CN116113967A - System and method for controlling digital knowledge dependent rights - Google Patents

System and method for controlling digital knowledge dependent rights Download PDF

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Publication number
CN116113967A
CN116113967A CN202180063278.1A CN202180063278A CN116113967A CN 116113967 A CN116113967 A CN 116113967A CN 202180063278 A CN202180063278 A CN 202180063278A CN 116113967 A CN116113967 A CN 116113967A
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China
Prior art keywords
knowledge
smart contract
digital
distribution system
distributed ledger
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Chinese (zh)
Inventor
查尔斯·霍华德·塞拉
安德鲁·卡德诺
泰勒·D·卡隆
泰莫尔·S·埃尔塔里
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Strong Trading Portfolio 2018 Ltd
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Strong Trading Portfolio 2018 Ltd
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Abstract

Systems and methods for controlling digital knowledge related rights are disclosed. The sample system may comprise: an input system for receiving digital knowledge from a user; a tagging system for tagging the digital knowledge; ledger management systems for creating, managing and storing things on distributed ledgers and providing provable access to the digital knowledge. The smart contract system may create a smart contract that includes a trigger action and respond with a defined smart contract action when the trigger event occurs. The smart contract system may also process commitments to the smart contract.

Description

System and method for controlling digital knowledge dependent rights
Cross reference
This application claims the benefit of priority from the following U.S. provisional patent application: U.S. provisional patent application No. 63/052,475 (attorney docket No. SFTX-0018-P01), filed on 7/16 in 2020, entitled "system and method for controlling digital knowledge related rights"; U.S. provisional patent application No. 63/054,603 (attorney docket No. SFTX-0017-P02) filed on day 21 of 7 in 2020 entitled "digital twin systems and methods for financial systems"; U.S. provisional patent application No. 63/127,980 (attorney docket SFTX-0016-P01), entitled "market orchestration system for facilitating electronic market transactions", filed on 12 months 18 in 2020.
The above applications are each incorporated by reference in their entirety.
Background
A large amount of information is periodically exchanged digitally and the amount of information is increasing. Such information may include valuable and sensitive information such as trade secrets, proprietary technology, proprietary materials, and author works. Some information is limited in access and control, such as limitations on who can view, edit, alter, use, transmit, sell, purchase, rent, review, license, and acquire digital information (e.g., with respect to patent permissions, trademark permissions, contractual agreements, copyright permissions, etc.). Setting and enforcing access and control restrictions is difficult because any computer-based system used to perform this transaction presents potential drawbacks, such as the risk of improper or unreliable system owners or maintainers, or the risk of other parties gaining unauthorized access and illegal access, copying, editing, or otherwise tampering with the digital knowledge.
The lending transaction provides financing for housing and education to various needs such as corporate and government projects, while enabling the borrower to obtain financial benefits. However, lending transactions suffer from a number of problems including opacity and asymmetry of the information, moral risk due to the transfer of risk or consequences of improper behavior, complexity of the application and negotiation process, heavy regulatory and policy regimes, difficulty in determining the value of the property being used as a mortgage or debt guarantee, difficulty in determining the reliability or financial health of the entity, and so forth.
Machines and automated agents are increasingly being used for marketing activities, including data collection, forecasting, planning, transactional execution, and other activities. This includes increasingly higher performance systems, such as those used for high speed transactions. There is a need for methods and systems that can improve machines capable of markets, including improving efficiency, speed, reliability, etc. for participants in such markets.
Many markets are becoming more and more decentralized, rather than more and more focused, distributed ledgers (e.g., blockchains), point-to-point interaction models, and microtransactions replace or supplement traditional models involving centralized or intermediaries. There is a need for an improved machine that enables large numbers of participants (including human participants and automated agents) to conduct decentralized transactions on a large scale.
Operations on blockchains (e.g., operations using cryptocurrency) increasingly require energy intensive computing operations, such as computing very large hash functions on ever-growing blockchains. Systems using work certificates, equity certificates, etc., have resulted in "mining" operations by which computer processing power is applied on a large scale to perform calculations that support collective trust for transactions recorded in blockchains.
Many applications of artificial intelligence also require energy intensive computing operations, such as very large neural networks with very many interconnections to perform operations on a large number of inputs to produce one or more outputs, such as prediction, classification, optimization, control outputs, and so forth.
The growth of internet of things and cloud computing platforms has also led to a proliferation of devices, applications, and connections between them, such that data centers, house servers, and other IT components consume a significant portion of the energy consumption in the united states and other developed countries.
As a result of these trends and other trends, energy consumption has become a major factor in computing resource utilization, such that energy resources and computing resources (or simply "energy and computing") have begun to merge from various perspectives, such as applications, purchases, supplies, configurations, and management of inputs, activities, outputs, and the like. For example, projects have been developed to host large computing resource facilities (e.g., bitlock TM Or other cryptocurrency mining operations) are placed near large hydroelectric resources such as the niagara waterfall.
The main challenges faced by the owners and operators of the facilities are the uncertainties involved in optimizing the facilities, such as due to fluctuations in the cost and availability of inputs (especially in the case of less stable renewable resources), fluctuations in the cost and availability of computing and network resources (e.g. in the case of network performance variations) and fluctuations and uncertainties of the various end markets in which energy and computing resources can be applied (e.g. fluctuations in cryptocurrency, fluctuations in energy markets, fluctuations in various other market pricing and uncertainties in the utility of artificial intelligence in a wide range of applications), among other factors.
Disclosure of Invention
The exemplary embodiments herein disclose systems, processes, and aspects for providing an encrypted secure blockchain for a knowledge system capable of storing digital knowledge to provide convenient, secure control of the knowledge system. The example methods and systems herein provide improvements in determining property valuations, reliability of physical financial health, transparency, information symmetry, and application and negotiation processes in a lending environment. The example methods and systems herein provide improvements to machines that enable markets that provide greater efficiency, speed, and/or reliability to participants in such markets. The example methods and systems herein provide improvements to automated configuration of data collection, storage and processing, input, resources, and output, and methods for facility optimization of energy and computing facilities.
In one or more exemplary embodiments, a knowledge distribution system for controlling digital knowledge-related rights is disclosed. The knowledge distribution system may be a blockchain for a knowledge system that allows for storing digital knowledge, purchasing or selling digital knowledge, tagging digital knowledge, and/or auditing/auditing the digital knowledge by cryptographically secure distributed ledgers. The intelligent contract may be implemented by the distributed ledger to control rights to digital knowledge, transfer digital knowledge, and to enable parties to adhere to agreements related to the digital knowledge. The knowledge system blockchain may also facilitate third parties to review, audit, or verify information related to digital knowledge.
There may be many practical barriers to knowledge sharing, such as lack of trust between parties that may benefit from knowledge sharing. There are platforms for digital knowledge distribution systems that facilitate orchestration of the sharing of knowledge by providing a high degree of control over the extent to which a transacting adversary has access to the shared knowledge. Even with knowledge security and good control, certain types of knowledge are so sensitive that owners may be reluctant to share the entire knowledge set with a single adversary. In an embodiment, a platform for a digital knowledge distribution system is disclosed that facilitates processing and controlling knowledge subsets, including automatically processing knowledge aggregation or related output due to knowledge subset partitioning.
The knowledge distribution system may include a ledger management system for creating and managing distributed ledgers, which may be distributed over nodes of a network and may include blocks linked by encryption. An intelligent contract system may be in communication with the distributed ledger and may be used to implement and manage intelligent contracts with the distributed ledger. The smart contract may be stored in the distributed ledger and may include a trigger event. The smart contract may be configured to perform a smart contract action with respect to the digital knowledge in response to the occurrence of the trigger event. The knowledge distribution system may be configured to receive an instance of the digital knowledge from a user. The digital knowledge may be tagged such that the instance of the digital knowledge operates as a tag on the distributed ledger. The tagged digital knowledge may be stored by the distributed ledger. The commitments of the parties to the smart contract may be processed. The knowledge distribution system may be configured to: managing control and access rights to the marked digital knowledge according to the intelligent contract; and managing the smart contract actions in response to the trigger event.
One or more of the following exemplary features may be included. The digital intellectual property may include intellectual property, where the smart contract embeds intellectual property licensing terms for the intellectual property embedded in the distributed ledger, and performing operations on the distributed ledger may provide access to the intellectual property, and may also handle commitments of parties to the smart contract to the intellectual property licensing terms. An intelligent contract wrapper on the distributed ledger may allow operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; an operation may be allowed to be performed on the ledger to add intellectual property rights to agree to apportion licensing fees among the parties in the ledger; operations may be allowed to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and/or may allow operations to be performed on the ledger to handle commitments of the principal to the terms of the agreement. The digital knowledge of the tag may include an instruction set. The distributed ledger may be used to provide provable access to the instruction set and execute the instruction set on a system to record transactions in the distributed ledger. The digital knowledge of the tag may include executable algorithm logic, a three-dimensional (3D) printer instruction set, an instruction set for a coating process, an instruction set for a semiconductor manufacturing process, a firmware program, an instruction set for a field programmable gate array, server-less code logic, an instruction set for a crystal manufacturing system, an instruction set for a food preparation process, an instruction set for a polymer production process, an instruction set for a chemical synthesis process, an instruction set for a bio-production process, a data set for digital twinning, and/or a business secret with an expert wrapper. The system may be used to aggregate views of trade secrets into a chain that proves which knowledge recipients of the principal have viewed the trade secrets. The knowledge distribution system may include a reporting system for reporting analysis results based on operations performed on the distributed ledgers or the digital knowledge. The distributed ledger may be used to aggregate instruction sets, wherein performing operations on the distributed ledger may add at least one instruction to a pre-existing instruction set to provide a modified instruction set. The smart contract may be used to manage allocation of a subset of instructions to the distributed ledger and access to the subset of instructions. The distributed ledger may be used to record principals contributing to the instance of digital knowledge by storing data related to the principals in at least one of the blocks. The knowledge distribution system may be configured to record a source of the instance of digital knowledge by storing data related to the source in at least one of the blocks. The distributed ledger may be configured to enable a private network of authorized participants to establish encryption-based consensus requirements to verify new ones of the blocks to be added. The ledger administration system may be used to facilitate crowdsourcing of information added to one of the blocks of the distributed ledger. The distributed ledger may be used to store a crowd-sourced review of instances of the digital knowledge in one of the blocks. The distributed ledger may be used to store a signature of an instance of the digital knowledge by a crowdsourcer in one of the blocks. The distributed ledger may be used to store a verification of an instance of the digital knowledge by a crowdsourcer in one of the blocks. The ledger administration system may be used to establish cryptocurrency tokens that may be transacted between users of the distributed ledger. The knowledge distribution system may include an account management system in communication with the distributed ledger, which may be used to facilitate creation and management of user accounts related to users of the knowledge distribution system. The knowledge distribution system may include a user interface system in communication with the distributed ledger, which may be used to present a user interface to a user of the knowledge distribution system, wherein the user interface allows the user to view data related to an instance of the digital knowledge. The knowledge distribution system may include a marketplace system in communication with the distributed ledger, the marketplace system may be operable to establish and maintain a digital marketplace that may be operable to visually present data related to instances of the digital knowledge to users of the knowledge distribution system. The knowledge distribution system may include a knowledge data store in communication with the distributed ledger, which may be used to store data related to the digital knowledge. The knowledge distribution system may include a client data store in communication with the distributed ledger, which may be used to store data related to users of the knowledge distribution system. The knowledge distribution system may include a smart contract data store in communication with the distributed ledger, which may be used to store data related to the smart contract. The knowledge distribution system may include a reporting system in communication with the distributed ledger, the reporting system operable to analyze the tagged digital knowledge and report analysis results based on the analysis of the tagged digital knowledge. The smart contracts may be generated using parameterizable smart contract templates. The smart contract may include parameters based on the type of digital knowledge to be tagged. The parameters may include financial parameters, license fee parameters, usage parameters, yield parameters, price allocation parameters, identity parameters, and/or access condition parameters.
In other exemplary embodiments, the knowledge distribution system may use distributed ledgers and intelligent contracts to facilitate the management and exchange of access, permissions, and ownership of digital knowledge.
In other exemplary embodiments, a computer-implemented method for controlling digital knowledge-related rights is disclosed. The method may include: a distributed ledger is created and managed, which is distributed over nodes of the network and comprises blocks linked by encryption. A smart contract may be implemented and managed by the distributed ledger, wherein the smart contract may be stored in the distributed ledger and may include a trigger event. An intelligent contract action may be performed with respect to the digital knowledge in response to the occurrence of the trigger event. An instance of the digital knowledge may be received. The digital knowledge may be tagged such that the instance of the digital knowledge operates as a tag on the distributed ledger. The tagged digital knowledge may be stored by the distributed ledger. The commitments of the parties to the smart contract may be processed. The method may include: managing control and access rights to the marked digital knowledge according to the intelligent contract; and managing the smart contract actions in response to the trigger event.
One or more of the following exemplary features may be included. Knowledge exchange for exchanging digital knowledge of the tags based on the smart contracts may be orchestrated. The knowledge exchange of the tagged digital knowledge may be integrated with another exchange, wherein the knowledge exchange facilitates an exchange of valuable and/or sensitive knowledge related to the subject matter of the other exchange.
In other exemplary embodiments, a knowledge distribution system for controlling digital knowledge-related rights is disclosed. The knowledge distribution system may include a ledger management system for creating and managing distributed ledgers. The distributed ledger may be distributed over nodes of a network and may include blocks linked by encryption. An intelligent contract system may be in communication with the distributed ledger and may be used to implement and manage intelligent contracts with the distributed ledger. The smart contract may be stored in the distributed ledger and may include a trigger event. The smart contract may be configured to perform a smart contract action with respect to the digital knowledge in response to the occurrence of the trigger event. The knowledge distribution system may be configured to receive an instance of the digital knowledge from a knowledge provider device, the instance of the digital knowledge including a 3D printer instruction set for a three-dimensional (3D) print object. The digital knowledge may be tagged such that the instance of the digital knowledge operates as a tag on the distributed ledger. The tagged digital knowledge may be stored by the distributed ledger. The commitments of the knowledge provider and knowledge receiver of the 3D printer instruction set to the smart contract may be processed. The knowledge distribution system may be configured to: managing control and access rights to the marked digital knowledge according to the intelligent contract; and managing the smart contract actions based on the conditions and the trigger event.
One or more of the following exemplary features may be included. The 3D printer instruction set may include a 3D print schematic. The object may be at least one of a custom part, a custom product, a manufactured part, a replacement part, a toy, a medical device, and a tool. The knowledge receiver may download and use the 3D printer instruction set using a knowledge receiver device. The knowledge receiver device may be at least one of a computing device, a server, a 3D printer, and a manufacturing device. The knowledge receiver may purchase digital knowledge of the tag corresponding to the 3D printer instruction set using a knowledge receiver device. The knowledge distribution system may include an event listener for listening to an Application Programming Interface (API) that may provide a connection between the knowledge distribution system and a knowledge receiver device of the knowledge receiver. The smart contract may be to: when the 3D printer instruction set can transfer or use the control rights and the access rights based on the digital knowledge of the tag, a condition for payment by the knowledge recipient is triggered. The controlling of the digital knowledge of the tag and the accessing may include allowing a user to 3D print using multiple instances of the 3D printer instruction set. The control rights and the access rights to the marked digital knowledge may comprise at least one of: the 3D printer requires, the period of time that the object can be 3D printed, whether the marked digital knowledge is transferred to a downstream knowledge receiver, assurance, disclaimer, reimbursement, and authentication with respect to the object. When the 3D printer instruction set is subjected to at least one of purchasing, downloading, and using, information related to the 3D printer instruction set of the marked digital knowledge may be modified on the distributed ledger. In an example, the information related to the 3D printer instruction set may include at least one of: sources, creation dates, names of one or more contributing individuals, groups, and/or companies, pricing, market trends for related schematics, serial numbers, and component identifiers. The smart contract action may be one of: assigning a serial number to the 3D printed object; monitoring the trigger event; verifying performance of the obligation based on the condition; verifying payment and/or transfer of the marked digital knowledge; transferring the digital knowledge of the tag; recording one or more transactions in the distributed ledger; performing one or more operations on the distributed ledger; and creating one or more new blocks in the distributed ledger. The smart contract action may include verifying that the condition defined in the smart contract is satisfied, wherein the condition may be one of: printer requirements, money for payment or transfer received from the knowledge receiver device of the knowledge receiver and transferring the marked digital knowledge to the knowledge receiver device. When the marked digital knowledge can be transferred to a knowledge receiver device of a knowledge receiver, a 3D printer can be used to print the object according to the 3D printer instruction set. The knowledge distribution system may include an intelligent contract generator that may be used to parameterize an intelligent contract template based on at least one of the knowledge provider provided information, the conditions, and the triggering event.
In other exemplary embodiments, a computer-implemented method for controlling digital knowledge-related rights is disclosed. The method may include: a distributed ledger is created and managed, which is distributed over nodes of the network and comprises blocks linked by encryption. A smart contract may be implemented and managed by the distributed ledger, wherein the smart contract may be stored in the distributed ledger and may include a trigger event. An intelligent contract action may be performed with respect to the digital knowledge in response to the occurrence of the trigger event. The method may include: an instance of the digital knowledge is received from a knowledge provider device, the instance of the digital knowledge including a 3D printer instruction set for a three-dimensional (3D) print object. The digital knowledge may be tagged such that the instance of the digital knowledge operates as a tag on the distributed ledger. The tagged digital knowledge may be stored by the distributed ledger. The commitments of the knowledge provider and knowledge receiver of the 3D printer instruction set to the smart contract may be processed. The method may include: managing control and access rights to the marked digital knowledge according to the intelligent contract; and managing the smart contract actions based on the conditions and the trigger event.
One or more of the following exemplary features may be included. Elements of the instance of the digital knowledge may be crowd-sourced through the smart contract. The elements of the instance of the digital knowledge may be managed by a smart contract system according to the smart contract.
A lending transaction support platform is provided having a set of data-integrated micro services including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions. The platform enables a wide range of proprietary solutions that can share data collection and storage infrastructure and can share or exchange inputs, events, activities, and outputs to enhance learning, enable automation, and enable adaptive intelligence among various solutions.
Aspects of the present invention relate to a method for electronically facilitating one or more personality rights of a licensing party. The method may include receiving an access request from a licensee to obtain approval of a licensing personality right from a set of available licensees. The method may include selectively granting access to the licensee based on the access request. The method may include receiving a deposit confirmation of the funds amount from the licensee. The method may include issuing an encrypted monetary amount corresponding to the amount of funds deposited by the licensee to an account of the licensee. The method may include receiving a smart contract request to create a smart contract that manages permissions of the licensee for the one or more personals of the licensee. The smart contract request may indicate one or more terms including a crypto-currency value amount to be paid to the licensor in exchange for one or more obligations of the licensor. The method may include generating the smart contract based on the smart contract request. The method may include hosting the cryptocurrency value amount from the account of the licensee. The method may include deploying the smart contract to a distributed ledger. The method may include verifying, by the smart contract, that the licensor has fulfilled the one or more obligations. The method may include releasing at least a portion of the cryptocurrency amount into a licensor account of the licensor in response to receiving verification that the licensor has fulfilled the one or more obligations. The method may include outputting a record to the distributed ledger, the record indicating that a license transaction defined by the smart contract has been completed.
Other aspects of the invention relate to a system for electronically facilitating one or more personality rights of a licensing party. The system may include one or more hardware processors configured by machine-readable instructions. The one or more processors may be configured to receive an access request from a licensee to obtain approval of a licensing personality right from a set of available licensees. The one or more processors may be configured to selectively grant access to the licensee based on the access request. The one or more processors may be configured to receive a deposit confirmation of the funds amount from the licensee. The one or more processors may be configured to issue an amount of cryptocurrency corresponding to an amount of funds deposited by the licensee to an account of the licensee. The one or more processors may be configured to receive a smart contract request to create a smart contract that manages permissions of the licensee for the one or more personals of the licensee; the smart contract request may indicate one or more terms including a crypto-currency value amount to be paid to the licensor in exchange for one or more obligations of the licensor. The one or more processors may be configured to generate the smart contract based on the smart contract request. The one or more processors may be configured to host the cryptocurrency amount from the account of the licensee. The one or more processors may be configured to deploy the smart contract to a distributed ledger. The one or more processors may be configured to verify, via the smart contract, that the licensor has fulfilled the one or more obligations. The one or more processors may be configured to release at least a portion of the cryptocurrency amount into a licensor account of the licensor in response to receiving verification that the licensor has fulfilled the one or more obligations. The one or more processors may be configured to output a record to the distributed ledger, the record indicating that a license transaction defined by the smart contract has been completed.
Drawings
The following detailed description of the invention and certain embodiments thereof may be understood by reference to the following drawings:
FIG. 1 is a schematic diagram of components of a platform for implementing smart transactions according to an embodiment of the present invention;
FIGS. 2A and 2B are schematic diagrams of additional components of a platform for implementing smart transactions according to embodiments of the present invention;
FIG. 3 is a schematic diagram of additional components of a platform for implementing smart transactions according to an embodiment of the present invention;
FIGS. 4 through 31 are schematic diagrams of embodiments of a neural network system, according to embodiments of the present invention, connectable to, integrated in, and accessible by a platform for implementing intelligent transactions, including systems involving expert systems, self-organization, machine learning, and artificial intelligence, and including trained neural network systems for pattern recognition, classification for one or more parameters, characteristics, or phenomena, for supporting autonomous control, and other purposes;
FIG. 32 is a schematic diagram of components of an environment including intelligent energy and computing facilities, a host intelligent energy and computing facility resource management platform, a set of data sources, a set of expert systems, interfaces to a set of marketplace platforms and external resources, and a set of user or client systems and devices, according to an embodiment of the invention;
FIG. 33 illustrates components and interactions of a trading, finance, and market support system;
FIG. 34 illustrates components and interactions of a set of data processing layers of a transaction, finance, and market support system;
FIG. 35 illustrates the adaptive intelligence and robotic process automation capabilities of a trading, finance, and market support system;
FIG. 36 illustrates opportunity mining capabilities of a trading, finance, and market support system;
FIG. 37 illustrates the adaptive edge computing management and edge intelligence capabilities of a trading, finance, and market support system;
FIG. 38 illustrates protocol adaptation and adaptive data storage capabilities of a trading, financial, and market support system;
FIG. 39 illustrates the robotic operational analysis capabilities of a trading, financial, and market support system;
FIG. 40 illustrates a blockchain and smart contract platform for enabling a long-term marketplace to gain event access;
FIG. 41 illustrates an algorithm and control panel for a blockchain and smart contract platform for enabling a long-term marketplace to gain event access;
FIG. 42 illustrates a blockchain and smart contract platform for long-term market demand aggregation;
FIG. 43 illustrates an algorithm and control panel for a blockchain and smart contract platform for long-term market demand aggregation;
FIG. 44 illustrates a blockchain and smart contract platform for crowd-sourced innovations;
FIG. 45 illustrates an algorithm and control panel for a blockchain and smart contract platform for crowd-sourced innovations;
FIG. 46 illustrates a blockchain and smart contract platform for crowd sourcing evidence;
FIG. 47 illustrates an algorithm and control panel for a blockchain and smart contract platform for crowd sourcing evidence;
FIG. 48 illustrates components and interactions of an embodiment of a lending platform having a set of data-integrated micro services including data collection and monitoring services for processing lending entities and transactions;
FIG. 49 illustrates components and interactions of an embodiment of a lending platform in which a set of lending resolution schemes are supported by a set of data-integrated data collection and monitoring services, an adaptive intelligence system, and a data storage system;
FIG. 50 illustrates components and interactions of an embodiment of a lending platform having a set of data-integrated blockchain services, smart contract services, social network analysis services, crowdsourcing services, and Internet of things data collection and monitoring services for collecting, monitoring, and processing information about entities involved in or associated with lending transactions;
FIG. 51 illustrates components and interactions of a lending platform having an Internet of things and a sensor platform for monitoring at least one of a set of assets, a set of mortgages, and a guaranty for a loan, bond, or debt transaction;
FIG. 52 illustrates components and interactions of a lending platform having a crowdsourcing system for collecting information about entities involved in lending transactions;
FIG. 53 illustrates an embodiment of a crowdsourcing workflow enabled by a lending platform;
FIG. 54 illustrates components and interactions of an embodiment of a lending platform having an intelligent contract system that automatically adjusts interest rates of loans based on information collected through at least one of an Internet of things system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services;
FIG. 55 illustrates components and interactions of an embodiment of a lending platform having smart contracts that automatically reorganize liabilities based on monitored conditions;
FIG. 56 illustrates components and interactions of a lending platform having a set of data collection and monitoring systems for verifying the reliability of loan warranty, including an Internet of things system and a social network analysis system;
FIG. 57 illustrates components and interactions of a lending platform having a robotic process automation system for negotiating a set of terms and conditions of a loan;
FIG. 58 illustrates components and interactions of a lending platform having a robotic process automation system for retrieving loans;
FIG. 59 illustrates components and interactions of a lending platform having a robotic process automation system for merging a set of loans;
FIG. 60 illustrates components and interactions of a lending platform having a robotic process automation system for managing warranty loans;
FIG. 61 illustrates components and interactions of a lending platform having a robotic process automation system for brokering mortgage loans;
FIG. 62 illustrates components and interactions of a lending platform having a crowdsourcing and automated classification system for verifying the status of bond issuers, a social networking monitoring system employing artificial intelligence for classifying the status of bonds, and an Internet of things data collection and monitoring system employing artificial intelligence for classifying the status of bonds;
FIG. 63 illustrates components and interactions of a lending platform having a system that manages terms and conditions of loans based on parameters monitored by the IoT, parameters determined by a social network analysis system, or parameters determined by a crowdsourcing system;
FIG. 64 illustrates components and interactions of a lending platform having an automated blockchain custody service for managing a set of custody assets;
FIG. 65 illustrates components and interactions of a loan platform having a loan underwriting system for loans with a set of data integration micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting loan entities and transactions;
FIG. 66 illustrates components and interactions of a lending platform having a loan marketing system with a set of data integration micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing loans to a set of potential parties;
FIG. 67 illustrates components and interactions of a lending platform having a rating system with a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating a set of loan-related entities;
FIG. 68 illustrates components and interactions of a lending platform having a compliance system with a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for automatically facilitating compliance with at least one of laws, regulations, and policies applicable to lending transactions;
FIG. 69 illustrates a system for automated loan management;
FIG. 70 illustrates a system;
FIG. 71 illustrates a method for processing a loan;
FIG. 72 illustrates a system for adaptive intelligence and robotic process automation capability for trading, finance, and marketing support;
FIG. 73 illustrates a method for automated smart contract creation and mortgage distribution;
FIG. 74 illustrates a system for processing a loan;
FIG. 75 illustrates a method for processing a loan;
FIG. 76 illustrates a system for adaptive intelligence and robotic process automation;
FIG. 77 illustrates a method for loan creation and management;
FIG. 78 illustrates a system for adaptive intelligence and robotic process automation capability for trading, finance, and marketing support;
FIG. 79 illustrates a method of robotic process automation for trading, finance, and marketing activities;
FIG. 80 illustrates a system for adaptive intelligence and robotic process automation;
FIG. 81 illustrates a method for automating transactions, financial and marketing activities;
FIG. 82 illustrates a system for adaptive intelligence and robotic processes;
FIG. 83 illustrates a method for performing loan-related actions;
FIG. 84 illustrates a system for adaptive intelligence and robotic processes;
FIG. 85 illustrates a method for performing loan-related actions;
FIG. 86 illustrates a system for adaptive intelligence and robotic processes;
FIG. 87 illustrates a method for performing loan-related actions;
FIG. 88 illustrates a smart contract system for mortgages managing loans;
FIG. 89 illustrates a smart contract method for mortgages managing loans;
FIG. 90 illustrates a system for verifying the status of a mortgage or guarantor of a loan;
FIG. 91 illustrates a crowd sourcing method for verifying the condition of a mortgage or guarantor of a loan;
FIG. 92 illustrates a smart contract system for modifying loans;
FIG. 93 illustrates a smart contract method for modifying a loan;
FIG. 94 illustrates an intelligent contract system for modifying loans;
FIG. 95 illustrates a smart contract method for modifying a loan;
FIG. 96 illustrates a smart contract system for modifying loans;
FIG. 97 illustrates a smart contract method for modifying a loan;
FIG. 98 illustrates a monitoring system for verifying conditions of a loan guarantee;
FIG. 99 illustrates a monitoring method for verifying conditions of a loan guarantee;
FIG. 100 illustrates a robotic process automation system for negotiating loans;
FIG. 101 illustrates a robotic process automation method for negotiating loans;
FIG. 102 illustrates a system for adaptive intelligence and robotic process automation;
FIG. 103 illustrates a loan collection method;
FIG. 104 illustrates a system for adaptive intelligence and robotic process automation;
FIG. 105 illustrates a loan repaying method;
FIG. 106 illustrates a system for adaptive intelligence and robotic process automation;
FIG. 107 illustrates a loan merge method;
FIG. 108 illustrates a system for adaptive intelligence and robotic process automation;
FIG. 109 illustrates a loan warranty method;
FIG. 110 illustrates a system for adaptive intelligence and robotic process automation;
FIG. 111 illustrates a mortgage proxy method;
FIG. 112 illustrates a system for adaptive intelligence and robotic process automation;
fig. 113 illustrates a method for liability management;
FIG. 114 illustrates a system for adaptive intelligence and robotic process automation;
FIG. 115 illustrates a method for bond management;
FIG. 116 illustrates a system for monitoring conditions of a bond issuer;
FIG. 117 illustrates a method for monitoring conditions of a bond issuer;
FIG. 118 illustrates a system for monitoring conditions of a bond issuer;
FIG. 119 illustrates a method for monitoring conditions of a bond issuer;
FIG. 120 illustrates a system for automated subsidy loan management;
FIG. 121 illustrates a method for automatically modifying subsidy loan terms and conditions;
FIG. 122 illustrates a system for automatically modifying loan terms and conditions;
FIG. 123 illustrates a method for collecting social networking information about entities involved in subsidized loan transactions;
FIG. 124 illustrates a system for automatically processing subsidized loans using crowd sourcing;
FIG. 125 illustrates a method for automatically processing subsidized loans;
FIG. 126 illustrates a system for asset access control;
FIG. 127 illustrates a method for asset access control;
FIG. 128 shows a system for automatically processing loan redemption;
FIG. 129 shows a method for facilitating mortgage redemption prevention;
FIG. 130 illustrates an example energy and computing resource platform;
FIG. 131 illustrates an exemplary facility data record;
FIG. 132 illustrates an exemplary personal data recording mode;
FIG. 133 illustrates a cognitive processing system;
FIG. 134 illustrates a process for a thread generation system to generate a list of threads;
FIG. 135 illustrates a process for a cue generation system to determine facility output for an identified cue;
FIG. 136 illustrates a process for generating and outputting personalized content;
FIG. 137 illustrates a schematic diagram of an example of a portion of a transaction artificial intelligence information technology system utilizing digital twinning, in accordance with some embodiments of the invention;
FIG. 138 illustrates a schematic diagram of a compliance system that facilitates licensing of personal rights in accordance with some embodiments of the present invention;
FIG. 139 illustrates a schematic diagram of an exemplary set of components of a compliance system in accordance with some embodiments of the invention;
FIG. 140 illustrates a set of operations for a method for auditing potential licensees for the purpose of licensing the licensee's personality rights in accordance with some embodiments of the invention;
FIG. 141 illustrates a set of operations for a method for facilitating licensing of a licensee's personality rights to a licensee in accordance with some embodiments of the invention;
FIG. 142 illustrates a set of operations of a method for detecting potential avoidance of a rule or rule by a licensee and/or licensee in accordance with some embodiments of the invention;
FIG. 143 illustrates a method for selecting an AI solution;
FIG. 144 illustrates a method for selecting an AI solution;
fig. 145 shows an example of an assembled AT solution;
FIG. 146 illustrates a method for selecting an AI solution;
FIG. 147 illustrates a method for selecting an AI solution;
FIG. 148 illustrates an AI solution selection and configuration system;
FIG. 149 illustrates an AI solution selection and configuration system;
FIG. 150 illustrates an AI solution selection and configuration system;
FIG. 151 illustrates a component configuration circuit;
FIG. 152 illustrates an AI solution selection and configuration system;
FIG. 153 illustrates a system for selecting and configuring artificial intelligence models;
FIG. 154 illustrates a method of selecting and configuring an artificial intelligence model;
FIG. 155 is a schematic diagram illustrating an example architecture of a digital twinning system according to an embodiment of the invention;
FIG. 156 is a schematic diagram illustrating exemplary components of a digital twinning management system, in accordance with an embodiment of the invention;
FIG. 157 is a schematic diagram illustrating an example of a digital twin I/O system interfacing with an environment, digital twin system, and/or components thereof to provide bi-directional data transfer between coupled components in accordance with an embodiment of the present invention;
FIG. 158 is a schematic diagram illustrating an example of a set of identification states associated with an industrial environment that a digital twinning system may identify and/or store for access by a smart system (e.g., a cognitive smart system) or digital twinning system user in accordance with an embodiment of the invention;
FIG. 159 is a schematic diagram illustrating an exemplary embodiment of a method for updating a set of attributes of a digital twin of the present invention on behalf of a client application and/or one or more embedded digital twin;
FIG. 160 illustrates an exemplary embodiment of a display interface of the present invention that presents digital twinning of a dryer centrifuge and information related to the dryer centrifuge;
FIG. 161 is a schematic diagram illustrating one exemplary embodiment of a method for updating a set of vibration fault level states of a machine component, such as a bearing, in a digital twin of an industrial machine on behalf of a client application;
FIG. 162 is a schematic diagram illustrating one exemplary embodiment of a method for updating a set of vibration severity unit values for a machine component, such as a bearing, in a digital twin of a machine on behalf of a client application;
FIG. 163 is a schematic diagram illustrating one exemplary embodiment of a method for updating a digitally twinned set of fault probability values for a machine component on behalf of a client application;
FIG. 164 is a schematic diagram illustrating one exemplary embodiment of a method for updating a set of outage probability values for a machine in a digital twinning of a manufacturing facility on behalf of a client application;
FIG. 165 is a schematic diagram illustrating one exemplary embodiment of a method for updating a set of downtime probability values for a manufacturing facility in a digital twinning of an enterprise on behalf of a client application;
FIG. 166 is a schematic diagram illustrating one exemplary embodiment of a method for updating a set of downtime cost values for a machine in a digital twin of a manufacturing facility;
FIG. 167 is a schematic diagram illustrating one exemplary embodiment of a method for updating one or more manufacturing KPI values of a digital twinning of a manufacturing facility on behalf of a client application;
FIG. 168 is a schematic diagram of components of a knowledge distribution system and communication network for facilitating digital knowledge management, in accordance with an embodiment of the invention;
FIG. 169 is a schematic diagram of a ledger network of a knowledge distribution system, in accordance with an embodiment of the invention;
FIG. 170 is a schematic diagram of the knowledge distribution system shown in FIG. 168, wherein the knowledge distribution system includes details of a smart contract and a smart contract system of the knowledge distribution system, in accordance with an embodiment of the invention;
FIG. 171 is a schematic diagram of a plurality of data stores of a knowledge distribution system in accordance with an embodiment of the invention;
FIG. 172 illustrates a method of deploying knowledge tagging and related smart contracts via a knowledge distribution system, in accordance with an embodiment of the invention;
FIG. 173 illustrates a method of executing a brief process flow of a smart contract that distributes digital knowledge through a knowledge distribution system, in accordance with an embodiment of the present invention;
FIG. 174 is a schematic diagram of another embodiment of components of a knowledge distribution system and communication network for facilitating digital knowledge management in accordance with an embodiment of the invention;
FIG. 175 illustrates a knowledge distribution system for controlling digital knowledge dependent rights;
FIG. 176 illustrates a computer-implemented method for controlling digital knowledge dependent rights;
FIG. 177 illustrates a computer-implemented method for controlling digital knowledge-related rights;
FIG. 178 illustrates a knowledge distribution system for controlling digital knowledge dependent rights;
FIG. 179 illustrates possible components of a 3D printer instruction set;
FIG. 180 shows possible content of tagged digital knowledge;
FIG. 181 illustrates possible smart contract actions;
FIG. 182 illustrates possible conditions associated with a triggering event;
FIG. 183 shows possible control and access rights;
FIG. 184 illustrates a possible trigger event;
FIG. 185 illustrates a computer-implemented method for controlling digital knowledge dependent rights;
FIG. 186 illustrates a computer-implemented method for controlling digital knowledge-related rights;
FIG. 187 illustrates possible crowdsourcing information;
FIG. 188 illustrates possible content of a distributed ledger;
FIG. 189 shows possible parameters;
FIG. 190 illustrates an embodiment of a knowledge distribution system for controlling digital knowledge dependent rights;
fig. 191-196 illustrate embodiments of operations for controlling digital knowledge-related rights.
Detailed Description
The term "service/microservice" (and similar terms) as used herein should be construed broadly. Without limiting to any other aspect or description of the present invention, a service/microservice includes any system (or platform) for functionally executing service operations, wherein the system may be data-integrated, including data collection circuitry, blockchain circuitry, artificial intelligence circuitry, and/or smart contract circuitry, for processing lending entities and transactions. The services/micro-services may facilitate data processing and may include facilities for data extraction, conversion and loading, data cleaning and deduplication facilities, data normalization facilities; a data synchronization facility; data security facilities, computing facilities (e.g., for performing predefined computing operations on data streams and providing output streams), compression and decompression facilities, analysis facilities (e.g., automated production providing data visualization), data processing facilities and/or data storage facilities (including storage reservation, formatting, compression, migration, etc.), and the like.
The services/microservices may include controllers, processors, network infrastructure, input/output devices, servers, client devices (e.g., notebook computers, desktops, terminals, mobile devices, and/or dedicated devices), sensors (e.g., internet of things sensors associated with one or more entities, devices, and/or mortgages), actuators (e.g., auto locks, notification devices, lights, camera controls, etc.), virtualized versions of any one or more of the foregoing (e.g., outsourcing computing resources for cloud storage, computing operations, etc., virtual sensors, stock or commodity prices, logging subscription data waiting to be collected), and/or components including computer readable instructions that, when executed by a processor, cause the processor to perform one or more functions of the service, etc. The services may be distributed across multiple devices, and/or the functionality of the services may be performed by one or more devices in conjunction with performing a given function of the services.
The services/micro-services may include application programming interfaces that facilitate connections between system components (e.g., micro-services) that execute the services and between the system and entities (e.g., programs, websites, user devices, etc.) external to the system. Without being limited to any other aspect of the invention, example micro-services that may exist in some embodiments include (a) a set of multi-mode data collection circuits that collect information about and monitor entities related to debit transactions; (b) A blockchain circuit for maintaining a secure history ledger for loan-related events, the blockchain circuit having access control features that manage access to a set of parties involved in the loan; (c) An application programming interface, a data integration service, a data processing workflow, and a user interface for processing loan-related events and loan-related activities; and (d) a smart contract circuit for specifying the terms and conditions of a smart contract that governs at least one of loan terms and conditions, loan-related events, and loan-related activities. Any service/microservice may be controlled by or control the controller. Some systems may not be considered services/micro-services. For example, a point-of-sale device that simply charges a fixed cost of goods or services may not be a service. In another example, a service that tracks the cost of goods or services and triggers a notification when the value changes may not be the rating service itself, but instead rely on the rating service, and/or may form part of the rating service in some embodiments. It can be seen that in certain embodiments, a given circuit, controller, or device may be a service or part of a service, for example when the function or capability of the circuit, controller, or device is used to support a service or micro-service as described herein, but for other embodiments (e.g., where the function or capability of the circuit, controller, or device is not related to a service or micro-service as described herein) may not be a service or part of a service. In another example, a mobile device operated by a user may form part of a service as described herein at a first point in time (e.g., when the user accesses features of the service through an application or other communication from the mobile device, and/or when a monitoring function is performed via the mobile device), but may not form part of the service at a second point in time (e.g., after a transaction is completed, after the user uninstalls the application, and/or when the monitoring function is stopped and/or passed to another device). Accordingly, the advantages of the present invention may be applied in a variety of processes or systems, and any such process or system may be considered a service (or part of a service) herein.
Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit a particular system, how to combine processes and system configurations from the present invention, provide performance characteristics (e.g., bandwidth, computing power, time response, etc.), and/or provide operational capabilities (e.g., inspection interval time, uptime including longitudinal (e.g., continuous operation time) and/or sequential (e.g., time of day, calendar time, etc.) of service components sufficient to provide a given embodiment of a service, platform, and/or micro-service as described herein, sensing resolution and/or accuracy, data determination (e.g., accuracy, timing, data volume, etc.), and/or actuator validation capabilities. Certain considerations that may be taken into account by those skilled in the art in determining the configuration of components, circuits, controllers, and/or devices to implement the services, platforms, and/or microservices described herein (hereinafter "services") include, but are not limited to: a balance of capital cost and operating cost to implement and operate the service; availability, speed, and/or bandwidth of network services available to system components, service users, and/or other entities interacting with the service; response time of service consideration (e.g., how fast decisions within the service must be performed to support business functions of the service, operating time of various artificial intelligence or other advanced computing operations) and/or capital or operating costs to support a given response time; the location of service interaction components, and the impact of these locations on service operations (e.g., data storage locations and associated regulatory schemes, network communication restrictions and/or costs, power costs as a function of location, support availability for time zones associated with services, etc.); availability of certain sensor types, associated support for these sensors, and availability of sufficient alternatives for sensing purposes (e.g., a camera may require supportive lighting and/or high network bandwidth or local storage); an aspect of the underlying value of an aspect of the service (e.g., a principal amount of the loan, a value of the mortgage, a fluctuation in the value of the mortgage, an equity or relative equity of the borrower, a guarantor, and/or borrower, etc.), including a time sensitivity of the underlying value (e.g., where it changes rapidly or slowly relative to the service operation or loan term); trust indicators between transaction parties (e.g., history of performance between parties, credit ratings, social ratings or other external indicators, whether activities related to transactions meet industry standards or other normalized transaction types, etc.); and/or availability of cost reclamation options (e.g., subscription, fee, service payment, etc.) for a given configuration and/or functionality of the service, platform, and/or microservice. Without being limited to any other aspect of the invention, certain operations performed by the service herein include: performing real-time changes to the loan based on the tracking data; executing a mortgage-supported smart contract with the data; re-valuating the liability transaction in response to the tracked conditions or data, etc. Although specific examples of services/micro-services and considerations are described herein for illustrative purposes, any system that would benefit from the disclosure herein and any consideration that would be appreciated by one of ordinary skill in the art having the benefit of the disclosure herein are specifically contemplated as falling within the scope of the present disclosure.
Services include, but are not limited to, financial services (e.g., loan transaction services), data collection services (e.g., data collection services for collecting and monitoring data), blockchain services (e.g., data integration services for maintaining security data), data integration services (e.g., data integration services for aggregating data), smart contract services (e.g., smart contract services for determining aspects of smart contracts), software services (e.g., software services that extract entity-related data from public information websites), crowdsourcing services (e.g., crowdsourcing services that request and report information), internet of things services (e.g., internet of things services for monitoring an environment), publishing services (e.g., publishing services for publishing data), micro services (e.g., having a set of application programming interfaces that facilitate connections between micro services), valuation services (e.g., setting up the value of mortgage based on information using valuation models), artificial intelligence services, market value data collection services (e.g., monitoring and reporting market information), clustering services (e.g., grouping mortgage objects according to attribute similarities), network services (e.g., being able to be configured relative to parameters of a social network), asset identification services (e.g., identifying institutions, identity authorities, financial institutions, and/management institutions, etc. Example services herein that perform one or more functions include computing devices, servers, networking devices, user interfaces, communication protocols, inter-device interfaces such as shared information and/or information storage and/or Application Programming Interfaces (APIs), sensors (e.g., internet of things sensors operatively coupled to monitored components, devices, locations, etc.), distributed ledgers, circuitry, and/or computer readable code for causing a processor to perform one or more functions of a service. One or more aspects or components of the services herein may be distributed across multiple devices, and/or may be incorporated, in whole or in part, on a given device. In an embodiment, aspects or components of the services herein may be implemented at least in part by circuitry, such as in a non-limiting example, a data collection service is implemented at least in part as a data collection circuit configured to collect and monitor data, a blockchain service is implemented at least in part as a blockchain circuit configured to maintain secure data, a data integration service is implemented at least in part as a data integrated circuit configured to aggregate data, a smart contract service is implemented at least in part as a smart contract circuit configured to determine aspects of a smart contract, a software service is implemented at least in part as a crowdsourcing circuit configured to extract data related to an entity from publicly available information sites, a crowdsourcing service is implemented at least in part as an internet of things circuit configured to request and report information, an internet of things service is implemented at least in part as an internet of things circuit configured to monitor an environment, the posting service is implemented at least in part as a posting service circuit configured to post data, the micro-service is implemented at least in part as a micro-service circuit configured to interconnect a plurality of service circuits, the valuation service is implemented at least in part as a valuation service circuit configured to access a valuation model to set a value of a mortgage based on data, the artificial intelligence service is implemented at least in part as an artificial intelligence service circuit, the market value data collection service is implemented at least in part as a market value data collection service circuit configured to monitor and report market information, the cluster service is implemented at least in part as a cluster service circuit configured to group mortgages based on similarity of attributes, the social network service is implemented at least in part as a social network analysis service circuit configured to configure parameters about a social network, the asset identification service is implemented at least in part as an asset identification service circuit for identifying a set of assets that the financial institution is responsible for keeping, and the identity management service is implemented at least in part as an identity management service circuit that enables the financial institution to verify identities, credentials, and the like. Accordingly, the advantages of the present invention may be applied in a variety of systems, and any such system may be considered with respect to the articles and services herein, while in certain embodiments a given system may not be considered with respect to the articles and services herein. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit a particular system and/or how to combine the processes and systems in this summary to enhance the operation of the contemplated systems. Those skilled in the art may consider considerations for determining the configuration of a particular service to include: distribution and access devices available to one or more parties to a particular transaction; the storage of certain types of information, types, and jurisdictional restrictions of communications; security and authentication requirements or expectations aspects of service information communication; the algorithm, machine learning component and/or artificial intelligence component of the service collect information; inter-principal communication and a determined response time; cost considerations for services, including capital expenditures and operating costs, and which party or entity will bear costs and availability to reclaim costs, e.g., through subscriptions, service fees, etc.; the amount of information stored and/or transferred to support the service; and/or processing or computing power for supporting services.
The term "article and service" (and similar terms) as used herein should be construed broadly. Without being limited to any other aspect or description of the invention, the goods and services include any goods and services including, but not limited to, goods and services used as rewards, as mortgages, as co-branded goods, etc., such as, but not limited to, an item application guarantee or guaranty for goods as loan targets, loan mortgages, or the like (e.g., products, services, supplies, solutions, physical products, software, service levels, quality of service, financial instruments, debts, mortgages, service fulfillment, or other items). Without being limited to any other aspect or description of the present invention, articles and services include any article and service, including, but not limited to, articles and services applied to physical articles (e.g., vehicles, ships, airplanes, buildings, houses, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, antiques, fixtures, furniture, equipment items, tools, machinery, and personal property), financial articles (e.g., merchandise, securities, currency, value documents, tickets, cryptocurrencies), consumables (e.g., edible articles, beverages), high value articles (e.g., precious metals, jewelry, precious stones), intellectual articles (e.g., intellectual property items, intellectual property, contract rights), and the like. Accordingly, the advantages of the present invention may be applied in a variety of systems, and any such system may be considered with respect to the articles and services herein, while in certain embodiments a given system may not be considered with respect to the articles and services herein. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit a particular system and/or how to combine the processes and systems in this summary to enhance the operation of the contemplated systems.
The terms "agent," "automated agent," and similar terms as used herein should be construed broadly. Without being limited to any other aspect or description of the invention, an agent or automated agent may handle events related to at least one of the value, status, and ownership of a mortgage or asset. The agent or automated agent may also take actions related to loans, liabilities transactions, bond transactions, subsidized loans, etc. to which the mortgage or property belongs, such as in response to the event being processed. Agents or automated agents may interact with the marketplace to collect data, test spot market transactions, perform transactions, etc., where dynamic system behavior involves complex interactions that a user may wish to understand, predict, control, and/or optimize. Some systems may not be considered agents or automated agents. For example, if events are only collected and not processed, the system may not be an agent or an automated agent. In some embodiments, if the loan-related action is not taken in response to the processed event, it may not be taken by an agent or automated agent. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention include and/or benefit from agents or automated agents. Some considerations by those skilled in the art or embodiments of the present invention regarding agents or automated agents include, but are not limited to: rules for determining when a change in value, condition, or ownership of a property or mortgage occurs, and/or rules for determining whether a change warrants further action on a loan or other transaction, among other considerations. Although specific examples of market value and market information are described herein for illustrative purposes, any embodiment that would benefit from the disclosure herein, and any consideration that would be appreciated by one skilled in the art having the benefit of the disclosure herein, are specifically contemplated as within the scope of the present disclosure.
The terms "market information," "market value," and similar terms as used herein should be construed broadly. Without being limited to any other aspect or description of the invention, market information and market value describe the status or value of an asset, mortgage, food or service at a defined point in time or period of time. Market value may refer to an expected value set for an item in a market or auction environment, or pricing or financial data for an item similar to the item, asset, or mortgage in at least one public market. For a company, the market value may be the number of its circulation strands multiplied by the current stock price. The valuation services may include a market value data collection service that monitors and reports market information related to the value (e.g., market value) of mortgages, publishers, a set of bonds and a set of assets, a set of subsidized loans, parties, etc. Market value may be dynamic in nature because they depend on a variety of factors, from actual business conditions to economical climates to supply and demand dynamics. The market value may be affected by, and the market information may include, the following factors: proximity to other assets, inventory or supply of assets, demand for assets, sources of items, histories of items, potential current value of item components, bankruptcy status of an entity, cancellation redemption status of an entity, contract breach status of an entity, violation regulation status of an entity, crime status of an entity, export regulation status of an entity, banned status of an entity, tariff status of an entity, tax status of an entity, credit report of an entity, credit rating of an entity, website rating of an entity, a set of entity product customer reviews, social network rating of an entity, a set of entity credentials, a set of entity referrals, a set of entity certificates, a set of entity behaviors, an entity location, and an entity geographic location. In some embodiments, market value may include, for example, volatility of the value, sensitivity of the value (e.g., relative to other parameters having associated uncertainty), and/or particular value of the valuation object to a particular party (e.g., an item owned by a first party may be more valuable than an item owned by a second party).
Some information may not be market information or market value. For example, the value-related variables are not market-derived, they may be in-use or investment values. In some embodiments, the investment value may be considered a market value (e.g., when the valuating party intends to use the asset as a post-acquisition investment) rather than in other embodiments (e.g., when the valuating party intends to immediately clear the post-acquisition investment). Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit from market information or market value. Some considerations by those skilled in the art in determining whether the term "market value" refers to an asset, item, mortgage, good, or service include: other similar assets exist in the marketplace, location-dependent value changes, open prices for items exceeding bid prices, and other considerations. Although specific examples of market value and market information are described herein for illustrative purposes, any embodiment that would benefit from the disclosure herein, and any consideration that would be appreciated by one skilled in the art having the benefit of the disclosure herein, are specifically contemplated as within the scope of the present disclosure.
The term "apportioned value" or "apportioned value" and similar terms used herein should be construed broadly. Without limiting to any other aspect or description of the invention, apportioning value describes the process of apportioning or apportioning value, or apportioning a rule, of dividing and apportioning value. The allocation of value may be to several parties (e.g., each of several parties is a beneficiary of value), to several transactions (e.g., each transaction uses a portion of value), and/or in a many-to-many relationship (e.g., a group of objects have aggregated values allocated among multiple parties and/or transactions).
Some conditions or processes may be independent of the value of the allocation. For example, the total value of an item may provide its inherent value, but not the value held by each identified entity. Those skilled in the art, having the benefit of the disclosure herein and having the benefit of the value of the apportionment, can readily determine which aspects of the present invention will benefit from a particular application of apportioning value. Some considerations of the value of the person skilled in the art or of the embodiments of the present invention in relation to the apportionment include, but are not limited to: principal amount currency, expected transaction type (loan, bond or debt), specific type of mortgage, rate of loan to value, rate of mortgage to loan, total transaction/loan amount, principal amount, amount of entity owed, mortgage value, etc. Although specific examples of shared value are described herein for illustrative purposes, any embodiment that is consistent with the disclosure herein, as well as any consideration that would be appreciated by one skilled in the art having the benefit of the disclosure herein, are expressly contemplated as being within the scope of the present disclosure.
The term "financial condition" and similar terms as used herein should be understood broadly. Without limiting to any other aspect or description of the invention, a financial condition describes the current state of an entity's asset, liability, and equity status at a defined point in time or period. The financial status may be posted to a financial statement. The financial condition may further include an ability to evaluate the entity to survive or fulfill future or expired liabilities in the event of a future risk. The financial condition may be determined based on a set of attributes of the following entities: an entity of a public claim of an entity, a set of properties owned by the entity as indicated by a public record, a valuation of a set of properties owned by the entity, a bankruptcy condition of the entity, a redemption status of the entity, a contract breach status of the entity, a violation regulation status of the entity, a crime status of the entity, an export regulation status of the entity, a banned status of the entity, a tariff status of the entity, a tax status of the entity, a credit report of the entity, a credit rating of the entity, a set of product customer reviews of the entity, a social network rating of the entity, a set of entity credentials, a set of entity referrals, a set of entity certificates, a set of entity behaviors, an entity location, and an entity geographic location. The financial status may also describe requirements or thresholds for agreements or loans. For example, the conditions that allow a developer to start may be various authentications and their consent to financial expenditures. That is, the ability of a developer to start depends on financial factors and the like. Some conditions may not be financial. For example, the credit card balance itself may be a clue to the financial situation, but may not be the financial situation itself. In another example, a payment plan may determine how long a liability may be on an entity's liability statement, but may not accurately provide financial status. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention include and/or will benefit from the financial aspects. Some considerations by those skilled in the art in determining whether the term "financial status" refers to the current status of an entity at a defined point in time or period of time and/or asset, liability, and equity status for a given purpose include: reporting more than one financial data point, a ratio of loan to mortgage value, a ratio of mortgage to loan, a total transaction/loan amount, credit scores for borrowers and borrowers, and other considerations. Although specific examples of financial aspects are described herein for illustrative purposes, any embodiment that would benefit from the disclosure herein, and any consideration that would be appreciated by one skilled in the art having the benefit of the disclosure herein, are specifically contemplated as falling within the scope of the present disclosure.
The term "interest rate" and similar terms used herein should be construed broadly. Without limiting to any other aspect or description of the invention, the interest rate includes an interest amount that expires per time period in proportion to a loan, deposit, or borrow amount. The total interest in a loan or borrow may depend on the principal sum, interest rate, frequency of compound interest, and the length of time the loan, deposit, or borrow is made. Typically, interest rate is expressed in terms of annual percentage, but may be defined for any period of time. The interest rate may be related to the amount charged by the bank or other borrower, or to the deposit interest rate paid by the bank or other entity to the depositor. The interest rate may be variable or fixed. For example, interest rates may vary according to government or other stakeholder instructions, money to lend or borrow principal, investment terms, default probabilities of borrowers, market supply and demand, number of mortgage, economic conditions, or special circumstances (e.g., to collect a reserve of money). In some embodiments, the interest rate may be a relative interest rate (e.g., relative to a base interest rate, a commodity expansion index, etc.). In some embodiments, the interest rate may further consider the cost or fee (e.g., a "base point") applied to adjust the interest rate. The nominal interest rate may not be adjusted for inflation while the actual interest rate should take into account inflation. Some examples may not be interest rates for the purposes of particular embodiments. For example, bank accounts that grow annually with fixed dollar amounts and/or fixed fee amounts may not be examples of the interest rate of certain embodiments. Those skilled in the art, with the benefit of the disclosure and understanding of the present disclosure, may readily determine the characteristics of the interest rate for a particular embodiment. Some considerations of interest to those skilled in the art or embodiments of the present invention include, but are not limited to: the principal amount of money, the variables used to set the interest rate, the criteria used to modify the interest rate, the type of anticipated transaction (loan, bond or debt), the particular type of mortgage, the ratio of loan to value, the mortgage to loan ratio, the total transaction/loan amount, the principal amount, the appropriate deadline for the transaction and/or mortgage for the particular industry, the likelihood that the borrower will sell and/or consolidate the loan prior to the deadline, and so forth. Although specific examples of interest are described herein for illustrative purposes, any embodiment that is within the contemplation of the invention, and any consideration that would be appreciated by those skilled in the art, having the benefit of the disclosure herein, are specifically contemplated as falling within the scope of the invention.
The term "rating service" (and similar terms) as used herein should be construed broadly. Without limiting to any other aspect or description of the invention, a rating service includes any service that sets the value of a good or service. The valuation service may use a valuation model to set the value of the mortgage based on information provided by the data collection and monitoring service. The smart contract service may process output from a set of valuation services and allocate sufficient mortgage to provide loan assurance and/or apportion the value of the mortgage among a set of borrowers and/or transactions. The valuation service may include an artificial intelligence service that may iteratively refine the valuation model based on outcome data related to mortgage transactions. The valuation service may include a market value data collection service that may monitor and report market information related to mortgage value. Some flows may not be considered rating services. For example, a point-of-sale device that charges only a fixed cost for goods or services may not be a valuation service. In another example, a service that tracks the cost of goods or services and triggers a notification when the value changes may not be the rating service itself, but rather rely on and/or form part of the rating service. Accordingly, the advantages of the present invention are applicable to a variety of process systems, and any such process or system may be considered a valuation service herein, while in some embodiments a given service may not be considered a valuation service herein. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional intended systems available, can readily determine which aspects of the present invention will benefit a particular system and how to combine the processes and systems in this summary to enhance the operation of the intended system and/or provide valuation services. Certain considerations by those of skill in the art in determining whether a desired system is an evaluation service and/or whether aspects of the present invention may be useful or enhance the desired system include, but are not limited to: performing real-time changes to the loan based on the value of the mortgage; executing the mortgage-supported smart contracts using the market data; reevaluating the mortgage based on the storage conditions or geographic location; mortgage value fluctuations, trends in utilized and/or diverted; etc. Although specific examples of rating services and considerations are described herein for illustrative purposes, any system that would benefit from the disclosure herein and any consideration that would be appreciated by one skilled in the art having the benefit of the disclosure herein are specifically contemplated as within the scope of the present disclosure.
The term "mortgage attribute" (and similar terms) as used herein should be construed broadly. Without being limited to any other aspect or description of the invention, mortgage attributes include durability (the ability of a mortgage to withstand wear or the lifetime of a mortgage), value, identification (whether a mortgage has an explicit feature that is easy to identify or market), value stability (whether a mortgage maintains value over time), standardization, rating, quality, marketability, fluidity, transferability, desirability, traceability, deliverability (the ability of a mortgage to deliver or transfer without deterioration in value), market transparency (i.e., the value of a mortgage is easy to verify or widely agreed upon), entity or virtually any identification. Mortgage attributes may be measured in absolute or relative terms and/or may include qualitative (e.g., classification descriptions) or quantitative descriptions. Mortgage attributes may vary from industry to industry, product to product, element to use, etc. Mortgage attributes may be quantitative or qualitative. The values associated with the mortgage attributes may be based on a scale (e.g., 1-10) or relative names (high, low, better, etc.). Mortgages may include various components; each component may have mortgage properties. Thus, a mortgage may have multiple values for the same mortgage attribute. In some embodiments, multiple values of mortgage attributes may be combined to generate one value for each attribute. Certain mortgage attributes may only apply to specific portions of a mortgage. Certain mortgage attributes, even for a given portion of a mortgage, may have different values depending on the interested party (e.g., a party's valuation of one aspect of the mortgage over another party) and/or depending on the type of transaction (e.g., a mortgage may be more valuable or appropriate for a first type of loan than a second type of loan). Certain properties associated with a mortgage may not be the mortgage properties described herein, depending on the purpose of the mortgage properties herein. For example, a product may be rated durable relative to a similar product; however, if the lifetime of a product is well below the term of a particular loan, the durability of the product may be rated as a different rating (e.g., not durable) or as an irrelevant rating (e.g., the current inventory of the product acts as a mortgage and is expected to change over the term of the loan). Thus, the advantages of the present invention are applicable to a variety of attributes, and any such attribute may be considered a mortgage attribute herein, while in some embodiments, a given attribute may not be considered a mortgage attribute herein. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional expected mortgage attributes that are available, can readily determine which aspects of the invention will benefit from particular mortgage attributes. Certain considerations by those of skill in the art in determining whether the desired attribute is a mortgage attribute and/or whether aspects of the invention may be beneficial or enhancing the desired system include, but are not limited to: the source of the attributes and the source of the attribute values (e.g., whether the attributes and attribute values are from a good reputation source), the volatility of the attributes (e.g., whether the attribute values of the mortgage fluctuate, whether the attribute is a new attribute of the mortgage), the relative differences in the attribute values of similar mortgages, the particular attribute values (e.g., certain attribute values may be high, such as at the 98 th percentile or very low, such as at the 2 nd percentile, compared to similar class mortgages), the variability of the mortgage, the type of transaction associated with the mortgage, and/or the purpose of using the mortgage for a particular party or transaction. Although specific examples of mortgage attributes and considerations are described herein for illustrative purposes, any system that would benefit from the disclosure herein and any consideration that would be appreciated by one of ordinary skill in the art having the benefit of the disclosure herein are specifically contemplated as within the scope of the present disclosure.
The term "blockchain service" (and similar terms) as used herein should be construed broadly. Without being limited to any other aspect or description of the present invention, a blockchain service includes any service related to processing, recording, and/or updating of a blockchain, and may include operations for processing a block, calculating hash values, generating a new block in a blockchain, appending a block to a blockchain, creating a fork in a blockchain, merging branches in a blockchain, validating previous calculations, updating shared ledgers, updating distributed ledgers, generating encryption keys, validating transactions, maintaining a blockchain, updating a blockchain, validating a blockchain, generating random numbers. These services may be performed by executing computer-readable instructions on local computers and/or remote servers and computers. Some services may not be considered separately as blockchain services, but may be considered to be based on the end use of the service and/or the blockchain service in particular embodiments-for example, the calculation of hash values may be performed in a context other than the blockchain, such as in the context of secure communications. Some initial services may be invoked without first being applied to the blockchain, but further actions or services in combination with the initial services may associate the initial services with aspects of the blockchain. For example, random numbers may be periodically generated and stored in memory; these random numbers may not originally be generated for blockchain purposes, but may be used for blockchains. Thus, the advantages of the present invention are applicable to a variety of services, and any such service may be considered a blockchain service herein, while in some embodiments a given service may not be considered a blockchain service herein. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional expected blockchain services available, can readily determine which aspects of the present invention can be configured to implement and/or would benefit from the particular blockchain services. Certain considerations by those of skill in the art in determining whether an intended service is a blockchain service and/or whether aspects of the present invention may benefit or augment an intended system include, but are not limited to: service application, source of the service (e.g., if the service is associated with a known or verifiable blockchain service provider), responsiveness of the service (e.g., some blockchain services may have an expected completion time, and/or may be determined by utilization), cost of the service, amount of data requested for the service, and/or amount of data generated for the service (the blockchain blocks or keys associated with the blockchain may be of a particular size or a particular size range). Although specific examples of blockchain services and considerations are described herein for purposes of illustration, any system that would benefit from the disclosure herein and any consideration that would be appreciated by one of ordinary skill in the art having the benefit of the disclosure herein are specifically contemplated as within the scope of the present disclosure.
The term "blockchain" (and variations such as "crypto-currency ledgers") as used herein may be broadly understood as describing a crypto-currency ledger that records, manages, or otherwise processes online transactions. The blockchain may be public, private, or a combination thereof, but is not limited thereto. Blockchains may also be used to represent a set of digital transactions, agreements, terms, or other digital value. Without being limited to any other aspect or description of the invention, in the former case, the blockchain may also be used in conjunction with investment applications, credential transaction applications, and/or digital/cryptocurrency-based markets. Blockchains may also be associated with providing a price, such as providing goods, services, items, fees, access-restricted areas or events, data, or other valuable benefits. Various forms of blockchains may be included in discussing the value units, mortgages, currencies, cryptocurrencies, or any other form of value. The value symbolized or represented by a blockchain can be readily ascertained by one of ordinary skill in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available. Although specific examples of a blockchain are described herein for illustrative purposes, any embodiment that would benefit from the disclosure herein, and any consideration that would be appreciated by one skilled in the art having the benefit of the disclosure herein, are specifically contemplated as falling within the scope of the present disclosure.
The terms "ledger" and "distributed ledger" (and similar terms) as used herein should be construed broadly. The ledger may be, without limitation to any other aspect or description of the invention, a document, file, computer file, database, book, etc. that maintains a transaction record. Ledgers may be physical or digital. Ledgers may include records related to sales, accounts, purchases, transactions, assets, liabilities, revenues, expenditures, capital, and the like. The ledger may provide a time-dependent transaction history. Ledgers may be centralized or decentralized/distributed. A centralized ledger may be a document controlled, updated, or viewed by one or more selected entities or clearing houses, where changes or updates to the ledgers are managed or controlled by the entities or clearing houses. A distributed ledger may be a ledger that is distributed among multiple entities, participants, or areas that may independently, simultaneously, or consistently update or modify their ledger copies. Ledgers and distributed ledgers may include security measures and encryption functions for signing, hiding or verifying content. In the case of a distributed ledger, blockchain techniques may be used. In the case of a distributed ledger implemented using blockchains, the ledger may be a Merkle tree consisting of a linked list of nodes, where each node contains hashed or encrypted transaction data for the previous node. Some transaction records may not be considered ledgers. A file, computer file, database, or book may or may not be a ledger, depending on the data it stores, the organization, maintenance, or manner of protection of the data. For example, if the transaction list cannot be trusted or verified, and/or based on inconsistent, fraudulent, or incomplete data, the transaction list may not be considered a ledger. The data in the ledger may be organized in any format, such as tables, lists, binary data streams, etc., according to convenience, data source, data type, environment, application, etc. The ledgers shared between different entities may not be distributed ledgers, but the distinction of distributed ledgers may be based on which entities have the right to make changes to the ledgers and/or how the changes are shared and handled between different entities. Thus, the advantages of the present invention are applicable to a variety of data, and any such data may be considered a ledger herein, while in some embodiments, given data may not be considered a ledger herein. Those skilled in the art who have the benefit of the disclosure herein and who have knowledge of the conventional expected ledgers and distributed ledgers available can readily determine which aspects of the present invention are useful for implementing and/or will benefit from the present disclosure. Certain considerations by those skilled in the art in determining whether the expected data is a ledger and/or whether aspects of the present invention may be useful or enhance the expected ledger include, but are not limited to: data security in ledgers (whether data can be tampered with or modified), time associated with making changes to data in ledgers, cost of making changes (calculation and currency), details of the data, organization of the data (whether the data needs to be processed for use in an application), who controls the ledgers (whether the principal can be trusted or relied on to manage the ledgers), confidentiality of the data (who can view or track the data in the ledgers), size of the infrastructure, communication requirements (distributed ledgers may require a communication interface or specific infrastructure), elasticity. Although specific examples of blockchain services and considerations are described herein for purposes of illustration, any system that would benefit from the disclosure herein and any consideration that would be appreciated by one of ordinary skill in the art having the benefit of the disclosure herein are specifically contemplated as within the scope of the present disclosure.
The term "loan" (and similar terms) as used herein should be construed broadly. Without being limited to any other aspect or description of the invention, a loan may be a agreement regarding properties that are borrowed and are expected to be returned in physical terms (e.g., borrowed money and returned money) or as a contracted transaction (e.g., borrowing a first good or service, and returning money, a second good or service, or a combination of both). The asset may be money, property, time, physical, virtual, service, rights (e.g., ticket, license, or other rights), depreciated amount, credit (e.g., tax credit, emission credit, etc.), contracted risk or liability, and/or any combination thereof. The loan may be based on a formal or informal agreement between the borrower and the borrower, wherein the borrower may provide the borrower with the property for a predetermined time, variable period of time, or indefinitely. The borrower and borrower may be individuals, entities, companies, governments, groups, organizations, etc. The types of loans may include mortgage loans, personal loans, warranty loans, non-warranty loans, preferential loans, commercial loans, petty loans, and the like. The agreement between the borrower and the borrower may specify terms of the loan. The borrower may be required to return the asset or to repayment on a different asset than the borrowed asset. In some cases, the loan may require repayment of interest from the borrowed property. Borrowers and borrowers may be intermediaries between entities that may never own or use the property. In some embodiments, the loan may not be associated with direct transfer of the good, but may be associated with a right of use or shared right of use. In some embodiments, the agreement between the borrower and the borrower may be performed between the borrower and the borrower, and/or between intermediary persons (e.g., beneficiaries of loan rights, such as by selling a loan). In some embodiments, the agreement between the borrower and the borrower may be performed by the service herein, such as by an intelligent contract service that determines at least a portion of the terms and conditions of the loan, and in some embodiments, the borrower and/or the borrower may adhere to the terms of the agreement, which may be an intelligent contract. In some embodiments, the smart contract service may populate the terms of the agreement and present them to the borrower and/or borrower for execution. In some embodiments, the smart contract service may automatically cause one of the borrower or borrower to adhere to terms (at least as an offer) and may present the offer to the other of the borrower or borrower for execution. In some embodiments, the loan agreement may include multiple borrowers and/or multiple borrowers, e.g., where a group of loans includes multiple payment beneficiaries for the group of loans and/or multiple borrowers for the group of loans. In some embodiments, the risk and/or liabilities of the set of loans may be individual (e.g., each borrower and/or borrower is associated with a particular loan of the set of loans), shared (e.g., a violation of a particular loan has an associated loss shared among the borrowers), and/or combinations (e.g., one or more subsets of the set of loans are processed and/or shared separately).
Some agreements may not be considered loans. Depending on the property being transferred, the manner in which the property is transferred, or the parties involved, the agreement to transfer or borrow the property may not be considered a loan. For example, in some cases, asset transfer may be indefinite, and may be considered to be selling the asset or permanent transfer. Likewise, a property may not be considered a loan in some cases if it is borrowed or transferred without explicit or clear terms or lack of consensus between the borrower and borrower. Even if the formal agreement is not directly written into the written agreement, the agreement may be considered a loan as long as the parties voluntarily and informed agree on the arrangement, and/or the transaction may be considered a loan by common practice (e.g., in a particular industry). Thus, the advantages of the present invention are applicable to a variety of agreements, and any such agreement may be considered a loan herein, while in some embodiments, a given agreement may not be considered a loan herein. Those skilled in the art, having the benefit of the disclosure herein and knowing the usual expected loans available, can readily determine which aspects of the invention will realize a loan, utilize a loan, or benefit a particular loan transaction. Certain considerations by those skilled in the art in determining whether the anticipated data is a loan and/or whether aspects of the invention may be useful or enhance the anticipated loan include, but are not limited to: the value of the property involved, the borrower's ability to return or repay the loan, the type of the property involved (e.g., whether the property is consumed through use), the repayment terms associated with the loan, the interest of the loan, the arrangement of the loan agreements, the form of the agreement, details of the loan agreement, mortgage attributes associated with the loan, and/or ordinary business expectations of any of the above under certain circumstances. Although specific examples of loans and considerations are described herein for illustrative purposes, any system that would benefit from the disclosure herein and any consideration that would be appreciated by one skilled in the art having the benefit of the disclosure herein are specifically contemplated as falling within the scope of the present disclosure.
The term "loan-related event" (and similar terms, including loan-related events) as used herein should be construed broadly. Without being limited to any other aspect or description of the invention, a loan-related event may include any event related to the terms of a loan or an event triggered by a loan-related agreement. Loan-related events may include loans, default, performance, repayment, payment, interest changes, late fee assessment, refund assessment, allocation, and the like. Loan-related events may be triggered by explicit agreement terms; for example, the agreement may specify that the interest rate increases over a period of time after the loan is started; the agreement-induced interest rate increase may be a loan-related event. The loan related event may be implicitly triggered by the related loan agreement terms. In some embodiments, any occurrence that may be considered to be related to a hypothesis of the loan agreement and/or a principal's expectations of the loan agreement may be considered an event occurrence. For example, if a mortgage of an anticipated loan is replaceable (e.g., as an inventory of mortgages), a change in inventory level may be considered an occurrence of a loan-related event. In another example, a lack of mortgage access, failure or malfunction of a monitoring sensor, etc. may be considered to be a loan related event if a mortgage is expected to be reviewed and/or validated. In some embodiments, the circuitry, controller, or other device described herein may automatically trigger the determination of a loan-related event. In some embodiments, the loan-related event may be triggered by an entity managing the loan or the loan-related contract. The loan-related event may be conditionally triggered according to one or more conditions in the loan agreement. The loan-related event may be related to a task or requirement that a borrower, or third party needs to complete. Certain events may be considered loan-related events in certain embodiments and/or certain contexts, but may not be considered loan-related events in another embodiment or context. Many events may be related to the loan, but may be caused by external triggers that are not related to the loan. However, in some embodiments, the external trigger event (e.g., a change in price of a commodity associated with a mortgage) may be a loan-related event in some embodiments. For example, if the terms and/or fulfillment of an existing loan agreement do not trigger a renegotiation, the borrower-initiated loan terms renegotiation may not be considered a loan-related event. Thus, the advantages of the present invention may be applied to a variety of events, and any such event may be considered a loan-related event herein, while in some embodiments a given event may not be considered a loan-related event herein. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional anticipated system available, can readily determine which aspects of the present invention can be considered loan-related events for the anticipated system and/or the particular transaction that the system supports. Certain considerations by those skilled in the art in determining whether the expected data is a loan-related event and/or whether aspects of the invention may be useful or enhance the expected transaction system include, but are not limited to: the impact of the related event on the loan (the event that caused the breach or termination of the loan may have a higher impact), the costs associated with the event (capital and/or operating costs), the costs associated with monitoring the occurrence of the event (capital and/or operating costs), the entity responsible for responding to the event, the time period and/or response time associated with the event (e.g., the time required to complete the event and the time allocated from when the event triggered to when the event needs to be processed or detected), the entity responsible for the event, the data required to process the event (e.g., confidential information may have different protective measures or restrictions), the mitigation measures that may be taken when the undetected event occurs, and/or the remedies that may be taken when the undetected event occurs. Although specific examples of loan-related events and considerations are described herein for illustrative purposes, any system that would benefit from the disclosure herein and any consideration that would be appreciated by one of ordinary skill in the art having the benefit of the disclosure herein are specifically contemplated as falling within the scope of the present disclosure.
The term "loan-related activity" (and similar terms) as used herein should be construed broadly. Without being limited to any other aspect or description of the invention, loan-related activities may include activities related to the generation, maintenance, termination, collection, execution, servicing, billing, marketing, executive capability, or negotiation of a loan. The loan-related activities may include activities related to signing a loan agreement or ticket, reviewing a loan document, processing a payment, evaluating a mortgage, evaluating a borrower or borrower for compliance with a loan term, renegotiating a term, perfecting a loan guarantee or mortgage, and/or canceling a term. The loan-related activity may be related to an event associated with the loan, such as an activity associated with an initial negotiation, prior to the formal agreement being made with terms. The loan-related activities may be related to events during the loan and after the end of the loan. The loan-related activities may be performed by a borrower, or third party. Some activities may not be considered loan-related activities alone, but may be considered loan-related activities based on the specificity of the activity to the loan period-e.g., invoicing or invoicing related to outstanding loans may be considered loan-related activities, however, when the invoicing or invoicing of a loan is combined with the invoicing or invoicing of non-loan-related elements, the invoicing may not be considered loan-related activities. Whether or not the loan is related to a property, certain activities may be related to the property; in these cases, the activity may not be considered a loan-related activity. For example, whether or not a property is associated with a loan, periodic audits associated with the property may occur or may not be considered a loan-related activity. In another example, periodic audits related to properties may be required by a loan agreement and typically will not occur unless related to a loan, in which case the activity may be considered a loan-related activity. In some embodiments, an activity may be considered a loan-related activity if no activity occurs with the loan inactive or absent, but in some cases (e.g., if an audit occurs normally but the borrower has no ability to perform or audit the audit, the audit may be considered a loan-related activity even if the audit has occurred in other ways). Thus, the advantages of the present invention may be applied to a variety of events, and any such event may be considered a loan-related event herein, while in some embodiments a given event may not be considered a loan-related event herein. Loan-related activities for the intended system purpose can be readily determined by those skilled in the art, having the benefit of the disclosure herein and understanding the conventional intended system available. Certain considerations by those skilled in the art in determining whether the desired data is a loan-related activity and/or whether aspects of the invention may be useful or enhance the desired loan include, but are not limited to: the necessity of the loan activity (whether the loan agreement or term may be met without the activity), the cost of the activity, the specificity of the activity to the loan (whether the activity is similar or identical to other industries), the time involved in the activity, the impact of the activity on the loan period, the entity performing the activity, the amount of data required for the activity (whether the activity requires confidential information related to the loan or personal information related to the entity), and/or the ability of the principal to perform and/or review the activity. Although specific examples of loan-related events and considerations are described herein for illustrative purposes, any system that would benefit from the disclosure herein and any consideration that would be appreciated by one of ordinary skill in the art having the benefit of the disclosure herein are specifically contemplated as falling within the scope of the present disclosure.
The terms "loan terms," "terms and conditions," and the like as used herein should be construed broadly ("loan terms"). Without being limited to any other aspect or description of the invention, loan terms may relate to conditions, rules, limitations, contractual obligations, and the like, related to the time of borrower and borrower agreement, repayment, initiation, and other executable conditions. The loan terms may be specified in a formal contract between the borrower and the borrower. The loan terms may specify interest rates, mortgages, redemption stopping conditions, debt consequences, payment options, payment plans, contracts, and the like. The loan terms may be negotiated or may be changed during the loan period. The loan terms may change or be affected by external parameters, such as market price, bond price, conditions associated with the borrower or borrower, etc. Certain aspects of the loan may not be considered as terms of the loan. In some embodiments, aspects of the loan that have not been formally agreed upon between the borrower and/or aspects of the loan that are not generally understood during the business process (and/or the particular industry) may not be considered as terms of the loan. Certain aspects of the loan may be preliminary or informal before they are formally agreed or confirmed in a contractual agreement or agreement. Certain aspects of a loan may not be considered a loan term alone, but may not be considered a loan term based on the specificity of a particular loan aspect. Certain aspects of the loan may not be considered a loan term at a particular time during the loan, but may be considered a loan term at another time during the loan (e.g., obligations and/or relinquishments that may occur when a party performs and/or the loan term expires). For example, interest rates are not generally considered to be loan terms until they are defined as related to the loan and as how to calculate compound benefits (years, months), etc. If an aspect of the loan is uncertain or not executable, it may not be considered a term. Some aspects may be a manifestation of or related to a loan term, but may not itself be a loan term. For example, the loan terms are a repayment period of the loan, such as one year. The terms may not specify how to repay the loan within a year. The loan may be repaid for 12 months or one year. In this case, the monthly payment plan may not be considered the terms of the loan because it is simply one or more repayment options in the loan that are not directly specified. Accordingly, the advantages of the present invention may be applied to various loan aspects, and any such aspect may be considered a loan term herein, while in certain embodiments, a given aspect may not be considered a loan term herein. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional prospective systems available, can readily determine which aspects of the invention are loan conditions of the prospective systems.
Certain considerations by those skilled in the art in determining whether the expected data is a loan term and/or whether aspects of the invention may be useful or enhance the expected loan include, but are not limited to: the term's performability (whether a condition can be performed by a borrower or borrower), the cost of performing the term (the time or effort required to ensure compliance with the term), the complexity of the term (how easily the involved parties are in compliance with or understand the term, whether the term is prone to error or misunderstanding), the entity responsible for the term, the fairness of the term, the stability of the term (the frequency of term changes), the observability of the term (whether the term can be verified by another party), the availability of the term to one party (whether the term is beneficial to the borrower or borrower), the risk associated with the loan (the term may depend on the probability that the loan may not be repayment), the characteristics of the borrower or borrower (which satisfy the ability of the term), and/or general expectations of the loan and/or related industries.
Although specific examples of loan terms and considerations are described herein for illustrative purposes, any system that would benefit from the disclosure herein and any consideration that would be appreciated by one skilled in the art having the benefit of the disclosure herein are specifically contemplated as within the scope of the present disclosure.
The terms "loan conditions", "terms and conditions", and the like as used herein are to be construed broadly ("loan conditions"). Without being limited to any other aspect or description of the invention, the loan conditions may relate to rules, limitations, and/or obligations related to the loan. The loan conditions may be related to rules or necessary obligations to obtain a loan, maintain a loan, apply for a loan, transfer a loan, etc. Loan conditions may include bond principal, bond balance, fixed interest rate, variable interest rate, payment amount, payment plan, end-of-line clearance plan, mortgage description, mortgage substitutability description, mortgage processing, mortgage usage rights, principal, insured, guarantor, personal guarantor, retention rights, deadline, contractual, redemption stopping conditions, default conditions, conditions related to the borrower's other bonds, and default consequences.
Certain aspects of the loan may not be considered a loan condition. Aspects of the loan that have not been formally agreed upon between the borrower and the borrower, and/or aspects of the loan that are not generally understood during the business (and/or the particular industry), may not be considered loan conditions. Certain aspects of the loan may be preliminary or informal before they are formally agreed or confirmed in a contractual agreement or agreement. Certain aspects of a loan may not be considered a loan condition alone, but may be considered a loan condition based on the specificity of a particular loan aspect. Certain aspects of the loan may not be considered a loan condition at a particular time during the loan, but may be considered a loan condition at another time during the loan (e.g., obligations and/or relinquishments that may occur with respect to principal performance and/or expiration of the loan condition). Thus, the advantages of the present invention may be applied to various loan aspects, and any such aspect may be considered a loan condition herein, while in certain embodiments, a given aspect may not be considered a loan condition herein. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional prospective systems available, can readily determine which aspects of the invention are the loan terms of the prospective systems. Certain considerations by those skilled in the art in determining whether the expected data is a loan condition and/or whether aspects of the invention may be useful or enhance the expected loan include, but are not limited to: the performability of a condition (whether a borrower or borrower can perform the condition), the cost of performing the condition (the time or effort required to ensure compliance with the condition), the complexity of the condition (how easily the involved parties are in compliance with or understand the condition, whether the condition is prone to error or misunderstanding), the entity responsible for the condition, the fairness of the condition, the observability of the condition (whether the condition can be verified by another party), the availability of terms to one party (whether the condition is beneficial to the borrower or borrower), the risk associated with the loan (the condition may depend on the probability that the loan may not be repairable), and/or the general expectations of the loan and/or related industries.
Although specific examples of loan conditions and considerations are described herein for illustrative purposes, any system that would benefit from the disclosure herein and any consideration that would be appreciated by one of ordinary skill in the art having the benefit of the disclosure herein are specifically contemplated as within the scope of the present disclosure.
The terms "loan mortgage," "mortgage," and the like as used herein should be construed broadly. Without limiting any other aspect or description of the invention, a loan mortgage may refer to any property or property that a borrower promises to a borrower as a reserve for loans and/or as a guarantee for loans. A mortgage may be any item of value that is accepted in the form of an alternate repayment in the event of a loan breach. Mortgages may include any number of physical or virtual items, such as vehicles, ships, airplanes, buildings, houses, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a set of inventory, merchandise, securities, currencies, value certificates, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contract rights, antiques, fixtures, furniture, equipment items, tools, machinery, and personal property. Mortgages may include more than one item or item type.
Mortgages may describe a property, value, or other item defined as a loan or transaction guarantee. A set of mortgages may be defined and replacement, removal, or addition of mortgages may be accomplished in the set of mortgages. For example, a mortgage may not be limited to: vehicles, ships, aircraft, buildings, homes, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, merchandise, securities, money, value certificates, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment items, tools, machinery or personal property, and the like. If a set of one or more mortgages is defined, the mortgage may be replaced, removed, or added, for example, to a set of mortgages. Without being limited to any other aspect or description of the invention, a mortgage or set of mortgages may also be used in conjunction with other terms of an agreement or loan, such as statement, guarantee, reimbursement, agreement, liability balance, fixed interest rate, variable interest rate, payment amount, payment plan, end-of-day maximum clearance plan, mortgage description, mortgage substitutability description, guarantee, personal guarantee, lien, deadline, redemption-stopping condition, default condition, and default outcome. In some embodiments, the smart contract may calculate whether a borrower meets conditions or contracts, and in the event that the borrower does not meet such conditions or contracts, may enable automatic actions or trigger other conditions or terms that may affect mortgage status, ownership, or transfer, or initiate replacement, removal, or addition of mortgages to a set of loan mortgages. Those skilled in the art, having the benefit of the disclosure herein and understanding the conventional mortgages available, may readily determine the purpose and use of the mortgage, including substitution, removal, and addition, in the various embodiments and contexts disclosed herein.
Although specific examples of mortgages and considerations are described herein for illustrative purposes, any system that would benefit from the disclosure herein and any consideration that would be appreciated by one of ordinary skill in the art having the benefit of the disclosure herein are specifically contemplated as within the scope of the present disclosure.
The term "smart contract service" (and similar terms) as used herein should be construed broadly. Without being limited to any other aspect or description of the invention, smart contract services include any service or application that manages smart contracts or smart loan contracts. For example, the smart contract service may specify the terms and conditions of the smart contract, such as in a rules database, or process the output of a set of valuation services, and allocate mortgages sufficient to provide a guarantee for the loan. The smart contract service may automatically execute a set of rules or conditions embodying the smart contract, where the execution may be based on or utilize the collected data. The smart contract service may automatically initiate loan payment requirements, automatically initiate redemption-stopping flows, automatically initiate actions to claim replacement or backup mortgages or transfer mortgage ownership, automatically initiate inspection flows, automatically alter mortgage-based payments or interest rate terms, and may configure smart contracts to automatically perform loan-related actions. The smart contract may manage at least one of loan terms and conditions, loan-related events, and loan-related activities. The smart contract may be a protocol encoded as a computer protocol that may facilitate, verify, or enforce negotiations or fulfillment of the smart contract. The smart contract may or may not be one or more of partially or fully self-executing or partially or fully self-enforcing.
Some processes may not be considered individually as being related to a smart contract, but may be considered to be a smart contract related to an aggregated system-for example, in one instance, automatically performing loan-related actions may not be related to a smart contract, but in another instance, may be governed by the terms of a smart contract. Accordingly, the advantages of the present invention are applicable to a variety of process systems, and any such process or system may be considered a smart contract or smart contract service herein, while in some embodiments a given service may not be considered a smart contract service herein.
Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit a particular system and how to combine the processes and systems in this summary to implement smart contract services and/or enhance the operation of the contemplated systems. Certain considerations by those of skill in the art in determining whether a desired system includes a smart contract service or smart contract and/or whether aspects of the present invention may benefit or augment the desired system include, but are not limited to: the ability to automatically transfer mortgage ownership in response to an event; automatic actions that can be taken when a contract is found to be compliant (or non-compliant); whether a mortgage is suitable for clustering, rebalancing, distributing, adding, replacing, and removing the mortgage's items; an aspect of the loan responds to the modified parameters of the event (e.g., time, complexity, suitability of the loan type, etc.); the complexity of system loan terms and conditions, including the advantages of quickly determining and/or predicting changes in entities related to the loan (e.g., mortgages, principal's financial status, countering mortgages and/or industries related to the principal); automatic generation of terms and conditions and/or whether execution of terms and conditions is applicable to the type of loan, party, and/or industry expected by the system; etc. Although specific examples of smart contract services and considerations are described herein for illustrative purposes, any system that would benefit from the disclosure herein and any consideration that would be appreciated by one skilled in the art having the benefit of the disclosure herein are specifically contemplated as within the scope of the present disclosure.
The term "IoT system" (and similar terms) as used herein should be construed broadly. Without limiting any other aspect or description of the invention, an internet of things system includes any system comprised of uniquely identified and associated computing devices, mechanical and digital machines, sensors, and objects that are capable of transmitting data over a network without intervention. Certain components may not be considered individually as an internet of things system, but may be considered as an internet of things system in an aggregate system-e.g., a single internet of things.
The sensors, smart speakers, and/or medical devices may not be part of the internet of things system, but may be part of a larger system and/or aggregated with many other similar components to be considered part of the internet of things system and/or the internet of things system. In some embodiments, for some purposes but not for other purposes, the system may be considered an internet of things system-e.g., the intelligent speakers may be considered part of an internet of things system for some operations (e.g., for providing surround sound, etc.), but not for other operations (e.g., transmitting content directly from a single local network source). Additionally, in some embodiments, appearance systems that are similar in appearance may be distinguished in determining whether such systems are IoT systems and/or what type of IoT system. For example, one group of medical devices may not share the aggregated HER database at a given time, while another group of medical devices may share data to the aggregated HER for purposes of clinical research, so one group of medical devices may be an internet of things system while another group is not. Accordingly, the advantages of the present invention may be applied in a variety of systems, and any such system may be considered an internet of things system herein, while in certain embodiments, a given system may not be considered an internet of things system herein. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional desired system available, can readily determine which aspects of the present invention will benefit a particular system and how to combine the processes and systems in this summary to enhance the operation of the desired system and/or which circuits, controllers, and/or devices comprise an IoT system for the desired system. Certain considerations by those of skill in the art in determining whether a desired system is an internet of things system and/or whether aspects of the present invention may benefit or augment the desired system include, but are not limited to: the transmission environment of the system (e.g., low power availability, inter-device networking); a shared data store for a group of devices; establishing a geofence by a set of devices; serving as a blockchain node; execution of asset, mortgage, or entity monitoring; relay data between devices; the ability to aggregate data from multiple sensors or monitoring devices, etc. Although specific examples of IoT systems and considerations are described herein for illustrative purposes, any system that would benefit from the disclosure herein and any consideration that would be appreciated by one of ordinary skill in the art having the benefit of the disclosure herein are specifically contemplated as within the scope of the present disclosure.
The term "data collection service" (and similar terms) as used herein should be construed broadly. Without being limited to any other aspect or description of the invention, a data collection service includes any service that collects data or information, including any circuit, controller, device, or application that can store, send, transmit, share, process, organize, compare, report, and/or aggregate data. The data collection service may include and/or may be in communication with a data collection device (e.g., a sensor). The data collection service may monitor the entity, for example, identifying data or information to collect. The data collection service may be event driven, run periodically, or retrieve data from an application at a particular point of application execution. Some processes may not be considered data collection services alone, but may be considered data collection services in an aggregated system-for example, in one instance, a network storage device may be a component of a data collection service, but in another instance, may have a separate functionality. Accordingly, the advantages of the present invention may be applied to a variety of process systems, and any such process or system may be considered a data collection service herein, while in some embodiments a given service may not be considered a data collection service herein. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit a particular system and how to combine the processes and systems in this summary to implement a data collection service and/or enhance the operation of the contemplated system. Certain considerations by those skilled in the art in determining whether a desired system is a data collection service and/or whether aspects of the present invention may be useful or enhance the desired system include, but are not limited to: the ability to dynamically modify business rules and change data collection protocols; monitoring the event in real time; connecting the data collection device to the monitoring infrastructure, executing computer-readable instructions, causing the processor to record or track the event; using an automated inspection system; sales are made at networked points of sale; data from one or more distributed sensors or cameras is required; etc. Although specific examples of data collection services and considerations are described herein for illustrative purposes, any system that would benefit from the disclosure herein and any consideration that would be appreciated by one skilled in the art having the benefit of the disclosure herein are specifically contemplated as falling within the scope of the present disclosure.
The term "data integration service" (and similar terms) as used herein should be construed broadly. Without limiting to any other aspect or description of the present invention, a data integration service includes any service that integrates data or information, including any device or application that can extract, convert, load, normalize, compress, decompress, encode, decode, and otherwise process data packets, signals, and other information. The data integration service may monitor entities, for example, identify data or information for integration. The data integration service can integrate data regardless of the frequency, communication protocol, or business rules required for the complex integration pattern. Accordingly, the advantages of the present invention may be applied to a variety of process systems, and any such process or system may be considered a data integration service herein, while in some embodiments a given service may not be considered a data integration service herein. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit a particular system and how to combine the processes and systems in this summary to achieve a data integration service and/or enhance the operation of the contemplated systems. Certain considerations by those skilled in the art in determining whether a desired system is a data integration service and/or whether aspects of the present invention may be useful or enhance the desired system include, but are not limited to: the ability to dynamically modify business rules and change data integration protocols; integrating the pull-in data with a third party database; synchronizing data across different platforms; connected to a central data repository; data storage capacity, processing capacity, and/or communication capacity distributed throughout the system; connecting independent automatic workflows; etc. Although specific examples of integrated services and considerations are described herein for illustrative purposes, any system that would benefit from the disclosure herein and any consideration that would be appreciated by one skilled in the art having the benefit of the disclosure herein are specifically contemplated as falling within the scope of the present disclosure.
The term "computing service" (and similar terms) as used herein should be construed broadly. Without being limited to any other aspect or description of the invention, a computing service may be included as part of one or more services, platforms, or micro-services, such as a blockchain service, a data collection service, a data integration service, a valuation service, a smart contract service, a data monitoring service, a data mining, and/or any service that facilitates data collection, access, processing, conversion, analysis, storage, visualization, or sharing. Some processes may not be considered computing services. For example, a process may not be considered a computing service depending on the kind of rules governing the service, the end product of the service, or the intent of the service. Accordingly, the advantages of the present invention are applicable to a variety of process systems, and any such process or system may be considered a computing service herein, while in some embodiments a given service may not be considered a computing service herein. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit a particular system and how to combine the processes and systems in this summary to implement one or more computing services and/or enhance the operation of the contemplated systems. Certain considerations by those skilled in the art in determining whether a desired system is a computing service and/or whether aspects of the present invention may be useful or enhance the desired system include, but are not limited to: access to a service based on a protocol; coordinating exchanges between different services; providing on-demand computing power to Web services; monitoring, collecting, accessing, processing, converting, analyzing, storing, integrating, visualizing, mining, or sharing one or more data is accomplished. Although specific examples of computing services and considerations are described herein for illustrative purposes, any system that would benefit from the disclosure herein and any consideration that would be appreciated by one skilled in the art having the benefit of the disclosure herein are specifically contemplated as falling within the scope of the present disclosure.
The term "sensor" as used herein should be construed broadly. Without limiting any other aspect or description of the invention, a sensor may be a device, module, machine, or subsystem that detects or measures a physical quality, event, or change. In embodiments, the detection or measurement may be recorded, indicated, communicated, or otherwise responded to. Examples of sensors may be a sensor for sensing movement of an entity, a sensor for sensing temperature, pressure or other properties about the entity or its environment, a camera capturing still or video images of the entity, a sensor collecting data about mortgages or assets (e.g. about location, condition (health, physical or other), quality, security, possession, etc.). In an embodiment, the sensor may be sensitive to the property to be measured, but not to the property, but not to other properties. The sensor may be analog or digital. The sensor may include a processor, transmitter, transceiver, memory, power supply, sensing circuitry, electrochemical reservoir, light source, and the like. Further examples of sensors contemplated for use in the system include biological sensors, chemical sensors, black silicon sensors, infrared sensors, acoustic sensors, inductive sensors, motion sensors, optical sensors, opacity sensors, proximity sensors, inductive sensors, eddy current sensors, passive infrared proximity sensors, radar, capacitance sensors, capacitance displacement sensors, hall effect sensors, magnetic sensors, GPS sensors, thermal imaging sensors, thermocouples, thermistors, photoelectric sensors, ultrasonic sensors, infrared laser sensors, inertial motion sensors, MEMS internal motion sensors, ultrasonic three-dimensional motion sensors, accelerometers, inclinometers, force sensors, piezoelectric sensors, rotary encoders, linear encoders, ozone sensors, smoke sensors, thermal sensors, magnetometers, carbon dioxide sensors, carbon monoxide sensors, oxygen sensors, glucose sensors, smoke detectors, metal detectors, volume sensors, altimeters, GPS, outdoor detection, environmental detection, activity detection, object detection (e.g., position detectors, such as Radio Frequency (RF) tags, radio Frequency (RF) distance detectors, radio Frequency (RF) tags, radio Frequency (RF) range detectors, radio frequency (e.g., power) detectors, radio Frequency (RF) range detectors, radio Frequency (RF) range(s). In some embodiments, the sensor may be a virtual sensor—for example, the interest parameter is determined as a result of a calculation based on other sensed parameters in the system. In some embodiments, the sensor may be an intelligent sensor-e.g., reporting the sensed value as abstract communication of the sensed value (e.g., as network communication). In some embodiments, the sensor may provide the sensed value directly (e.g., as a voltage level, frequency parameter, etc.) to circuitry, a controller, or other devices in the system. Those skilled in the art, having the benefit of the disclosure herein and understanding the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit from the sensors. Certain considerations by those of skill in the art in determining whether a desired device is a sensor and/or whether aspects of the present invention may benefit from or be enhanced by the desired sensor include, but are not limited to: adjusting activation/deactivation of the system based on the environmental quality; converting the electrical output into a measurement quantity; the ability to implement geofences; automatically modifying the loan in response to the change in the mortgage; etc. Although specific examples of sensors and considerations are described herein for illustrative purposes, any system that would benefit from the disclosure herein and any consideration that would be appreciated by one of ordinary skill in the art having the benefit of the disclosure herein are specifically contemplated as falling within the scope of the present disclosure.
The term "storage condition" and similar terms used herein should be construed broadly. Without limiting to any other aspect or description of the invention, storage conditions include an environment, physical location, environmental quality, exposure level, security measures, maintenance descriptions, accessibility descriptions, etc. associated with storing a property, mortgage or entity or support contract, loan or other agreement specified and monitored in the contract, loan or agreement, etc. Based on the stored conditions of the mortgage, asset or entity, actions may be taken to maintain, improve and/or confirm the condition of the asset or use the asset as a mortgage. Based on the storage conditions, actions may be taken to change the terms or conditions of the loan or bond. The storage conditions may be categorized according to various rules, thresholds, condition programs, workflows, model parameters, etc., and may be based on self-reporting or data from the internet of things device, data from a set of environmental condition sensors, data from a set of social network analysis services, and a set of algorithms for querying network domains, social media data, crowd-sourced data, etc. The storage conditions may be related to mortgage, distributor, borrower, fund distribution, or other geographic location. Examples of internet of things data may include images, sensor data, location data, and the like. Examples of social media data or crowd-sourced data may include the behavior of a party to a loan, the financial condition of a party, the compliance of a party with terms or conditions of a loan or bond, and so forth. The lender may include a bond issuer, a related entity, a borrower, a third party to the liability equity. The storage conditions may be related to an asset or mortgage type, such as a vehicle, a ship, an airplane, a building, a home, a real estate, an undeveloped property, a farm, a crop, a municipal facility, a warehouse, a set of inventory, merchandise, securities, money, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contract rights, antiques, fixtures, furniture, equipment items, tools, machinery, and personal property. The storage condition may include an environment, wherein the environment may include an environment selected from a municipal environment, a corporate environment, a securities trading environment, a real estate environment, a commercial facility, a warehouse facility, a transportation environment, a manufacturing environment, a storage environment, a house, or a vehicle. Actions based on mortgage, property, or entity storage conditions may include managing, reporting, altering, joining, merging, terminating, maintaining, modifying terms and/or conditions, redeeming, or otherwise processing a loan, contract, or agreement. Those skilled in the art, having the benefit of the disclosure herein and knowing the expected storage conditions, can readily determine which aspects of the present invention will benefit from the particular application of the storage conditions. Some considerations by those skilled in the art or embodiments of the present invention in selecting appropriate storage conditions for management and/or monitoring include, but are not limited to: the validity of the conditions of a given transaction jurisdiction, the available data for a given mortgage, the type of transaction expected (loan, bond or debt), the particular type of mortgage, the ratio of loan to value, the ratio of mortgage to loan, the total transaction/loan amount, the credit score of borrowers and borrowers, common practices of the industry, and other considerations. Although specific examples of storage conditions are described herein for illustrative purposes, any embodiment that would benefit from the disclosure herein, and any consideration that would be appreciated by one skilled in the art having the benefit of the disclosure herein, are specifically contemplated as falling within the scope of the present disclosure.
The term "geographic location" and similar terms used herein should be construed broadly. Without being limited to any other aspect or description of the invention, geographic location includes identifying or estimating a real world geographic location of an object, including generating a set of geographic coordinates (e.g., latitude and longitude) and/or a street address. Based on the geographic location of the mortgage, asset, or entity, actions may be taken to maintain or improve the condition of the asset or to use the asset as a mortgage. Based on the geographic location, an action may be taken to change the terms or conditions of the loan or bond. Based on geographic location, determination or prediction related to transactions may be made based on weather, internal clutter in a particular region, and/or local disasters (e.g., earthquakes, floods, tornados, hurricanes, industrial accidents, etc.). The geographic location may be determined according to various rules, thresholds, conditional programs, workflows, model parameters, etc., and may be based on data from reports or from internet of things devices, data from a set of environmental condition sensors, data from a set of social network analysis services, and a set of algorithms for querying network domains, social media data, crowd-sourced data, etc. Examples of geographic location data may include GPS coordinates, images, sensor data, street addresses, and the like. The geographic location data may be quantitative (e.g., longitude/latitude, relative to a platform map, etc.) and/or qualitative (e.g., categorical, such as "coast", "rural", etc.; "within new york city", etc.). The geographic location data may be absolute (e.g., GPS location) or relative (e.g., within 100 codes of the expected location). Examples of social media data or crowd-sourced data may include behavior of a borrowing party inferred from a geographic location, financial status of a party inferred from a geographic location, compliance of a party with terms or conditions of a loan or bond, and the like. The geographic location of an asset or mortgage type may be determined, such as municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farms, crops, municipalities, warehouses, a set of inventory, merchandise, securities, currencies, value documents, tickets, consumables, edible items, beverages, precious metals, jewelry items, precious stones, antiques, fixtures, furniture, equipment items, tools, machinery, or personal property. The geographic location of an entity may be determined, such as one of the principal, a third party (e.g., inspection service, maintenance service, cleaning service, etc. associated with the transaction), or any other entity associated with the transaction. The geographic location may include an environment selected from a municipal environment, a corporate environment, a securities trading environment, a real estate environment, a commercial facility, a warehouse facility, a transportation environment, a manufacturing environment, a storage environment, a house, or a vehicle. Mortgage, property, or entity geographic location-based actions may include managing, reporting, altering, joining, merging, terminating, maintaining, modifying terms and/or conditions, redeeming, or otherwise processing a loan, contract, or agreement. Those skilled in the art, having the benefit of the disclosure herein and knowing the intended system, can readily determine which aspects of the present invention will benefit a particular application of geographic location, and which positional aspect of an article is the geographic location of the intended system. Some considerations by those skilled in the art or embodiments of the present invention in selecting an appropriate geographic location for management include, but are not limited to: the legitimacy of a geographic location of a given transaction jurisdiction, the available data for a given mortgage, the type of expected transaction (loan, bond or debt), the particular type of mortgage, the ratio of loan to value, the mortgage to loan ratio, the total transaction/loan amount, the frequency and other considerations of borrowers' travel to certain jurisdictions, the liquidity of mortgage, and/or the likelihood of a particular location event occurring in connection with a transaction (e.g., weather, location of related industrial facilities, availability of related services, etc.). Although specific examples of geographic locations are described herein for illustrative purposes, any embodiments that benefit from the disclosure herein, and any considerations that would be understood by one of ordinary skill in the art having the benefit of the disclosure herein, are specifically contemplated as within the scope of the present disclosure.
The term "jurisdiction" and similar terms used herein should be construed broadly. Without being limited to any other aspect or description of the present invention, jurisdictional refers to law and legal authority that governs loan entities. The jurisdiction may be based on the geographic location of the entity, the registration location of the entity (e.g., flag country of a ship, enterprise registration country, etc.), the grant country of certain rights (e.g., intellectual property priority), etc. In some embodiments, the jurisdictional location may be one or more geographic locations of entities in the system. In some embodiments, the jurisdictional location may be different from the geographic location of any entity in the system (e.g., the protocol specifies some other jurisdiction). In some embodiments, the jurisdiction may differ for entities in the system (e.g., borrower at A, borrower at B, mortgage at C, agreement enforced at D, etc.). In some embodiments, the jurisdictional location of a given entity may vary during operation of the system (e.g., due to movement of mortgage, changes in related data, terms and conditions, etc.). In some embodiments, a given entity of the system may have multiple jurisdictions (e.g., due to the operation of related laws and/or options available to one or more parties), and/or may have different jurisdictions for different purposes. The jurisdictional location of a mortgage, asset, entity or action may indicate certain terms or conditions of a loan or bond and/or may indicate different obligations to issue notifications, redemption and/or default executions to an individual, mortgage and/or debt vouching processes, and/or various data processing within the system. Although specific examples of a jurisdiction are described herein for illustrative purposes, any embodiment that would benefit from the disclosure herein, and any consideration that would be appreciated by one skilled in the art having the benefit of the disclosure herein, are specifically contemplated as within the scope of the present disclosure.
The terms "value-added credential," "credential," and variants thereof, such as "cryptocurrency credential," as used herein in the context of value-added may be construed broadly as describing: (a) Currency or cryptocurrency units (e.g., cryptocurrency vouchers) and (b) may also be used to represent vouchers that may be exchanged for goods, services, data, or other valuable value (e.g., value vouchers). Without being limited to any other aspect or description of the invention, in the former case, the voucher may also be used in connection with investment applications, voucher transaction applications, and a voucher based marketplace. The credentials may also be associated with providing a price, such as providing goods, services, items, fees, access to a restricted area or event, data, or other valuable benefits. The credentials may be either optional (e.g., or with access credentials) or not. For example, value vouchers may be exchanged for accommodations (e.g., hotel rooms), dining/food and services, spaces (e.g., shared spaces, work spaces, meeting spaces, etc.), fitness/health goods or services, event tickets or event tickets, travel, flights or other vehicles, digital content, virtual goods, license keys, or other valuable goods, services, data, or valuations. Various forms of credentials may be included in discussing the price, mortgage, or units of value (whether monetary, cryptocurrency, or any other form of value, such as goods, services, data, or other benefits). The value represented or represented by the voucher, whether monetary, cryptocurrency, merchandise, services, data, or other value, can be readily ascertained by those skilled in the art having the benefit of the disclosure herein and understanding the voucher. Although specific examples of the vouchers are described herein for illustrative purposes, any embodiment that benefits from the disclosure herein, and any consideration that would be appreciated by one skilled in the art having the benefit of the disclosure herein, are specifically contemplated as falling within the scope of the present disclosure.
The term "pricing data" as used herein may be construed broadly to describe quantity information, such as price or cost, of one or more items in a market. Without limiting to any other aspect or description of the invention, pricing data may also be used in conjunction with spot market pricing, forward market pricing, pricing discount information, promotional pricing, and other information related to item costs or prices. The pricing data may satisfy one or more conditions or may trigger the application of one or more rules of the smart contract. Pricing data may be used in conjunction with other forms of data such as market value data, accounting data, access data, asset and facility data, worker data, event data, underwriting data, claim data, or other forms of data. Pricing data may be adjusted based on the item of value (e.g., condition, liquidity, location, etc.) and/or the context of the particular party. The purpose and use of pricing data in the various embodiments and contexts disclosed herein can be readily ascertained by one of ordinary skill in the art having the benefit of the disclosure herein and understanding the pricing data.
Without being limited to any other aspect or description of the invention, the vouchers include, but are not limited to, value vouchers, e.g., mortgage, asset, rewards, e.g., in vouchers that are representations of value, e.g., value-holding vouchers that may be exchanged for goods or services. Some components may not be considered separately as credentials, but may be considered credentials in an aggregate system-e.g., the value set on an asset may not itself be a credential, but the value of an asset may be set in a value credential, e.g., store, exchange, transaction, etc. For example, in a non-limiting example, the blockchain circuitry may be configured to provide a mechanism for a borrower to store the value of the property, wherein the value attributed to the credential is stored in a distributed ledger of the blockchain circuitry, but the credential itself that assigned the value may be exchanged or transacted through a credential marketplace or the like. In some embodiments, the credential may be considered a credential for some purpose but not for other purposes-e.g., the credential may be used as an indication of ownership of an asset, but the purpose of such a credential would not be to transact as a value that the credential may exist to include the value of the asset. Thus, the advantages of the present invention may be applied in a variety of systems, and any such system may be considered a credential system herein, while in some embodiments a given system may not be considered a credential system herein. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit a particular system and/or how to combine the processes and systems in this summary to enhance the operation of the contemplated systems. Certain considerations by those skilled in the art in determining whether the intended system is a credential and/or whether aspects of the present invention may be beneficial or enhanced for the intended system include, but are not limited to, access data, such as data related to access rights, notes and credentials; for investment applications such as investment shares, equity and vouchers; a credential transaction application; a credential-based marketplace; price forms such as monetary rewards and vouchers; converting the value of the resource using the credentials; encrypting the currency document; ownership indications such as identity information, event information, and credential information; trading blockchain-based access credentials in a marketplace application; pricing applications, such as setting and monitoring or pricing with access rights, basic access rights, credentials, and fees; transaction applications, such as transactions or exchanges or having access or potential access or credentials; creating and storing credentials on the blockchain for generating ownership or access rights (e.g., tickets); etc.
The term "financial data" as used herein may be broadly understood to describe the collection of financial information about an asset, mortgage, or other or item. Financial data may include revenue, expense, assets, liabilities, equity, bond ratings, violations, rate of Return On Asset (ROA), rate of Return On Investment (ROI), past performance, expected future performance, revenue per share (EPS), internal Rate of Return (IRR), surplus announcements, rates, etc., with statistical analysis of any of the above (e.g., moving average), etc. Financial data may also be used in combination with pricing data and market value data without being limited to any other aspect or description of the invention. The financial data may satisfy one or more conditions, or may trigger the application of one or more rules of the smart contract. Financial data may be used in conjunction with other forms of data, such as market value data, pricing data, accounting data, access data, asset and facility data, worker data, event data, underwriting data, claim data, or other forms of data. The purpose and use of pricing data in the various embodiments and contexts disclosed herein can be readily ascertained by one of ordinary skill in the art having the benefit of the disclosure herein and understanding the financial data.
The term "contract" as used herein may be construed broadly to describe terms, agreements or commitments, such as fulfilling some as or not as a whole. For example, the contract may relate to the principal's behavior or the principal's legal status. Without being limited to any other aspect or description of the invention, a contract may also be used in connection with other relevant terms of an agreement or loan, such as statement, guarantee, reimbursement, debt balance, fixed interest rate, variable interest rate, payment amount, payment plan, end-of-maximum payback plan, mortgage description, mortgage substitutability description, principal, insured, guaranty, personal guaranty, retention, deadline, redemption conditions, default conditions, and outcome of the violation. A contract or unfulfilled contract may satisfy one or more conditions, or may trigger a payment, a violation, or other terms and conditions. In some embodiments, the smart contract may calculate whether the contract is satisfied, and in the event that the contract is not satisfied, may enable automatic actions or trigger other conditions or terms. The purpose and use of the various embodiments and contextually contracts disclosed herein can be readily ascertained by one of ordinary skill in the pertinent art having the benefit of the disclosure and understanding the contracts herein.
The term "entity" as used herein may be construed broadly to describe a principal, a third principal (e.g., auditor, regulatory agency, service provider, etc.), and/or identifiable related objects, such as mortgages related to a transaction. Example entities include individuals, partnerships, companies, limited-responsibility companies, or other legal organizations. Other example entities include identifiable mortgages, countermortgages, potential mortgages, and the like. For example, the entity may be a given party, such as a person, to an agreement or loan. Data or other terms herein may be data having a context associated with an entity, such as an entity-oriented data. The entity may be a particular context or application, such as, but not limited to, a human entity, a physical entity, a transaction entity, or a financial entity. An entity may have a representation that represents it or represents its behavior. Without limiting any other aspect or description of the invention, an entity may also be used in connection with other related entities or terms of an agreement or loan, such as statements, guarantees, benefits, debt balances, fixed interest rates, variable interest rates, payment amounts, payment plans, end-of-maximum payback plans, mortgage descriptions, mortgage substitutability descriptions, parties, insured persons, guarantors, personal guaranties, liens, deadlines, redemption conditions, default conditions, and violation consequences. An entity may have a set of attributes, such as: the valuation of public claims, the valuation of a set of properties owned by an entity, the bankruptcy condition, the redemption prevention condition, the contract violation condition, the supervision violation condition, the crime condition, the export regulation condition, the bankruptcy condition, the tariff condition, the tax condition, the credit report, the credit rating, the website rating, the set of entity product customer reviews, the social network rating, the set of credentials, the set of transfer media, the set of proofs, the set of behaviors, the location, and the geographic location, but are not limited thereto. In some embodiments, the smart contract may calculate whether an entity satisfies a condition or contract, and in the event that such condition or contract is not satisfied by the entity, may enable automatic actions or trigger other conditions or terms. The purpose and use of the entities in the various embodiments and contexts disclosed herein may be readily ascertained by one of ordinary skill in the pertinent art having the benefit of the disclosure herein and knowing the entities.
The term "principal" as used herein may be understood broadly as describing a member of an agreement, such as a person, partner, corporation, finite-responsibility corporation, or other legal organization. For example, the principal may be a primary borrower, a secondary borrower, a lending agent, a corporate borrower, a government borrower, a banking borrower, a guaranteed borrower, a bond issuer, a bond buyer, an unsecured borrower, a guarantor provider, a borrower, a debtor, an underwriter, an inspector, an evaluator, an auditor, a valuation professional, a government officer, an accounting or other entity with agreements, transaction or loan rights or obligations. A party may define different terms as different terms, such as a transaction among a plurality of parties involved in a transaction, etc., but is not limited thereto. The principal may have a representative that represents it or represents its conduct. In some embodiments, the term "principal" may refer to a potential principal or an intended principal-e.g., an intended borrower or borrower interacting with the system that may not have promised to reach an actual agreement during interaction with the system. Without being limited to any other aspect or description of the invention, a principal may also be used in connection with other interested parties or terms of an agreement or loan, such as statements, guarantees, benefits, debt balances, fixed interest rates, variable interest rates, payment amounts, payment plans, maximum end point payback plans, mortgage descriptions, mortgage substitutability descriptions, entities, insured persons, guaranty, personal guaranty, retention rights, deadlines, redemption conditions, default conditions, and violation consequences. The principal may have a set of attributes, such as: identity, reputation, activity, behavior, business practices, contract fulfillment status, receivability information, accounts payable information, mortgage value information, and other types of information, but are not limited thereto. In some embodiments, the smart contract may calculate whether the principal satisfies a condition or contract, and in the event that the principal does not satisfy such a condition or contract, may enable automatic actions or trigger other conditions or terms. The purpose and use of the parties in the various embodiments and contexts disclosed herein can be readily ascertained by one of ordinary skill in the art having the benefit of the disclosure herein and an understanding of the parties.
The terms "principal attribute," "entity attribute," or "principal/entity attribute" as used herein may be construed broadly to describe a value, feature, or state of a principal or entity. For example, the attributes of a principal or entity may be, but are not limited to: value, quality, location, equity, price, physical condition, health condition, security, ownership, identity, reputation, activity, behavior, business practices, contract fulfillment status, accounts receivable information, accounts payable information, mortgage value information, and other types of information, and the like. In some embodiments, the smart contract may calculate a value, state, or condition related to an attribute of the principal or entity, and in the event that the principal or entity does not satisfy such a condition or contract, may enable an automatic action or trigger other conditions or terms. The purpose and use of these attributes in the various embodiments and contexts disclosed herein can be readily ascertained by one of ordinary skill in the art having the benefit of the disclosure herein and knowing the attributes of the principal or entity.
The term "borrower" as used herein is to be construed broadly to describe an agreed party that provides loan properties, loan benefits, and may include individuals, partnerships, companies, finite liabilities, or other legal organizations. For example, the borrower may be a primary borrower, a secondary borrower, a corporate borrower, a government borrower, a banking borrower, a guaranteed borrower, an unsecured borrower, or other parties having agreements, transaction or loan rights or obligations to provide a loan to the borrower, but is not so limited. The borrower may have a representative that represents it or represents its conduct. Without being limited to any other aspect or description of the invention, a principal may also be used in conjunction with other interested parties or terms of agreements or loans, such as borrowers, insurers, statements, guarantees, reimbursements, debt balances, fixed interest rates, variable interest rates, payment amounts, payment plans, end-of-line clearing plans, mortgage descriptions, mortgage substitutability descriptions, vouchers, personal vouchers, liens, deadlines, redemption conditions, default conditions, and default consequences. In some embodiments, the smart contract may calculate whether the borrower satisfies a condition or contract, and in the event that the borrower does not satisfy such a condition or contract, may enable automatic actions, notifications, or alarms, or trigger other conditions or terms. The purpose and use of the borrower in the various embodiments and contexts disclosed herein may be readily ascertained by one of ordinary skill in the pertinent art having the benefit of the present disclosure and understanding the borrower.
The term "crowd-sourced services" as used herein may be understood broadly to describe services provided or presented in connection with a crowd-sourced model or transaction, wherein a large number of people or entities provide contributions to meet the needs of the transaction, such as loans. The crowdsourcing service may be provided by a platform or system, but is not limited thereto. The crowdsourcing request may be communicated to a group of information providers, and responses to the request may be collected and processed by the crowdsourcing request to provide compensation to at least one successful information provider. The request and parameters may be used to obtain information related to the status of a set of loan mortgages. Crowd-sourced requests may be issued. In some embodiments, but not limited to, the crowdsourcing service may be performed by a smart contract, where rewards are managed by the smart contract, which processes responses to the crowdsourcing request and automatically assigns rewards to information meeting a set of parameters configured for the crowdsourcing request. The purpose and use of the crowdsourcing services in the various embodiments and contexts disclosed herein may be readily ascertained by one of ordinary skill in the art having the benefit of the disclosure herein and the knowledge of the crowdsourcing services.
The term "publishing service" as used herein may be understood to describe a set of services for publishing crowd-sourced requests. The publication service may be provided by a platform or system, but is not limited thereto. In some embodiments, but not limited to, the publishing service may be performed by a smart contract, wherein the crowdsourcing request is published by the smart contract or the publication is initiated by the smart contract. The purpose and use of the publication service in the various embodiments and contexts disclosed herein may be readily ascertained by one of ordinary skill in the art having the benefit of the disclosure herein and the knowledge of the publication service.
The term "interface" as used herein may be broadly interpreted to describe components by which interaction or communication is effected, such as components of a computer, which may be implemented in software, hardware, or a combination thereof. For example, the interface may serve many different purposes or for different applications or contexts, such as, but not limited to: an application programming interface, a graphical user interface, a software interface, a marketplace interface, a demand aggregation interface, a crowdsourcing interface, a secure access control interface, a network interface, a data integration interface, or a cloud computing interface, or a combination thereof. The interface may be, but is not limited to, a means of inputting, receiving or displaying data within the loan, re-financing, collecting, combining, warranting, proxy, or redemption stopping scope. The interface may be an interface to another interface. Without being limited to any other aspect or description of the invention, an interface may be used in connection with an application, process, module, service, layer, device, component, machine, article, subsystem, interface, or connection, or as part of a system. In some embodiments, the interface may be embodied in software, hardware, or a combination thereof, and may be stored in a medium or memory. The purpose and use of the interfaces in the various embodiments and contexts disclosed herein can be readily ascertained by one of ordinary skill in the pertinent art having the benefit of the disclosure herein and knowing the interfaces.
The term "graphical user interface" as used herein may be understood as the type of interface that allows a user to interact with a system, computer, or other interface, where the interaction or communication is accomplished through a graphical device or representation. The graphical user interface may be a component of a computer, which may be embodied as computer readable instructions, hardware, or a combination thereof. The graphical user interface may serve many different purposes or may be configured for different applications or contexts. Such an interface may be used as a way to receive or display data using visual representation, stimulus, or interaction data, but is not limited thereto. The graphical user interface may be used as an interface to another graphical user interface or other interfaces. Without being limited to any other aspect or description of the invention, the graphical user interface may be used in conjunction with an application, process, module, service, layer, device, component, machine, product, subsystem, interface, or connection, or as part of a system. In some embodiments, the graphical user interface may be embodied in computer readable instructions, hardware, or a combination thereof, and stored in a medium or memory. The graphical user interface may be used for any type of input, including a keyboard, mouse, touch screen, etc. The graphical user interface may be used in any desired user interaction environment including, for example, a dedicated application, a web interface, or a combination of these. The purpose and use of the graphical user interface in the various embodiments and contexts disclosed herein may be readily ascertained by one of ordinary skill in the pertinent art having the benefit of the disclosure herein and understanding the graphical user interface.
The term "user interface" as used herein may be understood as the type of interface that allows a user to interact with a system, computer, or other device, where the interaction or communication is accomplished through a graphical device or representation. The user interface may be a component of a computer, which may be implemented in software, hardware, or a combination thereof. The user interface may be stored in a medium or memory. The user interface may include drop-down menus, tables, forms, and the like, including default, templated, recommended, or preconfigured conditions. In some embodiments, the user interface may include voice interactions. Without being limited to any other aspect or description of the invention, the user interface may be used in connection with an application, circuit, controller, process, module, service, layer, device, component, machine, product, subsystem, interface, or connection, or as part of a system. The user interface may serve many different purposes or be configured for different applications or contexts. For example, the borrower side user interface may include features for viewing multiple customer profiles, but may be restricted from making certain changes. The debtor side user interface may include features for viewing detailed information and making changes to the user account. A third party neutral side interface (e.g., a third party willing to participate in a base transaction, such as a regulatory agency, auditor, etc.) may have the ability to view corporate supervision and anonymous user data, but not be able to manipulate any data, and may have predetermined access depending on the third party and access purpose. The third party wished-side interface (e.g., a third party who may be willing to participate in the underlying transaction, such as a payee, debtor, surveyor, part of the owner, etc.) may include functionality that allows viewing of particular user data and limits changes. Further features of these user interfaces may be used to implement embodiments of the systems and/or processes described in this disclosure. Accordingly, the advantages of the present invention may be applied in a variety of processes and systems, and any such process or system may be considered a service herein. The purpose and use of the user interface in the various embodiments and contexts disclosed herein may be readily ascertained by one of ordinary skill in the pertinent art having the benefit of the disclosure herein and the knowledge of the user interface. Certain considerations by those skilled in the art in determining whether the desired interface is a user interface and/or whether aspects of the present invention may be useful or enhance the desired system include, but are not limited to: configurable views, limited ability to manipulate or view, reporting functions, ability to manipulate user profiles and data, implementing regulatory requirements, providing desired user functions for borrowers, third parties, and the like.
The terms "interface" and "control panel" as used herein may also be broadly interpreted to describe components by which interaction or communication is effected, such as components of a computer, which may be implemented in software, hardware, or a combination thereof. The interface and control panel may acquire, receive, present, or otherwise evaluate an item, service, offer, or other aspect of a transaction or loan. For example, the interface and control panel may serve many different purposes or be configured for different applications or contexts, such as, but not limited to: an application programming interface, a graphical user interface, a software interface, a marketplace interface, a demand aggregation interface, a crowdsourcing interface, a secure access control interface, a network interface, a data integration interface, or a cloud computing interface, or a combination thereof. The interface or control panel may be used as a means of receiving or displaying data within a loan, re-financing, collection, merger, warranty, proxy, or redemption-suppressing context, but is not so limited. The interface or control panel may serve as an interface or control panel for another interface or control panel. Without limiting any other aspect or description of the invention, an interface may be used in conjunction with an application, circuit, controller, process, module, service, layer, device, component, machine, article, subsystem, interface, or connection, or as part of a system. In some embodiments, the interface or control panel may be embodied in computer readable instructions, hardware, or a combination thereof, and stored in a medium or memory. The purpose and use of the interfaces and/or control panels in the various embodiments and contexts disclosed herein can be readily ascertained by one of ordinary skill in the pertinent art having the benefit of the disclosure herein and understanding the conventional contemplated systems available.
The term "domain" as used herein may be construed broadly to describe the scope or context of a transaction and/or communications associated with a transaction. For example, a domain may serve many different purposes or for different applications or contexts, such as, but not limited to: the domain for executing, the domain for digital assets, the target domain for issuing requests, the target domain for applying social networking data collection and monitoring services, the target domain for applying internet of things data collection and monitoring services, the network domain, the geographic location domain, the jurisdictional location domain, and the time domain. Without being limited to any other aspect or description of the invention, one or more domains may be used with respect to any application, circuit, controller, process, module, service, layer, device, component, machine, product, subsystem, interface, or connection, or as part of a system. In some embodiments, the domains may be embodied in computer readable instructions, hardware, or a combination thereof, as well as stored in a medium or memory. The purpose and use of the various embodiments and fields of context disclosed herein may be readily ascertained by one of ordinary skill in the art having the benefit of the disclosure herein and the knowledge of the same.
The term "request" (and variants) as used herein may be broadly understood to describe an action or instance that initiates or requests provision of things (e.g., information, responses, objects, etc.). A particular type of request may also serve many different purposes or be configured for different applications or contexts, such as, but not limited to: formal legal requests (e.g., citations), re-financing requests (e.g., loans), or crowd-sourced requests. The system may be used to execute requests and to fulfill requests. Various forms of requests may be included in discussing legal actions, loan re-financing, or crowd-sourced services, but are not limited thereto. The value of the request implemented in an embodiment may be readily determined by one of ordinary skill in the art, having the benefit of the disclosure herein and knowing the intended system. Although specific examples of the subject matter are described herein for illustrative purposes, any embodiments that benefit from the disclosure herein, and any considerations that would be understood by one of ordinary skill in the art having the benefit of the disclosure herein, are specifically contemplated as falling within the scope of the present disclosure.
The term "reward" (and variants) as used herein may be understood broadly as describing what is received or provided in response to an action or stimulus. May be of the financial type or of the non-financial type, but is not limited thereto. The particular type of consideration may also serve many different purposes or be configured for different applications or contexts, such as, but not limited to: consideration events, consideration claims, monetary consideration, consideration captured as a data set, consideration points, and other forms of consideration. Consideration may trigger, assign, generate for innovation, provide for submitting evidence, request, provide, select, manage, configure, assign, communicate, identify, but is not limited to such, and other actions. The system may be used to perform the actions described above. Various forms of consideration may be included in discussing or encouraging specific actions, but are not limited thereto. In some embodiments herein, the reward may be used as a specific incentive (e.g., a specific person whose reward is responsive to the crowd-sourced request) or a general incentive (e.g., a reward that is responsive to the successful crowd-sourced request is provided in addition to or instead of providing the reward to the specific person who is responsive). Those skilled in the art, having the benefit of the disclosure herein and having the knowledge of the rewards, can readily determine the value of the rewards implemented in the embodiments. Although specific examples of a reward are described herein for illustrative purposes, any embodiment that would benefit from the disclosure herein, and any consideration that would be appreciated by one skilled in the art having the benefit of the disclosure herein, are specifically contemplated as within the scope of the present disclosure.
The term "robotic process automation system" as used herein may be understood broadly to describe a system capable of performing a task or providing a need for the system of the present invention. For example, robotic process automation systems, but are not limited thereto, may be used to: the method comprises the steps of negotiating a set of terms and conditions of a loan, negotiating loan resurfacing, loan withdrawal, merging a set of loans, managing warranty loans, mortgage agents, redemption-stopping negotiating training, configuring crowd-sourcing requests based on a set of attributes of the loan, setting rewards, determining a set of domains to which the requests are to be issued, configuring request content, configuring data collection and monitoring actions based on a set of attributes of the loan, determining a set of domains to which the data collection and monitoring service of the internet of things is to be applied, and performing iterative training and improvement based on a set of results. The robotic process automation system may include: a set of data collection and monitoring services, an artificial intelligence system, and another robotic process automation system that is a component of a higher level robotic process automation system. The robotic process automation system may include: at least one of the set of mortgage activities and the set of mortgage interactions includes the activities of: marketing campaigns, identifying a set of prospective borrowers, identifying properties, identifying mortgage, borrower qualifications, ownership searches, ownership verification, property assessment, property inspection, property valuation, revenue verification, borrower demographic analysis, identifying capital providers, determining available interest rates, determining available payment terms and conditions, analyzing existing mortgage loans, comparing existing and new mortgage loan terms, completing application workflows, populating application fields, preparing mortgage agreements, completing mortgage agreement plans, negotiating mortgage terms and conditions with capital providers, negotiating mortgage terms and conditions with borrowers, transferring ownership, setting liens, and ending mortgage agreements. An exemplary and non-limiting robotic process automation system may include one or more user interfaces, interfaces with circuitry and/or controllers in the overall system for providing, requesting, and/or sharing data, and/or one or more artificial intelligence circuits for iteratively improving one or more operations of the robotic process automation system. Those skilled in the art, having the benefit of the disclosure herein and with the knowledge of conventional intended robotic process automation systems available, can readily determine the circuitry, controllers, and/or devices for implementing the robotic process automation system that performs selected functions of the intended system. Although specific examples of robotic process automation systems are described herein for purposes of illustration, any embodiment would benefit from the disclosure herein and understand any consideration.
The term "loan-related activity" (and other related terms such as "loan-related event" and "loan-related activity") as used herein may be construed broadly to describe one or more actions, events, or activities related to a transaction, including a loan in a transaction. The actions, events, or activities may occur in many different loan environments, such as, but not limited to, loans, re-financing, merging, warranty, agency, redemption prevention, management, negotiation, collection, purchasing, execution, and data processing (e.g., data collection), or a combination thereof. The loan-related actions may be used in noun form (e.g., the default notification has been communicated to the borrower through a formal notification, which may be considered a loan-related action). Loan-related actions, events, or activities may refer to a single instance, and may represent a group of actions, events, or activities. For example, a single action such as providing a borrower with a specific notification of an overdue payment may be considered a loan-related action. Likewise, a set of actions that are related to a breach throughout may also be considered loan-related actions. Assessment, inspection, financing, and logging (but not limited thereto) may also be considered as loan-related actions that have occurred, and loan-related events may also be considered as loan-related events. Likewise, these activities that accomplish these actions may also be considered loan-related activities (e.g., evaluating, checking, subsidizing, recording, etc.), but are not limited thereto. In some embodiments, the smart contract or robotic process automation system may perform a loan-related action, a loan-related event, or a loan-related activity for one or more of the parties and process the appropriate tasks to accomplish the same task. In some cases, the smart contract or robotic process automation system may not be able to complete the loan-related actions, and depending on such results, this may enable automatic actions or trigger other conditions or terms. The purpose and use of this term in the various forms and embodiments described in this invention can be readily ascertained by one of ordinary skill in the pertinent art having the benefit of the present disclosure and understanding the loan related actions, events, and activities.
As described herein, the term "loan-related actions, events, and activities" may also be used more specifically to describe the context for the loan being earned. An equity loan is an action that a borrower may require to repay the loan, typically triggered by other conditions or terms (e.g., payment due). For example, when a borrower delinquents a loan three times in succession, a loan-related action of the offer may occur, resulting in a severe delinquent loan payment plan and a loan default. In this case, the borrower may initiate a loan-related action to collect the loan to protect his rights. In this case, the borrower may pay a fee to correct the default and fine, which may also be considered a loan-related action to collect the loan. In some cases, the smart contract or robotic process automation system may initiate or process loan-related actions, but is not limited to providing notification, investigation, and collection of payment history, or other tasks performed as part of the loan being collected. The purpose and use of the term in the event or other various embodiments and contexts disclosed herein may be readily ascertained by one of ordinary skill in the art having the benefit of the disclosure herein and understanding other forms of loan-related actions or terms of loan earnings and their various forms.
As described herein, the term "loan-related actions, events, and activities" may also be used more specifically to describe the context for loan payment. Typically, but not limited to, in transactions involving loans, the loan is repayment on a payment plan. Various actions may be taken to provide the borrower with information to repay the loan, as well as actions by which the borrower receives payment for the loan. For example, if the borrower pays for a loan, a loan-related action of the payment may occur. But are not limited to, such payments may include several actions related to loan payments, such as: payment submitted to the borrower, a loan ledger or accounting reflecting the paid, a payment receipt provided to the borrower, and the next payment requested by the borrower. In some cases, the smart contract or robotic process automation system may initiate, manage, or process such loan-related loan payment actions, but is not limited to issuing notifications to the borrower, investigating and collecting payment history, providing receipts to the borrower, providing notification of the next payable borrower's funds, or other actions related to loan payment. Other forms of loan-related actions or terms, and their various forms, that would benefit from the disclosure herein and understand loan payments, may be readily ascertained by one of ordinary skill in the art as the purpose and use of the terms in the event or other various embodiments and contexts disclosed herein.
As described herein, the term "loan-related actions, events, and activities" may also be used more specifically to describe the context for a payment plan or an alternative payment plan. Typically, but not limited to, in transactions involving loans, the loans are repayment according to a payment plan that may be modified over time. Alternatively, such payment plans may be formulated and agreed upon in the alternative, and provided as an alternative payment plan. In a borrower or borrower payment plan or alternate payment plan, various actions may be taken, such as: the amount of such payment, the fines or fees upon expiration of the payment, or other terms. For example, if the borrower pays for a loan ahead of time, loan payment planning and loan-related actions replacing the payment planning may occur; in this case, perhaps payment is applied as principal, while periodic payments should still expire. But are not limited to, loan actions related to a payment plan and an alternative payment plan may include several actions related to loan payment, such as: payment submitted to the borrower, a credit ledger or accounting reflecting the paid, a payment receipt provided to the borrower, additional or expired fee calculations, and the next payment requested by the borrower. In some embodiments, the activity that determines the payment plan or the alternative payment plan may be a loan-related action, event, or activity. In some embodiments, the activity conveying the payment plan or the alternative payment plan (e.g., to the borrower, or third party) may be a loan-related action, event, or activity. In some cases, the smart contract circuitry or robotic process automation system may initiate, manage, or process such loan-related actions with respect to the payment plan and the alternate payment plan, but is not limited to issuing notifications to the borrower, investigating, and collecting payment history, providing receipts to the borrower, calculating a next due date, calculating a final payment amount and date, providing a next payment notification to the borrower, determining a payment plan or alternate payment plan, communicating a payment plan or alternate payment plan, or other actions related to loan payment. The purpose and use of the term in the event or other various embodiments and contexts disclosed herein may be readily ascertained by one of ordinary skill in the art having the benefit of the disclosure herein and understanding other forms and variations of loan-related actions or terms of payment plans or alternate payment plans.
The term "regulatory notification requirement" (and any derivatives thereof) as used herein may be broadly understood to describe an obligation or condition to communicate a notification or information to another party or entity. Regulatory notification requirements may be required under one or more conditions that are triggered or typically required. For example, a borrower may have regulatory notice requirements that require a borrower to be provided notice of a loan breach, notice of a change in interest rate of the loan, or other notice related to a transaction or loan. Regulatory aspects of the term may be attributed to jurisdiction-specific laws, rules, or regulations requiring certain communication obligations. In some embodiments, the policy instructions may be considered regulatory notification requirements-e.g., when a borrower has an internal notification policy that may exceed regulatory requirements of one or more jurisdictions associated with the transaction. Notification aspects generally relate to formal communications, which may take many different forms, but may be specifically designated as a particular form of notification, such as authenticated mail, fax, email transmission, or other physical or electronic form for notifying content and/or time requirements associated with the notification. The requirement aspect relates to the obligation that the principal must complete to adhere to laws, rules, guidelines, policies, standard practices or agreements or loan terms. In some embodiments, the smart contract may process or trigger regulatory notification requirements and provide appropriate notifications to the borrower. This may be based on the location of at least one of: lenders, funds provided by loans, repayment of loans, mortgages of loans, or other locations specified by terms of the loans, transactions, or agreements. If the principal or entity does not meet such regulatory notification requirements, certain changes in both rights or obligations may be triggered-e.g., the borrower providing an out-of-specification notification to the borrower, taking automatic actions or triggers based on the loan terms and conditions, and/or based on external information (e.g., the borrower's regulatory practices, internal policies, etc.), may be implemented by the smart contract circuitry and/or the robotic process automation system. The purpose and use of the regulatory notification requirements of the various embodiments and contexts disclosed herein can be readily ascertained by one of ordinary skill in the art having the benefit of the disclosure herein and knowing the conventional contemplated systems available.
The term "regulatory notification requirements" may also be used to describe obligations or conditions for transmitting a notification or message to another party or entity based on a general or specific policy, rather than based on laws, rules, or criteria of a particular jurisdiction or a particular location, as in a jurisdiction-specific regulatory notification requirement. The regulatory notification requirements may be careful or suggested, rather than mandatory or required, under one or more conditions that are triggered or generally required. For example, a borrower may have policy-based regulatory notification requirements that require that the borrower be provided with a new information website or notification that a change in loan interest will be experienced in the future or other notification of consultancy or assistance in connection with a transaction or loan, but not mandatory (although mandatory notification may also be of policy basis). Thus, in using the term "regulatory notification requirements" based on policies, the smart contract circuitry may process or trigger the regulatory notification requirements and provide appropriate notifications to the borrower that may or may not be required by laws, rules, or guidelines. The basis for notification or communication may be for caution, politics, convention, or obligation.
The term "regulatory notification" may also be used herein to describe an obligation or condition that specifically communicates a notification or message to another party or entity (e.g., a borrower or borrower). The administrative notifications may be specific to any party or entity, or a group of parties or entities. For example, it may be suggested or required to provide a particular notification or communication to the borrower, such as in the event that the borrower fails to provide a predetermined payment for the loan resulting in an default. Thus, such regulatory notices for a particular user (e.g., borrower or borrower) may be the result of jurisdiction-specific or policy-based regulatory notice requirements, or for other reasons. Thus, in some cases, the smart contract may process or trigger the administrative notification, and providing the appropriate notification, law, rule or criteria to a particular party (e.g., borrower) may or may not require this, but such notification may be provided for cautious, polite or routine reasons. If a principal or entity does not meet such regulatory notification requirements for a particular principal or principals, it may be the case that certain rights may be exempted by the principal or principals or that an automatic action may be initiated or other conditions or terms triggered. The purpose and use of the regulatory notification requirements of the various embodiments and contexts disclosed herein can be readily ascertained by one of ordinary skill in the art having the benefit of the disclosure herein and knowing the conventional contemplated systems available.
The term "regulatory redemption request" (and any derivative) as used herein may be construed broadly to describe obligations or conditions that trigger, process, or complete a loan breach, mortgage redemption or withdrawal, or other related redemption-suppressing action. Regulatory redemption-stopping requirements may be required under one or more conditions that are triggered or typically required. For example, the borrower may have regulatory redemption requirements that require notification of the borrower's loan breach, or other notification regarding the loan breach prior to redemption. Regulatory aspects of the term may be attributed to jurisdiction-specific laws, rules, or regulations requiring certain communication obligations. The redemption-stopping aspect typically involves specific remedial action for the redemption of the redemption or mortgage property withdrawal and loan breach, which may take many different forms, but may also be specified in the loan terms. The requirement aspect relates to the obligation that the principal must complete to adhere to or fulfill the laws, rules, guidelines or agreements or loan terms. In some embodiments, the smart contract circuitry can process or trigger regulatory redemption-stopping requirements and handle appropriate tasks related to such redemption-stopping actions. This may be based on the jurisdictional location of at least one of: lenders, borrowers, funds provided by loans, repayment of loans, mortgages of loans, or other locations specified by terms of the loans, transactions, or agreements. If a principal or entity does not meet such regulatory redemption requirements, the principal or entity (e.g., borrower) may be exempted from certain rights, or such actions that fail to comply with regulatory notification requirements may initiate automatic actions or trigger other conditions or terms. The purpose and use of the various embodiments and in the context of the present disclosure can be readily ascertained by one of ordinary skill in the art having the benefit of the disclosure herein and the knowledge of the conventional contemplated systems available.
The term "regulatory redemption requirements" may also be used herein to describe obligations to trigger, handle, or complete loan violations, mortgage redemption or retraction, or other related redemption stopping actions based on general or specific policies, rather than based on laws, rules, or guidelines of a particular jurisdiction or a particular location, as in a jurisdiction-specific regulatory redemption stopping requirement. Under one or more conditions of triggering or general demand, regulatory redemption requirements may be careful or suggested rather than mandatory or required. For example, a borrower may have policy-based regulatory redemption requirements that require that notification of a loan breach or other notification of consultancy or assistance in connection with a transaction or loan be provided to the borrower, but are not mandatory (although mandatory notifications may also be of policy basis). Thus, in using regulatory redemption requirements terminology based on policies, the smart contract may process or trigger the regulatory redemption requirements and provide the borrower with appropriate notifications that laws, rules, or guidelines may or may not be required. The basis for notification or communication may be for caution, politics, industry practices or obligations.
The term "regulatory redemption requirements" may also be used to describe obligations or conditions to be fulfilled with respect to a particular user (e.g., borrower or borrower). The administrative notifications may be specific to any party or entity, or a group of parties or entities. For example, it may be suggested or required to provide a particular notification or communication to the borrower, such as in the event that the borrower fails to provide a predetermined payment for the loan resulting in an default. Thus, such regulatory redemption requirements are specific to a particular user, such as a borrower or borrower, and may be a result of jurisdiction-specific or policy-based regulatory redemption requirements, or other reasons. For example, the redemption request may be related to a particular entity involved in the transaction (e.g., the current borrower has been the customer for 30 years, so he/she has acquired a unique treatment), or to a class of entities (e.g., a "priority" borrower or a "first violation" borrower). Thus, in some cases, the smart contract circuitry may handle or trigger obligations or actions that must be taken in accordance with redemption, which laws, rules, or guidelines may or may not require to do so if the actions are directed or initiated by a particular party (e.g., borrower or borrower), but may be otherwise provided for caution, politics, or convention. In some embodiments, the obligations or conditions to be fulfilled with respect to a particular user may form part of, or otherwise be known to, the particular user to whom it applies (e.g., an insurance company or bank advertising specific practices for a particular class of customers, such as a first-time offending customer, first-time accident customer, etc.), and in some embodiments the obligations or conditions to be fulfilled with respect to the particular user may be unknown to the particular user to whom it applies (e.g., the bank has policies related to the class of users to which the particular user belongs, but the particular user does not know the classification).
The terms "value," "valuation model" (and similar terms) as used herein should be construed broadly to describe a method of evaluating and determining the estimated value of a mortgage. Without being limited to any other aspect or description of the invention, the valuation model may be used in conjunction with: mortgages (e.g., vouching for property), artificial intelligence services (e.g., improving valuation models), data collection and monitoring services (e.g., setting valuation amounts), valuation services (e.g., notifying, using, and/or improving the course of valuation models), and/or results related to mortgage transactions (e.g., as a basis for improving valuation models). The "particular jurisdiction valuation model" is also used as a valuation model for a particular geography/jurisdiction or region; wherein the jurisdiction may be specific to a borrower, funds delivery, payment of a loan or mortgage of a loan, or a combination thereof. In some embodiments, the particular jurisdiction assessment model considers jurisdiction impact on mortgage assessments, including at least: rights and obligations of borrowers and borrowers in the relevant jurisdiction; jurisdiction impact on movement, import, export, replacement, and/or clearance mortgage capabilities; the effect of jurisdiction on the time between the breach and redemption of a redemption or mortgage collection; and/or the influence of jurisdictions on the volatility and/or sensitivity of the wager value determination. In some embodiments, the particular geographic location valuation model considers the geographic location impact on mortgage valuations, which may include a similar list of relative jurisdiction impacts (although jurisdictional locations may be different from geographic locations), but may also include additional impacts, such as: weather influences; the distance of the mortgage from the monitoring, maintenance or withholding service; and/or proximity risk phenomena (e.g., faulty lines, industrial sites, nuclear power plants, etc.). The valuation model may utilize a valuation of the countermortgage (e.g., a general value like a mortgage, a market value like or alternative mortgage, and/or a value of an item related to a mortgage value) as part of the mortgage valuation. In some embodiments, the artificial intelligence circuitry includes one or more machine learning and/or artificial intelligence algorithms to improve the valuation model, including, for example, utilizing information related to or counteracting time-varying changes between multiple transactions of mortgages, and/or utilizing result information from the same or other transactions (e.g., success or failure of a loan transaction to complete, and/or a mortgage sequestration or clearing event determined in response to a mortgage valuation proving the real world) to iteratively improve the valuation model. In some embodiments, the artificial intelligence circuit is trained based on mortgage valuation data sets, such as previously determined valuations and/or by interaction with a trainer (e.g., human, accounting valuations, and/or other valuation data). In some embodiments, the valuation model and/or parameters of the valuation model (e.g., assumptions, calibration values, etc.) may be determined and/or negotiated as part of the terms and conditions of the transaction (e.g., a loan, a set of loans, and/or a subset of a set of loans). Those skilled in the art, having the benefit of the disclosure herein and understanding the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit from particular applications of the estimation model and how to select or combine the estimation models to implement specific examples of the estimation model. Some considerations by those skilled in the art or embodiments of the present invention in selecting an appropriate valuation model include, but are not limited to: legal considerations given a valuation model of the vouchers jurisdiction; the available data for a given mortgage; expected transaction/loan types; a particular type of mortgage; a loan-to-value ratio; mortgage to loan ratio; total transaction/loan amount; credit rating of borrowers; loan types and/or accounting practices for related industries; uncertainty associated with any of the above; and/or sensitivity associated with any of the above. Although specific examples of valuation models and considerations are described herein for illustrative purposes, any embodiment that would benefit from the disclosure herein and any consideration that would be appreciated by one of ordinary skill in the art having the benefit of the disclosure herein are specifically contemplated as within the scope of the present disclosure.
The term "market value data" or "market information" (and other forms or variants) as used herein may be broadly understood to describe data or information related to the valuation of property, mortgage, or other items of value that may be used as a loan, mortgage, or trade target. Market value data or market information may change from time to time and may be estimated, calculated, or objectively or subjectively determined based on various sources of information. The market value data or market information may be directly related to the mortgage or countermortgage. Market value data or market information may include financial data, market ratings, product ratings, customer data, market research to learn about customer needs or preferences, and competitive intelligence. Competitors, suppliers, etc., physical sales, transactions, customer acquisition costs, customer lifetime value, brand awareness, churn rate, etc. The terms may occur in many different contexts of contracts or loans, such as, but not limited to, loans, re-financing, merging, warranty, agency, redemption prevention, and data processing (e.g., data collection), or a combination thereof. Market value data or market information may be used as nouns to identify a single number or multiple numbers or data. For example, a borrower may utilize market value data or market information to determine whether the property or property will be a mortgage of a warranty loan, or, if the loan is contraband, may alternatively be used to determine redemption of a redemption, but is not limited to those cases in which the term is used. Market value data or market information may also be used to determine the number or calculation of loan versus value. In some embodiments, the collection service, smart contract circuitry, and/or robotic process automation system may estimate or calculate market value data or market information from one or more data or information sources. In some cases, market data value or market information (depending on the data/information contained therein) may enable automatic actions or trigger other conditions or terms. The purpose and use of the term in the various forms, embodiments, and contexts disclosed herein can be readily ascertained by one of ordinary skill in the pertinent art having the benefit of the disclosure herein and understanding the conventional contemplated systems and the relevant market information available.
The terms "mortgage-like," "mortgage-counteracting," and other forms or variations as used herein may be construed broadly to describe properties, assets, or valuable items that are similar in nature to mortgages (e.g., valuable items held in a guarantee) associated with a loan or other transaction. Similar mortgages may refer to property, asset, mortgage, or other items of value that may be aggregated with, replaced by, or otherwise used in conjunction with other mortgages, whether the similarity occurs in the form of a common attribute, such as the type of mortgage, the age of the mortgage, the condition of the mortgage, the history of the mortgage, the ownership of the mortgage, caretaker of the mortgage, security of the mortgage, conditions of the mortgage owner, the retention of the mortgage, storage conditions of the mortgage, geographic location of the mortgage, jurisdictional location of the mortgage, and the like. In some embodiments, the mortgage references an item that has a value correlation with the mortgage-e.g., the mortgage may exhibit similar price changes, volatility, storage requirements, etc. In some embodiments, similar mortgages may be aggregated to form larger guaranty equities or mortgages for additional loans, allocations, or transactions. In some embodiments, the countermortgage may be used to inform the mortgage of the estimate. In some embodiments, the smart contract circuitry or robotic process automation system may estimate or calculate numbers, data, or information related to similar mortgages, or may perform functions related to aggregating similar mortgages. The purpose and use of mortgages, countermortgages, or related terms, similar to mortgages, in the various forms, embodiments, and contexts disclosed herein, may be readily ascertained by one of ordinary skill in the art having the benefit of the disclosure herein and understanding the conventional contemplated systems available.
The term "reorganization" (and other forms such as reorganization) as used herein may be construed broadly to describe modifications to terms or conditions, properties, mortgages, or other considerations affecting a loan or transaction. Recombination may result in successful results with modified terms or conditions between the two parties, or unsuccessful results without modification or recombination. Reorganization may occur in many instances of a contract or loan, such as, but not limited to, application, loan, repayment, collection, consolidation, warranty, agency, redemption prevention, and combinations thereof. The liabilities may also be reorganized, which may indicate that the liabilities owed to the parties have changed in time, amount, mortgage, or other conditions. For example, the borrower may reorganize the liabilities of the loan to accommodate changes in financial conditions, or the borrower may suggest reorganizing the liabilities to the borrower for his own needs or caution. In some embodiments, the smart contract circuitry or robotic process automation system may automatically or manually reorganize liabilities based on monitored conditions, or create options to reorganize liabilities, manage negotiations of liabilities or implement processes of reorganization, or other actions related to reorganizing or modifying loan or transaction terms. The purpose and use of the term in the various embodiments and contexts disclosed herein (whether in the context of debt or otherwise) can be readily ascertained by one of ordinary skill in the art having the benefit of the disclosure herein and knowing the conventional contemplated systems available.
The terms "social network data collection," "social network monitoring service," and "social network data collection and monitoring service" (and various forms or derivatives thereof) as used herein may be construed broadly to describe services related to acquiring, organizing, observing, or otherwise acting on data or information derived from one or more social networks. The social network data collection and monitoring service may be part of a related service system or may be a set of separate services. Social network data collection and monitoring services may be provided by a platform or system, but are not limited thereto. Social network data collection and monitoring services may be used in a variety of environments, such as, but not limited to, loan, re-financing, negotiations, collection, consolidation, warranty, brokering, redemption prevention, and combinations thereof. Requesting social network data collection and monitoring using configuration parameters may be triggered by other service requests, automatically initiated, or upon occurrence of a condition or situation. An interface may be provided for configuring, initiating, displaying, or otherwise interacting with social network data collection and monitoring services. Social networking, as used herein, refers to any mass platform where data and communications occur between individuals and/or entities, where the data and communications are at least partially accessible by an embodiment system. In some embodiments, the social networking data includes information that is publicly available (e.g., accessible without any authorization). In some embodiments, the social networking data includes information that is appropriately accessible to the embodiment system, but may also include subscribed or other access to information that is not freely available to the public but accessible (e.g., in compliance with the privacy policies of the social network and its users). Social networks may be primarily social in nature, but may additionally or alternatively include professional networks, alumni networks, industry-related networks, academic-oriented networks, and the like. In some embodiments, the social network may be a crowdsourcing platform, such as a platform for accepting queries or requests directed to users (and/or subsets of users, possibly meeting specified criteria), where the user may be aware that certain communications are to be shared and accessed and/or publicly available by at least a portion of the users of the requestor or platform. In some embodiments, but not limited to, the social network data collection and monitoring service may be performed by a smart contract circuit or a robotic process automation system. The purpose and use of the social network data collection and monitoring service in the various embodiments and contexts disclosed herein may be readily ascertained by one of ordinary skill in the art having the benefit of the disclosure herein and knowing the conventional contemplated systems available.
The term "crowd sourcing and social network information" as used herein may also be understood broadly to describe information obtained or provided in connection with a crowd sourcing model or transaction, or information obtained or provided on or in connection with a social network. Crowd sourcing and social networking information may be provided by a platform or system, but is not limited thereto. The crowd sourcing and social networking information may be obtained, provided, or communicated to or from a set of information providers, and responses to requests collected and processed by those information providers. Crowd sourcing and social networking information may provide information, conditions or factors related to loans or agreements. Crowd sourcing and social networking information may be private or public, or a combination thereof, but is not limited thereto. In some embodiments, but not limited to, crowd sourcing and social networking information may be acquired, provided, organized, or processed by, but not limited to, a smart contract circuit, where the crowd sourcing and social networking information may be managed by the smart contract circuit that processes the information to satisfy a set of configuration parameters. The purpose and use of the term in the various embodiments and contexts disclosed herein can be readily ascertained by one of ordinary skill in the pertinent art having the benefit of the disclosure herein and understanding the conventional contemplated systems available.
The term "negotiation" (and other forms, such as negotiations or negotiations) as used herein may be construed broadly to describe discussions or communications that proceed to achieve a compromise, result or agreement between parties or entities. The negotiation may result in a successful outcome of the agreement of terms by both parties, or an unsuccessful outcome of the disagreement of specific terms or a combination thereof by both parties, but is not limited thereto. The negotiation may be successful on the one hand or for a particular purpose and unsuccessful on the other hand or for another purpose. Negotiations may occur in many situations of a contract or loan, such as, but not limited to, loan, repayment, collection, consolidation, insurance, agency, redemption prevention, and combinations thereof. For example, the borrower may negotiate interest rates or loan terms with the borrower. In another example, the default borrower may negotiate an alternative solution to avoid redemption with the borrower. In some embodiments, the smart contract circuitry or robotic process automation system may negotiate for one or more of the parties and process the appropriate tasks for completing or attempting to complete the negotiation of terms. In some cases, negotiations by the smart contract or robotic process automation system may be incomplete or successful. Successful negotiations may initiate automatic actions or trigger other conditions or terms of execution by the smart contract circuitry or robotic process automation system. The purpose and use negotiated in the various embodiments and contexts disclosed herein may be readily ascertained by one of ordinary skill in the pertinent art having the benefit of the disclosure herein and understanding the conventional contemplated systems available.
The term "negotiation" in various forms may be used herein more specifically in verb form (e.g., negotiations) or noun form (e.g., negotiations) or other forms to describe the context of mutual discussion that results. For example, the robotic process automation system may negotiate terms and conditions on behalf of the principal for use as a verb clause. In another example, the robotic process automation system may negotiate terms and conditions for loan modification or negotiate a consolidated offer or other terms. As noun clauses, negotiations (e.g., events) may be performed by a robotic process automation system. Thus, in some cases, the smart contract circuitry or robotic process automation system may negotiate (e.g., as a verb clause) terms and conditions, or a description of this may be considered to be a negotiation (e.g., as a noun clause). The purpose and use of the term in the various embodiments and contexts disclosed herein can be readily ascertained by one of ordinary skill in the art having the benefit of the disclosure herein and understanding the negotiation.
The term "negotiation" in various forms may also be used in particular to describe a result, e.g. a negotiation that compromises each other or that completes the result. For example, the loan may be through a robotic process automation system or other means, which may be considered to result in successful results of the principal agreeing upon the negotiation having been completed. Thus, in some cases, the smart contract circuitry or robotic process automation system may have negotiated a set of terms and conditions or negotiated a loan. One of ordinary skill in the art, having the benefit of the disclosure herein and knowing the conventional contemplated system available, can readily ascertain the purpose and use of the term in the various embodiments and contexts disclosed herein relate to the results achieved by both parties through completion of negotiations.
The term "negotiation" in various forms may also be used specifically to describe an event, such as negotiating an event or event negotiation, including achieving a set of agreed upon terms between parties. An event requiring agreement or compromise between parties may be considered a negotiation event, but is not limited thereto. For example, the process of achieving a set of mutually accepted terms and conditions between parties during a loan purchase process may be considered a negotiation activity. Thus, in some cases, the smart contract circuitry or robotic process automation system may accommodate negotiating the communication, actions, or behaviors of the event principals.
The term "collect" (and other forms such as collect) as used herein may be construed broadly to describe obtaining tangible (e.g., physical), intangible (e.g., data, license, or rights) or monetary (e.g., payment) items or other liabilities or assets from a source. The term may generally relate to the complete intended acquisition of an item or the complete completion of an item acquisition from an early stage related task to a later stage related task. Collecting successful results that may result in bidding for the item to the party, unsuccessful results that may result in not bidding for the item or obtaining the item from the party, or a combination thereof (e.g., delayed or defective) may also result, but is not limited to such. The collection may occur in many different contexts of contracts or loans, such as, but not limited to, loan, repayment, consolidation, warranty, agency, redemption-stopping and data processing (e.g., data collection) or combinations thereof. The collection may be used in noun form (e.g., collection of data or collection of overdue payments, where it relates to an event or describes an event), may refer to a classification of an item as a noun (e.g., collection of loan mortgages, where it relates to the number of items in a transaction), or may be used in verb form (e.g., collection of payments to a borrower). For example, the borrower may collect the over-payment to the borrower via an online payment, or may successfully collect the over-payment via a customer service call. In some embodiments, the smart contract circuitry or robotic process automation system may perform collection for one or more of the parties and process appropriate tasks for completing or attempting to collect one or more items (e.g., overdue payments). In some cases, negotiations by smart contracts or robotic process automation systems may be incomplete or unsuccessful, and depending on these results, this may result in automatic actions or triggering other conditions or terms. The purpose and use of the various forms, embodiments, and collections disclosed herein, can be readily ascertained by one of ordinary skill in the art having the benefit of the disclosure herein and the knowledge of the conventional contemplated systems available.
The term "collect" in various forms may also be used herein more specifically in noun form to describe the context of an event or thing, such as collecting an event or withdrawing payment. For example, a collection event may refer to a communication with a party or other activity related to acquiring an item in such an activity, but is not limited thereto. For example, collecting payment may involve the borrower obtaining payment through a collection process or through a collection department of the borrower. While not limited to overdue, delinquent, or default loans, collections may describe events, payments, or departments, or other terms related to transactions or loans, as remedial measures for what has been overdue. Thus, in some cases, the smart contract circuitry or robotic process automation system may collect payments or installments from borrowers, and the activity of doing so may be considered collection events, without limitation.
The term "collect" in various forms may also be used more specifically herein in adjectives or other forms to describe the context relevant to litigation, such as the outcome of a collection litigation (e.g., litigation regarding overdue or default payments of a loan). For example, the outcome of a collection litigation may relate to delinquent funds owed by a borrower or other party, and the collection work relating to these delinquent funds may be lifted by the party. Thus, in some cases, the smart contract circuitry or robotic process automation system may receive, determine, or otherwise manage the results of a collection litigation.
The term "collect" in various forms may also be used herein in adjectives or other forms to describe the context in relation to a purchase action, such as a collection action (e.g., an action that causes a bid or acquisition of overdue or default payments on a loan or other obligation). The collection rate of return, the collection financial rate of return, and/or the collection financial rate of return may be used. The result of such a collection action may or may not produce financial benefits. For example, the act of collecting may result in the payment of one or more outstanding payments of the loan, which may bring financial benefits to another party (e.g., a borrower). Thus, in some cases, the smart contract circuitry or robotic process automation system may obtain financial benefits from, or otherwise manage or somehow assist in, the collection of financial benefits of the actions. In an embodiment, the collecting action may include a need for collection litigation.
The term "collection" (collection ROI, ROI on collection activity, collection activity ROI, etc.) in various forms may also be used more specifically herein to describe the context associated with an action of receiving value, such as a collection action with a Return On Investment (ROI) (e.g., an action of bidding or obtaining that causes overdue or default payments on a loan or other obligation). The result of such a cash-out action may or may not have an ROI, relative to the cash-out action itself (as the ROI of the cash-out action), or as the ROI of a broader loan or transaction of the cash-out action subject. For example, the ROI of the collection action may be discreet or unobtrusive relative to the default loan, but is not limited thereto, depending on whether the ROI is to be provided to the lender or the like. The predicted ROI for collection may be estimated or calculated given the actual event that occurs. In some cases, the smart contract circuitry or robotic process automation system may present an estimated ROI of the checkout action or checkout event, or may calculate an ROI of an actual event occurring in the checkout action or checkout event, but is not limited thereto. In embodiments, such ROIs may be positive or negative, whether estimated or actual.
The terms "reputation," "reputation measure," "borrower reputation," and the like may include commonly and widely held beliefs, views, and/or mindsets that are commonly held to individuals, entities, mortgages, and the like. The measure of reputation can be determined based on social data including likes and dislikes, product and service reviews provided by an entity or entities, company or product ranks, current and historical markets, and financial data including prices, predictions, marketing suggestions, financial news about entities, competitors and partners. The reputation may be cumulative in that the product reputation and the reputation of the company leader or chief scientist may affect the overall reputation of the entity. The reputation of an entity's associated institution (e.g., a school that a student is reading) may affect the entity's reputation. In some cases, the intelligent contract circuitry or robotic process automation system may collect or initiate data collection related to the above and determine a measure or ranking of reputation. The smart contract circuitry or robotic process automation system may use a measure or ranking of entity reputation to determine whether to sign an agreement with an entity, determine terms and conditions of loan, interest rate, and so forth. In some embodiments, the indication of reputation determination may be related to the outcome of one or more transactions (e.g., comparing "likes" on a particular social media dataset to a result index, such as successful payments, successful negotiations of results, the ability to clear a particular type of mortgage, etc.) to determine a metric or ranking of entity reputation. The purpose and use of reputation, a measure or ranking of reputation, and/or the utilization of reputation in negotiations, the determination of terms and conditions, and the determination of whether to proceed with a transaction in various embodiments and contexts disclosed herein can be readily determined by those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available.
The term "collection" (e.g., payee) in various forms may also be used more particularly herein to describe a principal or entity that causes, manages, or facilitates a collection action, collection event, or other collection-related context. The reputation measurements of interested parties (e.g., payees) or during receipts can be estimated or calculated using objective, subjective or historical measurements or data. For example, a payee may participate in a payee action, and the reputation of the payee may be used to determine a decision, action or condition. Similarly, the collection may also be used to describe objective, subjective, or historical metrics or data to measure the reputation of interested parties (e.g., borrowers, or debtors). In some cases, the smart contract circuitry or robotic process automation system may present a cash or measurement, or implement a cash register, in the context of a transaction or loan.
The terms "collect" and "data collection" in various forms, including data collection systems, may also be used more specifically herein to describe the context in which data is acquired, organized, or processed, or a combination thereof, but are not limited thereto. The results of such data collection may be related to item collection (e.g., physical or logical grouping of items) or may be completely unrelated to actions taken in arrears (e.g., withdrawal of mortgages, debts, etc.), but are not limited thereto. For example, data collection may be performed by a data collection system, where data is acquired, organized, or processed for decision making, monitoring, or other purposes of an expected or actual transaction or loan. In some cases, the smart contract or robotic process automation system may incorporate a data collection or data collection system to perform some or all of the data collection tasks, without limitation. The purpose and use of the data or information used herein collected in the context can be readily ascertained by one of ordinary skill in the art having the benefit of the disclosure herein and knowing the conventional contemplated systems available.
The terms "re-financing," "re-financing activity," "re-financing interaction," "re-financing result," and similar terms as used herein should be construed broadly. Without being limited to any other aspect or description of the invention, the repayment and repayment campaign includes replacing an existing mortgage, loan, bond, liability transaction, etc. with a new mortgage, loan, bond, or liability transaction that pays or ends a previous financial arrangement. In some embodiments, any changes to and/or any substantial changes to the terms and conditions of the loan may be considered a re-financing activity. In some embodiments, the refinancing campaign is considered to be only those loan agreement changes that result in different loan agreement financial results. Typically, the new loan should be beneficial to the borrower or issuer, and/or to the agreement of both parties (e.g., improving the original financial results of a party, improving the securities or other results of another party). Re-financing may be used to reduce interest rates, reduce periodic payments, alter loan terms, alter mortgages associated with a loan, consolidate liabilities into a single loan, reorganize liabilities, alter loan types (e.g., from variable interest rates to fixed interest rates), repayment expired loans to account for improvements in credit scores, thereby expanding the loan size, and/or in response to changes in market conditions (e.g., interest rates, mortgage value, etc.).
The repayment campaign may include initiating a repayment offer, initiating a repayment request, configuring a repayment interest rate, configuring a repayment payment plan, configuring a repayment balance in response to an amount or term of the repayment loan, configuring mortgages for the repayment, including changes in mortgage terms and conditions used, changes in mortgage amounts, and the like, managing use of the repayment revenue, releasing or placing liens as appropriate for different mortgages as part of the repayment in the event of changes in terms and conditions, verifying ownership of new or existing mortgages for securing the repayment loan, managing inspection flows for new or existing guaranties for securing the repayment loan, filling out the repayment application, negotiating the terms and conditions of the repayment loan, and closing the repayment. The re-financing and re-financing activities may be disclosed in the context of a data collection and monitoring service that is a training set of interactions between loan re-financing activity collection entities. The re-financing and re-financing activities may be disclosed in the context of an artificial intelligence system that trains using a collected set of interactive training, including re-financing activities and results. The trained artificial intelligence can then be used to recommend resuscitations, evaluate resuscitations, make predictions about the expected outcome of resuscitations, and the like. The re-financing and re-financing activities may be disclosed in the context of an intelligent contract system that may automate a subset of the interactions and activities of re-financing. In one example, the smart contract system may automatically adjust loan interest rate based on information collected through at least one of an internet of things system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services. The interest rate may be adjusted based on rules, thresholds, model parameters that determine or suggest loan resurf interest rates based on interest rates that the borrower obtains from secondary borrowers, borrower risk factors (including predicted risk based on one or more predictive models using artificial intelligence), marketing factors (e.g., competing interest rates provided by other borrowers), and the like. The results and events of the re-financing activity may be recorded in a distributed ledger. Based on the results of the resurfacing campaign, the resurfacing loan intelligence contract may be automatically reconfigured to determine terms and conditions of the new loan, such as debt principal amount, debt balance, fixed interest rate, variable interest rate, payment amount, payment plan, end-of-maximum payback plan, mortgage description, mortgage substitutability description, principal, insured, guarantor, personal guaranty, lien, duration, contract, redemption prevention condition, default condition, and outcome of the violation.
Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit from the particular application of the re-financing activity, how to select or incorporate the re-financing activity, how to implement a system, service or circuit to automatically perform one or more (or all) aspects of the re-financing activity, and so forth. Some considerations by those skilled in the art or embodiments of the present invention when selecting an appropriate set of interactive training sets: training artificial intelligence takes action, suggests or predicts the outcome of certain re-financing activities. Although specific examples of re-financing and re-financing activities are described herein for illustrative purposes, any embodiment that would benefit from the disclosure herein, and any consideration that would be appreciated by one skilled in the art having the benefit of the disclosure herein, are specifically contemplated as falling within the scope of the present disclosure.
The terms "consolidate", "consolidate activities", "loan consolidate", "debt consolidate", "consolidate plan" and similar terms as used herein should be construed broadly. Without being limited to any other aspect or description of the invention, merging activities, loan merging, debt merging, merging plans relate to repayment of several smaller loans using a single large loan, and/or relate to repayment of at least a portion of one or more of a second set of loans using one or more of the set of loans. In embodiments, the loan merge may be guaranteed (i.e., supported by mortgage) or unsecured. The loans may be consolidated to achieve lower interest rates than one or more current loans, to reduce total monthly loan payments, and/or to allow the liabilities to adhere to the liabilities' consolidated loans or other liabilities. Loans that may be categorized as merge candidates may be determined based on models of the entity attributes involved in processing a set of loans, including principal identity, interest rate, payment balance, payment terms, payment plan, loan type, mortgage type, principal's financial status, payment status, mortgage status, and mortgage value. The merge activity may include managing the following: identifying a loan from a set of candidate loans, preparing a merge offer, preparing a merge plan, preparing content conveying the merge offer, arranging the merge offer, conveying the merge offer, negotiating modifications to the merge offer, preparing a merge agreement, executing the merge agreement, modifying mortgages of a set of loans, processing an audit workflow for merge, managing a check, managing an assessment, setting an interest rate, deferring payment requirements, setting a payment plan, or ending the merge agreement. In embodiments, there may be a system, circuitry, and/or services configured to create, configure (e.g., using one or more templates or libraries), modify, set, or otherwise process (e.g., in a user interface) various rules, thresholds, conditional processes, workflows, model parameters, etc., to determine or suggest a consolidated action or plan for a loan transaction or set of loans based on one or more events, conditions, states, actions, etc. In an embodiment, the merge plan may be based on various factors, such as a payment status, a set of interest rates for the loan, an actual interest rate for the flat market or external market, a borrower status for a set of loans, a mortgage or property status, a borrower's risk factors, one or more guarantors, a market risk factor, and so forth. The merging and merging activities may be disclosed in the context of a data collection and monitoring service that collects a training set of interactions between entities for a set of loans, and the merging and merging activities may be disclosed in the context of an artificial intelligence system that is trained using the collected interaction training set that includes the merging activities and results associated with the activities. The trained artificial intelligence may then be used to recommend consolidated activities, evaluate consolidated activities, make predictions about the expected outcome of the consolidated activities, and based on similar models, including debt conditions, mortgage or property conditions for securing or supporting a set of loans, status of business or business operations (e.g., accounts receivable, accounts payable, etc.), principal conditions (e.g., equity, financial, debt, location, and other conditions), principal behaviors (e.g., behaviors indicative of preferences, behaviors indicative of debt preferences), and the like. Liability consolidation, loan consolidation, and related consolidation activities may be disclosed in the context of an intelligent contract system that may automate a subset of consolidated interactions and activities. In an embodiment, the merge may include a merge of terms and conditions for the loan set, a selection of an appropriate loan, a payment term configuration for the merge loan, a pre-existing loan payment plan configuration, communication encouraging the merge, and so forth. In an embodiment, the artificial intelligence of the smart contract may automatically recommend or set rules, thresholds, actions, parameters, etc. (optionally, by learning such operations based on a training set of time-varying results) to generate a recommended merge plan that may specify a series of actions required to achieve a suggested or desired merge result (e.g., within an acceptable result range), which actions may be automated, and may involve conditionally performing steps based on monitored conditions and/or smart contract terms, which terms or conditions may be created, configured, and/or accounted for by the merge plan. The merge plan may be determined and executed based on market factors (e.g., competitive rates, mortgage values, etc., provided by other borrowers) and at least a portion of regulatory and/or compliance factors. The merge plan may be generated and/or executed for: creating a new consolidated loan, a secondary loan associated with the consolidated loan, a modification of an existing loan associated with the consolidated, repayment terms of the consolidated loan, redemption-stopping conditions (e.g., changing from a guaranteed loan interest rate to an unsecured loan interest rate), bankruptcy or weakness to repay liabilities, conditions involving market changes (e.g., current interest rate changes), and other consolidated conditions.
Some activities related to loans, mortgages, entities, etc. may be applicable to various loans, but may not be explicitly applicable to merging activities. Categorizing these activities as consolidated activities may be based on the context of the loan for which the activity occurred. However, those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit from the particular application of the merge event, how to select or combine the merge event, how to implement selected services, circuits and/or systems described herein to perform certain loan merge operations, and the like. Although specific examples of merging and merging activities are described herein for illustrative purposes, any embodiment that would benefit from the disclosure herein, and any consideration that would be appreciated by one skilled in the art having the benefit of the disclosure herein, are specifically contemplated as falling within the scope of the present disclosure.
The terms "warranty loan," "warranty loan transaction," "warranty person," "warranty loan interaction," "warranty property," or "collection of properties for warranty," and like terms, as used herein, are to be construed broadly. Without being limited to any other aspect or description of the invention, the policy may be applied to policy assets, such as invoices, inventory, accounts receivable, etc., where the value of the implementation of the item is in the future. For example, accounts receivable has a higher value after payment and a lower risk of default. Inventory and Work In Process (WIP) may be more valuable than parts as end products. References to corresponding receipts should be understood to include these terms, not to be limiting. The warranty service may include selling the accounts receivable at a present value discount rate (typically cash). The warranty may also include mortgages that use receivables as short-term loans. In both cases, the value of the receivables or invoices may be compromised for a variety of reasons, including future value of currency, due (e.g., 30 day net payment versus 90 day net payment), degree of breach risk of the receivables, condition of Work In Process (WIP), inventory status, delivery and/or shipping status, financial status of the receivables, shipping and/or billing status, payment status, borrower status, inventory status, borrower risk factor, borrower, one or more insurers, market risk factor, debt status (whether there is other lien rights to the receivables or payables of the inventory, mortgage asset status (e.g., inventory status (whether current or expired, an invoice that is delinquent), business or business operating conditions, conditions of the transaction party (e.g., equity, wealth, liability, location and other conditions, etc.), behaviors of the transaction party (e.g., behaviors that indicate preferences, behaviors that indicate negotiation styles, etc.), current interest rates, any current regulatory and compliance issues related to inventory or accounts receivable (e.g., if inventory is split, whether the expected product is properly approved), and legal suits for borrowers and many others, including predicted risk based on one or more predictive models using artificial intelligence.) a guarantor refers to a person, business, entity, or group who agrees to provide a value exchange for mortgages that directly obtain or use the invoice as a value loan in sales. A policy plan specifying proposed receivables (e.g., receivables meeting all, part, only certain criteria), and the fit coefficients of the proposal, the process conditions to convey the plan to potential parties, provide and receive offers, verify the quality of accounts receivable, accounts receivable during loans. Although specific examples of an insurance and an insurance campaign are described herein for illustrative purposes, any embodiment that would benefit from the disclosure herein, and any consideration that would be appreciated by one skilled in the art having the benefit of the disclosure herein, are specifically contemplated as within the scope of the present disclosure.
The terms "mortgage," "proxy mortgage," "mortgage guaranty," "mortgage loan activity," and/or "mortgage-related activity" as used herein should be construed broadly. Without limiting to any other aspect or description of the invention, mortgages are interactive processes in which borrowers provide the borrower with ownership or lien on ownership of a valuable item (typically property) in exchange for money or other valuable items, typically in conjunction with interest compensation. The exchange includes a condition that ownership rights are released to the borrower and/or property lien when the loan is repayment. The proxy activity of a mortgage may include determining potential properties, borrowers, and other parties to the loan, and scheduling or negotiating terms of the mortgage. Certain components or activities may not be considered related to a mortgage alone, but may be considered related to a mortgage when used in conjunction with a mortgage, to an entity or party to a mortgage, and so forth, depending on the action taken by the mortgage. For example, the proxy activity may be adapted to provide various loans, including unsecured loans, directly selling property, and the like. Mortgage activities and mortgage interactions may include mortgage marketing activities, identifying a set of potential borrowers, identifying mortgage properties to be mortgage, borrower qualifications, prospective mortgage property ownership searches and/or ownership verifications, prospective mortgage property evaluations, property inspections or property valuations, revenue verifications, borrower demographic analysis, identifying fund providers, determining availability rates, determining available payment terms and condition definitions, existing mortgage analysis, comparative analysis of existing and new mortgage terms, completing application workflows (e.g., maintaining application continued operation through later steps in the appropriate start-up process), number of application fields, preparing mortgage agreements, completing mortgage agreement plans, negotiating mortgage terms and conditions with capital providers, negotiating mortgage terms and conditions with borrowers, ownership, setting up deposit rights and closing mortgage agreements on mortgage properties, and the like should be widely understood. Although specific examples of mortgages and mortgage agents are described herein for illustrative purposes, any embodiment that would benefit from the disclosure herein, and any consideration that would be appreciated by one skilled in the art having the benefit of the disclosure herein, are specifically contemplated as within the scope of the present disclosure.
The terms "liability management", "liability transaction", "liability action", "liability clause and condition", "joint liability", "combined liability" and/or "liability combination" as used herein are to be understood in a broad sense. Without limiting to any other aspect or description of the invention, the liabilities include monetary value of items owed to another party. The loan typically results in the borrower holding a liability (e.g., a payment that must be paid according to the terms of the loan, which may include interest). Liability consolidation includes the use of a new single loan to repay multiple loans (or various other configurations of liability structures as described herein and as understood by those skilled in the art). In general, a new loan may have better terms or lower interest rates. The liability portfolio includes a number of liability portions or groups, often with different characteristics including terms, risks, etc. Liability portfolio management may involve decisions regarding the number and quality of liabilities held, and how to best balance the various liabilities to determine the risk/return status needed to achieve based on investment policies, risk returns for individual liabilities or groups of liabilities. If multiple borrowers offer a borrower a loan (or group of loans), the liabilities may be silver group loans. The liability portfolio can be sold to a third party (e.g., at a liability rate). Liability compliance includes various measures taken to ensure that liabilities are paid. Proving compliance may include paying a file of the action taken by the debt.
The liability-related transactions (liability transactions) and liability-related actions (liability actions) may include providing liability transactions, underwriting liability transactions, setting interest rates, deferred payment requirements, modifying interest rates, verifying ownership, managing checks, recording changes in ownership, evaluating the value of assets, promoting loans, ending transactions, setting the terms and conditions of transactions, providing notification that a offer is required, redeeming a set of assets, modifying terms and conditions, setting a rating of an entity, joining liabilities, and/or merging liabilities. The debt terms and conditions may include a debt balance, a debt principal amount, a fixed interest rate, a variable interest rate, a payment amount, a payment plan, an end-of-line repayment plan, a bond guarantee asset description, an asset replacement description, a principal, a issuer, a purchaser, a insured person, a guarantor, a personal guaranty, an lien, a duration, a contract, a redemption prevention condition, a violation condition, and a violation outcome. Although specific examples of liability management and liability management activities are described herein for illustrative purposes, any embodiments that benefit from the disclosure herein, and any considerations that will be apparent to those of skill in the art having the benefit of the disclosure herein, are specifically contemplated as falling within the scope of the present disclosure.
The terms "condition," "condition classification," "classification model," "condition management," and similar terms as used herein should be construed broadly. Without being limited to any other aspect or description of the invention, condition classification, classification model, condition management includes classifying or determining assets, issuers, borrowers, loans, debts, bonds, regulatory states, bond terms or conditions, loans or debt transactions, etc. specified and monitored in the offer. Based on the categorized condition of the asset, condition management may include actions to maintain or improve the condition of the asset or to use the asset as a mortgage. Based on the classification conditions of the issuer, borrower, third party administrative status, etc., the condition management may include an act of altering the loan or bond terms or conditions. The condition classification may include various rules, thresholds, condition programs, workflows, model parameters, etc., to classify conditions of a property, a issuer, a borrower, a loan, a debt, a bond, a regulatory state, a bond, terms or conditions of a loan or debt transaction, etc., based on data from an internet of things device, data from a set of environmental condition sensors, data from a set of social network analysis services, and a set of algorithms for querying a network domain, social media data, crowd-sourced data, etc. Condition classification may include grouping or marking or clustering entities that have similar positioning with respect to certain aspects of the classification condition (e.g., risk, quality, ROI, probability of reclamation, likelihood of default, or certain other aspects of the relevant liabilities).
Various classification models are disclosed, where the classification and classification models may relate to mortgage, distributor, borrower, fund distribution, or other geographic location related geographic locations. Classification and classification models are disclosed in which artificial intelligence is used to refine the classification model (e.g., refining the model by refining using artificial intelligence data). Thus, in some cases, artificial intelligence may be considered as part of a classification model and vice versa. Classification and classification models are disclosed in which social media data, crowd-sourced data, or internet of things data is used as input to a refinement model, or as input to a classification model. Examples of internet of things data may include images, sensor data, location data, and the like. Examples of social media data or crowd-sourced data may include the behavior of a party to a loan, the financial condition of a party, the compliance of a party with terms or conditions of a loan or bond, and so forth. The lender may include a bond issuer, a related entity, a borrower, a third party to the liability equity. Condition management may be discussed in connection with intelligent contract services, which may include condition classification, data collection and monitoring, bond, loan, and liability transaction management. In categorizing the issuer of the bond, the bond-related or mortgage assets, bond-supporting mortgage assets, bond parties, and collections thereof, the data collection and monitoring services will also be discussed in connection with the categorization and categorization model. In some embodiments, a classification model may be included in discussing bond types. The specific steps, factors, or improvements may be considered part of the classification model. In various embodiments, the classification model may vary in embodiments or in the same embodiment as is relevant to a particular jurisdiction. Different classification models may use different data sets (e.g., based on the issuer, borrower, mortgage property, bond type, loan type, etc.), and multiple classification models may be used in a single classification. For example, one bond type (e.g., municipal bond) may allow for a classification model based on municipal bond data from similar scale and economic prosperity, while another classification model may emphasize data from internet of things sensors associated with mortgage assets. Thus, depending on the embodiment and the specifics of the bond, loan, or debt transaction, different classification models will provide advantages or risks over other classification models. The classification model includes methods or concepts for classification. The classification criteria for a bond, loan, or debt transaction may include a balance of the bond, a principal amount of the bond, a fixed interest rate, a variable interest rate, a payment amount, a payment plan, a maximum clearing plan, a bond guarantee property description, a loan or debt transaction, a property substitutability description, a principal, a issuer, a purchaser, a insured person, a guarantor, a personal guaranty, a lien, a duration, a contract, a redemption condition, a violation condition, and a violation outcome. Classification criteria may include the type of bond issuer, such as municipalities, companies, contractors, government entities, non-government entities and non-profit entities. An entity may include a set of publishers, a set of bonds, a set of principals, and/or a set of assets. Classification conditions may include physical conditions (e.g., equity, wealth, liability, location, and other conditions), principal's behavior (e.g., behavior indicating preferences, behavior indicating liability preferences), and the like. Sorting criteria may include, for example, vehicles, ships, aircraft, buildings, houses, real estate, undeveloped property, farms, crops, municipalities, warehouses, a group of inventory, commodities, securities, currencies, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contract rights, antiques, fixtures, furniture, equipment items, tools, machinery, and personal property. The classification condition may include a bond type, wherein the bond type may include municipal bonds, government bonds, national library bonds, asset guarantee bonds, and corporate bonds. The classification condition may include a default condition, a redemption-suppressing condition, a condition indicating a contract violation, a financial risk condition, a behavioral risk condition, a policy risk condition, a financial health condition, a physical defect condition, a physical health condition, an entity risk condition, and an entity health condition. The classification condition may include an environment, wherein the environment may include a municipal environment, a corporate environment, a securities trading environment, a real estate environment, a commercial facility, a warehouse facility, a transportation environment, a manufacturing environment, a storage environment, a residential or vehicular environment. The bond transaction activity for a bond condition may include providing a bond transaction, a liability bond transaction, a setting up interest rate, a deferred payment requirement, a modification interest rate, verifying ownership, managing inspection, recording changes in ownership, evaluating the value of a property, urging a loan, ending a transaction, setting up terms and conditions of a transaction, providing a notification that needs to be provided, redeeming a set of properties, modifying and conditions, setting up a rating of an entity, and/or a merger of terms, etc.
Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit from the particular application of the classification model, how to select or combine the classification models to achieve the conditions and/or calculate the value of a mortgage from the required data. Some considerations by those skilled in the art or embodiments of the present invention in selecting appropriate conditions for management include, but are not limited to: the validity of the conditions of a given transaction jurisdiction, the available data for a given mortgage, the type of transaction expected (loan, bond or debt), the particular type of mortgage, the ratio of loan to value, the ratio of mortgage to loan, the total transaction/loan amount, the credit score of borrowers and borrowers, and other considerations. Although specific examples of conditions, classification models, and management of conditions are described herein for illustrative purposes, any embodiment that benefits from the disclosure herein, and any consideration that would be appreciated by one of ordinary skill in the art having the benefit of the disclosure herein, are specifically contemplated as within the scope of the present disclosure.
The term "classification" (and similar terms) as used herein should be broadly construed. Without being limited to any other aspect or description of the invention, classifying a condition or item may include an act of classifying the condition or item into groups or categories based on some aspect, attribute, or feature of the condition or item, wherein the condition or item is generic or similar to all items placed in the classification, although the classifications or categories based on other aspects or conditions are different at the time. Classification may include identifying one or more parameters, features, characteristics, or phenomena related to conditions or parameters of an item, entity, person, process, item, financial structure, or the like. The conditions classified by the condition classification system may include a contraband condition, a redemption-stopping condition, a condition indicating a contraband contract, a financial risk condition, a behavioral risk condition, a contract performance condition, a policy risk condition, a financial health condition, a physical defect condition, a physical health condition, an entity risk condition, and an entity health condition. The classification model may automatically classify items, entities, processes, projects, financial structures, etc., based on data received from various sources. The classification model may classify items based on a single attribute or combination of attributes and/or may utilize data regarding the items and models to be classified. The classification model may classify individual items, entities, financial structures, or the same category. The bonds may be categorized based on bond type (e.g., municipal bonds, corporate bonds, bond guarantees, etc.), rate of return, bond ratings (bond quality party indicators of third party bond issuer financial performance), and/or bond principal and interest balance capabilities, etc. The borrower or bond issuer may be categorized according to the type of borrower or issuer, the permissible attributes (e.g., based on revenue, wealth, location (domestic or foreign)), various risk factors, the status of the issuer, etc. Borrowers may be categorized according to allowable attributes (e.g., revenue, wealth, total assets, location, credit history), risk factors, current status (e.g., employment, student), principal behavior (e.g., behavior indicative of preference, reliability, etc.), and so forth. The condition classification system may classify the reception of the learning-aid loan based on the progress of the student in obtaining the academic position, the score or position of the student in the class, the condition of the student in the school (entrance, trial, etc.), the condition of the student taking part in the non-profit activities, the delay condition of the student, and the condition of the student taking part in the public welfare activities. The condition classified by the condition classification system may include a state of a set of mortgages of a loan or an entity state related to a loan guarantee. The conditions classified by the condition classification system may include medical conditions of borrowers, insurers, subsidies, and the like. The condition classified by the condition classification system may include adherence to at least one of laws, regulations, or policies associated with a loan transaction or a loan institution. The conditions classified by the condition classification system may include conditions of the bond issuer, conditions of the bond, ratings of loan related entities, and the like. The condition classified by the condition classification system may include identification of a machine, component, or mode of operation. The conditions classified by the condition classification system may include states or contexts (e.g., states of machines, processes, work traffic, markets, storage systems, networks, data collectors, etc.). The condition classification system may classify processes related to a state or context (e.g., a data storage process, a network coding process, a network selection process, a data market process, a power generation process, a manufacturing process, a refining process, a mining process, a drilling process, and/or other processes described herein). The condition classification system may classify the set of loan repayment actions based on the predicted results of the set of loan repayment actions. The condition classification system may classify a set of loans as merge candidates based on the identity of the principal, interest rate, payment balance, payment terms, payment plan, loan type, mortgage type, financial condition of the principal, payment status, mortgage condition, or mortgage value, etc. The condition classification system may classify entities involved in a set of warranty loans, bond issuing campaigns, mortgage loans, and the like. The condition classification system may classify a set of entities based on the expected outcome of various loan management activities. The condition classification system may classify the conditions of a set of publishers based on information from an internet of things data collection and monitoring service, a set of parameters associated with the publishers, a set of social network monitoring and analysis services, and the like. The condition classification system may classify a set of loan retraction actions, loan merge actions, loan negotiation actions, loan resubmission actions, etc., based on a set of predictions of these activities and entities.
The terms "subsidized loan," "subsidized loan" (and similar terms) as used herein should be construed broadly. Without being limited to any other aspect or description of the invention, a subsidy loan is a loan of money or valuable items, wherein the interest payment for the loan value may be deferred, delayed, or delayed, with or without accrued interest, such as when the borrower is at school, out of business, ill, etc. In an embodiment, the loan may be subsidized when interest payments for a portion or subset of the loan are borne or guaranteed by parties other than the borrower. Examples of subsidy loans may include municipal subsidy loans, government subsidy loans, learning-aid loans, property-guarantee subsidy loans, and corporate subsidy loans. Examples of subsidized study-aiding loans may include study-aiding loans that may be sponsored by a government, and interest may be delayed or not accumulated based on student's state of progression, student participation in non-profit activities, student's delay status, and student participation in public welfare activities. Examples of government subsidized housing loans may include government subsidies that may eliminate borrower payment settlement costs, first mortgage loan payments, and the like. Such subsidized loan conditions may include property location (rural or urban), borrower income, borrower military status, ability of purchased housing to meet health and safety standards, profit limits available for housing sales, and the like. Some uses of the term loan may not apply to subsidized loans, but rather to periodic loans. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit from consideration of the subsidized loan (e.g., determining the value of the loan, negotiations related to the loan, terms and conditions related to the loan, etc.), wherein borrowers may exempt some of the common loan liabilities of non-subsidized loans, wherein subsidies may include exempting, delaying or deferring the loan interest, or paying interest by third parties. Subsidies may include payment of settlement costs including points, pay-for-payment, etc. by parties or entities other than borrowers, and/or how to incorporate the processes and systems of the present invention to enhance or benefit from ownership verification.
The term "subsidy loan management" (and similar terms) as used herein should be construed broadly. Without limiting to any other aspect or description of the invention, subsidy loan management may include a number of activities and solutions for managing or responding to one or more events related to a subsidy loan, where such events may include applying for a subsidy loan, providing a subsidy loan, accepting a subsidy loan, providing underwriting information for a subsidy loan, providing an credit report for a borrower applying for a subsidy loan, deferring payment for a desired payment as part of the loan subsidy, setting an interest rate for a subsidy loan that may be part of the subsidy for a lower interest rate, deferring payment requirements as part of the loan subsidy, identifying mortgages of the loan, verifying ownership of the mortgage or guarantee, recording changes in ownership of the property, evaluating the value of the mortgage or guarantee of the loan, checking properties involved in the loan, identifying a change in status of an entity associated with a loan, a change in value of an entity associated with a loan, a change in an operating state of a borrower, a change in a financial rating of a borrower, a change in a financial value of an item provided as a guarantee, providing a loan insurance, providing evidence of property insurance associated with a loan, providing evidence of loan qualification, identifying a loan guarantee, underwriting a loan, paying a loan, delineating an earned loan, setting up terms and conditions of a loan, redeeming a property limited by a loan, modifying terms and conditions of a loan, setting up terms and conditions of a loan (e.g., principal of debt, balance of debt, fixed interest rate, variable interest rate, payment amount, payment plan, end-of-line clearing plan, mortgage description, mortgage alternatively description, principal, insured, guaranty, guarantee, personal guaranty, lien, deadlines, contracts, redemption-stopping conditions, default conditions, and outcome of default), or manage loan-related activities (e.g., without limitation, searching for parties willing to participate in a loan transaction, processing a loan application, underwriting a loan, contracting for a loan law, monitoring for loan performance, paying a loan, reorganizing or modifying a loan, settling a loan, monitoring for a mortgage of a loan, composing a silver group of a loan, redemption-stopping a loan, collecting a loan, merging a loan, analyzing for a loan performance, processing a loan violation, transferring ownership of a property or mortgage, ending a loan transaction), and the like. In an embodiment, a system for processing subsidy loans may include classifying a set of parameters of a set of subsidy loans based on data related to those parameters obtained from an internet of things data collection and monitoring service. Classifying a set of parameters of a set of subsidy loans may also be based on data obtained from one or more configurable data collection and monitoring services that utilize social network analysis services, crowdsourcing services, etc. to obtain parameter data (e.g., determining whether an individual or entity is eligible to obtain a subsidy loan, determining social value to provide or cancel a subsidy from a loan, determining whether a subsidy entity is legal, determining appropriate subsidy terms based on characteristics of a buyer and/or subsidy person, etc.).
The terms "redemption-suppressing condition," "redemption-suppressing mortgage," "default mortgage" (and similar terms) as used herein should be construed broadly. Without limiting to any other aspect or description of the invention, the redemption stopping condition, default, etc. describe the borrower failing to satisfy the loan terms. Without limiting to any other aspect or description of the invention, redemption prevention includes the process of a borrower attempting to withdraw a loan balance from a borrower in a redemption prevention or default condition or to replace the borrower's redemption of entitlement as a mortgage of a loan guarantee. Failing to satisfy the loan terms may include failing to pay a specified amount, failing to adhere to a payment plan, failing to make an end-of-line repayment, failing to properly secure the mortgage, failing to maintain the mortgage under specified conditions (e.g., well-maintained), failing to obtain a second loan, and so forth. Redemption prevention may include notifying borrowers, the public, judicial authorities to force the sale of mortgage by way of a redemption prevention auction, or the like. Once redeemed, the mortgage may be placed on a public auction website (e.g., an easy TM) or an auction site suitable for a particular type of property. The minimum opening price for a mortgage may be determined by the borrower and may cover the loan balance, loan interest, the fees associated with redemption prevention, and the like. Attempts to reclaim the loan balance may include transferring the mortgage's deed in lieu of a redemption stop (e.g., a borrower holding a real estate mortgage as the real estate deed of the mortgage). Redemption prevention may include possession or re-possession of a mortgage (e.g., an automobile, a sports car (e.g., a ship), an ATV, a ski, jewelry). Redemption prevention may include securing a mortgage associated with the loan (e.g., through a locking connected device, such as a smart lock, smart container, etc. that contains or secures the mortgage). Redemption prevention may include scheduling shipping mortgages for the carrier, freight forwarder, etc. Redemption prevention includes arranging for a drone, robot, or the like to transport the mortgage. In an embodiment, the loan may allow for an alternative mortgage or transfer of a lien from the mortgage originally used to guarantee the loan to an alternative mortgage, where the value of the alternative mortgage (to the borrower) is higher than the original mortgage, or the borrower has a greater interest in the item. As a result of the mortgage replacement, the replacement mortgage may be a target of the forced sale or the mortgage when the loan enters the redemption stop. Certain uses of the term default may not be applicable to conditions such as redemption of the offer, but rather to regular or default conditions of the item. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit from redemption prevention and/or how to combine the processes and systems of the present invention to enhance or benefit from redemption prevention. Some of the considerations by those skilled in the art in determining terms of redemption stopping, redemption stopping conditions, default, etc., refer to the borrower failing to satisfy the loan terms and the borrower making related attempts to collect a loan balance or obtain mortgage ownership.
The terms "ownership verification," "verifying ownership," "verifying clause," and similar terms as used in this document should be construed broadly. Without being limited to any other aspect or description of the present invention, ownership verification includes any work that verifies or validates ownership or equity of an individual or entity to property items such as vehicles, ships, airplanes, buildings, residences, real estate, undeveloped real estate, farms, crops, municipal facilities, warehouses, a group of inventory, merchandise, securities, currency, value certificates, tickets, cryptocurrency, consumer goods, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contract rights, antiques, fixtures, furniture, equipment items, tools, machinery, and personal property. The work to verify ownership may include referencing sales documents, government ownership transfer documents, legal ownership transfer, lien deregistration documents for property items, verifying transfer of intellectual property rights to borrowers in appropriate jurisdictions, and the like. Real estate verification may include court review contracts and records in a country, state, county, or region where buildings, houses, real estate, undeveloped real estate, farms, crops, municipal facilities, vehicles, vessels, aircraft, or warehouses have been located or registered. Some uses of the verification term may not be applicable to verification of ownership or ownership verification, but rather to confirming whether the process is functioning properly, whether biometric data has been used to properly identify individuals, whether intellectual property is valid, whether data is correct and meaningful, and so forth. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit from ownership verification and/or how to combine the processes and systems of the present invention to enhance or benefit from ownership verification. Certain considerations by those skilled in the art in determining whether the term validity refers to validity of ownership are specifically contemplated within the scope of the present invention.
Without being limited to any other aspect or description of the invention, verification includes any verification system including, but not limited to, verifying ownership of a loan mortgage or guarantee, verifying mortgage status of a guarantee or loan, verifying status of a loan guarantee, and the like. For example, the validation service may provide a mechanism for a borrower to more deterministically offer a loan, such as by validating a loan or a guarantee information component (e.g., revenue, employment, ownership, loan conditions, mortgage conditions, and property conditions). In a non-limiting example, the verification service circuit may be configured to verify the plurality of loan information components with respect to a financial entity for determining the property loan condition. Some components may not be considered individually as verification systems, but may be considered to be verified in an aggregate system-for example, the internet of things component itself may not be considered to be a verification component, however, when the internet of things component is associated with a mortgage asset, the internet of things component for asset data collection and monitoring may be considered to be a verification component when applied to verifying load reliability parameters of a personal assurance. In some embodiments, systems that are similar in appearance may be distinguished in determining whether such systems are used for verification. For example, a blockchain-based ledger may be used to verify identity in one instance and maintain confidential information in another instance. Thus, the advantages of the present invention are applicable in a variety of systems, and any such system may be considered a verification system herein, while in certain embodiments a given system may not be considered a verification system herein. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit a particular system and/or how to combine the processes and systems in this summary to enhance the operation of the contemplated systems. Certain considerations by those of skill in the art in determining whether an intended system is a verification system and/or whether aspects of the present invention may be useful or enhance the intended system include, but are not limited to: the loan platform is provided with a social network monitoring system for verifying the reliability of the loan guarantee; the loan platform is provided with an internet of things data collection and monitoring system for verifying the reliability of loan guarantee; the lending platform is provided with a crowdsourcing and automatic classification system for verifying the conditions of the bond issuer; the crowdsourcing system is used for verifying the quality, ownership or other conditions of the loan mortgage; biometric verification applications, for example using DNA or fingerprints; ioT devices are used to collectively verify the location and identity of fixed assets marked by virtual asset tags; a verification system using a voting or consensus protocol; training an artificial intelligence system to identify and verify events; verifying information such as title records, video clips, photos or testimonials; verification statements related to behavior, such as verifying the occurrence of compliance conditions, verifying the occurrence of default conditions, preventing improper behavior or false statements, reducing uncertainty, or reducing information asymmetry; etc.
The term "underwriting" (and similar terms) as used herein should be construed broadly. Underwriting includes, without limitation to any other aspect or description of the present invention, any underwriting including, but not limited to, underwriting related to underwriters, providing loan underwriting information, underwriting liability transactions, underwriting bond transactions, underwriting subsidy loan transactions, underwriting securities transactions, and the like. The underwriting service may be provided by a financial entity such as a bank, insurance company, or investment company, whereby the financial entity provides a guarantee for payment when determining a loss condition (e.g., damage or financial loss) and accepts financial risk of liability due to the guarantee. For example, a bank may underwire a loan through a mechanism that performs credit analysis that may result in determining that a granted loan will be obtained, such as by analyzing personal information components (e.g., employment history, payroll and financial statements, publicly available information, such as borrower's credit history) related to the person requesting the consumer's loan, analyzing business financial information components (e.g., tangible equity, debt to value ratio (leverage), availability liquidity (flow ratio), etc.) from the company requesting the business load. In a non-limiting example, the underwriting service circuit may be configured to underwrite a financial transaction that includes a plurality of financial information components associated with a financial entity for determining the financial status of an asset. In some embodiments, the underwriting component may be considered underwriting for some purpose, but not for other purposes-for example, an artificial intelligence system for collecting and analyzing transaction data may be used in conjunction with an intelligent contract platform for monitoring loan transactions, but may also be used for collecting and analyzing underwriting data, for example, using models trained by human expert underwriters. Thus, the advantages of the present invention may be applied in a variety of systems, and any such system may be considered herein as underwriting, while in certain embodiments, a given system may not be considered herein as underwriting. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit a particular system and/or how to combine the processes and systems in this summary to enhance the operation of the contemplated systems. Certain considerations by those skilled in the art in determining whether a desired system is underwriting and/or whether aspects of the present invention may be beneficial or enhanced for the desired system include, but are not limited to: the loan platform has a loan underwriting system with a set of data-integrated micro services including, for example, data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for underwriting loan entities and transactions; underwriting flows, operations and services; underwriting data, such as data relating to potential and actual principal identities relating to insurance and other transactions, refinement data, data relating to the probability and/or extent of risk occurrence associated with an activity, data relating to observed activity, and other data for underwriting or estimating risk; an underwriting application, such as, but not limited to, underwriting any insurance offer, any loan, or any other transaction, including detecting, describing, or predicting risk likelihood and/or scope, underwriting or inspection flow of the entity providing the loan solution, any application that analyzes the solution, or asset management solution; underwriting insurance policies, loans, guarantees, or guarantees; blockchain and smart contract platforms are used to aggregate identity and behavioral information of insurance underwriting, such as recording a set of events, transactions, activities, identities, facts, and other information related to underwriting flows using optional distributed ledgers; crowd-sourcing platforms, such as underwriting of various loans and vouchers; the loan underwriting system has a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services that underwrite loan entities and transactions; creating, configuring, modifying, setting, or otherwise handling underwriting solutions for various rules, thresholds, conditional procedures, workflows, or model parameters; an underwriting action or management plan for managing a set of loans of a given type or types based on one or more events, conditions, states, actions, secondary loans, or transactions supporting loans, for collecting, merging, redemption, bankruptcy, modification of existing loans, conditions involving market changes, redemption-stopping activities; the adaptive intelligence system includes an artificial intelligence model trained based on a set of underwriting activities and/or underwriting action results trained by an expert to generate a set of predictions, classifications, control specifications, plans, models; the loan underwriting system has a set of data-integrated micro services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for underwriting loan entities and transactions; etc.
The term "insurance" (and similar terms) as used herein should be construed broadly. Without being limited to any other aspect or description of the invention, insurance includes any insurance including, but not limited to, providing loan insurance, providing insurance evidence for properties related to a loan, accepting a first entity of another entity's risk or liability, and the like. Insurance or insurance may be a mechanism by which an insurance holder may be protected from financial losses, for example in the form of risk management, against the risk of accidental or indeterminate losses. The insurance mechanism may specify insurance, determine the need for insurance, determine evidence of insurance, etc., e.g., related to the property, property transaction, property loan, guarantee, etc. The entity that provides insurance may be referred to as an insurer, insurance company, underwriter, etc. For example, an insurance mechanism may provide a mechanism for a financial entity to determine insurance evidence of a loan-related property. In a non-limiting example, the insurance service circuit may be configured to determine an insurance proof condition for the property based on a plurality of insurance information components related to the financial entity used to determine the loan condition for the property. In some embodiments, components may be considered insurance for some purpose, but not for other purposes-e.g., blockchain and smart contract platforms may be used to manage aspects of loan transactions, such as for identity and confidentiality, but may also be used to aggregate identity and behavioral information for insurance underwriting. Thus, the advantages of the present invention may be applied in a variety of systems, and any such system may be considered as safe herein, while in certain embodiments, a given system may not be considered as safe herein. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit a particular system and/or how to combine the processes and systems in this summary to enhance the operation of the contemplated systems. Certain considerations by those skilled in the art in determining whether a desired system is insurance and/or whether aspects of the present invention may benefit or augment the desired system include, but are not limited to: insurance facilities such as branches, offices, storage facilities, data centers, underwriting services, and the like; insurance claims, such as for business outage insurance, product liability insurance, cargo, facility or equipment insurance, flood insurance, contract-related risk insurance, etc., as well as claim data related to product liability, general liability, worker reimbursement, contract-related injuries and other liability claims and claim data, such as supply contract reimbursement claims, product delivery claims, contract reimbursement claims, damage reimbursement claims, redemption or compensation claims, access claims, warranty reimbursement claims, energy production claims, delivery claims, time claims, nodes, key performance indicators, etc.; insurance related loans; insurance services, insurance brokerage services, life insurance services, health insurance services, retirement insurance services, property insurance services, accidental injury insurance services, financial insurance services, reinsurance services; the blockchain and intelligent contract platform is used for aggregating identity and behavior information of insurance underwriting; the identity of the insurance applicant, the identity of the party willing to provide insurance, information about the risk that may be applied (any type, but not limited to, e.g., property, life, travel, infringement, health, housing, business liability, product liability, car, fire, flood, casualties, retirement, loss of business, etc.); the distributed ledger may be used to facilitate offers and underwriting of small insurance, e.g., for defining a defined risk associated with a defined activity that is less extensive than a typical policy over a defined period of time; providing loan insurance, and providing insurance proof for loan related property; etc.
The term "polymerize" (and similar terms) as used herein should be interpreted broadly. Without limiting to any other aspect or description of the invention, aggregation includes any aggregation, including but not limited to any other method of aggregating items together, such as aggregating or linking similar items together (e.g., mortgages providing mortgages for a set of loans, mortgages for a set of loans aggregated in real-time based on similarity of states of the set of mortgages, etc., collecting data together (e.g., for storage, for communication, for analysis, as training data for models, etc.), aggregating polymeric items or data into a simpler description, or creating an ensemble formed by combining multiple (e.g., different) elements). In some embodiments, the aggregate may be considered an aggregate for some purposes, but not for others-e.g., an aggregate of mortgage conditions may be collected to aggregate the loans together in one instance and determine the default behavior in another instance-additionally, in some embodiments, in determining whether such a system is an aggregator and/or what type of aggregate system, systems that are similar in appearance may be distinguished, e.g., both the first and second aggregators may aggregate the financial entity data, wherein the first aggregator aggregates the financial entity data for building a training set of analytical model circuits and the second aggregator aggregates the financial entity data for storage in a blockchain-based distributed ledger. Thus, the advantages of the present invention may be applied in a variety of systems, and any such system may be considered herein to be polymeric, while in certain embodiments, a given system may not be considered herein to be polymeric. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit a particular system and/or how to combine the processes and systems in this summary to enhance the operation of the contemplated systems. Certain considerations by those skilled in the art in determining whether the desired system is an aggregation and/or whether aspects of the present invention may be beneficial or enhanced for the desired system include, but are not limited to, long-term market demand aggregation (e.g., blockchains and smart contract platforms for long-term market demand aggregation, expressed or submitted willingness in demand aggregation interfaces, blockchains for aggregating future demands on various products and services in a long-term market, the blockchains handling a set of potential configurations having different parameters for subset of configurations consistent with each other, and the subset of configurations for aggregating future demands submitted for offers meeting a sufficiently large subset at a profitable price, etc.); associating aggregate data (including trend information) of worker ages, seniorities, experiences (including by process type) with data of processes in which the workers participate; pre-aggregate and facilitate-fulfillment accommodation needs by automatically identifying conditions (e.g., distributed ledgers) that satisfy pre-configured commitments represented on blockchains; aggregated and fulfilled transportation offers (e.g., various predetermined or matters); goods and services on the aggregated blockchain (e.g., distributed ledgers for demand planning); with respect to demand syndication interfaces (e.g., presented to one or more consumers); aggregating a plurality of submissions; aggregate identity and behavioral information (e.g., insurance underwriting); accumulation and aggregation of multiple principals; data aggregation for a set of mortgages; aggregate value of mortgages or assets (e.g., based on real-time condition monitoring, real-time market data collection and integration, etc.); aggregating the loan portions; mortgages of smart contracts aggregated with other similar mortgages; etc.
The term "link" (and similar terms) as used herein should be construed broadly. Without being limited to any other aspect or description of the invention, links include any link, including but not limited to a link that is a relationship between two things or situations (e.g., one of which affects the other). For example, a subset of similar items (e.g., mortgages) are linked to provide mortgages for a set of loans. Some components may not be considered separately linked, but may be considered in the linking process in an aggregate system-for example, the smart contract circuitry may be configured to operate in conjunction with blockchain circuitry as part of a loan processing platform, but where the smart contract circuitry processes the aggregate while not storing information through the blockchain circuitry, however, the two circuits may be connected through the smart contract circuitry, connecting financial entity information through a distributed ledger on the blockchain circuitry. In some embodiments, the link may be considered a link for some purpose, but not for other purposes-e.g., linking goods and services for a user and the radio frequency link between access points is a different form of link, taken together as a link linking goods and services for a user when the RF link is a communication link between transceivers. Additionally, in some embodiments, systems that are similar in appearance may be distinguished in determining whether and/or what type of links such systems are. For example, linking similar data together for analysis is different than linking similar data together for drawing. Thus, the advantages of the present invention may be applied in a variety of systems, and any such system may be considered herein as a link, while in certain embodiments a given system may not be considered herein as a link. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit a particular system and/or how to combine the processes and systems in this summary to enhance the operation of the contemplated systems. Certain considerations by those skilled in the art in determining whether a desired system is linked and/or whether aspects of the present invention may be beneficial to or enhance the desired system include, but are not limited to, linking a marketplace or external marketplace with a system or platform; link data (e.g., a data cluster including links and nodes); storing and retrieving data linked to the local process; links in the public knowledge graph (e.g., about nodes); data linked to proximity or location (e.g., assets); links to environments (e.g., goods, services, assets, etc.); linking events (e.g., for storage in a blockchain, etc., for communication or analysis); linking ownership or access rights; links to access credentials (e.g., travel products linked to access credentials); links to one or more resources (e.g., protected by encryption or other techniques); linking the message to the smart contract; etc.
The term "willingness index" (and similar terms) as used herein should be construed broadly. Without being limited to any other aspect or description of the invention, the willingness index includes any willingness index including, but not limited to, a willingness index from a user or users or parties associated with a transaction, etc. (e.g., parties willing to participate in a loan transaction), recording or storing such willingness (e.g., circuitry for recording willingness inputs from users, entities, circuitry, systems, etc.), analyzing the willingness-related data, and setting the willingness index circuitry (e.g., circuitry that sets or communicates the index based on the circuitry inputs, e.g., from users, parties, entities, systems, circuitry, etc.), a model trained to determine the willingness index of the input data associated with the willingness through one of a plurality of inputs from users, parties, or financial entities, etc. Some components may not be considered solely as willingness indicators, but may be considered as willingness indicators in an aggregated system-for example, a principal may seek information related to a transaction, such as a transaction market seeking information through the principal's willingness, but may not be considered as willingness indicators for a transaction. However, when a party claims a particular intent (e.g., by having a control input user interface for indicating benefit), the party's intent may be recorded (e.g., in a storage circuit, in a blockchain circuit), analyzed (e.g., by an analysis circuit, a data collection circuit), monitored (e.g., by a monitoring circuit), and so forth. In a non-limiting example, willingness metrics from a set of parties associated with a product, service, etc., such as metrics defining parameters that the parties are willing to commit to purchasing the product or service, may be recorded (e.g., in a blockchain via a distributed ledger). In some embodiments, the willingness index may be considered a willingness index for some purpose, but not for other purposes-for example, the user may indicate the willingness of a loan transaction, but this does not necessarily mean that the user indicates a willingness to provide a mortgage type in connection with the loan transaction. For example, the data collection circuit may record the willingness index of the transaction, but may have a separate circuit structure for determining the mortgage willingness index. Additionally, in some embodiments, systems that are similar in appearance may be distinguished in determining whether such systems determine willingness metrics and/or what type of willingness metrics are present. For example, one circuit or system may collect data from multiple parties to determine willingness indicators in a loan guarantee, while a second circuit or system may collect data from multiple parties to determine willingness indicators in a loan guarantee to determine loan-related ownership. Thus, the advantages of the present invention may be applied in a variety of systems, and any such system may be considered a willingness index herein, while in some embodiments a given system may not be considered a willingness index herein. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit a particular system and/or how to combine the processes and systems in this summary to enhance the operation of the contemplated systems. Certain considerations by those skilled in the art in determining whether the intended system is a willingness indicator and/or whether aspects of the present invention may be beneficial or enhanced for the intended system include, but are not limited to, the principal indicating a willingness to participate in a transaction (e.g., a loan transaction), the principal indicating a willingness to protect a product or service, recording or storing the willingness indicator (e.g., via a storage circuit or blockchain circuit), analyzing the willingness indicator (e.g., via a data collection and/or monitoring circuit), and the like.
The term "accommodation" (and similar terms) as used herein should be construed broadly. Without limiting any other aspect or description of the invention, accommodation includes any service, activity, event, etc., including, for example, but not limited to, a room, a group of rooms, a table, a seat, a building, an event, a shared space provided by individuals (e.g., airBnB space), beds and breakfast, a workspace, a meeting room, a meeting space, a fitness facility, a health and wellness facility, a dining facility, etc., wherein a person may live, stay, sit, live, participate, etc. Thus, accommodation may be purchased (e.g., ticket purchased through a sports ticketing application), booked (e.g., booked through a hotel booking application), offered as a reward or gift, traded or exchanged (e.g., through a marketplace), offered as access (e.g., offered through aggregate demand), offered on an as-needed basis (e.g., booking rooms according to availability of nearby events), and so forth. Certain components may not be considered lodging alone, but may be considered lodging in an aggregate system-for example, a resource (e.g., a room in a hotel) may not itself be considered lodging, but a reservation of a room may be considered lodging. For example, a blockchain and smart contract platform for lodging forward market rights may provide a mechanism to provide access to lodging. In a non-limiting example, the blockchain circuitry may be configured to store access rights in the long-term requirements marketplace, where the access rights may be stored in a distributed ledger with associated shared access to a plurality of operational entities. In some embodiments, a accommodation may be considered to be an accommodation for some purpose but not for other purposes-e.g., a reservation of a room may be its own accommodation, but may not be a satisfied accommodation, e.g., a reservation, if related or ordered items are not satisfied by convention at the time of the reservation. Additionally, in some embodiments, systems that are similar in appearance may be distinguished in determining whether and/or what type of accommodation such systems are related to. For example, accommodation provision may be made based on different systems, e.g., a system in which accommodation provision is determined by a system that collects data related to long-term demand, and a second system in which accommodation provision is provided as a reward based on a system that processes performance parameters. Thus, the advantages of the present invention may be applied in a variety of systems, and any such system may be considered relevant to the accommodation herein, while in certain embodiments a given system may be considered irrelevant to the accommodation herein. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit a particular system and/or how to combine the processes and systems in this summary to enhance the operation of the contemplated systems. Certain considerations by those of skill in the art in determining whether a desired system is associated with accommodation and/or whether aspects of the present invention may be beneficial or enhanced for a desired system include, but are not limited to, determining provided accommodation, transaction or exchange services (e.g., via an application and/or user interface), accommodation offers as a combination of products, services, and access rights, etc., regarding processing (e.g., aggregate demand for long-term market offers), accommodation via advance reservation when certain conditions (e.g., relating to prices within a given time window) are met, and the like.
The term "or matter" (and similar terms) as used herein should be interpreted broadly. Without limiting to any other aspect or description of the invention, or matter includes any or matter including, but not limited to, any action that depends on the second action. For example, the service may be provided according to a certain parameter value, such as collecting data according to an asset tag indication from an internet of things circuit. In another example, an accommodation such as a hotel reservation may depend on whether the concert (local to the hotel, concurrent with the reservation) is scheduled. Certain components may not be considered individually related to or associated with an issue, but may be considered related to or associated with an issue in an aggregated system-e.g., data input collected from a data collection service circuit may be stored, analyzed, processed, etc., and not considered related to or associated with an issue, however, an intelligent contract service circuit may apply contract terms based on the collected data. For example, the data may indicate mortgage status regarding a loan transaction, and the smart contract service circuit may apply the data to the mortgage-dependent contract terms. In some embodiments, the or issue may be considered to be for some purpose but not for other purposes-e.g., delivery of future events or access rights may depend on whether the loan condition is satisfied, but if there is no or issue link between the loan condition and the usage rights, the loan condition itself may not be considered to be the or issue. Additionally, in some embodiments, systems that are similar in appearance may be distinguished in determining whether and/or what type of such systems are related to or have events. For example, both algorithms may create a long-term market event access right credential, but with the first algorithm creating no or a good credential and the second algorithm creating a good credential to provide the credential. Thus, the advantages of the present invention may be applied in a variety of systems, and any such system may be considered herein as being a matter or matter, while in certain embodiments a given system may not be considered herein as being a matter or matter. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit a particular system and/or how to combine the processes and systems in this summary to enhance the operation of the contemplated systems. Certain considerations by those of skill in the art in determining whether the desired system is or has something and/or whether aspects of the present invention may be beneficial or enhance the desired system include, but are not limited to, a forward market within or operated by the platform may be or have a forward market, e.g., a forward market that grants, triggers or otherwise brings future rights based on event occurrence, condition satisfaction, etc.; blockchains are used to create or market in any form of event or access credentials by securely storing access rights on a distributed ledger; setting and monitoring or having pricing for access rights, base access rights, credentials, fees, etc.; optimizing products, time, pricing, etc. to identify and predict patterns, establish rules and or matters; exchanging or having access or base access or credential access credentials and/or having access credentials; creating or having a long-term market event access ticket, wherein the ticket can be created and stored on a blockchain for use in possibly resulting in ticket ownership or access; discovery and delivery of or access to future events; influencing or representing future demands or matters, e.g. comprising a set of products, services, etc.; predetermined or have matters; optimizing products, time, pricing, etc. to identify and predict patterns, establish rules and or matters; creating or having future offers within the control panel; each smart contract that may result in ownership of the virtual good or have access to or purchase the virtual good if the virtual good is available under specified conditions; etc.
The term "service level" (and similar terms) as used herein should be construed broadly. Without being limited to any other aspect or description of the invention, a service level includes any service level, including but not limited to any qualitative or quantitative measure of the extent to which a service is provided, such as but not limited to a streaming service and a business-level service (e.g., travel reservation or postal delivery), the extent to which resources are available (e.g., service level a representing a high availability of resources and service level C representing a limitation of resources, such as in terms of road traffic flow limitations), the extent to which operating parameters are run (e.g., the system is running in a high service state and a low service state, etc.). In an embodiment, the service level may be multi-modal such that the service level is variable where the system or circuit provides a service rating (e.g., the service rating is used as an input to an analysis circuit based on the service rating determination). Some components may not be considered solely with respect to the service level, but may be considered with respect to the service level in the aggregate system-e.g., the system for monitoring traffic may provide current rate data but not represent the service level, but when a determined traffic flow is provided to the monitoring circuit, the monitoring circuit may compare the determined traffic flow rate to past traffic flows and determine the service level based on the comparison. In some embodiments, the service level may be considered a service level for some purpose but not for other purposes-for example, the availability of first class travel accommodations may be considered a service level for determining whether to purchase an air ticket rather than putting forward future demands on a flight. Further, in some embodiments, similar appearance systems may be distinguished in determining whether and/or which service levels are used by such systems. For example, an artificial intelligence circuit may be trained for past service levels for traffic flow patterns on a highway and used to predict future traffic flow patterns based on current traffic flow, but similar artificial intelligence circuits may predict future traffic flow patterns based on time of day. Thus, the advantages of the present invention may be applied to a variety of systems, and the service level of any such system may be considered relative to the service level herein, while in some embodiments a given system may not be considered relative to the service level herein. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit a particular system and/or how to combine the processes and systems in this summary to enhance the operation of the contemplated systems. Certain considerations by those skilled in the art in determining whether the desired system is a service level and/or whether aspects of the present invention can benefit or enhance the desired system include, but are not limited to, transportation or accommodation offers with predefined or pertinent matters and parameters regarding price, service mode, and service level; guarantee or guarantee applications, transportation markets, etc.
The term "payment" (and similar terms) as used herein should be construed broadly. Without being limited to any other aspect or description of the invention, payment includes, but is not limited to, any action or process of payment (e.g., loan payment) or paid (e.g., insurance payment), amount paid or payable (e.g., paying $ 1000), repayment (e.g., repayment), manner of payment (e.g., using loyalty programs, compensation points or specific currencies, including cryptocurrency), and the like. Some components may not be considered as payment alone, but may be considered as payment in an aggregated system-e.g., a submitted amount of money may not be considered as payment, but may be considered as payment (or repayment) when applicable to payments meeting loan requirements. For example, the data collection circuitry may provide a mechanism for a borrower to monitor loan repayment. In a non-limiting example, the data collection circuit may be configured to monitor payment of the plurality of loan components relative to a financial loan contract for determining a loan condition for the property. In some embodiments, the payment may be considered to be for some purpose, but not for other purposes—for example, the payment to a financial entity may be a payoff amount to repay a loan, or may be to satisfy an accompanying obligation under a loan violation condition. Additionally, in some embodiments, systems that are similar in appearance may be distinguished in determining whether and/or what type of payment such systems are associated with. For example, funds may be used to reserve a accommodation or to satisfy a service delivery after the accommodation is satisfied. Thus, the advantages of the present invention may be applied in a variety of systems, and any such system may be considered herein as payment, while in certain embodiments, a given system may not be considered herein as payment. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit a particular system and/or how to combine the processes and systems in this summary to enhance the operation of the contemplated systems. Certain considerations by those skilled in the art in determining whether the desired system is a payment and/or whether aspects of the present invention may be beneficial or enhanced for the desired system include, but are not limited to, delaying a desired payment, deferring a payment request, repaying a loan, paying an amount, payment plan, end-of-line clearing plan, payment fulfillment and satisfaction, payment means, and the like.
The term "location" (and similar terms) as used herein should be construed broadly. Without being limited to any other aspect or description of the invention, a location includes any location, including, but not limited to, a particular place or location of a person, place, or item, or location information regarding a location of a person, place, or item, such as a geographic location (e.g., a geographic location of a mortgage), a storage location (e.g., a storage location of a property), a personal location (e.g., a borrower, worker), location information related to the foregoing, and so forth. Some components may not be considered separately for location, but may be considered for location in an aggregated system-for example, a smart contract circuit may be configured to specify the requirement to store mortgages in a fixed location, but not to specify a particular location for a particular mortgage. In some embodiments, the location may be considered a location for some purpose but not for other purposes-e.g., processing a loan may require a borrower's address location in one instance, and processing a default condition may require a particular location in another instance. Additionally, in some embodiments, systems that are similar in appearance may be distinguished in determining whether such systems are locations and/or what type of locations. For example, in one instance, the location of a concert may need to be in a concert hall that accommodates 10000 people, but in another instance the location of the actual concert hall is specified. Thus, the advantages of the present invention may be applied in a variety of systems, and any such system may be considered relative to the locations herein, while in certain embodiments a given system may not be considered relative to the locations herein. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit a particular system and/or how to combine the processes and systems in this summary to enhance the operation of the contemplated systems. Certain considerations by those of skill in the art in determining whether an intended system regarding location is considered and/or whether aspects of the present invention may be beneficial or enhanced with respect to the intended system include, but are not limited to, the geographic location of an item or mortgage, the stored location of an item or property, location information, the location of a borrower or borrower, a location-based product or service objective application, a location-based fraud detection application, an indoor location monitoring system (e.g., camera, infrared system, motion detection system); worker location (including routes taken through the location), location parameters, event location, specific location of the event, and so forth.
The term "route" (and similar terms) as used herein should be construed broadly. Without being limited to any other aspect or description of the invention, a route includes any route including, but not limited to, a road or route from a starting point to a destination, sent or guided along a specified route, and the like. Some components may not be considered separately from the route, but may be considered as routes in an aggregate system-for example, a mobile data collector may specify a request for a route to collect data based on input from a monitoring circuit, but only upon receipt of input, the mobile data collector may determine which route to take and begin traveling along that route. In some embodiments, a route may be considered a route for some purpose but not for other purposes—for example, a possible route through a road system may be considered a particular route that is different from one location to another. Additionally, in some embodiments, systems that are similar in appearance may be distinguished in determining whether such systems are specified relative to location and/or which types of locations. For example, a route depicted on a map may indicate a possible route or an actual route taken by an individual. Thus, the advantages of the present invention may be applied in a variety of systems, and any such system may be considered with respect to the routes herein, while in some embodiments a given system may not be considered with respect to the routes herein. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit a particular system and/or how to combine the processes and systems in this summary to enhance the operation of the contemplated systems. Certain considerations by those skilled in the art in determining whether the intended system is a utilization route and/or whether aspects of the present invention may be useful or enhance the intended system include, but are not limited to, delivery routes; a route taken by the location; displaying a hotspot graph of a route of a customer or a worker traveling in an environment; determining which resources are deployed to which routes or travel types; a direct route or a multi-station route, for example from the consumer's destination to a specific location or to any location where an event occurs; a route for moving the data collector; etc.
The term "future offer" (and similar terms) as used herein should be construed broadly. Without being limited to any other aspect or description of the present invention, future offers include any future offer for goods or services, including, but not limited to, future offers to provide goods or services, future offers for proposed purchase, future offers by a long-term marketplace platform, future offers determined by smart contract circuitry, etc. Further, the future offer may be or have a future offer, or an offer based on a condition that causes the offer to be a future offer, for example, a condition that is imposed in the future offer depending on a predetermined condition (for example, securities of $1000 may be purchased on a set future date in accordance with a predetermined state indicated by the market). Some components are not considered individually as future offers, but may be considered future offers in an aggregated system-for example, if a loan offer is not authorized by a collective agreement between multiple parties related to the offer, the loan offer may not be considered a future offer, but may be considered a future offer once votes are collected and stored by a distributed ledger (e.g., by a blockchain circuit). In some embodiments, the future offer may be considered a future offer for some purposes but not for other purposes-e.g., the future offer may depend on the condition being met in the future, and thus the future offer may not be considered a future offer until the condition is met. Further, in some embodiments, systems that are similar in appearance may be distinguished in determining whether such systems are future products and/or types of future products. For example, two vouching offers may be determined as future offers, but one offer may have immediate or future issues to be met, and thus may not be considered a future offer, but rather may immediately provide a future statement. Accordingly, the advantages of the present invention may be applied to a variety of systems, and any such system may be considered in connection with future offerings herein, while in certain embodiments, a given system may not be considered in connection with future offerings herein. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit a particular system and/or how to combine the processes and systems in this summary to enhance the operation of the contemplated systems. Certain considerations by those of skill in the art in determining whether the desired system is associated with a future offer or whether aspects of the present invention may be beneficial or enhanced for the desired system include, but are not limited to, a long-term offer, including or with a long-term offer, a long-term offer in a long-term marketplace platform (e.g., for creating a future offer or with a future offer related to offer data identifying the platform's operating marketplace or external marketplace); future offers associated with contracting for intelligence (e.g., by performing an indication of commitments to purchase, participate in, or otherwise consume future offers, etc.).
The term "access rights" (and derivatives or variants) as used herein may be broadly understood to describe the right to obtain or hold property, item or other value. Or a condition with access may be that such access becomes authorized, obtained, or otherwise forensic upon satisfaction of a trigger or condition. The access rights or rights may also be provided for a particular purpose or for a different application or context configuration, such as, but not limited to, a loan-related action or any service or offer. But are not limited to, it may be desirable to provide notification to the owners of property, items, or items of value prior to such access or access. In discussing legal litigation, delinquent or default loans or agreements, or other situations where a borrower may seek remediation, but is not limited to, various forms of access and/or has access. The value of such rights to be implemented in the embodiments can be readily ascertained by one of ordinary skill in the art having the benefit of the disclosure herein and the knowledge of the conventional contemplated systems available. Although specific examples of access rights and/or access rights are described herein for illustrative purposes, any embodiment that would benefit from the disclosure herein, and any consideration that would be appreciated by one skilled in the art having the benefit of the disclosure herein, are specifically contemplated as falling within the scope of the present disclosure.
The term "smart contract" (and other forms or variants) as used herein may be broadly understood to describe a method, system, connected resource, or wide area network that provides one or more resources useful for assisting or performing an action, task, or thing through the embodiments disclosed herein. An intelligent contract may be a series of steps or processes that negotiate, manage, reorganize, or execute an agreement or loan between parties. The smart contract may also be implemented as an application, web site, FTP site, server, device or other connection component, or internet-related system that provides resources to negotiate, manage, reorganize, or conduct an agreement or loan between the parties. The smart contract may be a self-contained system or may be part of a larger system or component that is the smart contract. For example, a smart contract may refer to a loan or agreement itself, a condition or term, or to a system implementing such a loan or agreement. In some embodiments, the smart contract circuitry or robotic process automation system may be incorporated into or by the robotic process automation system to perform one or more purposes or tasks, whether part of a loan or transaction process, or in other forms. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily ascertain the purpose and use of the intelligent contracts in the various forms, embodiments, and contexts disclosed herein.
The term "reward distribution" (and variants) as used herein may be understood broadly to describe things or prices that are distributed or offered for purposes. The reward distribution may be of a financial type or a non-financial type, but is not limited thereto. The particular type of consideration allocation may also serve many different purposes or be configured for different applications or contexts, such as, but not limited to: consideration events, consideration claims, monetary consideration, consideration captured as a data set, consideration points, and other forms of consideration. Accordingly, the allocation of consideration may be provided as a price within the scope of the loan or agreement. The system may be used to assign rewards. Various forms of consideration allocation may be included in discussing or encouraging specific actions, but are not limited thereto. The consideration allocation may include an actual allocation of consideration and/or a record of consideration. The reward distribution may be performed by an intelligent contract circuit or a robotic process automation system. The value of the consideration allocation in the embodiments can be readily determined by those skilled in the art, given the benefit of the disclosure herein, and knowing the conventional contemplated systems available. Although specific examples of payment distribution are described herein for illustrative purposes, any embodiment that is benefit of the disclosure herein, and any consideration that would be appreciated by one skilled in the art having the benefit of the disclosure herein, are specifically contemplated as falling within the scope of the present disclosure.
The term "parameter or condition" (as well as other derivatives, forms, or variants) as used herein is intended to be broadly construed to describe the completion, existence, or demonstration of the parameter or condition that has been satisfied. The term may generally relate to a process of determining the degree of satisfaction of a parameter or condition, or may relate to the completion of the process that produces a result, but is not limited thereto. Meeting a successful outcome that may result in other triggers or conditions or clauses, but is not limited thereto. Satisfaction of parameters or conditions may occur in many different contexts of contracts or loans, such as, but not limited to, loan, re-financing, merging, warranty, brokering, redemption prevention, data processing (e.g., data collection), or a combination thereof. The satisfaction of the parameter or condition may take the form of nouns (e.g., satisfaction of debt payouts) or verbs to describe the process of determining the outcome of the parameter or condition. For example, the borrower may satisfy the parameter requirements by paying a certain amount of money on time, or in the event of a loan breach, the borrower may satisfy a condition that allows access to the owner, but is not limited thereto. In some embodiments, the smart contract or robotic process automation system may perform or determine satisfaction of a parameter or condition for one or more of the principals and process appropriate tasks to satisfy the parameter or condition. In some cases, satisfaction of parameters or conditions by intelligent contracts or robotic process automation systems may be incomplete or unsuccessful, and depending on these results, this may result in automatic actions or triggering other conditions or terms. The purpose and use of this term in the various forms, embodiments, and contexts disclosed herein can be readily ascertained by one of ordinary skill in the pertinent art having the benefit of the disclosure herein and understanding the conventional contemplated systems available.
The term "information" (and other forms such as, but not limited to, information) as used herein may be construed broadly in various contexts with respect to agreements or loans. The term may generally relate to a large number of contexts, such as information about agreements or loans, or may specifically relate to limited information (e.g., specific details of events occurring on a specific date). Thus, information may appear in many different contexts of contracts or loans, and may be used in contexts other than limited to evidence, transactions, accesses, and the like. Alternatively, but not limited to, the information may be used in connection with various stages of a protocol or transaction, such as loan, re-financing, consolidation, warranty, agency, redemption prevention and information processing (e.g., data or information collection), or a combination thereof. For example, information that is evidence, transaction, access, etc. may be used in noun form (e.g., the information is obtained from the borrower), or may refer to various items of information in noun form (e.g., information about the loan may be found in a smart contract), or may be described as adjective (e.g., the borrower provides information submission material). For example, the borrower may collect the over-payment to the borrower via an online payment, or may successfully collect the over-payment via a customer service call. In some embodiments, the smart contract circuitry or robotic process automation system may perform collection, management, calculation, provision, or other tasks for one or more of the parties and process appropriate tasks related to the information (e.g., providing notification of overdue payments). In some cases, the information provided by the smart contract circuitry or robotic process automation system may be incomplete, which may result in automatic actions or triggering other conditions or terms based on these results. The purpose and use of the various forms, embodiments, and contexts disclosed herein as evidence, transactions, access, etc. of information can be readily ascertained by one of ordinary skill in the art having the benefit of the disclosure herein and understanding the conventional contemplated systems available.
The information may be linked to external information (e.g., an external source). More specifically, the term may relate to acquiring, parsing, receiving, or other relationships with an external source or sources, but is not limited thereto. Thus, information associated with external information or sources may be used in connection with various stages of a agreement or transaction, such as loan, re-financing, merging, warranty, agency, redemption prevention and information processing (e.g., data or information collection), or a combination thereof. For example, the information linked to the external information may change as the external information changes, such as based on an external source borrower credit score. In some embodiments, the smart contract circuitry or robotic process automation system may perform acquisition, management, calculation, reception, update, provision, or other tasks for one or more of the parties and process appropriate tasks related to information linked to external information. In some cases, the information linked to external information by the smart contract or robotic process automation system may be incomplete and depending on these results, this may enable automatic actions or trigger other conditions or terms. The purpose and use of this term in the various forms, embodiments, and contexts disclosed herein can be readily ascertained by one of ordinary skill in the pertinent art having the benefit of the disclosure herein and understanding the conventional contemplated systems available.
The information that is part of the loan or agreement may be separate from the information displayed in the access location. More specifically, the term may relate to a description that information may be allocated, split, limited, or otherwise separated from other information in the context of a loan or agreement. Thus, the information presented or received at the access location is not necessarily all information available for a given context. For example, the information provided to the borrower may be different information received by the borrower from an external source and may be different from the information received or presented from the access location. In some embodiments, the smart contract circuitry or robotic process automation system may perform information separation or other tasks for one or more of the parties and process the appropriate tasks. The purpose and use of this term in the various forms, embodiments, and contexts disclosed herein can be readily ascertained by one of ordinary skill in the pertinent art having the benefit of the disclosure herein and understanding the conventional contemplated systems available.
The term "information encryption and access control" (and other related terms) as used herein may be broadly understood to broadly describe whether a principal or multiple principals may observe or possess certain information, actions, events, or activities related to a transaction or loan. Encryption of information may be used to prevent parties from accessing, observing, or receiving information, as well as to prevent parties outside of transactions or loans from accessing, observing, or receiving confidential (or other) information. Control of information access involves determining whether a principal has access to such information. Information encryption or access control may occur in many different loan environments, such as, but not limited to, loan, re-financing, merging, warranty, agency, redemption prevention, management, negotiation, collection, purchasing, execution, and data processing (e.g., data collection), or a combination thereof. Encryption of information or control of information access may refer to a single instance, or may describe a large amount of information, actions, events, or activities, but is not so limited. For example, a borrower or borrower may access information about a loan, but other parties outside of the loan or agreement may not have access to the loan information due to encryption of the information or access control to the details of the loan. In some embodiments, the smart contract circuitry or robotic process automation system may perform information encryption or information access control for one or more of the parties and process the appropriate tasks for encryption or information access control. The purpose and use of this term in the various forms, embodiments, and contexts disclosed herein can be readily ascertained by one of ordinary skill in the pertinent art having the benefit of the disclosure herein and understanding the conventional contemplated systems available.
The term "potential accessing principal list" (and other related terms) as used herein may be construed broadly to describe generally whether a principal or multiple principals can observe or possess certain information, actions, events, or activities related to a transaction or loan. The list of potential accessing principals may be used to authorize one or more principals to access, view, or receive information, or alternatively may be used to prevent the accessing principal from being able to do so. The potential accessing principal list information relates to determining whether the principal (whether on the potential accessing principal list or not) has access to such information. The list of potential accessing principals occurs in many different loan environments, such as, but not limited to, loan, re-financing, merger, insurance, agency, redemption prevention, management, negotiation, collection, purchasing, execution, and data processing (e.g., data collection), or a combination thereof. The list of potential accessing principals may refer to a single instance or may describe a large number of principals or information, actions, events, or activities, but is not so limited. For example, the list of potential accessing principals may grant (or deny) access to information about the loan, but other principals outside of the list of potential accessing principals may not be able (or may be granted) access to the loan information. In some embodiments, the smart contract circuitry or robotic process automation system may perform management or enforce a list of potential access principals for one or more of the principals and process the appropriate tasks for encryption or information access control. The purpose and use of this term in the various forms, embodiments, and contexts disclosed herein can be readily ascertained by one of ordinary skill in the pertinent art having the benefit of the disclosure herein and understanding the conventional contemplated systems available.
The terms "offer," "offer," and similar terms as used herein should be construed broadly. Without being limited to any other aspect or description of the invention, an offer includes any item or service offer, including, but not limited to, an insurance offer, a guarantee offer, an offer to provide an item or service, an offer for proposed purchase, an offer through a long-term marketplace platform, a future offer, or an offer, an offer related to a loan (e.g., loan, refeeding, collection, merger, warranty, agency, redemption-stopping), an offer determined by smart contract circuitry, an offer to a customer/debtor, an offer to a provider/borrower, a third party offer (e.g., regulatory agency, audit agency, part of owners, layered service provider), and the like. The offer may include a physical object, virtual item, software, physical service, access rights, entertainment content, accommodation, or many other objects, services, solutions, or considerations. For example, a third party offer may be to arrange a band, rather than merely offering a ticket for sale. Further, the offer may be based on predetermined conditions or matters. Certain components may not be considered individually as offers, but may be considered as offers in an aggregated system-for example, if an insurance offer is not approved by one or more parties associated with the offer, the insurance offer should not be considered an offer, but once approved, may be considered an offer. Accordingly, the advantages of the present invention may be applied to a variety of systems, and any such system may be considered in connection with the offers herein, while in certain embodiments a given system may not be considered in connection with the offers herein. Those skilled in the art, having the benefit of the disclosure herein and knowing the conventional contemplated systems available, can readily determine which aspects of the present invention will benefit a particular system and/or how to combine the processes and systems in this summary to enhance the operation of the contemplated systems. Certain considerations by those skilled in the art in determining whether an intended system is associated with an offer and/or whether aspects of the present invention may be beneficial or enhance the intended system include, but are not limited to, the goods or services provided, the or an issue related to the offer, the manner in which tracking of the or an issue or condition is met or not, approving the offer, performing an offer exchange, and the like.
The term "Artificial Intelligence (AI) solution" as used herein should be construed broadly. Without being limited to any other aspect of the invention, an AI solution includes a coordinated set of AI-related aspects to perform one or more tasks or operations throughout the invention. An example AI solution includes one or more AI components, including any AI component described herein (including at least neural networks, expert systems, and/or machine learning components). As one aspect, example AI solutions may include types of components of the solution, such as heuristic AI components, model-based AI components, selected types of neural networks (e.g., recursion, convolution, perceptrons, etc.), and/or any type of AI components having selected processing capabilities (e.g., signal processing, frequency component analysis, auditory processing, visual processing, speech processing, text recognition, etc.). Without being limited to any other aspect of the invention, a given AI solution may be formed from the number and type of AI components of the AI solution, the connectivity of the AI components (e.g., connected to each other, to system inputs from or interacting with the AI solution, and/or to system outputs including or interacting with the AI solution). A given AI solution may also be formed by connections between AI components within the AI solution and boundary elements (e.g., inputs, outputs, stored intermediate data, etc.) in communication with the AI solution. A given AI solution may also be formed from a configuration of each AI component of the AI solution, where the configuration may include the following: model calibration of AI components; connectivity and/or flow (e.g., serial and/or parallel coupling, feedback loops, logical connections, etc.) between AI components; the number of AI component inputs, selected input data, and/or input data processing; the depth and/or complexity of the neural network or other components; training data descriptions (e.g., training data parameters such as content, amount of training data, statistical descriptions of valid training data, etc.) of AI components; and/or a selection of AI component types and/or a hybrid description. The AI solutions include selection of AI elements, flow connectivity of the AI elements, and/or configuration of the AI elements.
Those skilled in the art, with the benefit of this disclosure, may readily determine the AI solutions of a given system and/or configure operations to perform the AI solution selection and/or configuration operations for a given system. Some considerations for determining AI solutions and/or configuration operations to perform AI solution selection and/or configuration operations include, but are not limited to: availability of AI components and/or component types for a given implementation; the availability of supporting infrastructure to implement a given AI component (e.g., available data input values including data quality, service level, resolution, sampling rate, etc., availability of appropriate training data for a given AI solution, availability of expert input, e.g., for an expert system and/or development model training dataset, regulatory and/or policy-based considerations including allowing actions to be taken through the AI solution, requirement to acquire and/or retain sensitive data, difficulty in acquiring data, and/or high data cost); operational considerations of the system including or interacting with the AI solution, including response time specifications, security considerations, liability considerations, etc.; available computing resources such as processing power, network communication power, and/or memory storage capacity (e.g., data to support initial data, training data, input data (e.g., buffered, or stored input data), iterative improvement state data, output data (e.g., buffered, and stored output data), and/or intermediate data storage (e.g., data to support ongoing computations, historical data, and/or accumulated data)); the type of tasks that the AI solution is to perform, the applicability of the AI component to these tasks, the sensitivity of the AI component performing the tasks (e.g., variability in the size of the disturbance of the output space relative to the input space); interaction of AI components throughout the AI solution (e.g., low-capability rationality AI components may be coupled with high-capability AI components that may provide high sensitivity and/or infinite response to inputs); and/or model implementation considerations (e.g., recalibration requirements, model aging constraints, etc.).
The AI solutions selected and/or configured may be used with any of the systems, programs, and/or aspects of the embodiments set forth in this disclosure. For example, a system utilizing an expert system may include the expert system as all or part of a selected, configured AI solution. In another example, a system utilizing a neural network and/or a combination of neural networks may include the neural network as all or part of an AI solution selected, configured. The described aspects of AI solutions, including the selection and configuration of AI solutions, are non-limiting illustrations.
Referring to FIG. 1, a set of systems, methods, components, modules, machines, articles, blocks, circuits, are providedServices, programs, applications, hardware, software, and other elements are interchangeably referred to herein collectively as system 100 or platform 100. Platform 100 is capable of a wide variety of improvements to various machines, systems, and other components to enable transactions involving the exchange of value (e.g., using currency, cryptocurrency, tokens, rewards, etc., as well as a wide variety of physical and other resources) for various goods, services, and resources in various markets, including current or spot markets 170, long-term markets 130, etc. "currency" as used herein is understood to include: legal currency issued or regulated by the government, cryptocurrency, value vouchers, tickets, loyalty points, reward points, coupons, and other elements representing or having value that may be exchanged. Resources, such as resources that may exchange value in the marketplace, are understood to include: goods, services, natural resources, energy resources, computing resources, energy storage resources, data storage resources, network bandwidth resources, processing resources, etc., including resources that exchange value and resources that are capable of conducting transactions (e.g., computing and processing resources, storage resources, network resources, and energy resources required to effect transactions). The platform 100 may include a set of forward purchasing and selling machines 110, each of which may be configured as an expert system or automated intelligent agent for interacting with one or more of a set of spot markets 170 and forward markets 130. Enabling a set of long-term purchase and sale machines 110 is: an intelligent resource purchasing system 164 having a set of intelligent agents for purchasing resources in spot and forward markets; an intelligent resource allocation and coordination system 168 for intelligently selling allocated or coordinated resources, such as computing resources, energy resources, and other resources that participate in or implement transactions; an intelligent sales engine 172 for intelligently coordinating sales of allocated resources in spot and futures markets; an automated spot market test and arbitrage trade execution engine 194 for executing spot tests of spot and forward markets, such as with micro-trading, and automatically executing transactions utilizing resources of favorable arbitrage conditions if the conditions indicate such favorable arbitrage conditions. Each of the engines may use model-based or rule-based expert A system (e.g., based on rules or heuristics) and a deep learning system by which rules or heuristics can be learned through experimentation involving a large number of inputs. The engine may use any of the expert systems and artificial intelligence capabilities described throughout the present invention. Interactions within platform 100, including interactions of all platform components, as well as interactions between them and interactions with various markets, may be tracked and collected, such as through data aggregation system 144, for example, for aggregating purchase and sales data in various markets through a set of machines described herein. The aggregated data may include tracking and outcome data that may be fed to the artificial intelligence systems and machine learning systems, for example, for training or supervising the artificial intelligence systems and machine learning systems. The various engines may run on a series of data sources including aggregated data from market transactions, tracking data regarding the behavior of each of the engines, and a set of external data sources 182, which may include social media data sources 180 (e.g., facebook TM And Twitter TM Etc.), internet of things (IoT) data sources (including data sources from sensors, cameras, data collectors, and instrumented machines and systems), such as IoT sources that provide information about machines and systems implementing transactions and machines and systems involved in resource production and consumption. External data sources 182 may include behavioral data sources such as automated agent behavioral data sources 188 (e.g., data sources that track and report automated agents for conversation and dialog management, agents for control functions of machines and systems, agents for purchase and sales, agents for data collection, agents for advertising, etc.), human behavioral data sources 184 (e.g., data sources that track online behavior, mobile behavior, energy consumption behavior, energy production behavior, network utilization behavior, computing and processing behavior, resource consumption behavior, resource production behavior, purchasing behavior, attention behavior, social behavior, etc.), and entity behavioral data sources 190 (e.g., behaviors of business organizations and other entities such as purchasing behavior, consumption behavior, production behavior, marketing activity, union behavior, trading behavior, site selection behavior, etc.). From sensors, machines, humans, practice The body and automated agents, as well as IoT, social and behavioral data regarding sensors, machines, humans, entities and automated agents, may be used together to populate expert systems, machine learning systems, and other intelligent systems and engines described in this disclosure, such as provided as input to deep learning systems, and as feedback or results for training, supervision and iterative improvement purposes of predictive, categorical, automated and control systems. These data may be organized as an event stream. Such data may be stored in a distributed ledger or other distributed system. These data may be stored in a knowledge graph, where nodes represent entities and links represent relationships. External data sources may be queried through various database query functions. The data sources 182 may be accessed through APIs, agents, connectors, protocols (e.g., REST and SOAP) and other data ingestion and extraction techniques. The data may be enriched with metadata and may be converted and loaded into a suitable form for use by the engine, such as by cleaning, normalization, deduplication, and the like.
The platform 100 may include a set of intelligent prediction engines 192 for predicting events, activities, variables, and parameters of the spot market 170, the forward market 130, resources traded in such market, resources supporting such market, behaviors (e.g., any behavior tracked in the external data source 182), transactions, and the like. The prediction engine 192 may perform operations on data from the data aggregation system 144 regarding elements of the platform 100 as well as data from the external data sources 182. The platform may include a set of intelligent transaction engines 136 for automatically executing transactions in the spot market 170 and the forward market 130. This may include performing a smart cryptocurrency transaction using the smart cryptocurrency execution engine 183, as described in more detail below. Platform 100 may utilize the assets of improved distributed ledgers 113 and improved smart contracts 103, including assets that embed and manipulate proprietary information, instruction sets, etc., that enable complex transactions between individuals while reducing (or not) reliance on intermediaries. These and other components are described in more detail in this disclosure.
Reference is made to the block diagram of fig. 2, which shows further details of the platform 100 and additional components and interactions between them. A set of forward purchasing and selling machines 110 may include: the renewable energy distribution engine 102, for example, is used to distribute energy generation or renewable energy, such as in a hybrid vehicle or system that includes energy generation or renewable energy, a renewable energy system with energy storage, or other energy storage system, wherein energy is distributed to one or more of sales on the long-term market 130, sales in the spot market 170, for completing transactions (e.g., mining cryptocurrency), or other purposes. For example, the renewable energy allocation engine 102 may explore available options for using stored energy, such as accepting sales of energy from producers in the current and long-term energy markets, storing energy for future use, or using energy for work (which may include processing tasks such as platform processing activities, such as data collection or processing, or processing tasks that perform transactions, including crypto-currency mining activities).
A set of forward purchasing and selling machines 110 may include: the energy purchase and sale machine 104 is used to purchase or sell energy, such as in an energy spot market 148 or an energy forward market 122. The energy purchasing and selling machine 104 may use expert systems, neural networks, or other intelligence to determine the time of purchase, e.g., based on current and expected status information related to energy pricing and availability, and based on current and expected status information related to energy demand, including energy demand to perform computing tasks, cryptocurrency mining, data collection operations, and other tasks, such as tasks performed by automated agents and systems, and tasks required by humans or entities based on their behavior. For example, an energy purchasing machine may identify through machine learning that an enterprise may need a block of energy in order to perform a higher level of manufacturing based on an increase in order or market demand, and may purchase energy on the futures market at a favorable price based on a combination of energy market data and entity behavioral data. Continuing with this example, market demand may be understood through machine learning, for example, by processing a human behavioral data source 184, such as social media posts, e-commerce data, etc., that indicates an increase in demand. The energy purchase and sale machine 104 may sell energy in the energy spot market 148 or the energy forward market 122. Sales may also be made by expert systems running on the various data sources described herein, including outcome training and human supervision.
A set of forward purchasing and selling machines 110 may include: renewable Energy Credits (REC) purchase and sell machines 108 that may purchase renewable energy credits, pollution credits, and other environmental or regulatory credits for such credits in the spot market 150 or the long-term market 124. Purchase may be configured and managed by an expert system running on any external data source 182 or on data aggregated by the platform's set of data aggregation systems 144. Renewable energy credits and other credits may be purchased by automated systems using expert systems, including machine learning or other artificial intelligence, for example, purchasing credits at favorable times based on understanding of supply and demand (as determined by processing input from data sources). Under historical input conditions, the expert system may train based on the purchase result dataset. The expert system may be trained based on a human purchase decision dataset and/or may be supervised by one or more human operators. The Renewable Energy Credit (REC) purchasing and selling machine 108 may also sell such credit in the spot market 150 or the long-term market 124, pollution credits, and other environmental or regulatory credits. Sales may also be made by expert systems running on the various data sources described herein, including outcome training and human supervision.
A set of forward purchasing and selling machines 110 may include: attention purchasing and selling machine 112, which may purchase one or more attention related resources, such as advertising space, search listings, keyword listings, banner advertisements, participation in a panelist or survey activity, participation in a trial or test spot, or the like, in attention spot market 152 or attention forward market 128. The attention resource may include the attention of an automated agent for searching, shopping, and purchasing, such as a robot, crawler, dialog manager, and the like. The purchase of attention resources may be configured and managed by an expert system running on any external data source 182 or on data aggregated by the platform's set of data aggregation systems 144. Attention resources may be purchased by automated systems using expert systems, including machine learning or other artificial intelligence, for example, purchasing resources at favorable times based on understanding of supply and demand (as determined by processing inputs from various data sources). For example, attention purchasing machine 112 may purchase advertising space for advertisements in a long-term marketplace based on learning from a broad input of data regarding market conditions, behavioral data, and activities related to agents and systems within platform 100. Under historical input conditions, the expert system may train based on the purchase result dataset. The expert system may be trained based on a human purchase decision dataset and/or may be supervised by one or more human operators. The attention purchasing and selling machine 112 may also sell one or more attention related resources, such as advertising space, search listings, keyword listings, banner advertisements, participation in a panelist or survey activity, participation in a trial or test spot, or the like, in the attention spot market 152 or the attention spot market 128, which may include providing or selling access to or attention by one or more automated agents of the platform 100. Sales may also be made by expert systems running on the various data sources described herein, including outcome training and human supervision.
A set of forward purchasing and selling machines 110 may include: the computing purchase and sales machine 114, which may purchase one or more computing-related resources, such as processing resources, database resources, computing resources, server resources, disk resources, input/output resources, temporary storage resources, memory resources, virtual machine resources, container resources, and the like, in the computing spot market 154 or the computing forward market 132. Purchase of computing resources may be configured and managed by an expert system running on any external data source 182 or on data aggregated by a set of data aggregation systems 144 of the platform. Computing resources may be purchased by automated systems using expert systems, including machine learning or other artificial intelligence, for example, purchasing resources at favorable times based on understanding of supply and demand (as determined by processing inputs from various data sources). For example, the computing purchasing machine 114 may purchase or reserve computing resources on a cloud platform of a computing resource long-term market based on learning from extensive input about market conditions, behavioral data, and data about activities of agents and systems within the platform 100, thereby obtaining such resources at a favorable price during a surge in computing demand. Under historical input conditions, the expert system may train based on the purchase result dataset. The expert system may be trained based on a human purchase decision dataset and/or may be supervised by one or more human operators. The computing purchase and sales machine 114 may also sell one or more computing-related resources, such as processing resources, database resources, computing resources, server resources, disk resources, input/output resources, temporary storage resources, memory resources, virtual machine resources, container resources, etc., connected to, belonging to, or managed by the platform 100 in the computing spot market 154 or the computing forward market 132. Sales may also be made by expert systems running on the various data sources described herein, including outcome training and human supervision.
A set of forward purchasing and selling machines 110 may include: the data store purchase and sale machine 118, which may purchase one or more data-related resources, such as database resources, disk resources, server resources, memory resources, RAM resources, network attached storage resources, storage Attached Network (SAN) resources, tape resources, time-based data access resources, virtual machine resources, container resources, and the like, in a storage resource spot market 158 or a data store forward market 134. Purchase of data storage resources may be configured and managed by an expert system running on any external data source 182 or on data aggregated by a set of data aggregation systems 144 of the platform. The data storage resources may be purchased by an automated system using expert systems, including machine learning or other artificial intelligence, for example, purchasing resources at favorable times based on understanding of supply and demand (as determined by processing inputs from various data sources). For example, the computing purchasing machine 114 may purchase or reserve computing resources on a cloud platform of a computing resource long-term market based on learning from extensive input of data about market conditions, behavioral data, and activities of agents and systems within the platform 100, thereby obtaining such resources at a favorable price during storage demand surges. Under historical input conditions, the expert system may train based on the purchase result dataset. The expert system may be trained based on a human purchase decision dataset and/or may be supervised by one or more human operators. The data store purchase and sale machine 118 may also sell one or more data store-related resources connected to, belonging to, or managed by the platform 100 in the storage resource spot market 158 or the storage forward market 134. Sales may also be made by expert systems running on the various data sources described herein, including outcome training and human supervision.
A set of forward purchasing and selling machines 110 may include: bandwidth purchase and sale machine 120, which may purchase one or more bandwidth-related resources, such as cellular bandwidth, wi-Fi bandwidth, radio bandwidth, access point bandwidth, beacon bandwidth, local area network bandwidth, wide area network bandwidth, enterprise network bandwidth, server bandwidth, storage input/output bandwidth, advertising network bandwidth, market bandwidth, or other bandwidth, in bandwidth spot market 160 or bandwidth forward market 138. The purchase of bandwidth resources may be configured and managed by an expert system running on any external data source 182 or on data aggregated by the platform's set of data aggregation systems 144. Bandwidth resources may be purchased by automated systems using expert systems, including machine learning or other artificial intelligence, for example, purchasing resources at favorable times based on understanding of supply and demand (as determined by processing inputs from various data sources). For example, bandwidth purchasing and selling machine 120 may purchase or reserve bandwidth on network resources for future network activities managed by platform based on learning from extensive inputs of data regarding market conditions, behavioral data, and activities regarding agents and systems within platform 100, thereby obtaining such resources at a favorable price during a surge in bandwidth demand. Under historical input conditions, the expert system may train based on the purchase result dataset. The expert system may be trained based on a human purchase decision dataset and/or may be supervised by one or more human operators. The bandwidth purchase and sale machine 120 may also sell one or more bandwidth-related resources connected to, belonging to, or managed by the platform 100 in the bandwidth resource spot market 160 or the bandwidth forward market 138. Sales may also be made by expert systems running on the various data sources described herein, including outcome training and human supervision.
A set of forward purchasing and selling machines 110 may include: a spectrum purchase and sale machine 142 that may purchase one or more spectrum related resources, such as cellular spectrum, 3G spectrum, 4G spectrum, LTE spectrum, 5G spectrum, cognitive radio spectrum, point-to-point network spectrum, emergency response spectrum, etc., in a spectrum spot market 162 or spectrum forward market 140. Purchase of spectrum resources may be configured and managed by an expert system running on any external data source 182 or on data aggregated by a set of data aggregation systems 144 of the platform. Spectral resources may be purchased by automated systems using expert systems, including machine learning or other artificial intelligence, for example, purchasing resources at favorable times based on understanding of supply and demand (as determined by processing inputs from various data sources). For example, spectrum buying and selling machine 142 can purchase or reserve spectrum on network resources for future network activities managed by the platform based on learning from extensive inputs of data regarding market conditions, behavioral data, and activities regarding agents and systems within platform 100, thereby obtaining such resources at a favorable price during a surge in spectrum demand. Under historical input conditions, the expert system may train based on the purchase result dataset. The expert system may be trained based on a human purchase decision dataset and/or may be supervised by one or more human operators. The spectrum purchase and sale machine 142 may also sell one or more spectrum related resources connected to, belonging to, or managed by the platform 100 in the spectrum resource spot market 162 or spectrum forward market 140. Sales may also be made by expert systems running on the various data sources described herein, including outcome training and human supervision.
In an embodiment, the intelligent resource coordination and allocation engine 168, including the intelligent resource purchase engine 164, sales engine 172, and test and arbitrage engine 194, may provide coordinated and automated allocation of resources and coordinated execution of transactions between the various forward markets 130 and spot markets 170 by coordinating the various purchase and sales machines, such as by expert systems, such as machine learning systems (which may be model or deep learning systems based and may be trained and/or supervised by humans based on the results). For example, the coordination and allocation engine 168 may coordinate the purchase of resources for a set of assets and the sale of resources available for a set of assets, such as fleet, data centers for processing and data storage resources, information technology networks (local, cloud, or hybrid), energy production system groups (renewable or non-renewable), smart homes or buildings (including appliances, machines, infrastructure components and systems, etc. that consume or produce resources thereof), and the like. The platform 100 may optimize the distribution of resource purchases, sales, and utilization based on the data aggregated in the platform, such as by tracking the activities of various engines and agents, and by retrieving input from external data sources 182. In an embodiment, the results may be provided as feedback for training the intelligent resource coordination and allocation engine 168, e.g., based on profitability, resource optimization, business objective optimization, objective satisfaction, user or operator satisfaction, etc. For example, as energy used for computing tasks becomes an important part of enterprise energy usage, platform 100 may learn to optimize how a set of machines with energy storage capabilities allocate that capability among computing tasks (e.g., crypto-currency mining, neural network applications, data computing, etc.), other useful tasks (which may generate profits or other benefits), storage for future use or sale to energy grid providers. Platform 100 may be used by fleet operators, businesses, governments, municipalities, military units, emergency units, manufacturers, energy manufacturers, cloud platform providers, and other businesses and operators that own or operate to consume or provide energy, computing, data storage, bandwidth, or spectrum. The platform 100 may also be used in an attention market, for example to support attention-based value exchanges using available resource capacity, such as in an advertising market, a microtransaction market, and the like.
Still referring to FIG. 2, the platform 100 may include a set of intelligent prediction engines 192 that predict one or more attributes, parameters, variables, or other factors, for example, for use as input to a set of long-term purchase and sales machines, the intelligent transaction engine 126 (e.g., for intelligent cryptocurrency execution), or for other purposes. Each of a set of intelligent prediction engines 192 may use data tracked, aggregated, processed, or otherwise processed by the data aggregation system 144, etc., within the platform 100, as well as input data from external data sources 182 (e.g., social media data sources 180, automated agent behavior data sources 188, human behavior data sources 184, entity behavior data sources 190, and IoT data sources 198). These collective inputs may be used to predict attributes, for example using models (e.g., bayesian, regression, or other statistical models), rules, or expert systems, such as machine learning systems with one or more classifiers, pattern identifiers, and predictors, such as any of the expert systems described in the present disclosure. In an embodiment, a set of intelligent prediction engines 192 may include one or more specialized engines that predict market attributes, such as capacity, demand, supply, and price, using specific data sources for a specific market. These engines may include: an energy price prediction engine 215 that predicts an automated agent-based behavior; a network spectrum price prediction engine 217 that predicts automated agent-based behavior; REC price prediction engine 219, which predicts automated agent-based behavior; a calculate price prediction engine 221 that predicts an automated agent-based behavior; a network spectrum price prediction engine 223 that predicts automated agent-based behavior. In each case, observations about the behavior of an automated agent, such as an automated agent for conversation, dialog management, e-commerce management, advertisement management, etc., may be provided as input to the engine's predictions. The intelligent prediction engine 192 may also include a series of engines that provide predictions based at least in part on entity behaviors, such as behaviors of businesses and other organizations, such as marketing behaviors, sales behaviors, product-providing behaviors, advertising behaviors, purchasing behaviors, transaction behaviors, merger behaviors, and other entity behaviors. These engines may include an energy price prediction engine 225 that uses entity behavior, a network spectrum price prediction engine 227 that uses entity behavior, a REC price prediction engine 229 that uses entity behavior, a calculation price prediction engine 231 that uses entity behavior, and a network spectrum price prediction engine 233 that uses entity behavior.
The intelligent prediction engine 192 may also include a series of engines that provide predictions based at least in part on human behavior, such as consumer and user behavior, such as purchasing behavior, shopping behavior, sales behavior, product interaction behavior, energy utilization behavior, movement behavior, activity level behavior, activity type behavior, transaction behavior, and other human behavior. These engines may include an energy price prediction engine 235 that uses human behavior, a network spectrum price prediction engine 237 that uses human behavior, a REC price prediction engine 239 that uses human behavior, a calculation price prediction engine 241 that uses human behavior, and a network spectrum price prediction engine 243 that uses human behavior.
Still referring to fig. 2, the platform 100 may include a set of intelligent transaction engines 136 that automatically execute transactions in the long-term market 130 and/or the spot market 170 based on determining that there are advantages, such as through the intelligent resource allocation and coordination engine 168 and/or using predictions from the intelligent prediction engine 192. The intelligent transaction engine 136 may be used to automatically execute transactions in each of the markets described above using available market interfaces (e.g., APIs, connectors, ports, network interfaces, etc.). In embodiments, these intelligent transaction engines may perform transactions based on event streams from external data sources (e.g., ioT data source 198 and social media data source 180). For example, these engines may include IoT forward energy trading engine 195 and/or IoT computing market trading engine 106, one or both of which may use data from the internet of things to determine the time and other attributes of market trading of one or more resources described herein in the market, such as energy market trading, computing resource trading, or other resource trading. IoT data may include: meters and control data for one or more machines (optionally coordinated as a group) that use or produce energy or use or possess computing resources; weather data that affects energy prices or consumption (e.g., wind data that affects wind energy production); sensor data from an energy production environment; sensor data from points of energy or computing resource usage (e.g., vehicles Vehicle traffic data, network traffic data, IT network utilization data, internet utilization and traffic data, camera data from job sites, intelligent building data, intelligent home data, etc.); the internet of things collects or transmits other data therein, including data stored in IoT platforms and data of cloud service providers such as amazon, IBM, etc. The engine 136 may include an engine that uses social data to determine the time of other attributes of the market transaction for one or more resources described herein, such as the social data forward energy transaction engine 199 and/or the social data calculation market transaction engine 116. The social data may include: from social networking sites (e.g., facebook TM 、YouTube TM 、Twitter TM 、Snapchat TM 、Instagram TM Etc.). Data from a website; data from an e-commerce website; data from other sites that contain information that may be relevant to determining or predicting the behavior of a user or entity, such as data representing interest in or attention to a particular topic, good or service, data representing activity type and level (e.g., data that may be observed through machine processing display image data of an individual's participation in an activity, including travel, work activity, leisure activity, etc.). Social data may be provided to machine learning, e.g., for learning user behavior or entity behavior, and/or as input to expert systems, models, etc., e.g., expert systems, models, etc., for determining parameters of transactions based on such social data. For example, an event or set of events in a social data stream may indicate a likelihood of a surge in interest in an online resource, product, or service, and computing resources, bandwidth, storage, etc. may be purchased in advance (avoiding peak-time pricing) to accommodate the surge in interest reflected by the social data stream.
Referring to fig. 3, platform 100 may include transaction execution capabilities involving one or more distributed ledgers 113 and one or more smart contracts 103, where distributed ledgers 113 and smart contracts 103 are used to implement specialized transaction features for a particular transaction domain. One area is intellectual property, where transactions are very complex compared to more direct sales of goods or services involving license terms and conditions that are somewhat unmanageable. In an embodiment, an intelligent contract wrapper 105, such as a wrapper that aggregates intellectual property rights, is provided using a distributed ledger, wherein an intelligent contract embeds IP licensing terms for the intellectual property rights embedded in the distributed ledger, and performing operations on the distributed ledger can provide access to the intellectual property rights and cause executing parties to adhere to the IP licensing terms. Licensing terms for various goods and services, including digital goods such as video, audio, video games, video game elements, music, electronic books, and other digital goods, may be managed by tracking transactions involving them in a distributed ledger whereby publishers can verify the licensing and re-licensing chain. The distributed ledger may be used to add each licensee to the ledger and the ledger may be retrieved at the point of use of the digital item, for example in a streaming platform, to verify that a license has occurred.
In an embodiment, the improved distributed ledger provides an intelligent contract wrapper 105, such as an IP wrapper, container, intelligent contract, or similar mechanism for aggregating intellectual property licensing terms, wherein the intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to the intellectual property aggregation stack. In many cases, intellectual property is built on top of other intellectual property, e.g. software code originates from other code, where: commercial secrets or proprietary technologies of elements of the process are combined to implement a larger process; patents covering sub-components of the system or steps in the process are pooled together; elements of a video game include subcomponent assets from different creators; one book contains contributions of multiple authors, and so on. In an embodiment, the intelligent IP wrapper aggregates licensing terms for different intellectual property items (including digital goods, including digital goods embodying different types of intellectual property rights), and transaction data relating to the relevant items and optionally one or more portions of the items corresponding to those transaction data are stored in a distributed ledger for enabling verification of consent to those licensing terms (e.g. at the time of designated use) and/or access control to the relevant items. In an embodiment, license fee apportionment wrapper 115 may be provided in a system having a distributed ledger for aggregating intellectual property license terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportion license fees among parties in the ledger. Thus, a ledger may accumulate contributions to the ledger with consent evidence of any licensing fees being shared among the contributors of the IP embedded in and/or controlled by the ledger. The ledger may record licensing terms and automatically alter them when a new contribution is made, such as by one or more rules. For example, a contribution may be allocated a share of the total licensing fees according to rules (e.g., based on partial contributions, e.g., based on contributing code lines, author lines, contributions to system components, etc.). In an embodiment, the distributed ledger may be bifurcated into versions representing different combinations of sub-components of the IP, for example, to enable a user to select the most common combination, thereby allowing the contributor contributing the greatest value to be rewarded. The change and result tracking may be iteratively improved, such as by machine learning.
In an embodiment, a distributed ledger is provided for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack.
In an embodiment, platform 100 may have an improved distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to commit parties to compliance with contract terms through the ledger's IP transaction wrapper 119. This may include operations involving cryptocurrency, tokens, or other operations, as well as conventional payments and physical transfers, such as the various resources described herein. The ledger may accumulate evidence of commitments of parties to the IP transaction, such as license fee terms, revenue sharing terms, IP ownership terms, assurance and liability terms, licensing rights and restrictions, usage domain terms, and the like.
In an embodiment, the improved distributed ledger may include a distributed ledger having a marked instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set. Parties desiring to share the rights of proprietary technology, trade secrets, or other valuable instructions may share the instruction set through a distributed ledger that captures and stores evidence of actions taken by third parties on the ledger, thereby proving access and consent to the terms and conditions of access. In an embodiment, platform 100 may have a distributed ledger that tags executable algorithm logic 121 such that performing operations on the distributed ledger can provide provable access to the executable algorithm logic. The distributed ledger may store various instruction sets, such as verifying access and verifying agreement to terms (e.g., smart contract terms). In an embodiment, the instruction set embodying the trade secret may be divided into sub-components such that operations must be performed on multiple ledgers to obtain (provable) access to the trade secret. This may allow parties desiring to share secrets with multiple SCRs or suppliers, etc., to maintain provable access control while separating components between different suppliers to avoid sharing the entire set with one party. Various sets of executable instructions may be stored in dedicated distributed ledgers, which may include intelligent wrappers for particular types of instruction sets, such that provable access control, clause verification, and utilization tracking may be performed by performing operations on the distributed ledgers (which may include triggering access control in a content management system or other system upon verification of actions taken in intelligent contracts on the ledgers). In an embodiment, platform 100 may have a distributed ledger marking 3D printer instruction set 123 such that performing operations on the distributed ledger can provide provable access to the instruction set.
In an embodiment, the platform 100 may have a distributed ledger marking an instruction set for the coating process 125 such that performing operations on the distributed ledger can provide provable access to the instruction set.
In an embodiment, platform 100 may have a distributed ledger marking instruction sets for semiconductor manufacturing process 129 such that performing operations on the distributed ledger can provide provable access to the manufacturing process.
In an embodiment, platform 100 may have a distributed ledger that marks firmware program 131 such that performing operations on the distributed ledger can provide provable access to the firmware program.
In an embodiment, platform 100 may have a distributed ledger marking the instruction set for FPGA 133 such that performing operations on the distributed ledger can provide provable access to the FPGA.
In an embodiment, platform 100 may have a distributed ledger that marks serverless code logic 135 such that performing operations on the distributed ledger can provide provable access to the serverless code logic.
In an embodiment, platform 100 may have a distributed ledger marking an instruction set for crystal manufacturing system 139 such that performing operations on the distributed ledger can provide provable access to the instruction set.
In an embodiment, platform 100 may have a distributed ledger marking an instruction set for food preparation process 141 such that performing an operation on the distributed ledger can provide provable access to the instruction set.
In an embodiment, platform 100 may have a distributed ledger marking an instruction set for polymer production process 143 such that performing operations on the distributed ledger can provide provable access to the instruction set.
In an embodiment, platform 100 may have a distributed ledger marking an instruction set for chemical synthesis process 145 such that performing operations on the distributed ledger can provide provable access to the instruction set.
In an embodiment, platform 100 may have a distributed ledger marking an instruction set for a biological production process 149 such that performing an operation on the distributed ledger can provide provable access to the instruction set.
In an embodiment, platform 100 may have a distributed ledger marking a trade secret with expert wrapper 151 such that performing an operation on the distributed ledger provides provable access to the trade secret and the wrapper provides an expert verification of the trade secret. An interface may be provided through which an expert accesses the trade secret on the ledger and verifies that the information is accurate and sufficient to allow a third party to use the secret.
In an embodiment, platform 100 may have a distributed ledger that aggregates a view of a trade secret into a chain that proves which and how many parties have viewed the trade secret. The view may be used to assign value to the creator of the trade secret, the operator of platform 100, etc.
In an embodiment, platform 100 may have a distributed ledger that marks instruction set 111 such that performing an operation on the distributed ledger provides provable access 155 to the instruction set, and executing the instruction set on the system results in a transaction being recorded in the distributed ledger.
In an embodiment, platform 100 may have a distributed ledger marking intellectual property items and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items.
In an embodiment, platform 100 may have a distributed ledger that aggregates instruction sets, wherein performing operations on the distributed ledger adds at least one instruction to pre-existing instruction set 161 to provide a modified instruction set.
Still referring to FIG. 3, the smart cryptocurrency execution engine 183 may provide intelligence to the time, location, and other attributes of the cryptocurrency transaction, such as mining the transaction, exchanging the transaction, storing the transaction, retrieving the transaction, and so forth. Like bitjoin TM Such cryptocurrencies are becoming more common, and specialized tokens for various uses, such as exchanging value in various specialized market fields, are emerging. The first Issuance (ICO) of such tokens is increasingly subject to securities lawsRegulatory regulations, in some cases tax collection is also required. Thus, although cryptocurrency transactions typically occur within a computer network, jurisdictional factors may be important in determining where, when, and how to perform transactions, store cryptocurrency, exchange value with it. In an embodiment, the smart cryptocurrency execution engine 183 may use features embedded in or wrapped with digital objects representing tokens, such as features that result in transactions being executed with the tokens with knowledge of various conditions, including geographic conditions, regulatory conditions, tax conditions, market conditions, and the like.
In an embodiment, the platform 100 may include a tax aware token 165 or smart wrapper for encrypting a monetary token that directs execution of a transaction involving the token to a geographic location in accordance with: tax processing of at least one of the token and the transaction in the geographic location.
In an embodiment, the platform 100 may include a location aware token 169 or smart wrapper that enables automatic execution of cryptocurrency tokens, submitting transactions upon identifying location-based parameters that provide for tax-beneficial processing.
In an embodiment, the platform 100 may include an expert system or AI agent 171 that uses machine learning to optimize execution of cryptocurrency transactions based on tax status. Machine learning may use one or more models or heuristics, e.g., populated with relevant jurisdictional tax data, may be trained based on a training set of human transaction operations, may be supervised by a human supervisor, and/or may use deep learning techniques based on results over time, e.g., when performing operations on a wide variety of internal system data and external data sources 182, as described herein.
In an embodiment, the platform 100 may include a regulatory aware token 173 having tokens, a smart wrapper, and/or an expert system for aggregating regulatory information covering cryptocurrency transactions and automatically selecting an operating jurisdiction based on such regulatory information. Machine learning may use one or more models or heuristics, e.g., populated with relevant jurisdictional regulatory data, may be trained based on a training set of human transaction operations, may be supervised by a human supervisor, and/or may use deep learning techniques based on results over time, e.g., when performing row operations on a wide variety of internal system data and external data sources 182, as described herein.
In an embodiment, the platform 100 may include an energy price aware token 175, wrapper, or expert system that uses machine learning to optimize execution of cryptocurrency transactions based on real-time energy price information of available energy. Cryptocurrency transactions (e.g., token mining and blockchain operations) can be very energy intensive. The energy price aware tokens may be used to time such operations based on energy price predictions, for example using one or more of the prediction engines 192 described in this disclosure.
In an embodiment, the platform 100 may include an energy aware token 179, wrapper, or expert system that uses machine learning to optimize execution of cryptocurrency transactions based on an understanding of available energy to drive computing resources to execute the transactions. For example, token mining may only be performed when renewable energy is available. Machine learning for optimizing transactions may use one or more models or heuristics, such as being populated with relevant energy data (e.g., may be captured in a knowledge graph that may contain energy information divided by type, location, and operating parameters), may be trained based on a training set of input-output data for human initiated transactions, may be supervised by a human supervisor, and/or may use deep learning techniques based on results over time, such as when performing operations on a wide variety of internal system data and external data sources 182, as described herein.
In an embodiment, the platform 100 may include a charge cycle aware token 181, wrapper, or expert system that uses machine learning to optimize the charge and recharge cycles of the rechargeable battery system to provide energy for the execution of cryptocurrency transactions. For example, the battery may be discharged for cryptocurrency transactions only when a minimum threshold of battery charge is maintained for other operations, known recharging resources are readily available, and the like. Machine learning for optimizing charging and recharging may use one or more models or heuristics, such as being populated with relevant battery data (e.g., may be captured in a knowledge graph that may contain energy information divided by type, location, and operating parameters), may be trained based on a training set of human operations, may be supervised by a human supervisor, and/or may use deep learning techniques based on results over time, such as when performing operations on a wide variety of internal system data and external data sources 182, as described herein.
Optimization of various intelligent token operations may be achieved through machine learning trained based on results (e.g., financial profitability). Any of the machine learning systems described in this invention may be used to optimize intelligent cryptocurrency transaction management.
In an embodiment, computing resources (such as those described in this disclosure) may be allocated for operations occurring within platform 100, operations managed by the platform, and operations involving activities, workflows, and processes of various assets that may be commonly owned, operated, or managed with the platform, such as groups or clusters of assets that own or use computing resources, to perform a series of computing tasks. Examples of computing tasks include, but are not limited to: cryptocurrency mining, distributed ledger calculation and storage, forecasting tasks, transaction execution tasks, spot market testing tasks, internal data collection tasks, external data collection tasks, machine learning tasks, and the like. As described above, energy, computing resources, bandwidth, spectrum, and other resources may be coordinated for these tasks through machine learning, etc. Results and feedback information may be provided for the machine learning, such as results and overall results of any single task, such as profitability and profitability of a business or other operation involving the task.
In an embodiment, network resources (such as those described in this disclosure) may be allocated to perform a series of network tasks for operations occurring within the platform 100, operations managed by the platform, and operations involving activities, workflows, and processes of various assets that may be commonly owned, operated, or managed with the platform, such as groups or clusters of assets that own or use network resources. Examples of network tasks include: cognitive network coordination, network coding, peer-to-peer bandwidth sharing (including, for example, cost-based routing, value-based routing, result-based routing, etc.), distributed transaction execution, spot-market testing, randomization (e.g., using genetic programming with result feedback to change network configuration and transmission paths), internal data collection, and external data collection. As described above, energy, computing resources, bandwidth, spectrum, and other resources may be coordinated for these network tasks through machine learning, etc. Results and feedback information may be provided for the machine learning, such as results and overall results of any single task, such as profitability and profitability of a business or other operation involving the task.
In an embodiment, data storage resources (such as those described herein) may be allocated for operations occurring within, managed by, and involving activities, workflows, and processes of various assets that may be commonly owned, operated, or managed with the platform, such as groups or clusters of assets that own or use network resources, to perform a series of data storage tasks. Examples of data storage tasks include: distributed ledger storage, internal data storage (e.g., operational data for the platform), cryptocurrency storage, intelligent wrapper storage, external data storage, feedback and result data storage, and the like. As described above, data storage, energy, computing resources, bandwidth, spectrum, and other resources may be coordinated for these data storage tasks through machine learning, or the like. Results and feedback information may be provided for the machine learning, such as results and overall results of any single task, such as profitability and profitability of a business or other operation involving the task.
In an embodiment, a smart contract, such as a smart contract that embodies terms related to intellectual property, trade secrets, proprietary technology, instruction sets, algorithm logic, and the like, may embody or include contract terms that may include: terms and conditions of options, license fee superposition terms, domain exclusivity, partial exclusivity, intellectual property collections, standard terms (e.g., terms related to essential and non-essential patent usage), technical transfer terms, counsel service terms, update terms, support terms, maintenance terms, derivative work terms, copy terms, performance related rights or indicators, and the like.
In embodiments where the instruction set is embodied in digital form, such as contained in or managed by a distributed ledger transaction system, the various systems may be configured with interfaces that allow them to access and use the instruction set. In an embodiment, such a system may include access control features that verify proper permissions by checking a distributed ledger, key, token, etc. that indicates whether there is access to the instruction set. Such systems that execute a distributed instruction set may include systems for 3D printing, crystal fabrication, semiconductor fabrication, coated articles, polymer production, chemical synthesis, and bio-production, among others.
It should be understood that network capabilities and network resources include a wide range of network systems, components and capabilities, including 3G, 4G, LTE, 5G and other cellular network type infrastructure elements, access points, routers and other Wi-Fi elements, cognitive network systems and components, mobile network systems and components, physical layer, MAC layer and application layer systems and components, cognitive network components and capabilities, point-to-point network components and capabilities, optical network components and capabilities, and the like.
Expert system and artificial intelligence building blocks
Neural network system
Referring to fig. 4-31, embodiments of the present invention, including embodiments involving expert systems, self-organization, machine learning, artificial intelligence, etc., may benefit from the use of neural networks, such as training for pattern recognition, classification of one or more parameters, features, or phenomena, neural networks for supporting autonomous control, and other purposes. References to neural networks throughout this disclosure should be understood to include various different types of neural networks, machine learning systems, artificial intelligence systems, and the like, such as feed forward neural networks, radial basis function neural networks, self-organizing neural networks (e.g., kohonen self-organizing neural networks), recurrent neural networks, modular neural networks, artificial neural networks, physical neural networks, multi-layer neural networks, convolutional neural networks, mixtures of neural networks with other expert systems (e.g., hybrid fuzzy logic-neural network systems), self-encoding neural networks, probabilistic neural networks, time-delay neural networks, convolutional neural networks, regulatory feedback neural networks, radial basis function neural networks, recurrent neural networks, hopfield neural networks, boltzmann machine neural networks, self-organizing map (SOM) neural networks, learning Vector Quantization (LVQ) neural networks, full recurrent neural networks, simple recurrent neural networks echo state neural networks, long term short term memory neural networks, two-way neural networks, layered neural networks, random neural networks, genetic scale RNN neural networks, machine neural network committee, associative neural networks, physical neural networks, transient training neural networks, spike neural networks, new cognitive neural networks, dynamic neural networks, cascade neural networks, neuro-fuzzy neural networks, combined pattern generation neural networks, memory neural networks, layered time memory neural networks, deep feed forward neural networks, gated recursive unit (GCU) neural networks, automatic encoder neural networks, variation automatic encoder neural networks, denoising automatic encoder neural networks, sparse automatic encoder neural networks, markov chain neural networks, limited Boltzmann machine neural networks, deep belief neural networks, deep convolution neural networks, deconvolution neural networks, deep convolution inverse graph neural networks, generation antagonism neural networks, liquid machine neural networks, extreme learning machine neural networks, echo state neural networks, deep residual error neural networks, support vector machine neural networks, neural graph machine neural networks, and/or holographic associative memory neural networks, or mixtures or combinations of the foregoing, or with other expert systems, such as rule-based systems, model-based systems (including systems based on physical models, statistical models, flow-based models, biological models, biomimetic models, etc.).
In an embodiment, fig. 5 to 31 illustrate an exemplary neural network, and fig. 4 illustrates a legend showing various components of the neural network illustrated in fig. 5 to 31. Fig. 4 illustrates various neural network components described in units assigned functions and requirements. In an embodiment, various neural network examples may include a feedback data/sensor unit, a noise input unit, and a hidden unit. The neural network component also includes probability hiding units, doping hiding units, output units, matching input/output units, recursion units, memory units, different memory units, kernels, and convolution or pool units.
In an embodiment, fig. 5 illustrates an exemplary sensory neural network that may be connected to platform 100, integrated with platform 100, or interfaced with platform 100. The platform may also be associated with other neural network systems, such as a feed forward neural network (fig. 6), a radial basis neural network (fig. 7), a deep feed forward neural network (fig. 8), a recurrent neural network (fig. 9), a long/short term neural network (fig. 10), and a gated recurrent neural network (fig. 11). The platform may also be associated with other neural network systems, such as an auto encoder neural network (fig. 12), a variational neural network (fig. 13), a denoising neural network (fig. 14), a sparse neural network (fig. 15), a markov chain neural network (fig. 16), and a Hopfield network neural network (fig. 17). The platform may also be associated with additional neural network systems, such as Boltzmann machine neural network (fig. 18), restricted BM neural network (fig. 19), deep belief neural network (fig. 20), deep convolution neural network (fig. 21), deconvolution neural network (fig. 22), and deep convolution inverse graph neural network (fig. 23). The platform may also be associated with other neural network systems, such as a generate inverse neural network (fig. 24), a liquid machine neural network (fig. 25), an extreme learning machine neural network (fig. 26), an echo state neural network (fig. 27), a depth residual neural network (fig. 28), a Kohonen neural network (fig. 29), a support vector machine neural network (fig. 30), and a neural turing machine neural network (fig. 31).
The aforementioned neural network may have various nodes or neurons that may perform various functions upon input, such as input received from sensors or other data sources (including other nodes). The functions may relate to weights, features, feature vectors, etc. Neurons may include perceptron, neurons mimicking biological functions (e.g., human touch, vision, taste, hearing, and smell), and the like. Continuous neurons, e.g., with S-shaped activation, can be used in the context of various forms of neural networks, e.g., cases involving back propagation.
In many embodiments, the expert system or neural network may be trained, for example by a human operator or supervisor, or based on a dataset, model, or the like. Training may include presenting to a neural network one or more training data sets representing values, such as sensor data, event data, parameter data, and other types of data (including many of the types described in this disclosure), as well as one or more outcome metrics, such as the outcome of a process, the outcome of a calculation, the outcome of an event, the outcome of an activity, and so forth. Training may include optimization training, such as training a neural network to optimize one or more systems based on one or more optimization methods, such as bayesian methods, parametric bayesian classifier methods, k-nearest neighbor classifier methods, iterative methods, interpolation methods, pareto optimization methods, algorithmic methods, and the like. Feedback may be provided during the course of the change and selection, for example using a genetic algorithm that evolves one or more solutions based on feedback through a series of rounds.
In an embodiment, multiple neural networks may be deployed in a cloud platform that receives data streams and other inputs collected in one or more transaction environments (e.g., collected by a mobile data collector) and sent to the cloud platform over one or more networks (including using network coding to provide efficient transmission). In a cloud platform, multiple different types (including modular, architecture adaptive, hybrid, etc.) of neural networks can be used to undertake prediction, classification, control functions, and provide other outputs related to the expert systems disclosed herein, by optionally using massively parallel computing capabilities. Different neural networks may be configured to compete with each other (optionally including the use of evolutionary algorithms, genetic algorithms, etc.), such that an appropriate type of neural network with appropriate input sets, weights, node types, functions, etc. may be selected, e.g., by an expert system, for a particular task involved in a given context, workflow, environmental process, system, etc.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use a feed forward neural network that moves information in one direction through a series of neurons or nodes to an output, such as from a data input (e.g., a data source related to at least one resource or a parameter related to a transaction environment) or any of the data sources mentioned in this disclosure. The data may be moved from the input node to the output node, optionally through one or more hidden nodes, without looping. In an embodiment, the feed forward neural network may be constructed with various types of elements (e.g., binary McCulloch-Pitts neurons, the simplest of which is a sensor).
In embodiments, the methods and systems described herein that relate to expert systems or ad hoc capabilities may use a encapsulated neural network, for example, for predictive, classification, or control functions with respect to transaction environments, such as those related to one or more of the machines and automation systems described herein.
In embodiments, the methods and systems described herein that relate to expert systems or organizational capabilities may use Radial Basis Function (RBF) neural networks, which may be preferable in some cases involving interpolation in multidimensional space (e.g., where interpolation helps to optimize multidimensional functions, such as for optimizing the data market described herein, optimizing the efficiency or output of a power generation system, factory systems, etc., or other cases involving multiple dimensions.
In embodiments, the methods and systems described herein that relate to expert systems or ad hoc capabilities may use Radial Basis Function (RBF) neural networks, such as those employing distance criteria (e.g., gaussian functions) relative to the center. In a multilayer sensor, radial basis functions may be applied as alternatives to hidden layers, such as S-shaped hidden layer transitions. The RBF network may have two layers, for example, with inputs mapped onto each RBF in the hidden layer. In an embodiment, the output layer may comprise a linear combination of hidden layer values, which represents, for example, an average prediction output. The output layer values may provide the same or similar output as the output of the regression model in the statistics. In the classification problem, the output layer may be an sigmoid function of a linear combination of hidden layer values, representing a posterior probability. Performance in both cases is typically improved by shrinkage techniques (e.g., ridge regression in classical statistics). This corresponds to a priori beliefs for small parameter values (and thus smooth output functions) in the bayesian framework. RBF networks can avoid local minima because the only parameter that is adjusted during learning is the linear mapping from the hidden layer to the output layer. Linearity ensures that the error surface is quadratic and therefore has a single minimum. In the regression problem, this can be found in a matrix operation. In classification problems, the fixed nonlinearities introduced by the sigmoid output function can be handled using iterative re-weighted least squares functions, or the like. RBF networks may use kernel methods such as Support Vector Machines (SVMs) and gaussian processes (where RBF is a kernel function). The input data may be projected into space using a nonlinear kernel function, where a linear model may be used to solve the learning problem.
In an embodiment, the RBF neural network may include an input layer, a hidden layer, and a summing layer. In the input layer, one neuron appears in the input layer for each prediction variable. In the case of a class variable, N-1 neurons are used, where N is the number of classes. In an embodiment, the input neuron may normalize the range of values by subtracting the median and dividing by the quartile range. The input neurons may then feed back a value to each neuron in the hidden layer. A variable number of neurons (determined by the training process) may be used in the hidden layer. Each neuron may consist of a radial basis function centered on a point having as many dimensions as a number of predicted variables. The expansion (e.g., radius) of the RBF function may be different for each dimension. The center and extension may be determined by training. When represented using vectors of input values from the input layer, the hidden neurons can calculate the Euclidean distance of the test case from the neuron's center point, and then apply the RBF kernel function to that distance, e.g., using an extension value. The resulting value may then be passed to a summation layer. In the summation layer, the values from neurons in the hidden layer may be multiplied by weights associated with neurons, and may be added to the weighted values of other neurons. This sum becomes the output. For classification problems, one output (with separate weight sets and summing units) is generated for each target class. The value output of a class is the probability that the evaluated situation has that class. In training of the RBF, various parameters may be determined, such as the number of neurons in the hidden layer, the center coordinates of each hidden layer function, the expansion of each function in each dimension, and the weights applied to the output when the weights are passed to the summing layer. Training may be used by clustering algorithms (e.g., k-means clustering), by evolutionary methods, and the like.
In an embodiment, the recurrent neural network may have a time-varying real value (not just 0 or 1) activation (output). Each connection may have a real-valued weight that may be modified. Some nodes are called marker nodes, some output nodes and other hidden nodes. For supervised learning in discrete time settings, the training sequence of real valued input vectors may become the activation sequence of input nodes, one input vector at a time. At each time step, each non-input unit may calculate its current activation as a non-linear function of the weighted sum of the activations of all units it receives the connection. The system may explicitly activate (independent of the input signal) some of the output cells at a specific time step.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use ad hoc neural networks, such as Kohonen ad hoc neural networks, for example, for visualization of data views, such as low-dimensional views as high-dimensional data. The ad hoc neural network may apply competition learning to a set of input data, such as from one or more sensors or other data inputs from or associated with a transaction environment, including any machines or components related to the transaction environment. In an embodiment, the ad hoc neural network may be used to identify structures in data, such as unlabeled data, such as data sensed from a series of data sources or sensors in a transaction environment, where the data sources are unknown (e.g., an event may come from any one of a series of unknown sources). The ad hoc neural network may organize structures or patterns in the data so that it may be identified, analyzed, and tagged, for example, to identify market behavioral structures as corresponding to other events and signals.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use recurrent neural networks, which may allow bi-directional flow of data, such as where connected units (e.g., neurons or nodes) form a directed loop. Such networks may be used to model or present dynamic time behavior, such as that involved in dynamic systems such as the various automated systems, machines and devices described in this disclosure, such as automated agents that interact with markets for collecting data, testing spot market transactions, executing transactions, etc., where dynamic system behavior involves complex interactions that a user may wish to understand, predict, control, and/or optimize. For example, recurrent neural networks may be used to predict market states, such as market states involving dynamic processes or actions that train or implement state changes of resources of a trading environment market, such as in the trading environment market. In embodiments, the recurrent neural network may use internal memory to process various types of input sequences described herein, such as from other nodes and/or from sensors or other data inputs provided by or related to the transaction environment. In embodiments, recurrent neural networks may also be used for pattern recognition, for example, to identify machines, components, agents, or other items based on behavioral signatures, profiles, a set of feature vectors (e.g., in an audio file or image), and so forth. In a non-limiting example, the recurrent neural network may identify transitions in the operating mode of the market or machine by learning to classify transitions from a training data set that includes data streams from one or more data sources applied to or about sensors of one or more resources.
In embodiments, the methods and systems described herein that relate to expert systems or ad hoc capabilities may use a modular neural network, which may include a series of independent neural networks (e.g., the various types of neural networks described herein) tuned by intermediaries. Each individual neural network in the modular neural network may work with a separate input to complete the sub-tasks that make up the task to be performed by the entire modular network. For example, the modular neural network may include a recurrent neural network for pattern recognition, e.g., to identify which type of machine or system is being sensed by one or more sensors provided as input channels to the modular network and the RBF neural network for optimizing the understood machine or system behavior. The intermediary may accept inputs from each individual neural network, process them, and create outputs for the modular neural network, such as appropriate control parameters, condition predictions, and the like.
Combinations between any two, three, or larger combinations of the various neural network types described herein are encompassed in the present invention. This may include a combination in which the expert system uses one neural network for identifying patterns (e.g., patterns indicative of problems or fault conditions) and uses a different neural network for self-organizing activities or workflows based on the identified patterns (e.g., providing output of management system autonomous control in response to the identified conditions or patterns). This may also include a combination in which the expert system uses one neural network for classifying the item (e.g., identifying a machine, component, or mode of operation) and a different neural network for predicting a condition of the item (e.g., fault condition, operating condition, expected condition, maintenance condition, etc.). The modular neural network may also include a situation in which the expert system uses one neural network for determining a situation or context (e.g., a situation of a machine, process, workflow, market, storage system, network, data collector, etc.) and a different neural network for self-organizing a process (e.g., a data storage process, network encoding process, network selection process, data market process, power generation process, manufacturing process, refining process, mining process, boring process, or other process described herein) related to the situation or context.
In embodiments, the methods and systems described herein that relate to expert systems or ad hoc capabilities may use a physical neural network in which neural behavior is performed or simulated using one or more hardware elements. In an embodiment, one or more hardware neurons may be used to stream voltage values, current values, etc. representing sensor data, e.g., by one or more machines providing energy or consuming energy for one or more transactions, calculating information from analog sensor inputs representing energy consumption, energy production, etc. One or more hardware nodes may be used to stream output data generated by the activity of the neural network. The hardware nodes may include one or more chips, microprocessors, integrated circuits, programmable logic controllers, application specific integrated circuits, field programmable gate arrays, etc., which may be used to optimize a machine that is generating or consuming energy, or to optimize another parameter of some portion of any type of neural network described herein. The hardware nodes may include hardware for accelerating computations (e.g., special purpose processors for performing basic or more complex computations on input data to provide output, special purpose processors for filtering or compressing data, special purpose processors for decompressing data, special purpose processors for compressing specific files or data types (e.g., for processing image data, video streams, acoustic signals, thermal images, heatmaps, etc.). The physical neural network may be embodied in a data collector, including a data collector that may be reconfigured by switching or routing inputs in a varying configuration, such as by providing different neural network configurations within the data collector for processing different types of inputs (with switching and configuration optionally under the control of an expert system, which may include a software-based neural network located on or remote from the data collector). A physical or at least partially physical neural network may include physical hardware nodes located in a storage system, such as input/output functions for storing data in a machine, a data storage system, a distributed ledger, a mobile device, a server, a cloud resource, or a transaction processing environment, for example, to accelerate one or more storage elements providing data to or retrieving data from the neural network. A physical or at least partially physical neural network may include physical hardware nodes located in the network, for example, for transmitting data within, to, or from an industrial environment, for example, for accelerating input/output functions of one or more network nodes in the network, for accelerating relay functions, and so forth. In embodiments of a physical neural network, an electrically tunable resistive material may be used to simulate the function of a nerve synapse. In an embodiment, the physical hardware simulates neurons and the software simulates neural networks between neurons. In an embodiment, the neural network supplements a conventional algorithm computer. These computers are general-purpose and can be trained to perform appropriate functions without any instructions such as classification functions, optimization functions, pattern recognition functions, control functions, selection functions, evolution functions, etc.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use a multi-layer feed forward neural network, such as complex pattern classification for one or more projects, phenomena, patterns, conditions, and the like. In an embodiment, the multi-layer feedforward neural network may be trained by optimization techniques such as genetic algorithms, e.g., exploring large-scale and complex option spaces to find optimal or near-optimal global solutions. For example, a multi-layer feedforward neural network may be trained using one or more genetic algorithms to classify complex phenomena, such as identifying complex modes of operation of the machine, such as modes involving complex interactions between machines (including interference effects, resonance effects, etc.), modes involving nonlinear phenomena, modes involving critical faults, such as where multiple faults occur simultaneously, making it difficult to analyze root causes, etc. In an embodiment, the multi-layer feed forward neural network may be used to categorize results from market monitoring, including, for example, monitoring systems operating within the market, such as automated agents, and monitoring resources implementing the market, such as computing, networking, energy sources, data storage, energy storage, and other resources.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use feed forward, back propagation multi-layer perceptive (MLP) neural networks, for example, for processing one or more remote sensing applications, for example, for taking input from sensors distributed in various transaction environments. In embodiments, the MLP neural network may be used for trading environments and resource environment classification, such as spot markets, forward markets, energy markets, renewable Energy Credit (REC) markets, networking markets, advertising markets, spectrum markets, ticketing markets, rewards markets, computing markets, and other environments mentioned in this disclosure, as well as physical resources and environments in which they are generated, such as energy resources (including renewable energy environments, mining environments, exploration environments, drilling environments, etc.), as well as for geologic formation (including subsurface and above-ground feature) classification, material (including fluid, mineral, metal, etc.) classification, and other issues. This may include fuzzy classification.
In embodiments, the methods and systems described herein that relate to expert systems or ad hoc capabilities may adapt a neural network using a structure, where the structure of the neural network is adapted based on, for example, rules, sensed conditions, environmental parameters, and the like. For example, if the neural network does not converge to a solution, such as classifying an item or predicting arrival, then when operating on a set of inputs after a certain amount of training, the neural network, such as from a feedforward to recurrent neural network, may be modified, such as by switching the data paths between some subset of nodes from unidirectional to bidirectional data paths. The structural adaptation may occur under the control of an expert system, for example, to trigger the adaptation in the event of a trigger, rule or event occurrence, for example to identify the occurrence of a threshold (e.g., no convergence to a solution for a given time) or to identify a phenomenon requiring a different or additional structure (e.g., to identify that the system is changing dynamically or in a nonlinear manner). In one non-limiting example, the expert system may switch from a simple neural network structure (e.g., feed forward neural network) to a more complex neural network structure (e.g., recurrent neural network, convolutional neural network, etc.) upon receiving an indication that the continuously variable transmission in the system being analyzed is being used to drive a generator, turbine, etc.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use an automatic encoder, automatic connector, or Diabolo neural network, which may be similar to a multi-layer perceptron (MLP) neural network, e.g., there may be an input layer, an output layer, and one or more hidden layers connecting them. However, the output layer in an auto-encoder may have the same number of cells as the input layer, where the purpose of the MLP neural network is to reconstruct its own input (rather than just issuing the target value). Thus, the automatic encoder may operate as an unsupervised learning model. For example, an automatic encoder may be used for unsupervised learning efficient encoding, e.g., for dimension reduction, for learning a generation model of data, etc. In embodiments, the automatically encoded neural network may be used to self-learn an effective network encoding for transmitting analog sensor data from a machine or digital data from one or more data sources from the machine over one or more networks. In an embodiment, an automatically encoded neural network may be used to self-learn an efficient storage method for storing a data stream.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use Probabilistic Neural Networks (PNNs), which in embodiments may include a multi-layer (e.g., four-layer) feed-forward neural network, where each layer may include an input layer, a hidden layer, a mode/summing layer, and an output layer. In one embodiment of the PNN algorithm, the parent Probability Distribution Function (PDF) for each class may approximate, for example, a Parzen window function and/or a non-parametric function. Then, using the PDF of each class, the class probability of the new input is estimated, and bayesian rules may be employed, for example, to assign it to the class with the highest posterior probability. PNN may comprise a bayesian network and may use statistical algorithms or analysis techniques, such as nuclear Fisher discriminant analysis techniques. PNN may be used for classification and pattern recognition in any of the wide range of embodiments disclosed herein. In one non-limiting example, a probabilistic neural network may be used to predict a fault condition of an engine based on data input collection of sensors and instrumentation of the engine.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use Time Delay Neural Networks (TDNNs) that may include feed forward structures for identifying sequence data that is independent of the characteristics of the sequence position. In an embodiment, to account for time offsets in the data, a time delay is added to one or more inputs, or between one or more nodes, so that multiple data points (from different points in time) are analyzed together. The time-lapse neural network may form part of a larger pattern recognition system using, for example, a perceptron network. In an embodiment, TDNN may be trained using supervised learning, e.g., using back propagation or training connection weights under feedback. In an embodiment, TDNN may be used to process sensor data from different streams, such as velocity data streams, acceleration data streams, temperature data streams, pressure data streams, etc., where time delays are used to match the data streams in time, such as to help understand patterns related to various streams (e.g., changes in price patterns in spot or long-term markets).
In embodiments, the methods and systems described herein involving expert systems or self-organizing capabilities may use convolutional neural networks (in some cases referred to as CNN, convNet, translationally invariant neural networks, or spatially invariant neural networks) in which units are connected in a pattern similar to that of the visual cortex of the human brain. Neurons may respond to stimuli in a restricted spatial region (known as the receptive field). The experience fields may partially overlap such that they collectively cover the entire (e.g., visual) field. The node response may be calculated mathematically, for example by convolution operations, using a multi-layer perceptron that uses minimal preprocessing. Convolutional neural networks may be used for identification in image and video streams, for example, to identify machine types in large environments using a camera system provided on a mobile data collector on, for example, a drone or mobile robot. In an embodiment, convolutional neural networks may be used to provide recommendations based on data inputs, including sensor inputs and other contextual information, such as recommended routes for mobile data collectors. In an embodiment, a convolutional neural network may be used to process input, such as natural language processing for instructions provided by one or more participants involved in a workflow in an environment. In an embodiment, a convolutional neural network may be deployed with a large number of neurons (e.g., 100,000, 500,000, or more), multiple (e.g., 4, 5, 6, or more) layers, and many (e.g., millions) of parameters. Convolutional neural networks may use one or more convolutional networks.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use a management feedback network, for example, for identifying incidents (e.g., new types of behavior that were not previously understood in a transactional environment).
In embodiments, the methods and systems described herein involving expert systems or self-organizing capabilities may use self-organizing maps (SOMs), involving unsupervised learning. A set of neurons may learn to map points in input space to coordinates in output space. The input space may have different dimensions and topologies than the output space, and the SOM may save these dimensions and topologies while mapping the phenomena into groups.
In embodiments, the methods and systems described herein that relate to expert systems or ad hoc capabilities may use learning vector quantization neural networks (LVQ). Prototype representations of classes may be parameterized along with appropriate distance measures in a distance-based classification scheme.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use an Echo State Network (ESN), which may include a recurrent neural network with a sparsely connected random hidden layer. The weights of the output neurons may change (e.g., the weights may be trained based on feedback). In an embodiment, the ESN may be used to process a time series pattern, e.g., in an example, identify a pattern of events associated with the marketplace, e.g., a pattern of price changes in response to incentives.
In embodiments, the methods and systems described herein involving expert systems or self-organizing capabilities may use bi-directional recurrent neural networks (BRNNs), for example, using a finite sequence of values (e.g., voltage values from sensors) to predict or tag each element of a sequence based on past and future contexts of the element. This may be accomplished by adding the outputs of two RNNs, e.g., one processing sequence from left to right and the other processing sequence from right to left. The combined output is a prediction of the target signal, such as a signal provided by a teacher or supervisor. The bidirectional RNN may be combined with a long and short term memory RNN.
In embodiments, the methods and systems described herein that relate to expert systems or ad hoc capabilities may use a hierarchical RNN that connects elements in various ways to decompose hierarchical behavior, e.g., into useful subroutines. In an embodiment, the hierarchical RNN may be used to manage one or more hierarchical templates for data collection in a transaction environment.
In embodiments, the methods and systems described herein that relate to expert systems or ad hoc capabilities may use a random neural network that may introduce random variants into the network. Such random variations may be regarded as forms of statistical sampling, such as monte carlo sampling.
In embodiments, the methods and systems described herein that relate to expert systems or self-organizing capabilities may use genetic scale recurrent neural networks. In such an embodiment, RNNs (typically Long Short Term Memories (LSTM)) are used to break down sequences into several scales, where each scale forms a main length between two consecutive points. The first order scale consists of one normal RNN, the second order scale consists of all points separated by two exponentials, etc. The N-th order RNN connects the first node and the last node. The outputs from all the different scales may be considered as member committees and the associated scores may be used for genetic use for the next iteration.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use a machine committee (CoM) comprising a collection of different neural networks that collectively "vote" on a given example. Since neural networks may suffer from local minimisation, starting from the same architecture and training, but using randomly different initial weights often gives different results. CoM tends to stabilize the results.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use associative neural networks (ASNNs), such as extensions to the machine committee that involve combining multiple feed forward neural networks and k nearest neighbor technologies. In the case of analysis of KNN, correlation between integrated responses can be used as a measure of distance. This corrects for deviations in neural network integration. The associative neural network may have a memory coincident with the training set. If new data becomes available, the network immediately increases its predictive capability and provides data estimation (self-learning) without retraining. Another important feature of ASNN is that it is feasible to interpret neural network results by analyzing correlations between data instances in model space.
In embodiments, the methods and systems described herein involving expert systems or self-organizing capabilities may use an Instantaneous Trained Neural Network (ITNN) in which weights of the hidden layer and the output layer are mapped directly from training vector data.
In embodiments, the methods and systems described herein that relate to expert systems or ad hoc capabilities may use spiking neural networks, which may explicitly take into account the time of input. The network inputs and outputs may be represented as a series of spikes (e.g., a pulse function or a more complex shape). The sns may process information in the time domain (e.g., time-varying signals, such as signals related to dynamic behavior of a market or transaction environment). They are typically implemented as a recursive network.
In embodiments, the methods and systems described herein involving expert systems or self-organizing capabilities may use dynamic neural networks that handle nonlinear multivariate behavior and include learning of aging behavior, such as transient phenomena and time delay effects. Transients may include changing behavior of market variables such as price, available quantity, available opponents, etc.
In an embodiment, the cascading correlations may be used as an architecture and supervised learning algorithm, supplementing the adjustment of weights in a fixed topology network. The cascading correlations may start with a minimal network and then automatically train and add new hidden units one by one, creating a multi-layer structure. Once a new hidden unit is added to the network, its input side weights can be frozen. The unit then becomes a permanent feature detector in the network, which can be used to generate output or to create other more complex feature detectors. The cascade correlation architecture can learn quickly, determine its own size and topology, and preserve its built structure even if the training set changes and does not need to be back-propagated.
In embodiments, the methods and systems described herein that relate to expert systems or self-organizing capabilities may use a neural fuzzy network, such as a fuzzy inference system that relates to in the body of an artificial neural network. Depending on the type, several layers may simulate the processes involved in fuzzy reasoning, such as fuzzification, reasoning, aggregation, and defuzzification. The fuzzy system is embedded into the general structure of the neural network as a benefit of using the available training methods to find the parameters of the fuzzy system.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use a combined pattern to generate a network (CPPN), such as a variation of an Associated Neural Network (ANN), that is different from the set of activation functions and the manner in which they are applied. While a typical ANN generally contains only sigmoid functions (and sometimes gaussian functions), CPPN may include both types of functions and many others. Furthermore, CPPN may also be applied over the entire space of possible inputs so that these inputs can represent a complete image. Since these inputs are a combination of functions, the CPPN encodes an image at virtually infinite resolution, and can sample a particular display at any optimal resolution.
This type of network can add new patterns without retraining. In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use a one-time associative memory network that assigns each new pattern to an orthogonal plane using a hierarchical array of adjacent connections, for example, by creating a specific memory structure.
In embodiments, the methods and systems described herein that relate to expert systems or self-organizing capabilities may use Hierarchical Time Memory (HTM) neural networks, such as those that relate to the structural and algorithmic characteristics of new cortex. The HTM may use a biomimetic model based on memory prediction theory. HTM can be used to discover and infer high-level causes of observed input patterns and sequences.
Holographic associative memory
In embodiments, the methods and systems described herein involving expert systems or self-organizing capabilities may use Holographic Associative Memory (HAM) neural networks, which may include analog, correlation-based, associative, stimulus response systems. The information may be mapped onto a complex phase orientation. The memory is effective for associative memory tasks, generalization, and pattern recognition with variable attention.
In embodiments, various embodiments involving network coding may be used to code transmission data between network nodes in a neural network, such as nodes located in one or more data collectors or machines in a transaction environment.
Integrated circuit building block
In embodiments, one or more of the controllers, circuits, systems, data collectors, storage systems, network elements, etc., as described throughout the present invention, may be embodied in or on an integrated circuit, such as an analog, digital, or mixed signal circuit, such as a microprocessor, programmable logic controller, application specific integrated circuit, field programmable gate array, or other circuit, such as one or more chips provided on one or more circuit boards, such as in hardware (with possibly accelerated speed, energy performance, input output performance, etc.), to provide one or more of the functions described herein. This may include providing circuits with up to billions of logic gates, flip-flops, multiplexers, and other circuits in a small space, facilitating high speed processing, low power consumption, and reduced manufacturing costs compared to board level integration. In embodiments, digital ICs (typically microprocessors, digital signal processors, microcontrollers, etc.) may use Boolean (Boolean) algebra to process digital signals to embody complex logic such as those involved in the circuits, controllers, and other systems described herein. In embodiments, the data collector, expert system, storage system, etc. may be implemented as a digital integrated circuit, such as a logic IC, memory chip, interface IC (e.g., level shifter, serializer, deserializer, etc.), power management IC, and/or programmable device; analog integrated circuits, such as linear ICs, RFICs, etc., or mixed signal ICs, such as data acquisition ICs (including a/D converters, D/a converters, digital potentiometers) and/or clock/timing ICs.
Referring to fig. 32, the environment includes intelligent energy and computing facilities (e.g., large facilities that possess many computing resources and have access to large energy sources (e.g., hydroelectric resources), as well as host intelligent energy and computing facility resource management platforms, referred to for convenience in some cases as energy and information technology platforms (with the networks, data storage, data processing, and other resources described herein), a set of data sources, a set of expert systems, interfaces with a set of marketing platforms and external resources, and a set of user (or client) systems and devices.
Intelligent energy and computing facility
The facility may be used to access inexpensive (at least for some period of time) power sources (e.g., hydro dams, wind farms, solar arrays, nuclear power plants, or grids) to contain a large number of network information technology resources including processing units, servers, etc. that can be flexibly utilized (e.g., by switching inputs, switching configurations, switching programming, etc.), and to provide a range of outputs that can also be flexibly configured (e.g., to deliver power to a smart grid, to provide computing results (e.g., for cryptocurrency mining, artificial intelligence, or analysis)). The facility may include a power storage system, for example for large scale storage of available power.
Intelligent energy and computing facility resource management platform
In operation, a user may access the energy and information technology platform to initiate and manage a set of activities that involve optimizing energy and computing resources among a different set of available tasks. The energy resources can comprise hydroelectric power, nuclear energy, wind energy, solar energy, electric power of a power grid and the like, and energy storage resources such as batteries, gravitational energy and the like and energy storage by using thermal materials such as fused salt and the like. The computing resources may include GPUs, FPGAs, servers, chips, ASICs, processors, data storage media, network resources, and the like. Available tasks may include cryptocurrency hashing, expert system processing, computer vision processing, NLP, path optimization, model application (e.g., for analysis), and the like.
In an embodiment, the platform may include various subsystems that may be implemented as micro-services such that other subsystems of the system access the functionality of the subsystem providing the micro-services through an Application Programming Interface (API). In some embodiments, the various services provided by the subsystem may be deployed in a bundled form, for example, integrated through a set of APIs. Each subsystem is described in more detail in connection with diagram 130.
An external data source may include any system or device that may provide data to the platform. Examples of data sources may include market data sources (e.g., financial markets, business markets (including e-commerce), advertising markets, energy markets, telecommunications markets, etc.). The energy and computing resource platform accesses external data sources via a network (e.g., the internet) in any suitable manner (e.g., a crawler, an Extract Transform Load (ETL) system, a gateway, a proxy, an Application Programming Interface (API), a spider program, a distributed database query, etc.).
A facility is a facility having, among other things, energy resources (e.g., hydropower resources) and a set of computing resources (e.g., a set of flexible computing resources, such as GPUs, FPGAs, etc., that can be configured and managed to perform computing tasks; a set of flexible network resources, such as by adjusting network coding protocols and parameters, that can be similarly configured and managed).
The user and client systems and devices may include any system or device that may consume one or more computing or energy resources provided by the energy and computing resource platform. Examples include cryptocurrency systems (e.g., for bitcoin and other cryptocurrency mining operations), expert and artificial intelligence systems (e.g., neural networks and other systems, e.g., for computer vision, natural language processing, path determination and optimization, pattern recognition, deep learning, supervised learning, decision support, etc.), energy management systems (e.g., smart grid systems), and the like. The user and client systems may include user devices such as smartphones, tablet devices, notebook computing devices, personal computing devices, smart televisions, gaming devices, and the like.
The energy and computing resource platform components in diagram 130.
FIG. 130 illustrates an example energy and computing resource platform according to some embodiments of the invention. In an embodiment, the energy and computing resource platform may include a processing system 13002, a storage system 13004, and a communication system 13006.
The processing device 13002 may include one or more processors and memory. The processors may operate alone or in a distributed fashion. The processors may or may not be located in the same physical device, or in different devices, which may or may not be located in the same facility. The memory may store computer-executable instructions that are executed by the one or more processors. In an embodiment, the processing device 13002 may execute a facility management system 13008, a data acquisition system 13010, a cognitive process system 13012, a cue generation system 13014, a content generation system 13016, and a workflow system 13018.
Storage 13004 may include one or more computer-readable storage media. These computer-readable storage media may or may not be located in the same physical device or in different devices. Such computer readable storage media may include flash memory devices, solid state memory devices, hard disk drives, and the like. In an embodiment, the storage device 13004 stores one or more of a facility data store 13020, a personal data store 13022, and an external data store 13024.
The communication system 13006 can include one or more transceivers for enabling wireless or wired communication with one or more external devices (including user devices and/or servers) through a network (e.g., the internet and/or a cellular network). Communication system 13006 may implement any suitable communication protocol. For example, communication system xxx may implement an IEEE 801.11 wireless communication protocol and/or any suitable cellular communication protocol to enable wireless communication with external devices and external data 13024 over a wireless network.
Energy and computing resource management platform
Energy and computing resources are discovered, provided, managed and optimized by learning a set of results using artificial intelligence and expert systems that are sensitive to market and other conditions. Inventory of resources is discovered and facilitated, optionally through user input and/or automatic detection (including peer-to-peer detection). A graphical user interface may be implemented to receive relevant information about available energy sources and computing resources. This may include "digital twinning" of energy and computing facilities that allow modeling, prediction, etc. A set of data records may be generated that define the facility or a set of facilities commonly owned or operated by a host. These data records may have any suitable architecture. In some embodiments (e.g., fig. 131), the facility data record may include a facility identifier (e.g., a unique identifier corresponding to the facility), a facility type (e.g., an energy system and capability, a computing system and capability, a network system and capability), a facility attribute (e.g., a facility name, a facility sponsor name, a facility description, a facility key, a goal of the facility, a time element, a schedule, etc.), a participant/potential participant in the facility (e.g., an identifier of an owner, an operator, a host, a service provider, a consumer, a customer, a user, a worker, etc.), and any suitable metadata (e.g., a creation date, a start-up date, a predetermined requirement, etc.). Content, such as documents, messages, alerts, reports, web pages, and/or application pages, may be generated from the content of the data records. For example, a data record of a facility may be obtained and a web page template may be populated with data contained therein. In addition, existing facilities may be managed, data records of facilities updated, results determined (e.g., energy generated, computing tasks completed, results processed achieved, financial results achieved, service levels achieved, etc.), and information sent to individuals and systems (e.g., updates, alarms, requests, instructions, etc.).
The data acquisition system may acquire various types of data from different data sources and organize the data into one or more data structures. In an embodiment, the data acquisition system receives data (e.g., user input profile information) from a user through a user interface. In an embodiment, the data acquisition system may retrieve data from a passive electron source. In an embodiment, the data acquisition system may implement a crawler to crawl different websites or applications. In an embodiment, the data acquisition system may implement an API to retrieve data from an external data source or user device (e.g., a user phone or various contact lists in an email account). In an embodiment, the data acquisition system may construct the acquired data into a suitable data structure. In an embodiment, the data acquisition system generates and maintains personal records based on collected data about the individual. In an embodiment, the personal data store stores personal records. In some of these embodiments, the personal data store may include one or more databases, indexes, tables, and the like. Each individual record may correspond to a respective individual and may be organized according to any suitable architecture.
Fig. 132 illustrates an exemplary architecture of personal records. In this example, each person record may include a unique person identifier (e.g., a user name or value), and may define all data related to the person, including the person's name, the facility to which it belongs or is related (e.g., a list of facility identifiers), the person's attributes (age, place, work, company, role, skill, competency, educational experience, work experience, etc.), the person's contact or relationship list (e.g., in a role hierarchy or graph), and any suitable metadata (e.g., date of addition, date of action taken, date of receipt of input, etc.).
In an embodiment, the data acquisition system generates and maintains one or more graphs based on the retrieved data. In some embodiments, the graphics data store may store the one or more graphs. The graph may be facility specific or may be a global graph. The graph may be used in many different applications (e.g., to identify a set of roles, such as for personal authentication, approval, etc., or to identify a system configuration, capability, etc., such as a hierarchy of energy production, computing, networking, or other systems, subsystems, and/or resources).
In an embodiment, the graph may be stored in a graph database, where the data is stored in a collection of nodes and edges. In some embodiments, the graph has nodes representing entities and edges representing relationships, each node may have a node type (also referred to as an entity type) and an entity value, each edge may have a relationship type and may define a relationship between two entities. For example, a personal node may include a personal ID identifying the person represented by the node, and a corporate node may include a corporate identifier identifying the corporation. The "effectiveness" edge from the individual node to the company node may represent the company effectiveness of the individual represented by the edge node as represented by the company node. In another example, a personal node may include a personal ID identifying a person represented by the node, and a facility node may include a facility identifier identifying a facility. The "management" edge directed from a personal node to a facility node may represent that the person represented by the personal node is the manager of the facility represented by the facility node. Further, in embodiments, an edge or node may contain or reference additional data. For example, a "management" edge may include functionality that indicates a particular function within a facility managed by an individual. The graph may be used in many different applications, which will be discussed in connection with a cognitive processing system.
In an embodiment, the authenticated identity information may be imported from one or more identity information providers, and from LinkedIn TM And other social network sources. In an embodiment, the data acquisition system may include an identity management system (not shown) of the platform that may manage identity stitching, identity resolution, identity normalization, etc., for example, to determine that individuals represented in different social networking sites and email contacts are in fact the same person. In an embodiment, the data acquisition system may include a profile aggregation system (not shown) that looks up and aggregates the profileDifferent pieces of information to generate a comprehensive profile of the individual. The profile aggregation system may also perform deduplication on individuals.
Cognitive processing system
The cognitive processing system 13312 may implement one or more of a machine learning process, an artificial intelligence process, an analysis process, a natural language processing process, and a natural language generation process. Fig. 133 illustrates an exemplary cognitive processing system according to some embodiments of the inventions. In this example, the cognitive processing system may include a machine learning system 13302, an Artificial Intelligence (AI) system 13304, an analysis system 13306, a natural language processing system 13308, and a natural language generation system 13310.
Machine learning system
In embodiments, the machine learning system may train models, such as predictive models (e.g., various types of neural networks, regression-based models, and other machine learning models). In embodiments, training may be supervised, semi-supervised, or unsupervised. In an embodiment, training may be accomplished using training data, which may be collected or generated for training purposes.
The facility output model (or predictive model) may be a model that receives facility attributes and outputs one or more predictions regarding production or other output of the facility. Examples of predictions may be the amount of energy that a facility will generate, the amount of processing that a facility will perform, the amount of data that a network will be able to transmit, the amount of data that can be stored, the price of a component or service, etc. (e.g., provided to or by a facility), the profit generated to complete a given task, the cost required to perform an action, etc. In each case, the machine learning system may choose to train the model based on the training data. In an embodiment, the machine learning system may receive vectors containing facility attributes (e.g., facility type, facility capabilities, goals sought, constraints or rules applicable to utilization of resources or facilities, etc.), personal attributes (e.g., roles, managed components, etc.), and results (e.g., energy generated, computational tasks completed, financial results completed, etc.). Each vector corresponds to a respective result and a property of a respective facility and a respective action resulting in the result. The machine learning system receives these vectors and generates a predictive model based thereon. In an embodiment, the machine learning system may store these predictive models in a model data store.
In an embodiment, training may also be based on feedback received by the system, also referred to as "reinforcement learning. In an embodiment, a machine learning system may receive a set of environments and results related to a facility that lead to predictions (e.g., attributes of the facility, attributes of the model, etc.), and may update the model based on feedback.
In an embodiment, training may be provided from a training data set created by observing the actions of a set of people, such as a facility manager managing facilities having various capabilities and various scenarios and situations involved. This may include using robotic process automation to learn training data sets of human interactions with interfaces (e.g., graphical user interfaces) of one or more computer programs (e.g., control panels, control systems, and other systems for managing energy and computing management facilities).
Artificial Intelligence (AI) system
In an embodiment, the AI system utilizes a predictive model to make predictions about the facility. Examples of predictions include predictions related to inputs to the facility (e.g., available energy, energy costs, computing resource costs, network capacity, etc., as well as various market information, such as pricing information for the terminal usage market), predictions related to components or systems of the facility (including performance predictions, maintenance predictions, uptime/downtime predictions, capacity predictions, etc.), predictions related to functions or workflows of the facility (e.g., those involving conditions or states that may result in following one or more different possible paths in a workflow, process, etc.), predictions related to outputs of the facility, etc. In an embodiment, the AI system receives a facility identifier. In response to the facility identifier, the AI system may retrieve attributes corresponding to the facility. In some embodiments, the AI system may obtain the facility attributes from the map. Additionally or alternatively, the AI system may obtain facility attributes from facility records corresponding to facility identifiers and personal attributes from personal records corresponding to personal identifiers.
Examples of additional attributes that may be used to make predictions about a facility or related system process include: related facility information; owner goals (including financial goals); a customer objective; and further additional or alternative attributes. In an embodiment, the AI system may output a score for each possible prediction, where each prediction corresponds to a possible result. For example, when using a predictive model to determine the likelihood that a hydroelectric power generation source of a facility will produce 5MW of power, the predictive model may output a score that "will produce" results and a score that "will not produce" results. The AI system may then select the highest scoring result as the prediction. Alternatively, the AI system may output the corresponding score to the requesting system.
Clustering system
In an embodiment, the clustering system clusters records or entities based on the attributes contained herein. For example, similar facilities, resources, individuals, customers, etc. may be clustered. The clustering system may implement any suitable clustering algorithm. For example, when personal records are clustered to identify a list of customer cues that correspond to resources that a facility may sell, the clustering system may implement k nearest neighbor clustering whereby the clustering system identifies k personal records that are most closely related to the attributes defined for the facility. In another example, the clustering system may implement k-means clustering such that the clustering system identifies k different clusters of personal records, whereby the clustering system or another system selects items from the clusters.
Analysis system
In an embodiment, an analysis system may perform analysis related to various aspects of the energy and computing resource platform. The analysis system may analyze certain communications to determine which configurations of the facility produce the greatest yield, which conditions tend to indicate potential faults or problems, etc.
Thread generation system
FIG. 134 illustrates the manner in which the thread generating system generates a list of threads. The cue generation system receives a list of potential cues (13402) (e.g., a list of potential cues for consumers of available products or resources). The thread generation system may provide a list of threads to the clustering system (13404). The clustering system clusters (13406) the cue profile with the facility attribute clusters to identify one or more clusters. In an embodiment, the clustering system returns a list of cues (13410). In other embodiments, the clustering system returns a cluster (13408), and the thread generation system selects a list of threads from the clusters to which the potential clients belong (13410).
FIG. 135 illustrates the manner in which the thread generation system determines the facility output of the threads identified in the thread list. In an embodiment, the thread generation system provides the AI system with the thread identifier of the corresponding thread (step 13502). The AI system may then obtain the cue properties of the cue and the facility properties of the facility, and may feed the respective properties into the predictive model (step 13504). The prediction model outputs a prediction, which may be a score associated with each possible outcome, or a single predicted outcome (e.g., the outcome with the highest score) selected based on its respective score (step 13506). In this way, the thread generation system may iterate through each thread in the list of threads. For example, the cue generation system may generate cues that are consumers of computing power, energy power, prediction forecasting, optimizing results, and the like.
In an embodiment, the thread generation system classifies the threads (step 13508) and generates a list of threads (step 13512) that is provided to the facility operator or system host, including an indication of why the thread may be willing to participate in the facility, e.g., the thread is a dense user of computing resources, e.g., to predict behavior of complex, multi-variable markets, or mine cryptocurrency. In embodiments where more threads are stored and/or categorized, the thread generation system continues to examine the list of threads (step 13510).
Content generation system
In an embodiment, the content generation system of the platform generates content of the contact event, such as an email, text message, or web post, or machine-to-machine message, such as communicating through an API or point-to-point system. In an embodiment, the content is customized based on attributes of the facility, attributes of the recipient (e.g., based on a profile of the person, a role of the person, etc.), and/or with respect to items or activities associated with the facility using artificial intelligence. The content generation system may be implanted with a set of templates that may be customized, for example, by training the content generation system based on data created by a human author, the content generation system may be further trained by feedback based on results tracked by the platform, for example, indicating that a particular form of communication successfully raised a donation to a facility, and other indications mentioned in this disclosure. The content generation system may customize the content based on attributes of the facility, item, and/or one or more persons, etc. For example, a facility manager may receive short messages, including codes, acronyms, and jargon, about events related to facility operations, while an external consumer of the facility output may receive a more formal report related to the same event.
FIG. 136 illustrates the manner in which the content generation system may generate personalized content. The content generation system receives the recipient ID, the sender ID (which may be a person or a system, etc.), and the facility ID (step 13602). The content generation system may determine the appropriate templates to use based on relationships between the recipients, senders, and facilities, and/or based on other considerations (e.g., busy manager recipients are more likely to reply to less formal messages or more formal messages) (step 13604). The content generation system may provide templates (or identifications thereof), recipient IDs, sender IDs, and facility IDs to the natural language generation system. The natural language generation system may obtain facility attributes based on the facility ID, obtain personal attributes corresponding to the recipient or sender based on their ID (step 13606). The natural language generation system may then generate personalized or customized content based on the selected templates, facility parameters, and/or other attributes of the various types described herein (step 13608). The natural language generation system may output the generated content to the content generation system (step 13610).
In an embodiment, a facility manager or the like may approve and/or edit the generated content provided by the content generation system and then send the content via email and/or other channels or the like. In an embodiment, the platform will track contact events.
Referring to fig. 137, the adaptive intelligence system 13704 may include an artificial intelligence system 13748, a digital twin system 13720, and an adaptive device (or edge) intelligence system 13730. The artificial intelligence system 13748 can define a machine learning model 13702 for performing analyses, simulations, decisions, and predictions related to data processing, data analysis, simulation creation, and simulation analysis of one or more of the transaction entities. The machine learning model 13702 is an algorithmic and/or statistical model that does not use explicit instructions but relies on patterns and reasoning to perform specific tasks. The machine learning model 13702 builds one or more mathematical models to make predictions and/or decisions based on training data without being explicitly programmed to perform particular tasks. The machine learning model 13702 may receive sensor data inputs as training data through the data collection system 13718 and the monitoring system 13706 and the connection facility 13716, including event data 13724 and status data 13772 related to one or more of the transaction entities. Event data 13724 and status data 13772 may be stored in a data storage system 13710. Sensor data input to the machine learning model 13702 may be used to train the machine learning model 13702 to perform analysis, simulation, decision, and prediction related to data processing, data analysis, simulation creation, and simulation analysis of one or more of the transaction entities. The machine learning model 13702 may also use input data from one or more users of the information technology system. The machine learning model 13702 may include an artificial neural network, a decision tree, a support vector machine, a bayesian network, a genetic algorithm, any other suitable form of machine learning model, or a combination thereof. The machine learning model 13702 may be used to learn by supervised learning, unsupervised learning, reinforcement learning, self-learning, feature learning, sparse dictionary learning, anomaly detection, association rules, combinations thereof, or any other suitable learning algorithm.
The artificial intelligence system 13748 can also define a digital twinning system 13720 to create a digital copy of one or more of the transaction entities. The digital copy of one or more of the transaction entities may use the substantially real-time sensor data to provide a substantially real-time virtual representation of the transaction entity and to provide a simulation of one or more possible future states of the one or more transaction entities. The digital copy is present concurrently with the one or more transaction entities being replicated. The digital copy provides one or more simulations of physical elements and attributes of the replicated transaction entity or entities and their dynamics in embodiments throughout the life style of the replicated transaction entity or entities. The digital replica may provide a hypothetical simulation of one or more transaction entities, such as during a design phase prior to construction or manufacture of one or more transaction entities, or during or after construction or manufacture of one or more transaction entities, by allowing the sensor data to be extrapolated to simulate the state of one or more transaction entities, such as during high stress, after component wear may have been problematic for some time, during maximum throughput operation, after one or more assumptions or plan improvements have been made to one or more manufacturing entities, or in any other suitable hypothetical case. In some embodiments, the machine learning model 13702 may automatically predict hypothetical conditions for simulation using the digital replica, such as by predicting likely improvements to one or more transaction entities, predicting when one or more components of one or more transaction entities may fail, and/or suggesting likely improvements to one or more transaction entities, such as changes to time settings, arrangements, components, or any other suitable changes to transaction entities. The digital copy allows simulation of one or more transaction entities during the design and operational phases of the one or more transaction entities, as well as simulation of hypothetical operating conditions and configurations of the one or more transaction entities. The digital replica enables very valuable analysis and simulation of one or more transaction entities by enabling, not only in, on and around each component of the one or more transaction entities, but in some embodiments, also the observation and measurement of almost any type of metric, including temperature, wear, light, vibration, etc., within the one or more transaction entities. In some embodiments, the machine learning model 13702 may process sensor data including event data 13724 and state data 13772 to define simulation data used by the digital twin system 13720. For example, the machine learning model 13702 may receive status data 13772 and event data 13724 associated with a particular transaction entity of the plurality of transaction entities and perform a series of operations on the status data 13772 and event data 13724 to format the status data 13772 and event data 13724 into a format suitable for use by the digital twin system 13720 in creating a digital copy of the transaction entity. For example, one or more transaction entities may include robots for enhancing products on adjacent assembly lines. The machine learning model 13702 may collect data from one or more sensors located on, near, in, and/or around the robot. The machine learning model 13702 may perform operations on the sensor data to process the sensor data into analog data and output the analog data to the digital twin system 13720. The digital twinning system 13720 simulation may use the simulation data to create one or more digital copies of the robot, the simulation including metrics including temperature, wear, speed, rotation, and vibration of the robot and components of the robot, and the like. The simulation may be a substantially real-time simulation such that a human user of the information technology may view the simulation of the robot, metrics related to the robot, and metrics related to components of the robot in substantially real-time. The simulation may be a predictive or hypothetical situation such that a human user of the information technology may view the predictive or hypothetical simulation of the robot, metrics related to the robot, and metrics related to components of the robot.
In some embodiments, the machine learning model 13702 and the digital twinning system 13720 may process the sensor data and create a digital copy of a set of transaction entities of the plurality of transaction entities to facilitate design, real-time simulation, predictive simulation, and/or hypothetical simulation of a set of related transaction entities. The digital copy of the set of transaction entities may use the substantially real-time sensor data to provide a substantially real-time virtual representation of the set of transaction entities and to provide a simulation of one or more possible future states of the set of transaction entities. The digital copy exists concurrently with the replicated set of transaction entities. The digital copy provides one or more simulations of the physical elements and attributes of the replicated set of transaction entities and their dynamics in embodiments throughout the life style of the replicated set of transaction entities. The one or more simulations may include visual simulations, such as a wire-frame virtual representation of one or more transaction entities that may be viewed on a display using an Augmented Reality (AR) device or using a Virtual Reality (VR) device. The visual simulation may be manipulated by a human user of the information technology system, such as by zooming or highlighting components of the simulation and/or providing an exploded view of one or more transaction entities. The digital replica may provide a hypothetical simulation of the set of transaction entities, such as during a design phase prior to construction or manufacture of the one or more transaction entities, or during or after construction or manufacture of the one or more transaction entities, by allowing the hypothetical extrapolation of sensor data to simulate the state of the set of transaction entities, such as during high stress, after component wear may have been problematic for some time, during maximum throughput operation, after one or more assumptions or plan improvements have been made to the set of transaction entities, or in any other suitable hypothetical case. In some embodiments, the machine learning model 13702 may automatically predict hypothetical conditions for modeling using the digital copy, such as by predicting possible improvements to the set of transaction entities, predicting when one or more components of the set of transaction entities may fail, and/or suggesting possible improvements to the set of transaction entities, such as changes to time settings, arrangements, components, or any other suitable changes to the transaction entities. The digital copy allows simulation of the set of transaction entities during the design and operational phases of the set of transaction entities, as well as simulation of hypothetical operating conditions and configurations of the set of transaction entities. The digital copy enables very valuable analysis and simulation of one or more transaction entities by enabling, not only in, on and around each component of the set of transaction entities, but in some embodiments, also the observation and measurement of almost any type of metric within the set of transaction entities, including temperature, wear, light, vibration, etc. In some embodiments, the machine learning model 13702 may process sensor data including event data 13724 and state data 13772 to define simulation data used by the digital twin system 13720. For example, the machine learning model 13702 may receive status data 13772 and event data 13724 related to a particular transaction entity of the plurality of transaction entities and perform a series of operations on the status data 13772 and event data 13724 to format the status data 13772 and event data 13724 into a format suitable for use by the digital twin system 13720 in creating digital copies of the set of transaction entities. For example, a set of transaction entities may include: a die press for placing the product on a conveyor belt; the conveyor belt, the die stamping machine is used for placing the products on the conveyor belt; a plurality of robots for adding parts to the product as it moves along an assembly line. The machine learning model 13702 may collect data from one or more sensors located on, near, in, and/or around each of the die machine, the conveyor belt, and the plurality of robots. The machine learning model 13702 may perform operations on the sensor data to process the sensor data into analog data and output the analog data to the digital twin system 13720. The digital twinning system 13720 simulation may use these simulation data to create one or more digital copies of the die machine, the conveyor belt, and the plurality of robots, the simulation including metrics including temperature, wear, speed, rotation, and vibration of the die machine, the conveyor belt, and the plurality of robots, and their components, etc. The simulation may be a substantially real-time simulation such that a human user of the information technology may view the simulation of the die machine, the conveyor belt, and the plurality of robots, metrics associated therewith, and metrics associated with components thereof in substantially real-time. The simulation may be a predictive or hypothetical case such that a human user of the information technology may view the predictive or hypothetical simulation of the die machine, the conveyor belt, and the plurality of robots, metrics associated therewith, and metrics associated with components thereof.
In some embodiments, the machine learning model 13702 may preferentially collect sensor data for digital replica simulation of one or more of the transaction entities. The machine learning model 13702 may be trained using sensor data and user input to learn which types of sensor data are most effective in creating a digital copy simulation of one or more of the transaction entities. For example, the machine learning model 13702 may find that a particular transaction entity has dynamic properties such as component wear and throughput that are affected by temperature, humidity, and load. The machine learning model 13702 may preferentially collect sensor data related to temperature, humidity, and load through machine learning, and may preferentially process the sensor data of the priority type as analog data for output to the digital twinning system 13720. In some embodiments, the machine learning model 13702 may suggest to a user of the information technology system that more and/or different priority type sensors be implemented in the vicinity and surrounding of the transaction entity being modeled in the information technology so that more and/or better priority type data may be used in the modeling of the transaction entity through its digital copy.
In some embodiments, the machine learning model 13702 may be used to learn based on one or both of the modeling targets and the quality or type of sensor data to determine which types of sensor data are to be processed as analog data for transmission to the digital twinning system 13720. The modeled target may be a target set by a user of the information technology system, or may be predicted or learned by the machine learning model 13702. Examples of modeling targets include creating digital copies capable of displaying throughput dynamics on an assembly line, which may include collecting, modeling, and modeling heat, power, component wear, and other metrics of conveyor belts, assembly machines, one or more products, and other components of a transaction ecosystem, among others. The machine learning model 137102 may be used to learn to determine which types of sensor data need to be processed as analog data for transmission to the digital twinning system 13720 to implement such a model. In some embodiments, the machine learning model 13702 may analyze which types of sensor data are being collected, the quality and quantity of sensor data being collected, and what the sensor data being collected represents; decisions, predictions, analyses, and/or determinations of which types of sensor data are relevant and/or irrelevant to achieving the modeling objective may be made; decisions, predictions, analyses, and/or determinations can be made to prioritize, refine, and/or implement the quality and quantity of sensor data that is processed into analog data for use by the digital twinning system 13720 to achieve modeling goals.
In some embodiments, a user of the information technology system may input modeling targets into the machine learning model 13702. The machine learning model 13702 may learn to analyze the training data to output suggestions to a user of the information technology system as to which types of sensor data are most relevant to achieving the modeling objective, e.g., one or more types of sensors in, on, or near a transaction entity or entities that are relevant to achieving the modeling objective are sufficient and/or insufficient to achieve the modeling objective, and how different configurations of the types of sensors (e.g., by adding, removing, or repositioning sensors) better facilitate the machine learning model 13702 and the digital twinning system 13720 to achieve the modeling objective. In some embodiments, the machine learning model 13702 may automatically increase or decrease the collection rate, processing, storage, sampling rate, bandwidth allocation, bit rate, and other attributes of sensor data collection to achieve or better achieve modeling goals. In some embodiments, the machine learning model 13702 may make suggestions or predictions to a user of the information technology system regarding: the collection rate, processing, storage, sampling rate, bandwidth allocation, bit rate, and other attributes of sensor data collection are increased or decreased to achieve or better achieve modeling goals. In some embodiments, the machine learning model 13702 may automatically create and/or propose modeling targets using sensor data, simulation data, previous, current, and/or future digital replica simulations of one or more of the plurality of transaction entities. In some embodiments, the modeling objective automatically created by the machine learning model 13702 may be automatically achieved by the machine learning model 13702. In some embodiments, the modeling targets automatically created by the machine learning model 13702 may be proposed to a user of the information technology system and implemented only after the user accepts and/or partially accepts, e.g., after the user modifies the proposed modeling targets.
In some embodiments, the user may input one or more modeling targets by entering one or more modeling commands into the information technology system, or the like. The one or more modeling commands may include, for example: commands for the machine learning model 13702 and the digital twinning system 13720 to create a digital replica simulation of a transaction entity or set of transaction entities; the digital replica simulation is set to a command of one or more of a real-time simulation and a hypothesis simulation. Modeling commands may also include, for example, parameters regarding what type of sensor data should be used, the sampling rate of the sensor data, and other parameters regarding the sensor data used in one or more digital replica simulations. In some embodiments, the machine learning model 13702 may be used to predict modeling commands, for example, by using previous modeling commands as training data. The machine learning model 13702 may present predictive modeling commands to users of the information technology system to facilitate simulation of one or more of the transaction entities, etc., which may be useful for management of the transaction entities and/or to enable users to easily identify potential problems or possible improvements to the transaction entities. The system shown in fig. 137 may include a transaction management platform and an application.
In some embodiments, the machine learning model 13702 may be used to evaluate a set of hypothesis simulations of one or more of the transaction entities. The set of hypothetical simulations may be created by the machine learning model 13702 and the digital twinning system 13720 due to: one or more modeling commands; one or more modeling targets, one or more modeling commands, predictions of the machine learning model 13702, or combinations thereof. The machine learning model 13702 may evaluate the set of hypothesis simulations based on one or more metrics defined by a user, one or more metrics defined by the machine learning model 13702, or a combination thereof. In some embodiments, the machine learning model 13702 may evaluate each hypothesis simulation in the set of hypothesis simulations independently of each other. In some embodiments, the machine learning model 13702 may evaluate one or more hypothesis simulations of the set of hypothesis simulations with respect to each other, for example by ranking the hypothesis simulations or creating a hierarchy of hypothesis simulations based on one or more metrics.
In some embodiments, the machine learning model 13702 may include one or more model interpretive systems to facilitate human understanding of the output of the machine learning model 13702, as well as information and insight related to the cognition and processes of the machine learning model 13702, i.e., the one or more model interpretive systems enable humans to understand not only what the machine learning model 13702 is outputting, but also what the machine learning model 13702 will output, and what processes cause the machine learning model 13702 to form those outputs. The one or more model interpretability systems may also be used by a human user to refine and guide training of the machine learning model 13702 to help debug the machine learning model 13702 to help identify deviations in the machine learning model 13702. The one or more model interpretability systems may include one or more of the following: linear regression, logistic regression, generalized Linear Model (GLM), generalized Additive Model (GAM), decision trees, decision rules, rule fit, naive bayes classifier, K nearest neighbor algorithm, partial dependency graph, individual condition expectation graph (ICE), cumulative local effect (ALE) graph, feature interactions, permutation feature importance, global proxy model, local proxy (LIME) model, scope rules (i.e., anchor points), shape values, shape additive interpretation (SHAP), feature visualization, network profiling, or any other suitable machine learning interpretable embodiment. In some embodiments, the one or more model interpretability systems may include a model dataset visualization system. The model dataset visualization system is used to automatically provide a human user of the information technology system with visual analysis related to the sensor data, simulation data, and value distribution of data nodes of the machine learning model 13702.
In some embodiments, the machine learning model 13702 may include and/or implement an embedded model interpretive system, such as a Bayesian Case Model (BCM) or a glass box model. The bayesian case model uses bayesian case-based reasoning, prototype classification, and clustering to assist humans in understanding the data of the sensor data, simulation data, and data nodes of the machine learning model 13702. In some embodiments, the model interpretive system may include and/or implement a glass-box interpretive method, such as a gaussian process, to assist humans in understanding sensor data, simulation data, data nodes, and the like, of the machine learning model 13702.
In some embodiments, the machine learning model 13702 may include and/or implement Testing (TCAV) using concept activation vectors. TCAV allows machine learning model 13702 to learn human interpretable concepts from examples, such as "running," "not running," "powering up," "not powering up," "robotic," "human," "truck," or "ship," through a process that includes defining concepts, determining concept activation vectors, and calculating directional derivatives. By learning human interpretable concepts, objects, states, etc., the TCAV can allow the machine learning model 13702 to output useful information related to transaction entities and data collected therefrom in a format that is readily understood by a human user of the information technology system.
In some embodiments, the machine learning model 13702 may be and/or include an artificial neural network, e.g., a connective system, for "learning" to perform tasks by considering examples without being explicitly programmed with task-specific rules. The machine learning model 13702 may be based on a collection of connected units and/or nodes, which may behave like artificial neurons, which may in some aspects simulate neurons in a biological brain. Each of these units and/or nodes may have one or more connections to other units and/or nodes. These units and/or nodes may be used to transmit information (e.g., one or more signals) to other units and/or nodes, process signals received from other units and/or nodes, and forward processed signals to other units and/or nodes. One or more of these units and/or nodes and the connections between them may have one or more digital "weights" assigned. The assigned weights may be used to facilitate learning, i.e., training, of the machine learning model 13702. The assigned weights may increase and/or decrease one or more signals between one or more units and/or nodes, and in some embodiments may have one or more thresholds associated with one or more of the weights. The one or more thresholds may be configured such that: signals are only transmitted between one or more units and/or nodes if the signals and/or aggregate signals exceed a threshold. In some embodiments, these units and/or nodes may be assigned to multiple layers, each layer having one or both of an input and an output. The first layer may be configured to receive training data, transform at least a portion of the training data, and transmit signals associated with the training data and its transforms to the second layer. The final layer may be used to output estimates, conclusions, products, or other results from the machine learning model 13702 processing one or more inputs. Each layer may perform one or more types of transformations, and one or more signals may pass through one or more layers one or more times. In some embodiments, the machine learning model 13702 may employ deep learning and be modeled and/or configured, at least in part, as a deep neural network, a deep belief network, a recurrent neural network, and/or a convolutional neural network, for example, by being configured to include one or more hidden layers.
In some embodiments, the machine learning model 13702 may be and/or include a decision tree, e.g., a tree-based predictive model, for identifying one or more observations and determining one or more conclusions based on the input. These observations may be modeled as one or more "branches" of the decision tree, and these conclusions may be modeled as one or more "branches and leaves" of the decision tree. In some embodiments, the decision tree may be a classification tree that may include one or more branches and leaves representing one or more class labels and one or more branches representing one or more feature combinations for guiding to the class labels. In some embodiments, the decision tree may be a regression tree. The regression tree may be configured such that one or more target variables may take on consecutive values.
In some embodiments, the machine learning model 13702 may be and/or include a support vector machine, e.g., a set of related supervised learning methods configured for one or both of classification and regression modeling of data. The support vector machine may be used to predict whether the new instance belongs to one or more categories that are configured during training of the support vector machine.
In some embodiments, the machine learning model 13702 may be used to perform regression analysis to determine and/or estimate a relationship between one or more inputs and one or more features of the one or more inputs. The regression analysis may include linear regression, wherein the machine learning model 13702 may calculate a single line to best fit the input data according to one or more mathematical criteria.
In an embodiment, inputs to the machine learning model 13702 (e.g., regression model, bayesian network, supervisory model, or other type of model) may be tested, such as by using a set of test data that is independent of the dataset used to create and/or train the machine learning model, to test the effect of various inputs on the accuracy of the model 13702, and so forth. For example, inputs to the regression model may be removed, including single inputs, paired inputs, triplet inputs, etc., to determine if none of these inputs would severely impact the success of model 13702. This may help identify inputs that are actually correlated (e.g., are linear combinations of the same underlying data), overlapping, etc. The successful comparison of the models may assist in selecting among alternative input data sets that provide similar information, e.g., to identify inputs that produce the least "noise" in the model, provide the greatest impact on model effectiveness at the lowest cost, etc. (among several similar inputs). Thus, the effect of input variations and test input variations on model effectiveness may be used to cut or enhance model performance of any machine learning system described in this disclosure.
In some embodiments, the machine learning model 13702 may be and/or include a bayesian network. The bayesian network may be a probabilistic graph model representing a set of random variables and conditional independence of the set of random variables. The bayesian network can be used to represent the random variables and conditional independence through directed acyclic graphs. The bayesian network may include one or both of a dynamic bayesian network and an impact graph.
In some embodiments, the machine learning model 13702 may be defined by supervised learning, i.e., one or more algorithms, for constructing a mathematical model containing a set of training data for one or more inputs and desired outputs. The training data may include a set of training examples, each having one or more inputs and a desired output, i.e., a supervisory signal. Each training example may be represented in the machine learning model 13702 with an array and/or vector (i.e., feature vector). The training data may be represented in a matrix in the machine learning model 13702. The machine learning model 13702 may learn one or more functions by iteratively optimizing an objective function, thereby learning an output that predicts an associated new input. After optimization, the objective function enables the machine learning model 13702 to accurately determine the output of inputs other than those contained in the training data. In some embodiments, the machine learning model 13702 may be defined by one or more supervised learning algorithms (e.g., active learning, statistical classification, regression analysis, and similarity learning). Active learning may include interactively querying a user and/or information source through machine learning model 13702 to tag new data points with desired output. Statistical classification may include identifying, by the machine learning model 13702, a set of subcategories, i.e., sub-populations, to which a new observation belongs based on a training set of data containing observations having known categories. Regression analysis may include estimating relationships between dependent variables (i.e., result variables) and one or more independent variables (i.e., predicted variables, covariates, and/or characteristics) via the machine learning model 13702. Similarity learning may include learning from examples through the machine learning model 13702 using a similarity function designed to measure the degree of similarity or relatedness of two objects.
In some embodiments, the machine learning model 13702 may be defined by unsupervised learning, i.e., one or more algorithms for constructing a mathematical model containing only an input set of data by looking up structures in the data (e.g., groupings or clusters of data points). In some embodiments, the machine learning model 13702 may learn from test data (i.e., training data) that has not been labeled, categorized, or categorized. The unsupervised learning algorithm may include identifying commonalities in the training data through the machine learning model 13702 and learning by reacting to the presence or absence of identified commonalities in the new data. In some embodiments, the machine learning model 13702 may generate one or more probability density functions. In some embodiments, the machine learning model 13702 may learn by performing cluster analysis, for example, by assigning a set of observations into subsets (i.e., clusters) according to one or more pre-specified criteria (e.g., similarity metrics based on internal compactness, separation, estimated density, and/or graph connectivity being factors).
In some embodiments, the machine learning model 13702 may be defined by semi-supervised learning, i.e., one or more algorithms that use training data, some of which may lack training labels. Semi-supervised learning may be weakly supervised learning, where training labels may be noisy, limited, and/or inaccurate. The generation of noisy, limited, and/or inaccurate training labels may be less costly and/or less labor intensive, thereby enabling the machine learning model 13702 to be trained based on a larger training data set at less cost and/or effort.
In some embodiments, the machine learning model 13702 may be defined by reinforcement learning, e.g., one or more algorithms using dynamic programming techniques, such that the machine learning model 13702 may be trained by taking action in the environment to maximize accumulated rewards. In some embodiments, the training data is represented as a Markov decision process.
In some embodiments, the machine learning model 13702 may be defined by self-learning, wherein the machine learning model 13702 is used to train using training data without external rewards and without external teaching, for example, by employing a Cross Adaptive Array (CAA). CAA may compute decisions about actions and/or emotions of the resulting situation in a cross-wise fashion, driving the teaching of the machine learning model 13702 through interactions between cognition and emotion.
In some embodiments, the machine learning model 13702 may be defined by feature learning, i.e., one or more algorithms, for finding increasingly accurate and/or appropriate representations of one or more inputs (e.g., training data) provided during training. Feature learning may include training through principal component analysis and/or cluster analysis. The feature learning algorithm may include attempting to preserve the input training data by the machine learning model 13702 while also converting the input training data such that the converted input training data is useful. In some embodiments, the machine learning model 13702 may be used to transform the input training data before performing one or more classifications and/or predictions of the input training data. Thus, the machine learning model 13702 may be used to reconstruct input training data from one or more unknown data generating distributions without having to conform to an unreasonable configuration of input training data from the distributions. In some embodiments, the feature learning algorithm may be executed by the machine learning model 13702 in a supervised, unsupervised, or semi-supervised manner.
In some embodiments, the machine learning model 13702 may be defined by anomaly detection, i.e., by identifying rare and/or outlier instances of one or more items, events, and/or observations. These rare and/or outlier instances may be identified by instances that differ significantly from the patterns and/or attributes of most training data. Unsupervised anomaly detection may include detecting anomalies in unlabeled training data sets by machine learning model 13702 under the assumption that most training data is "normal". Monitoring for anomaly detection may include training a dataset in which at least a portion of the training data has been marked as "normal" and/or "anomaly.
In some embodiments, the machine learning model 13702 may be defined by robotic learning. Robotics learning may include generating one or more courses through machine learning model 13702, which is a sequence of learning experiences, and cumulatively obtaining new skills through exploration guided by machine learning model 13702 and social interactions of machine learning model 13702 with humans. Acquisition of new skills may be facilitated by one or more guidance mechanisms (e.g., active learning, maturation, motor coordination, and/or imitation).
In some embodiments, the machine learning model 13702 may be defined by association rule learning. Association rule learning may include discovering relationships between variables in a database by machine learning model 13702 in order to identify strong rules using some "interestingness" metric. Association rule learning may include identifying, learning, and/or evolving rules to store, manipulate, and/or apply knowledge. The machine learning model 13702 may be used to learn by identifying and/or utilizing a set of relationship rules that collectively represent the knowledge captured by the machine learning model 13702. Association rule learning may include one or more of learning classifier systems, inductive logic programming, and artificial immune systems. A learning classifier system is an algorithm that may combine a discovery component (e.g., one or more genetic algorithms) with a learning component (e.g., one or more algorithms for supervised learning, reinforcement learning, or unsupervised learning). Inductive logic programming may include the use of the machine learning model 13702 to represent rule learning of one or more of input examples, background knowledge, and assumptions determined by the machine learning model 13702 during training. The machine learning model 13702 may be used to derive a hypothetical logic program containing all positive examples given a known background knowledge of the code and a set of examples of a logical database expressed as facts.
Referring to fig. 138, a compliance system 13800 that facilitates licensing of checkers using distributed ledgers and cryptocurrency is illustrated. As used herein, personality rights may refer to an entity's ability to control its identity usage for business purposes. The term "entity" as used herein may refer to an individual or organization (e.g., university, school, sports team, company, etc.) who agrees to grant their personality rights, unless the context indicates otherwise. This may include the ability of the entity to control its use of names, images, similarity, sounds, etc. For example, an individual exercising his personality for business purposes may include appearing in a business, television program, or movie, making sponsored social media posts (e.g., instragram posts, facebook posts, twitter posts, etc.), appearing their name on clothing (e.g., jersey, body shirts, jersey, etc.) or other merchandise, appearing in a video game, etc. In embodiments, individuals may refer to student athletes or professional athletes, but may also include other categories of individuals. Although the present description refers to NCAA, the system may be used to monitor and facilitate transactions with other individuals and organizations. For example, the system may be used in an office sports environment where organizations may circumvent payroll limits or other tournament rules (e.g., international football fair competition rules) using sponsorship and other licensing agreements.
In an embodiment, compliance system 13800 maintains one or more digital ledgers that record transactions related to the entity's personality right permissions. In an embodiment, the digital ledgers may be distributed ledgers distributed among a set of computing devices 13870, 13880, 13890 (also referred to as nodes), and/or may be encrypted. In other words, each participating node may store a copy of the distributed ledger. An example of a digital ledger is a blockchain ledger. In some embodiments, the distributed ledgers are stored on a set of common nodes. In other embodiments, the distributed ledgers are stored on a set of whitelisted participant nodes (e.g., on a server participating in a university or sports team). In some embodiments, the digital ledger is maintained privately by compliance system 13800. The digital ledger maintenance means provided by the latter configuration is more energy-saving; while the former configuration (e.g., distributed ledger) provides a more secure/verifiable means of digital ledger maintenance.
In an embodiment, a distributed ledger may store tokens. The tokens may be cryptocurrency tokens that are transferable to licensees and licensees. In some embodiments, a distributed ledger may store ownership data for each token. The token (or a portion thereof) may be owned by a compliance system, a regulatory organization (e.g., NCAA), a licensor, a licensee, a sports team, an institution, an individual, or the like. In an embodiment, the distributed ledger may store event records. The event records may store information related to events associated with entities related to the compliance system. For example, the event records may record agreements entered by both parties, cases where the licensor has completed obligations, allocation of funds to the licensor based on the licence, cases where the licensor has not completed obligations, allocation of funds to entities associated with the licensee (e.g., teammates, institutions, sports teams, etc.), and so forth.
In an embodiment, the digital ledger may store an intelligent contract that manages an agreement between the licensee and the licensee. As described herein, a licensee may be an organization or individual desiring to sign up for a licensee personal right licensing agreement. Examples of licensees may include, but are not limited to: an auto dealer where a star student player is expected to appear in a printed advertisement, a company where a portrait of a licensor (e.g., player and/or team) appears in a commercial, a video game making company where a team name, team clothing, player name and/or number is expected to be used in a video game, a company where a team name, player name or number is expected to be used in a game, a shoe maker where a player is expected to speak sports shoes, a television program making person where a player is expected to appear in a television program, etc. In an embodiment, compliance system 13800 generates a smart contract that records agreements between individuals and licensees and facilitates transfer of a price (e.g., funds) when individuals recognize that the individuals have fulfilled their requirements set forth in the agreements. For example, an athlete may agree to appear in an advertisement on behalf of a local car dealer. The smart contracts in this example may include an athlete's identifier (e.g., personal ID and/or personal account ID), an organization's identifier (e.g., organization ID and/or organization account ID), a person's requirements (e.g., appearing in a commercial, making sponsored social media posts, appearing at a close signature, etc.), and a price (e.g., monetary amount). In an embodiment, the smart contract may include additional terms. In an embodiment, the additional terms may include allocation rules defining the manner in which a price is allocated to an athlete and one or more other parties (e.g., an agent, manager, university, sports team, teammate, etc.). For example, in the case of a student athlete, the smart contract may define a division between a licensee, a student athlete's university's sports family, and a student athlete's teammate. In a specific example, universities may have a policy that requires that players present in any advertisement divide funds in 60/20/20 divisions, with 60% of the funds allocated to student players present in the advertisement, 20% of the funds allocated to sports sectors, and 20% of the funds allocated to teammates of the student players. When the smart contract verifies that the athlete has fulfilled its responsibility for the smart contract (e.g., appears in an advertisement), the smart contract may transfer the contracted amount from the licensee's account to the athlete's account and to any other entity's account, which may allocate a proportion of funds in the smart contract (e.g., sports and teammates).
In an embodiment, compliance system 13800 facilitates funds transfer using cryptocurrency. In an embodiment, the cryptocurrency is mined by the participant node and/or generated by the compliance system. The crypto-currency may be an established type of crypto-currency (e.g., bitcoin, ethercoin, lye coin, etc.), or may be a proprietary crypto-currency. In some embodiments, the crypto-currency is a hook crypto-currency hooked with a particular legal currency (e.g., hooked with dollars, pounds, euros, etc.). For example, a single unit of cryptocurrency (also referred to as a "token") may be hooked with a single unit of legal currency (e.g., dollars). In an embodiment, the licensee may exchange legal currency for a corresponding amount of crypto-currency. For example, if the cryptocurrency is hooked with dollars, the licensee may exchange a quantity of dollars for a corresponding quantity of cryptocurrency. In an embodiment, the compliance system 13800 may maintain a percentage of real world currency as a transaction fee (e.g., 5%). For example, in exchange for $10000, compliance system 13800 may assign an encrypted currency of $9500 to the licensee's account and may take $5000 as the transaction fee. Once the cryptocurrency is deposited into the licensee's account, the licensee may conduct transactions with an individual.
In an embodiment, compliance system 13800 may allow an organization to create a smart contract template that defines one or more conditions/constraints of a contract. For example, an organization may predefine assignments between licensees, organizations, and any other person (e.g., coaches, teammates, representatives). Additionally or alternatively, the organization may agree on a minimum and/or maximum amount. Additionally or alternatively, the organization may limit when a protocol may be signed and/or executed. For example, athletes may be restricted from appearing in commercials or advertisement campaigns during a season and/or during an examination. These details may be stored in an organization data store 13856a, and the organization may set other conditions/restrictions in the smart contract. In these embodiments, the individual and licensee desiring to sign up for an agreement must use the intelligent contract template provided by the organization to which the individual belongs. In other words, if the smart contract is defined or otherwise approved by an organization, compliance system 13800 may only allow individuals having an active relationship with the organization (e.g., racing in a college sports team) to participate in the smart contract.
In an embodiment, compliance system 13800 manages clearing house flows that approve potential licensees. The licensee may provide information about the licensee before the licensee can participate in the agreement facilitated by compliance system 13800. This may include tax ID numbers, entity names, company information (e.g., status and type), lists of key people (e.g., board of directors, high management, board of directors members, approved decision makers, etc.), and any other suitable information. In an embodiment, a potential licensee may be required to sign (e.g., an electronic signature or an ink signature) a document, indicating that the organization would be unwilling to use compliance system 13800 to circumvent any rules, laws, or regulations (e.g., they would not circumvent NCAA regulations). In an embodiment, compliance system 13800 or another entity (e.g., NCAA) may authenticate the licensee. Once authenticated, this information is stored in licensee data store 13856B and the licensee can participate in the transaction.
In an embodiment, once the licensor joins the organization (e.g., signed a sports prize with university), compliance system 13800 may create an account for the licensor. Once the licensor is verified as being associated with an organization, compliance system 13800 may create an account for the licensor and may create a relationship between the person and the organization, whereby the licensor may be required to use the organization's approved or provided smart contracts. If the licensor joins another organization (e.g., goes to another school), compliance system 13800 may terminate the relationship with the previous organization and may create a new relationship with the other organization. Similarly, once the licensor is no longer associated with any organization (e.g., athlete graduation, entry into professional tournament, retirement, etc.), the compliance system 13800 may prevent the licensor from participating in a transaction on the compliance system 13800.
In an embodiment, compliance system 13800 may provide a graphical user interface that allows a user to create a smart contract that manages personal rights permissions. In these embodiments, the compliance system allows a user (e.g., licensor) to select an intelligent contract template. In some embodiments, compliance system 13800 may limit users to selecting only smart contract templates associated with the licensor's institution. In an embodiment, the graphical user interface allows the user to define certain terms (e.g., one or more types of obligations placed on the licensor, amount to be paid, date on which the licensor obligation must complete, place at which the obligation is completed, and/or other suitable terms). When a user provides input for parameterizing a smart contract template, compliance system 13800 may generate a smart contract by parameterizing one or more variables in the smart contract using the provided input. Compliance system 13800 may deploy smart contracts when parameterizing instances of smart contracts. In some embodiments, compliance system 13800 may deploy the smart contracts by broadcasting parameterized smart contracts to participant nodes, which in turn may update each respective instance of the distributed ledger with a new smart contract. In some embodiments, the licensor's institution must approve the parameterized smart contract before the parameterized smart contract can be deployed to the distributed ledger.
In an embodiment, the compliance system 13800 may provide a graphical user interface to verify the performance of the obligation by the licensor. In some of these embodiments, compliance system 13800 may include applications accessed by licensees that allow the licensees to prove that they are meeting obligations. In some of these embodiments, the application may allow the user to record the location to which the licensor is going (e.g., the location where a movie or photograph was taken), upload a record (e.g., a screenshot of a social media post), or provide other evidence of a license that the licensor has fulfilled its obligations related to the licensing transaction. In this way, the licensor can prove that it has completed the task required by the licensing agreement. In some embodiments, the application may interact with the wearable device, or may capture other digital information, such as social media posts of the user (e.g., licensor), to collect evidence that the supporting or refuting licensor claims that it is fulfilling obligations under the transaction agreement. In an embodiment, the evidence of the evidence collected by the application may be recorded by the application and stored on a distributed ledger as licensor data store 13856C.
In an embodiment, compliance system 13800 (or an intelligent contract issued in conjunction with compliance system 13800) may complete transactions related to intelligent contracts that govern permissions of a licensor after verifying that the licensor has fulfilled its defined obligations in an agreement. As previously described, the licensor may use the application to provide evidence of the compliance with the present agreement obligation. Further, the licensee may provide proof that the licensee has fulfilled its obligations (e.g., use of an application). In an embodiment, the smart contract of the management protocol may receive verification that the licensor has fulfilled its obligations of the protocol definition. In response, the smart contract may release (or initiate release) the cryptocurrency amount defined in the smart contract. The cryptocurrency amount may be assigned to an account of the licensor and any other party defined in the agreement (e.g., teammates of the licensor, plans of the licensor, regulatory authorities, etc.).
In an embodiment, compliance system 13800 is used to perform analysis and provide reports to regulatory authorities and/or other entities (e.g., other organizations). In these embodiments, the analysis may be used to identify individuals who potentially circumvent the regulatory body's rules and regulations. Further, in some embodiments, transaction records may be maintained on a distributed ledger, whereby different organizations may be able to view agreements signed by individuals associated with other organizations, such that increased transparency and level of supervision may prevent individuals, organizations, and/or licensees from circumventing rules and regulations.
In an embodiment, the compliance system 13800 may train and/or utilize a machine learning model to identify potential instances of avoidance rules or regulations. In these embodiments, the compliance system 13800 may use the result data to train a machine learning model. Examples of outcome data may include data related to a set of transactions in which an organization (e.g., sports team or university), a licensee (e.g., company), and/or a licensee (e.g., athlete) are determined to circumvent rules or regulations, and data related to a set of transactions, and/or a licensee is found to comply with rules and regulations. Examples of machine learning models include neural networks, regression-based models, decision trees, random forests, hidden markov models, bayesian models, and the like. In an embodiment, compliance system 13800 may utilize a machine learning model by obtaining a set of records from a distributed ledger related to transactions by licensees, and/or organizations (e.g., sports teams or universities). The compliance system may extract relevant characteristics such as the amount paid by the licensee to a particular licensor, funds paid to other licensees in other sports teams, affiliations of the licensor, amounts paid by other licensees to the licensor, and the like, and may feed back the characteristics to the machine learning model. The machine learning model may issue a score indicating the likelihood that the transaction is legal (or illegal) based on the extracted features. In an embodiment, compliance system 13800 may provide notifications to interested parties (e.g., regulatory authorities) when the output of the machine learning model indicates that the transaction may be illegal.
FIG. 139 illustrates an example system 13900 for electronically facilitating licensing of one or more personality rights of a licensor in accordance with some embodiments of the invention. In some embodiments, the system 13900 can include one or more computing platforms 13902. The computing platform 13902 may be used to communicate with one or more remote platforms 13904 according to a client/server architecture, a peer-to-peer architecture, and/or other architectures. The remote platform 13904 may be used to communicate with other remote platforms via the computing platform 13902 and/or according to a client/server architecture, peer-to-peer architecture, and/or other architectures. A user may access system 13900 via remote platform 13904.
In an embodiment, the computing platform 13902 may be configured by machine-readable instructions 13906. Machine-readable instructions 13906 may include one or more instruction modules. The instruction modules may include computer program modules. The instruction modules may include one or more of an access module 13108, a funds management module 13112, a ledger management module 13116, a verification module 13118, an analysis module 13120, and/or other instruction modules.
In an embodiment, the access module 13108 may be configured to receive an access request from a licensee to obtain approval of a licensing square from a set of available licensees. In an embodiment, the access module 13108 may be used to selectively grant access to licensees based on access requests. For example, the access module 13108 can receive a name of a potential licensee (e.g., a company name), a list of responsible persons of the potential licensee (e.g., a high manager and/or owner), a licensee location, affiliations of the licensee and its responsible persons, and so forth. In an embodiment, the access module 13108 may provide this information to the access rights grantor and/or may feed this information into an artificial intelligence system that reviews potential licensees. In an embodiment, the access module 13108 may selectively grant access to a licensee by verifying that a licensee is allowed to associate with a set of licensees, including the licensee, based on the set of affiliations. Selectively granting access to the licensee may include approving the licensee for association with the set of licensees in response to verifying that the licensee is allowed to associate with the set of licensees. The set of affiliations of the licensee may include an organization to which the licensee belongs or an organization to which a principal associated with the licensee donates or owns.
In an embodiment, the funds management module 13112 may be configured to receive a deposit confirmation of the amount of funds from the licensee. In some embodiments, the funds management module 13112 may be operable to issue an encrypted monetary amount corresponding to the amount of funds deposited by the licensee to the account of the licensee. In an embodiment, the funds management system 13112 may be used to keep the monetary amount of the value of the crypto-currency from the licensee's account until the funds are released by the smart contract.
In an embodiment, ledger administration module 13116 may be configured to receive a smart contract request to create a smart contract that manages licensees' permissions of one or more personality rights of a licensee. In an embodiment, ledger administration module 13116 may be used to generate smart contracts based on smart contract requests. The smart contract may be generated using a smart contract template provided by a third party of interest (e.g., university, regulatory agency, etc.) and one or more parameters provided by a user (e.g., licensor, team of licensor, institution, and/or licensee). As non-limiting examples, the third party of interest may be a university, sports team, or university sports governance organization. The smart contract request may indicate one or more terms including a crypto-currency value amount to be paid to the licensor in exchange for one or more obligations of the licensor. In an embodiment, ledger management module 13116 may be used to deploy intelligent contracts to distributed ledgers. The distributed ledger may be audited by a set of third parties, including interested third parties. The distributed ledger may be a public ledger. The distributed ledger may be a dedicated ledger that is only hosted on computing devices associated with interested third parties. In an embodiment, the distributed ledger may be a blockchain.
In an embodiment, the verification module 13118 may be used to verify that the licensor has fulfilled one or more obligations. In some embodiments, verifying that the licensor has fulfilled one or more obligations may include receiving location data from a wearable device associated with the licensor, and verifying that the licensor has fulfilled the one or more obligations based on the location data, whereby the location may be used to display that the licensor is in a particular location at a particular time (e.g., photographed or photographed). In an embodiment, verifying that the licensor may have fulfilled one or more obligations includes receiving social media data from a social media website and verifying that the licensor has fulfilled one or more obligations based on the social media data, whereby the social media data may be used to indicate that the licensor has made a desired social media posting. In an embodiment, verifying that the licensor may have fulfilled one or more obligations includes receiving media content from an external data source and verifying that the licensor has fulfilled one or more obligations based on the media content, whereby the licensor and/or licensee may upload the media content to prove that the licensor has appeared in the media content. As non-limiting examples, the media content may be one of a video recording, a photo, or an audio recording. In an embodiment, the verification module 13118 may generate and output an event record to the participating node when the verifying licensor has fulfilled its obligation. In an embodiment, the verification module 13118 may generate and output to the participating nodes an event record indicating that the compliance system 13100 has received evidence of evidence (e.g., social media data, location data, and/or media content) that the licensor has fulfilled its obligations. In an embodiment, verification module 13118 may be configured to output an event record to a distributed ledger indicating completion of a licensed transaction defined by a smart contract.
In an embodiment, the verification module 13118 may be configured to verify that the licensor has fulfilled the one or more obligations by the smart contract. In an embodiment, the verification module 13118 and/or the smart contract may be configured to release at least a portion of the crypto-currency value into the licensor account of the licensor in response to receiving a verification that the licensor has fulfilled one or more obligations. Releasing at least a portion of the monetary value to the licensee account of the licensee may include identifying an allocation smart contract associated with the licensee and allocating the monetary value according to allocation rules. As non-limiting examples, additional entities may include one or more of a licensor's teammate, a licensor's coach, a licensor's team, a licensee's university, and a regulatory agency (e.g., NCAA).
In an embodiment, analysis module 13120 may be configured to obtain a set of records from a distributed ledger indicating completion of a set of corresponding transactions. The set of records may include records indicating that a transaction defined by the smart contract has been completed. The analysis module 13120 may determine whether an organization associated with the licensor may violate one or more regulations based on the set of records and the fraud detection model. The fraud detection model may be trained using training data indicating allowed transactions and fraudulent transactions.
In some embodiments, the allocation intelligent contract may define allocation rules that govern the manner in which funds resulting from licensing one or more personality rights will be allocated between the licensor and one or more additional entities.
In some embodiments, the provision may be provided by one of NCAA, FIFA, NBA, MLB, NFL, MLS, NHL, etc., as a non-limiting example.
In some embodiments, computing platform 13902, remote platform 13904, and/or external resources 13934 may be operably linked via one or more electronic communication links. Such an electronic communication link may be established, for example, at least in part via a network such as the internet and/or other networks. It should be understood that this is not intended to be limiting and that the scope of the invention includes embodiments in which computing platform 13902, remote platform 13904, and/or external resources 13934 may be operatively linked via some other communication medium.
A given remote platform 13904 may include one or more processors for executing computer program modules. Computer program modules can be used to enable an expert or user associated with a given remote platform 13904 to interface with the compliance system 13100 and/or external resources 13934, and/or to provide other functionality attributed herein to the remote platform 13904. As non-limiting examples, a given remote platform 13904 and/or a given computing platform 13902 may include one or more of a server, desktop computer, notebook computer, handheld computer, tablet computing platform, netbook, smartphone, game console, and/or other computing platform.
The external resources 13934 may include sources of information external to the compliance system 13100, external entities participating in the compliance system 13100, and/or other resources. In some implementations, some or all of the functionality attributed herein to an external resource 13934 may be provided by a resource included in the compliance system 13100.
The computing platform 202 may include electronic storage 13936, one or more processors 13938, and/or other components. Computing platform 1202 may include a communication line or port to enable information exchange with a network and/or other computing platforms. The illustration of computing platform 13902 in fig. 139 is not limiting. Computing platform 13902 may include a plurality of hardware, software, and/or firmware components that operate together to provide the functionality attributed herein to computing platform 13902. For example, computing platform 13902 may be implemented by a cloud of computing platforms that operate together as computing platform 13902.
Electronic storage 13936 may include non-transitory storage media that electronically store information. The electronic storage media of electronic storage 13936 may include one or both of system memory and/or removable memory integrated with (i.e., substantially non-removable from) computing platform 13902, which may be removably connected to computing platform 13902 by, for example, a port (e.g., a USB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.). The electronic storage 13936 may include one or more optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, hard drive, floppy drive, etc.), electrical charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), and/or other electronically readable storage media. The electronic storage 13936 may include one or more virtual storage resources (e.g., cloud storage, virtual private networks, and/or other virtual storage resources). Electronic storage 13936 may store software algorithms, information determined by processor 13938, information received from computing platform 13902, information received from remote platform 13904, and/or other information that enables computing platform 13902 to operate as described herein.
Processor 13938 may be used to provide information processing capabilities in computing platform 13902. Accordingly, processor 13938 may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information. Although the processor 13938 is shown as a single entity in fig. 139, this is for illustrative purposes only. In some embodiments, the processor 13938 can include a plurality of processing units. These processing units may be physically located within the same device, or the processor 13938 may represent processing functionality of a plurality of devices operating in concert. The processor 13938 can be used to execute the modules 13108, 13112, 13116, 13118, 13120 and/or other modules. The processor 13938 may be used for software; hardware firmware; some combination of software, hardware, and/or firmware; and/or other mechanisms for configuring processing capabilities on the processor 13938, the modules 13108, 13112, 13116, 13118, 13120, and/or other modules. As used herein, the term "module" may refer to any component or collection of components that perform the function attributed to that module. This may include one or more physical processors, processor-readable instructions, circuits, hardware, storage media, or any other component during execution of processor-readable instructions.
It should be appreciated that while modules 13108, 13112, 13116, 13118, and 13120 are illustrated in fig. 139 as being implemented within a single processing unit, in embodiments where processor 13938 includes multiple processing units, one or more of modules 13108, 13112, 13116, 13118, and 13120 may be implemented remotely from the other modules. The description of the functionality provided by the different modules 13108, 13112, 13116, 13118, and 13120 described below is for illustrative purposes, and is not intended to be limiting, as any of modules 13108, 13112, 13116, 13118, and/or 13120 may provide more or less functionality than is described. For example, one or more of modules 13108, 13112, 13116, 13118, and/or 13120 may be eliminated, and some or all of its functionality may be provided by other ones of modules 13108, 13112, 13116, 13118, and/or 13120. As another example, the processor 13938 can be used to execute one or more additional modules that can perform some or all of the functionality attributed below to one of the modules 13108, 13112, 13116, 13118, and/or 13120.
Fig. 140 and/or fig. 141 illustrate an example method 14000 for electronically facilitating licensing of one or more personality rights of a licensor in accordance with some embodiments of the invention. The operation of method 14000 presented below is for illustrative purposes. In some embodiments, the method 14000 may be implemented using one or more additional operations not described and/or without using one or more operations discussed. Furthermore, the order in which the operations of method 14000 are illustrated in fig. 140 and/or fig. 141 and described below is not intended to be limiting.
In some embodiments, the method 14000 may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, digital circuitry designed to process information, analog circuitry designed to process information, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices that perform some or all of the operations of method 14000 in response to instructions stored electronically on an electronic storage medium. The one or more processing devices may include one or more devices configured by hardware, firmware, and/or software, which are specifically designed to perform one or more operations of method 14000.
Fig. 140 illustrates a method 14000 in accordance with one or more embodiments of the invention.
At 14002, the method may include receiving an access request from a licensee to obtain approval of the licensing square rights from a set of available licensees. In accordance with one or more embodiments, operation 14002 may be performed by one or more hardware processors configured in accordance with machine-readable instructions, the one or more hardware processors comprising the same or similar modules as access module 13108.
At 14004, the method includes selectively granting access to the licensee based on the access request. In accordance with one or more embodiments, operation 14004 may be performed by one or more hardware processors configured in accordance with machine-readable instructions, the one or more hardware processors comprising the same or similar modules as access module 13108.
At 14006, the method includes receiving a deposit confirmation of the funds amount from the licensee. In accordance with one or more embodiments, operation 14006 may be performed by one or more hardware processors configured in accordance with machine-readable instructions, the one or more hardware processors comprising the same or similar modules as funds management module 13112.
At 14008, the method includes issuing an encrypted monetary amount corresponding to the amount of funds deposited by the licensee to the account of the licensee. In accordance with one or more embodiments, operation 14008 may be performed by one or more hardware processors configured in accordance with machine-readable instructions, the one or more hardware processors comprising the same or similar modules as funds management module 13112.
Fig. 141 illustrates a method 14100 in accordance with one or more embodiments of the invention.
At 14122, the method includes receiving a smart contract request to create a smart contract that manages a licensee's license for one or more personality rights of the licensee. The smart contract request may indicate one or more terms including a crypto-currency value amount to be paid to the licensor in exchange for one or more obligations of the licensor. In accordance with one or more embodiments, operation 14122 may be performed by one or more hardware processors configured in accordance with machine-readable instructions, including the same or similar modules as ledger administration module 13116.
At 14124, the method includes generating a smart contract based on the smart contract request. In accordance with one or more embodiments, operation 14124 may be performed by one or more hardware processors configured in accordance with machine-readable instructions, including the same or similar modules as ledger administration module 13116.
At 14126, the method includes keeping a monetary value of the cryptocurrency from the account of the licensee. In accordance with one or more embodiments, the operations 14126 may be performed by one or more hardware processors configured in accordance with machine-readable instructions, including the same or similar modules as the funds management module 13112.
At 14128, the method includes deploying an intelligent contract to the distributed ledger. In accordance with one or more embodiments, operation 14128 may be performed by one or more hardware processors configured in accordance with machine-readable instructions, including the same or similar modules as ledger administration module 13116.
At 14130, the method includes verifying, by the smart contract, that the licensor has fulfilled the one or more obligations. In accordance with one or more embodiments, operation 14130 may be performed by one or more hardware processors configured in accordance with machine-readable instructions, including the same or similar modules as authentication module 13118.
At 14132, the method includes releasing at least a portion of the crypto-currency value into a licensor account of the licensor in response to receiving verification that the licensor has fulfilled the one or more obligations. In accordance with one or more embodiments, operation 14132 may be performed by one or more hardware processors configured in accordance with machine-readable instructions, including the same or similar modules as authentication module 13118.
At 14134, the method includes outputting a record to the distributed ledger indicating completion of the license transaction defined by the smart contract. In accordance with one or more embodiments, operation 14134 may be performed by one or more hardware processors configured in accordance with machine-readable instructions, including the same or similar modules as authentication module 13118 and/or ledger management module 13116.
FIG. 142 illustrates a method 14200 in accordance with one or more embodiments.
At 14202, the method includes obtaining a set of records from the distributed ledger indicating that a set of corresponding transactions have been completed. The set of records may include records indicating that a transaction defined by the smart contract has been completed. In accordance with one or more embodiments, operation 14202 may be performed by one or more hardware processors configured in accordance with machine-readable instructions, comprising the same or similar modules as analysis module 13120.
At 14204, the method includes determining, based on the set of records and the fraud detection model, whether an organization associated with the licensor is likely to violate one or more regulations. In accordance with one or more embodiments, operation 14204 may be performed by one or more hardware processors configured in accordance with machine-readable instructions, including the same or similar modules as analysis module 13120.
Referring to fig. 143, a computer-implemented method 14300 for selecting an AI solution for use in a robotic or automated process is shown. The computer-implemented method may include receiving one or more functional media (14302). The functional media may include information indicating brain activity of workers engaged in tasks to be automated. The functional medium may be functional imaging, such as MRI, FMRI, etc., from which the neocortical active region may be identified. The functional media may be images, video streams, audio streams, etc., from which the type of brain activity may be inferred. The functional media may be acquired at the time the worker performs the work or at the time a simulation of the work is performed, such as in an augmented reality, virtual reality environment, or on a model of the device and/or environment. After receipt, the functional media (14304) is analyzed to identify an activity level (14306) in the at least one brain region. Based on the activity level, brain region parameters and/or activity parameters are identified (14308). The brain region parameters may represent specific areas of the neocortex, such as frontal, parietal, occipital and temporal (including primary visual and primary auditory) or subdivision of the neocortex (including ventral prefrontal (cloth Luo Kaou) and orbital prefrontal (prefrontal) the activity parameters may represent functional areas of the brain, such as visual processing, inductive reasoning, audio processing, olfactory processing, muscle control, etc. the activity parameters may represent the type of activity in which the worker is engaged, such as visual processing (watching), audio processing (listening), olfactory processing (sniffing), locomotor activity, hearing the sound of the device, observing another interviewee, etc. the activity level may represent the intensity or level of the activity, such as the extent of the brain region concerned, signal intensity, whether the brain region is engaged, etc.
Based on one or more of the brain region parameters, activity parameters, or activity levels, an action parameter may be identified (14310). The action parameters may provide additional information about the activity parameters. For example, the motion parameters indicate motion, and the motion parameters may describe a range of motion, a speed of motion, a repetition of motion, utilization of muscle memory, smoothness of motion, a flow of motion, timing of motion, and the like. Based on one or more of the brain region parameters, activity parameters, or activity levels, a component to be incorporated into the final AT solution may be selected (14312). The components may include one or more of models, expert systems, neural networks, and the like. After components of the AI solution are selected, configuration parameters may be determined (14314). The configuration parameters may be based in part on the type of component selected, brain region parameters, activity levels, or motion parameters. Configuring and configuring parameters may include selecting inputs of a machine learning process, identifying outputs to be provided by the machine learning process, identifying inputs of an operating solution process (14316), identifying outputs of an operating solution process, adjusting learning parameters, identifying rates of change, identifying weighting factors, identifying parameters for inclusion, identifying parameters for exclusion of parameters, setting thresholds for input data, setting output thresholds for operating a robotic process, or setting parameter thresholds. Further, analyzing the functional media (14304) may include identifying a second brain region parameter or a second activity parameter (14318). The component of the AI solution may be modified based on the second brain region parameter or the second activity parameter (14320). A second component (14322) of the AI solution may be selected based on a second brain region parameter or a second activity parameter. The final AI solution may be assembled from components (14324) or from a second component (14326). In an embodiment, the final AI solution may be assembled from the first component and the second component, optionally with any standard or requisite components to effect operation.
Referring to FIG. 144, a computer-implemented method 14400 for selecting an AI solution for use in a robotic or automated process is shown. The method may include receiving a user-related input (14402) including a timestamp and analyzing the user-related input (14404). The user-related inputs may include audio feeds, motion sensors, video feeds, heartbeat monitors, eye trackers, biosensors (e.g., galvanic skin response), and the like. The analysis may enable identification of a series of user actions and associated activity parameters (14406). A component of the AI solution may be selected (14408) based on a user action in a series of user actions. The analysis may enable identification of a second user action in the series of user actions (14410). Based on the second user action, the selected component (14412) for the AI solution may be modified. A second component for the AI solution may be selected based on a second user action (14414). The action parameters may be identified based on the user action and/or associated activity parameters (14416). For example, if the user action is a motion, the action parameters may include a range of motion, a speed of motion, a repetition of motion, utilization of muscle memory, a smoothness of motion, a flow of motion, timing of motion, and the like. The selected component of the AI solution may be configured based on the action parameters (14418). In an embodiment, at least one device input performed by a user may be received (14420). The device input may be synchronized with the user action based on the timestamp and the correlation between the device input and the determined user action (14419). The component (14423) can be modified based on the correlation. The selection of components of the AI solution may be based in part on a correlation between the device input and the user-related input (14421). AI solutions may be assembled (14422) from components. The AI solution may be assembled (14424) from the second component. In an embodiment, the AI may be assembled from components and a second component, optionally with any standard or requisite components to effect operation.
Referring to fig. 145, an illustrative and non-limiting example of an assembled AI solution 14502 is shown. The assembled AI solution 14502 may include the selected component 14504 and the second selected component 14506, as well as other components 14508. Configuration data 14514 for a first selected component and configuration data 14512 for a second selected component may be provided. The runtime input data 14510 may be designated as part of a component configuration process. The components may be configured to run in series (e.g., the selected component 14504 and the second selected component 14506 receiving input from the selected component 14504) or in parallel (e.g., the second component 14506 and the other components 14508). Some components may provide input to other components (e.g., a selection component 14504 that provides input to a second selection component 14506). Multiple components may provide various portions of the overall AI solution output 14518 (e.g., the second selected component 14506 and other components 14508). The description is not meant to be limiting, and the final solution may include a different number of components, configuration data and inputs, as well as other components (e.g., sensors, voice modulators, etc.), and may be interconnected in various configurations.
Referring to fig. 146-147, a computer-implemented method for selecting an AI solution for a robotic or automated process is shown. The method may include receiving temporal biometric data of a worker performing a task (14602), and receiving spatiotemporal environmental data experienced by the worker performing the task (14604). Using the received data, a spatiotemporal activity pattern may be identified (14606). Based on the spatiotemporal activity pattern, an activity area of the worker's new cortex may be identified (14608). The type of reasoning used when performing the task may be identified based on the active region of the neocortex and/or biometric data or spatiotemporal environmental data (14610). The components used in the AI solution may be selected to replicate the inference type (14612). The components of the AI solution may be configured based on the spatiotemporal environment input (14614). It may be determined whether the serial or parallel AI solution is optimal (14616). A set of configuration inputs of the components may be identified (14618), and an ordered input of the components of the AI solution may be identified (14620). Training the machine may include providing various subsets of the spatiotemporal environmental inputs to determine appropriate input weights and to identify efficiencies from the spatiotemporal environmental input combinations (14622). Desired or undesired combinations of spatiotemporal environmental data may also be identified (14624). Based on the identified desired input, the input environmental data may be processed to reduce input noise (14626) (e.g., improve signal-to-noise ratio of the signal of interest), filtered to provide an appropriate input signal to the component, etc.
With continued reference to fig. 147, second time biometric data of the same worker performing the task may be received (14702) and a plurality of performed tasks identified from the biometric measurements (14704). Performance parameters (14706) may be extracted from biometric measurements (e.g., worker heart rate, galvanic skin response, etc.). In some embodiments, the component (14707) may be configured based on performance parameters. In some embodiments, the second temporal biometric measurement may be provided as a training set to a configuration module (14709). Result data related to the task may be received (14708), and second temporal biometric data may be related to the received result data (14710). In some embodiments, the component (14711) can be selected based at least in part on the correlation. A series of time intervals between each of the plurality of performed tasks may be identified (14712), and components of the AI solution are configured based on at least one of the time intervals (14714). For example, if a worker examines an object for a long time before moving to the next action, this may indicate complex visual processing as well as psychological processing, and may indicate that the corresponding component of the task is for depth, fine detail processing, and the like.
Referring to fig. 141, an AI solution selection and configuration system 14102 is shown. The example selection and configuration system 14102 can include a media input module 14104 configured to receive user-related functional media 14114. The user-related functional media 14114 may include images, recordings, video feeds, biometric data (e.g., heartbeat data, galvanic skin response data, etc.), movement data, etc. of persons engaged in the task to be automated. The media analysis module 14106 may analyze the received media and identify action parameters. The action parameters may represent the type of activity that the person appears to be engaged in, such as viewing, listening, moving, thinking, etc. In some embodiments, the functional media indicates a type of brain activity of a person engaged in a task to be automated, and the media analysis module 141206 identifies an activity level in at least one brain region and provides brain region parameters corresponding to the identified activity level in the brain region. The media analysis module may also identify activity parameters indicating a participation level, such as participation, non-participation, activity level, activity type, and the like. The solution selection module 14108 can be configured to select at least one component of the AI solution for an automated process based at least in part on the motion parameters, brain region parameters, or activity parameters. The brain region parameter or action parameter may suggest the type of component to be selected, and the activity parameter may suggest the level of processing required for that component. For example, the action parameters of the viewing will suggest selecting components suitable for visual processing. If the activity parameter represents an olfactory process, the input specification module may identify at least one chemical sensor as an input. If the activity parameter represents a visual process, the input specification module 13116 may identify at least one visual sensor as a robotic input. In some embodiments, the vision sensor may be selected to be sensitive to a portion of the visible spectrum having wavelengths between about 380 and 700 nanometers. If the activity parameter represents auditory processing, the input specification module 13116 may identify at least one microphone as a robotic input. If the activity parameter represents a very high concentration level, the solution selection module 14108 may suggest a level of treatment that will be needed, a location where treatment may occur, and so forth. The component configuration module 14110 may configure the components 14112. The configuration component may include: selecting an input of a machine learning process for a selected component, identifying an output to be provided by the machine learning process, identifying an input of an operational solution process, identifying an output of an operational solution process, adjusting a learning parameter, identifying a rate of change, identifying a weighting factor, identifying a parameter for inclusion, identifying a parameter for exclusion of the parameter, setting a threshold for input data, setting an output threshold for operating a robotic process, setting a parameter threshold, and the like. The solution assembly module 14118 may assemble a final AI solution based on one or more selected components, configuration components, and a desired runtime. The input specification module 14116 may suggest input sources based on selected components, action parameters, brain region parameters, activity parameters, and the like.
Referring to fig. 149, an AI solution selection and configuration system 14002 is shown. An exemplary selection system 14202 may include an image input module 14904 configured to receive a functional image 1494 of the brain, such as a functional MRI or other magnetic imaging, an electroencephalogram (EEG), or other imaging, for example by identifying a broad range of brain activities (e.g., active bands such as delta, theta, alpha, and gamma waves), by identifying a set of brain regions that have been activated and/or deactivated while a worker is performing one of the tasks to be automated. The image input module 14904 may provide a subset of the functional images 14914 to the image analysis module 14106. In some embodiments, the image input module 14904 may perform some preprocessing, such as noise reduction, histogram adjustment, filtering, etc., on a subset of the functional images 14914 before providing the subset of the functional images 14914 to the image analysis module 1496. The image analysis module 14106 may identify activity levels in at least one brain region and provide brain region parameters based on a subset of the functional images. The brain region parameters may represent specific regions of the neocortex, such as frontal, parietal, occipital and temporal (including primary visual cortex and primary auditory cortex) of the neocortex, or subdivision of the neocortex (including ventral frontal cortex (cloth Luo Kaou) and orbital frontal cortex), the brain region parameters may represent functional regions of the brain, such as visual processing, inductive reasoning, audio processing, olfactory processing, muscle control, etc. the solution selection module 1408 may select components for an AI solution based on brain region parameters and provide inputs to component configuration modules (e.g., select inputs to a machine learning process, identify outputs to be provided by the machine learning process, identify inputs to operate a solution process, identify outputs to operate a solution process, adjust learning parameters, identify rates of change, identify weighting factors, identify parameters for inclusion, identify parameters for exclusion of parameters, set thresholds for input data, set output thresholds for operating a robot process, and set parameter thresholds, etc. the component configuration module 1492 may use inputs to configure components 14912. The solution selection module 1408 may also provide inputs to the components 1496, may be set up the components to be configured as components of the assembly specification, and may be set up as part of the assembly specification, may be an assembly specification may be set up as part of the assembly specification may be an assembly specification may be made
Referring to fig. 150-151, an AI solution selection and configuration system 15002 is shown. The exemplary AI solution selection and configuration system 15002 may include an input module 15004 configured to receive various user-related inputs, such as video, audio recordings, heartbeat monitors, galvanic skin response data, motion data, and the like. There may be time data associated with the user-related input. The input module 15004 may provide a subset of the user-related input data 15014 to the input analysis module 15006. The analysis module 15006 may include a time analysis module 15018 to identify the timing of user-related actions. The time analysis module 15018 may implement timing to identify user actions. In some embodiments, the input module 15004 may perform some preprocessing on a subset of the user-related input data 15014, such as noise reduction, correlation between input data types, etc., before providing the subset of the user-related input data 15014 to the input analysis module 15006. The input analysis module 15006 may identify the type (e.g., visual processing, auditory processing, olfactory processing, motion control, etc.) and activity intensity level of brain activity being engaged in based on data of heartbeat data, galvanic skin response data, and the like. The component selection module 15008 may select components for AI solutions based on the type of brain activity and provide inputs to the component configuration module 15010, which may include an ML input selection module 15102 for selecting inputs to a machine learning process, an MP output identification module 15104 for identifying outputs to be provided by the machine learning process, a runtime input selection module 15106 for identifying inputs to operate the solution process, a runtime output identification module 15108 for identifying outputs to the components, a setting module 15110 for identifying rates of change, identifying weighting factors, setting thresholds for input data, setting output thresholds for operating the robotic process, etc., a parameter setting module 15112 for adjusting learning parameters, identifying parameters to be included, identifying parameters to be excluded, setting parameter thresholds, etc. The component configuration module 15010 may configure selected components 15012. The component selection module 15008 may also provide data to the input specification module 15016. The AI solution assembly module 15020 may combine the configured components with other components and any standard or requisite components to create an AI solution. The AI solution may be configured to receive an input specified by the input specification module 15016. Although one iteration of selecting components is shown in this figure, it is contemplated that multiple components may be selected, configured, and assembled as part of an AI solution.
In an embodiment, referring to fig. 152, an AI solution selection and configuration system 15202 is shown. The exemplary AI solution selection and configuration system 15202 may include a data input module 15204 for receiving an input stream including temporary user-related data 15214, which temporary user-related data 15214 may include video streams, audio streams, device interactions (e.g., mouse clicks, mouse movements, physical inputs to a machine), user biometric features such as heart beat, galvanic skin response, eye tracking, and the like. The data input module 15204 may also receive temporal environment input data 15220 representing environmental inputs being received by a user, such as visual environments, auditory environments, olfactory environments, device displays, device user interfaces, and the like. The data input module 15204 may also receive time result input data 15203. The data input module 15204 may provide a subset of the received data 15214, 15220, 15203 to the input analysis module 15216. The data input module 15204 may process the received data 15214, 15220, 15203 to reduce noise, compress data, correlate some data, and the like. The analysis module 15216 may identify a plurality of user actions to be provided to the component selection module 15208. The image analysis module 15216 may include a time analysis module 15218 to identify the timing of user actions. The temporal analysis module 15218 may allow correlation between temporal user-related data 15214, environmental data 15220, and result data 15203. Based on the user actions, the component selection module 15208 can select components that will simulate one or more mental processes of the user required to perform at least one of the plurality of user actions. Factors identifying the selected component may include a desired level of computational intensity, time sensitivity, and the like. This may specify the type of component, the location of the component (in-vehicle, in the cloud, edge computing, etc.). The input analysis module 15216 may also provide information about user actions and environmental data to the component configuration module 15210. This data may be used by the component configuration module along with the result data as inputs to the machine learning algorithm to identify which inputs are advantageous and which inputs are disadvantageous, to enable the component to achieve the desired result, and to identify appropriate weighting of inputs, parameter settings, and the like. The component configuration module 15210 configures the component 15212, and the component 15212 is provided to the entire AI solution 15224 along with configuration information.
As described elsewhere herein, the present invention relates to systems and methods for discovering solutions to increased opportunities for automation and intelligence, including domain-specific problems. Furthermore, the present invention relates to the selection and configuration of artificial intelligence solutions (e.g., neural networks, machine learning systems, expert systems, etc.) after finding opportunities.
Referring now to FIG. 153, the controller 15308 includes an opportunity mining module 153, an artificial intelligence configuration module 15304, and an artificial intelligence search engine 15310, optionally with a collaborative filter 15328 and a clustering engine 15330. The opportunity mining module 153 receives input 15302, such as attribute input regarding attributes of a task, domain, or domain-related problem.
Input 15302 may be processed by opportunity mining module 153 to determine whether the artificial intelligence system is applicable to a task or domain. For example, attribute inputs 15302 may include attributes of tasks, domains, or questions, such as negotiation tasks, drafting tasks, data input tasks, email response tasks, data analysis tasks, document review tasks, device operation tasks, prediction tasks, NLP tasks, image recognition tasks, pattern recognition tasks, motion detection tasks, route optimization tasks, and the like. The opportunity mining module 153 may determine whether one or more attributes of a task are similar to other tasks that have been automated or that have applied intelligence, or whether a task is potentially automatable or suitable for application intelligence based on attributes of the task, regardless of whether it has been completed before. For example, attributes of the drafting task may include elucidating a first idea, elucidating a second idea, elucidating a plurality of ideas, combining the plurality of ideas in groups of two, and combining the ideas in groups of three. The task of expressing ideas may not be applicable to automation, but combining ideas in groups of two or three may be applicable to automation or application intelligence.
If it is determined that the artificial intelligence system is applicable to a task or domain, an output 15312 regarding the determination may be used to trigger the artificial intelligence search engine 15310 to perform a search of the artificial intelligence memory 157. The artificial intelligence memory 157 may include a plurality of domain-specific and general artificial intelligence models 15318, and components 15318 of domain-specific and general artificial intelligence models. The artificial intelligence memory 157 may be organized by category. The category may be at least one of an artificial intelligence model component type, a domain, an input type, a processing type, an output type, a computational demand, a computational capability, a cost, a training state, or an energy use. The artificial intelligence memory may include at least one electronic commerce feature. The at least one e-commerce feature may include at least one of a rating, comment, link to related content, provisioning mechanism, licensing mechanism, delivery mechanism, or payment mechanism. Model 15318 may be pre-trained or may be used for training. The components of the domain-specific and generic artificial intelligence model 15318 may include artificial intelligence building blocks, such as components that detect and translate languages, or components that provide highly personalized customer recommendations. One or more models 15318 and/or components of models 15318 may be identified in a search of artificial intelligence memory 157. The components of the model 15318 may be identified as separate elements for use in custom AI model 15318 assembly, or as complete, optionally pre-trained, model 15318 components.
The artificial intelligence memory 157 may include metadata 15324 or other descriptive material indicating the applicability of the artificial intelligence system to at least one of address a particular type of problem or to operate on domain-specific inputs, data, or other entities. The metadata 15324 or other descriptive materials, categories, or e-commerce features may be searched using the attribute inputs 15302 and/or other selection criteria 15314. For example, the artificial intelligence memory 157 and its metadata 15324 may be searched for attributes of tasks related to 2D object classification to reveal that the artificial intelligence model 15318 suitable for tasks related to 2D object classification may be a convolutional neural network. Continuing with this example, model diversity may exist even in Convolutional Neural Network (CNN) classes in artificial intelligence memory 157, such as a CNN calibrated to a particular type of 2D object recognition (e.g., straight edge) and another CNN calibrated to another 2D object recognition (e.g., a combination of curved and straight edges). In this example, if another edge and bending attribute of the 2D object type is searched, the artificial intelligence memory 157 will present the CNN most appropriate for the 2D object to be classified.
In an embodiment, in addition to input 15302, artificial intelligence search engine 15310 can use at least one selection criterion 15314 to search artificial intelligence models 15318 and/or components thereof in artificial intelligence memory 157. Selection criteria used in recommending artificial intelligence models 15318 or model components may include at least one of the following: whether the model is pre-trained, availability of at least one artificial intelligence model 15318 or model component for execution in a user environment, availability of at least one artificial intelligence model 15318 or model component to a user, governance guidelines, governance policies, computational factors, network factors, data availability, task specific factors, performance factors, quality of service factors, model deployment considerations, safety considerations, or human-machine interfaces, as may be described elsewhere herein. For example, governance guidelines, such as requirements for anti-bias scrutiny of pedestrian accident avoidance systems, may be used to search artificial intelligence memory 157 for artificial intelligence models applied to autopilot tasks. In another example, the selection criteria for an artificial intelligence solution to be used with an air traffic control system may be a requirement that resistance attacks and spoofing input training have been accepted. In yet another example, the selection criteria for an artificial intelligence solution to be used for a stock exchange task may be human supervision, in particular a requirement based on a final decision of a human.
The artificial intelligence search engine 15310 can rank the one or more results of the search according to the dominance or disadvantance of at least one artificial intelligence model 15318 or model component with respect to at least one selection criterion 15314. The ranked search results may be presented to the user for evaluation and consideration, and ultimately selected. In an embodiment, the artificial intelligence search engine 15310 may also include a collaborative filter 15328, the collaborative filter 15328 receiving an indication of at least one element of the artificial intelligence model 15318 or model component from a user, the indication for filtering search results. In an embodiment, the artificial intelligence search engine 15310 may also include a clustering engine 15330, the clustering engine 15330 configured to cluster search results including at least one artificial intelligence model 15318 or model component. The clustering engine 15330 may be at least one of a similarity matrix or k-means clustering. The clustering engine 15330 may associate at least one of similar developers, similar domain-specific questions, or similar artificial intelligence solutions in the search results.
Once the artificial intelligence search engine 15310 identifies the artificial intelligence model 15318 or components thereof by searching using the input 15302 alone or through both the input 15302 and the selection criteria 15314, the artificial intelligence configuration module 15304 can configure one or more data inputs 15320 for use with at least one artificial intelligence model 15318 or model component. In some embodiments, the artificial intelligence configuration module 15304 may be used to discover and select which inputs 15320 may enable artificial intelligence to be effectively and efficiently used for a given problem. In an embodiment, the artificial intelligence configuration module 15304 can further configure at least one artificial intelligence model 15318 or model component in accordance with at least one configuration standard 15322. In embodiments, a single data input and model component may be configured via one or more configuration criteria, while in other embodiments a single configuration criteria governs the configuration of data inputs, AI component assemblies, etc.
In an embodiment, the at least one configuration standard 15322 may include at least one of: the availability of at least one artificial intelligence model 15318 or model component to execute in a user environment, the availability of at least one artificial intelligence model 15318 or model component to a user, governance guidelines, governance policies, computing factors, network factors, data availability, task specific factors, performance factors, quality of service factors, model deployment considerations, safety considerations, or human-machine interfaces. In an embodiment, the at least one configuration criterion may include at least one of: identifying desired outputs, identifying training data, identifying parameters for exclusion or inclusion in training or operation of a model, inputting data thresholds, outputting data thresholds, selection of neural network types, selection of input model types, setting of initial model weights, setting of model sizes, selection of computing deployment environments, selection of input data sources for training, selection of input data sources for operation, selection of feedback functions/outcome metrics, selection of data integration languages for input and output, configuration of APIs 13114 for model training, configuration of APIs 13114 for model input, setting of APIs for output, configuration of access control, configuration of security parameters, configuration of network protocols, configuration of storage parameters, configuration of economic factors, configuration of data flows, configuration of high availability, one or more fault tolerant environments, price-based data acquisition strategies, heuristics, decision-making decisions for decision-making models, or coordination of massively parallel decision-making environments. In an embodiment, the at least one configuration criterion may include parameters for assembling the AI solution from the plurality of identified model components, optionally together with other criteria or requisite model components. For example, model components may be used for parallel operation, serial operation, or a combination of serial and parallel.
For example, the artificial intelligence configuration module 15304 can configure the artificial intelligence model 15318 such that one data input 15320 is weighted more than another. For example, in rain, an autopilot solution may weigh inputs from a traction control system and a forward radar system more than sensors targeted to improve fuel efficiency (e.g., sensors measuring road grade and vehicle speed). After rain, the weights may be reversed.
In another example, the artificial intelligence configuration module 15304 can configure the artificial intelligence model 15318 to operate within certain thresholds of the data input 15320. For example, the artificial intelligence model 15318 can be used to combine drawing tasks. When only two clear ideas are provided to the model 15318, the model 15318 may not be triggered to run. However, once the model 15318 receives the third clear idea, the combining process of its clear ideas may begin.
The artificial intelligence configuration module 15304 can configure which sensors are used as data inputs 15320, the frequency of sampling data, the frequency of transmitting output, the weighting of various data inputs 15320, thresholds applied to data from the data inputs 15320, whether to use the output of one component of the model 15318 as an input to another component of the model 15318, the order of operation of components of the model 15318, the positioning of model components within the model workflow, and the like.
The artificial intelligence configuration module 15304 can configure the artificial intelligence model 15318 in accordance with one or more model components identified by the artificial intelligence search engine 15310. For example, if the search results include only model components, AI configuration module 15304 can configure the locations where the identified 127 components are placed relative to each other, such as in a workflow or data stream, and other components that may be needed to run relative to model 15318.
In an embodiment, the artificial intelligence memory 157 may include a set of interfaces to the artificial intelligence system, such as enabling the downloading of related artificial intelligence applications, establishing links or other connections to the artificial intelligence system (e.g., links to cloud-deployed artificial intelligence systems through APIs, ports, connectors, or other interfaces), and so forth.
Referring now to FIG. 154, a method of artificial intelligence model identification and selection may include receiving input regarding attributes of a task or domain (15402) and processing the input to determine if an artificial intelligence system can be applied to the task or domain (15404); performing a search of an artificial intelligence memory of a plurality of domain-specific and general artificial intelligence models and model components using the input and at least one selection criterion to identify at least one of an artificial intelligence model or model component to be applied to a task or domain (15408); one or more data inputs are configured for the at least one artificial intelligence model or model component (15410). The artificial intelligence memory may include metadata or other descriptive material indicating the applicability of the artificial intelligence system to at least one of solve a particular type of problem or operate on domain-specific inputs, data, or other entities.
The method may also include ranking 15412 one or more results of the search according to a dominance or disadvantage of the at least one artificial intelligence model relative to the at least one selection criterion. The method may also include configuring at least one artificial intelligence model or model component (15414) according to at least one configuration criteria. The method may also include collaborative filtering search results including the at least one artificial intelligence model using elements of the at least one artificial intelligence model or model component selected by the user (15416). The method may also include clustering (15418) search results including the at least one artificial intelligence model or model component using a clustering engine.
Fig. 155 illustrates an exemplary environment for a digital twinning system 15500. In an embodiment, the digital twinning system 15500 generates a set of digital twinning of the set of industrial environments 15520 and/or the industrial entities in the set of industrial environments. In an embodiment, the digital twinning system 15500 uses sensor data or the like acquired from the respective sensor system 15530 monitoring the industrial environment 15520 to maintain a set of states of the respective industrial environment 15520. In an embodiment, the digital twinning system 15500 can include a digital twinning management system 15502, a digital twinning I/O system 15504, a digital twinning simulation system 15506, a digital twinning dynamic model system 15508, a cognitive intelligence system 15510, and/or an environmental control module 15512. In an embodiment, digital twinning system 15500 can provide a real-time sensor API that provides a set of capabilities for enabling a set of interfaces for sensors of a respective sensor system 15530. In embodiments, digital twin system 15500 may include and/or employ other suitable APIs, agents, connectors, bridges, gateways, hubs, ports, routers, switches, data integration systems, peer systems, etc. to facilitate the transfer of data to and from digital twin system 15500. In these embodiments, these connection components may allow IoT sensors or intermediate devices (e.g., relays, edge devices, switches, etc.) in the sensor system 15530 to transmit data to the digital twin system 15500 and/or receive data (e.g., configuration data, control data, etc.) from the digital twin system 15500 or other external systems. In an embodiment, the digital twinning system 15500 can also include a digital twinning data store 15516 that stores digital twinning 15518 of various industrial environments 15520, as well as objects 15522, devices 15524, sensors 15526, and/or humans 15528 in the environment 15520.
Digital twinning may refer to a digital representation of one or more industrial entities, such as industrial environment 15520, physical object 15522, device 15524, sensor 15526, human 15528, or any combination thereof. Examples of industrial environments 15520 include, but are not limited to, factories, power plants, food production facilities (which may include inspection facilities), commercial kitchens, indoor planting facilities, natural resource excavation sites (e.g., mines, oil fields, etc.), and the like. The types of objects, devices and sensors found in an environment will also vary depending on the type of environment. Non-limiting examples of physical objects 15522 include raw materials, manufactured products, excavated material, containers (e.g., boxes, garbage cans, cooling towers, vats, trays, drums, boxes, etc.), furniture (e.g., tables, counters, workstations, shelves, etc.), and the like. Non-limiting examples of the device 15524 include robots, computers, vehicles (e.g., automobiles, trucks, tank trucks, trains, forklifts, cranes, etc.), machines/devices (e.g., tractors, tillers, drills, presses, assembly lines, conveyors, etc.), and the like. The sensor 15526 can be any sensor device and/or sensor aggregation device found in the sensor system 15530 in the environment. Non-limiting examples of sensors 15526 that may be implemented in the sensor system 15530 may include temperature sensors 15532, humidity sensors 15534, vibration sensors 15536, LIDAR sensors 15538, motion sensors 15540, chemical sensors 15542, audio sensors 15544, pressure sensors 15546, weight sensors 15548, radiation sensors 15550, video sensors 15552, wearable devices 15554, relays 15556, edge devices 15558, cross-point switches 15560, and/or any other suitable sensors. Examples of different types of physical objects 15522, devices 15524, sensors 15526, and environments 15520 are referenced herein.
In some embodiments, device-in-sensor fusion and data storage for industrial IoT devices is supported, including device-in-sensor fusion and data storage for industrial IoT devices, wherein data from multiple sensors is multiplexed in a device for storing a fused data stream. For example, in a byte-like structure (where time, pressure, and temperature are bytes in a data structure such that pressure and temperature remain associated in time without requiring separate processing of the streams by an external system), or by addition, division, multiplication, subtraction, etc., pressure and temperature data may be multiplexed into a data stream that combines pressure and temperature in a time series such that the fused data may be stored on the device. Any of the sensor data types described throughout the present disclosure (including vibration data) may be fused in this manner and stored in a local data pool, memory, or on an IoT device, such as a data collector, machine component, or the like.
In some embodiments, a set of digital twins may represent an entire organization, such as an energy production organization, an oil and gas organization, a renewable energy production organization, an aerospace manufacturer, a vehicle manufacturer, a heavy equipment manufacturer, a mining organization, a drilling organization, an offshore platform organization, and the like. In these examples, digital twinning may include digital twinning of one or more industrial facilities of the organization.
In an embodiment, digital twin management system 15502 generates digital twin. Digital twinning may include (e.g., by reference) other digital twinning. In this way, discrete digital twinning may include a set of other discrete digital twinning. For example, digital twinning of a machine may include digital twinning of sensors on the machine, digital twinning of components that make up the machine, digital twinning of other devices incorporated in or integrated with the machine (e.g., a system that provides input to or takes output from the machine), and/or digital twinning of products or other items manufactured by the machine. Still further to this example, digital twinning of an industrial facility (e.g., a plant) may include digital twinning that represents the layout of the industrial facility, including the placement of physical assets and systems within or around the facility, as well as digital assets of assets within the facility (e.g., digital twinning of machines), as well as digital twinning of storage areas within the facility, digital twinning of humans collecting vibration measurements from machines of the entire facility, and so forth. In this second example, digital twinning of an industrial facility may refer to embedded digital twinning, and then other digital twinning embedded in these digital twinning may be referred to.
In some embodiments, digital twinning may represent an abstract entity, such as a workflow and/or process, including inputs, outputs, sequences of steps, decision points, processing loops, etc., that make up such a workflow and process. For example, digital twinning may be a digital representation of a manufacturing process, a logistics workflow, an agricultural process, or a mineral extraction process, among others. In these embodiments, the digital twinning may include a reference to an industrial entity contained in a workflow or process. Digital twinning of a manufacturing process may reflect various stages of the process. In some of these embodiments, the digital twinning system 15500 receives real-time data from an industrial facility (e.g., from the sensor system 15530 of the environment 15520) where the manufacturing process occurs and reflects the current (or substantially current) state of the process in real-time.
In an embodiment, the digital representation may include a set of data structures (e.g., categories) that collectively define a set of attributes of the physical object 15522, device 15524, sensor 15526, or environment 15520 of the representation and/or its possible behavior. For example, the set of properties of the physical object 15522 may include a type of the physical object, a size of the object, a mass of the object, a density of the object, one or more materials of the object, physical characteristics of the one or more materials, a surface of the physical object, a state of the physical object, a location of the physical object, an identifier of other digital twins contained in the object, and/or other suitable properties. Examples of behavior of a physical object may include a state of the physical object (e.g., solid, liquid, or gas), a melting point of the physical object, a density of the physical object when in a liquid state, a viscosity of the physical object when in a liquid state, a freezing point of the physical object, a density of the physical object when in a solid state, a hardness of the physical object when in a solid state, a ductility of the physical object, a buoyancy of the physical object, a conductivity of the physical object, a burning point of the physical object, a manner of influence of humidity on the physical object, a manner of influence of water or other liquid on the physical object, a terminal velocity of the physical object, and so forth. In another example, a set of properties of a device may include a type of the device, a size of the device, a mass of the device, a density of the device, one or more materials of the device, a physical property of the one or more materials, a surface of the device, an output of the device, a state of the device, a location of the device, a trajectory of the device, a vibration characteristic of the device, an identifier of the device connection and/or other digital twinning involved, and so forth. Examples of behavior of a device may include maximum acceleration of the device, maximum velocity of the device, range of motion of the device, heating profile of the device, cooling profile of the device, processes performed by the device, operations performed by the device, and so forth. Exemplary properties of an environment may include the size of the environment, the boundaries of the environment, the temperature of the environment, the humidity of the environment, the airflow of the environment, physical objects in the environment, the flow of water (if a body of water) of the environment, and so forth. Examples of the behavior of an environment may include scientific laws governing the environment, processes performed in the environment, rules or regulations that must be complied with in the environment, and the like.
In an embodiment, the properties of digital twinning may be adjusted. For example, the temperature of the digital twin, the humidity of the digital twin, the shape of the digital twin, the material of the digital twin, the dimensions of the digital twin, or any other suitable parameter may be adjusted. As the properties of the digital twinning are adjusted, other properties may also be affected. For example, if the temperature of the environment 15520 increases, the pressure in the environment may also increase, such as a gas pressure according to the ideal gas law. In another example, if the digital twinning of a sub-zero environment is warmed to an above-zero temperature, the embedded twinning properties of solid water (i.e., ice) may become liquid over time.
Digital twinning can take many different forms. In an embodiment, the digital twinning may be visual digital twinning presented by a computing device such that a human user may view the environment 15520 and/or a digital representation of physical objects 15522, devices 15524, and/or sensors 15526 in the environment. In an embodiment, the digital twinning may be presented and output to a display device. In some of these embodiments, the digital twinning may be presented in a graphical user interface so that a user may interact with the digital twinning. For example, a user may "get in-depth" of a particular element (e.g., a physical object or device) to view additional information for the element (e.g., a state of the physical object or device, properties of the physical object or device, etc.). In some embodiments, the digital twinning may be presented and output in a virtual reality display. For example, a user may view a 3D presentation of the environment (e.g., using a display or virtual reality headset). While doing so, the user may view/check the digital twinning of physical assets or devices in the environment.
In some embodiments, the data structure of the visual digital twinning (i.e., digital twinning configured to be displayed in 2D or 3D) may include a surface (e.g., spline, mesh, polygonal mesh, etc.). In some embodiments, the surface may include texture data, shading information, and/or reflection data. In this way, the surface can be displayed in a more realistic manner. In some embodiments, such surfaces may be presented by a visualization engine (not shown) when the digital twinning is within the field of view and/or when present in a larger digital twinning (e.g., a digital twinning of an industrial environment). In these embodiments, digital twinning system 15500 may present surfaces of a digital object, whereby the digital twinning presented may be described as a set of adjacent surfaces.
In an embodiment, a user may provide input through a graphical user interface that controls one or more properties of a digital twin. For example, a user may provide input that alters the properties of a digital twin. In response, digital twinning system 15500 may calculate the effect of the changed attribute and may update the digital twinning and any other digital twinning affected by the attribute change.
In an embodiment, a user may view a process performed for one or more digital twins (e.g., manufacturing a product, extracting minerals from a mine or well, livestock inspection line, etc.). In these embodiments, the user may view the entire process or specific steps in the process.
In some embodiments, digital twinning (and any digital twinning embedded therein) may be represented in a non-visual representation (or "data representation"). In these embodiments, the digital twinning and any embedded digital twinning exist in a binary representation, but the relationship between the digital twinning is maintained unchanged. For example, in an embodiment, each digital twin and/or component thereof may be represented by a set of physical dimensions defining the shape of the digital twin (or component thereof). Further, the data structure embodying the digital twin may include a location of the digital twin. In some embodiments, the digitally twinned position may be provided using a set of coordinates. For example, digital twinning of an industrial environment can be defined for a coordinate space (e.g., cartesian coordinate space, polar coordinate space, etc.). In an embodiment, the embedded digital twinning may be represented as a set of one or more ordered triples (e.g., [ x-coordinate, y-coordinate, z-coordinate ] or other vector-based representation). In some of these embodiments, each ordered triplet may represent a location of a particular point (e.g., a center point, a vertex, a nadir, etc.) on an industrial entity (e.g., an object, device, or sensor) relative to an environment in which the industrial entity is located. In some embodiments, the data structure of the digital twin may include a vector that indicates the motion of the digital twin relative to the environment. For example, a fluid (e.g., liquid or gas) or solid may be represented by a vector that indicates the velocity (e.g., direction and magnitude of velocity) of an entity represented by a digital twin. In an embodiment, the vector in twinning may represent a microscopic sub-assembly, such as a particle in a fluid; digital twinning may represent physical properties such as displacement, velocity, acceleration, momentum, kinetic energy, vibration properties, thermal properties, electromagnetic properties, etc.
In some embodiments, a set of two or more digital twins may be represented by a graph database that includes nodes and edges connecting the nodes. In some implementations, edges may represent spatial relationships (e.g., "adjoining," "attached," "including," etc.). In these embodiments, each node in the graph database represents a digital twin of an entity (e.g., an industrial entity) and may include a data structure defining the digital twin. In these embodiments, each edge in the graph database may represent a relationship between two entities represented by connected nodes. In some embodiments, edges may represent spatial relationships (e.g., "adjoining," "attached," "joined," "having," "including," etc.). In embodiments, various types of data may be stored in nodes or edges. In an embodiment, a node may store attribute data, status data, and/or metadata related to a facility, system, subsystem, and/or component. The types of attribute data and state data may vary based on the entity represented by the node. For example, a node representing a robot may include attribute data indicating the material of the robot, the size of the robot (or its components), the mass of the robot, etc. In this example, the state data of the robot may include a current pose of the robot, a position of the robot, and the like. In an embodiment, an edge may store relationship data and metadata related to a relationship between two nodes. Examples of relationship data may include the nature of the relationship, whether the relationship is a permanent relationship (e.g., a fixed component will have a permanent relationship with the structure to which it is attached or attached), and so forth. In an embodiment, an edge may include metadata about a relationship between two entities. For example, if a product is produced on an assembly line, one relationship between product digital twinning that can be recorded and the assembly line may be "creation mode". In these embodiments, an exemplary edge representing a "creation style" relationship may include a timestamp indicating the date and time of product creation. In another example, a sensor may make measurements related to the status of a device, whereby one relationship between the sensor and the device may include "measured" and may define the type of measurement measured by the sensor. In this example, the metadata stored in the edge may include a list of N measurements taken and a timestamp for each respective measurement. In this way, temporal data relating to the nature of the relationship between two entities may be maintained, allowing an analysis engine, machine learning engine, and/or visualization engine to utilize such temporal relationship data, such as by aligning different data sets with a series of points in time, for example, to facilitate causal analysis for a predictive system.
In some embodiments, the graphic database may be implemented in a hierarchical manner such that the graphic database relates to a set of facilities, systems, and components. For example, a digital twin of a manufacturing environment may include nodes representing the manufacturing environment. The graphic database may also include nodes representing various systems in the manufacturing environment, such as nodes representing HVAC systems, lighting systems, manufacturing systems, etc., all of which may be connected to nodes representing the manufacturing systems. In this example, each system may also be connected to various subsystems and/or components of the system. For example, in an HVAC system, the HVAC system may be connected to a subsystem node representing a cooling system of a facility, a second subsystem node representing a heating system of the facility, a third subsystem node representing a fan system of the facility, and one or more nodes representing a thermostat (or thermostats) of the facility. Further implementing the example, the subsystem node and/or the component node may be connected to a lower level node, which may include the subsystem node and/or the component node. For example, a subsystem node representing a cooling subsystem may be connected to a component node representing an air conditioning unit. Similarly, a component node representing a thermostat device may be connected to one or more component nodes representing various sensors (e.g., temperature sensors, humidity sensors, etc.).
In embodiments implementing a graphic database, the graphic database may involve a single environment or may represent a larger enterprise. In the latter case, the company may have various manufacturing and distribution facilities. In these embodiments, an enterprise node representing an enterprise may be connected to an environmental node of each respective facility. In this way, digital twinning system 15500 can maintain digital twinning for multiple industrial facilities of an enterprise.
In an embodiment, digital twin system 15500 may use a graphic database to generate digital twin that may be presented and displayed and/or may be represented in a data representation. In the former case, digital twinning system 15500 may receive a request to present digital twinning, whereby the request includes one or more parameters indicating the view to be shown. For example, the one or more parameters may indicate an industrial environment and a presentation type to be shown (e.g., a "real world view" showing the environment in a manner that is viewable by humans, an "infrared view" showing objects as a function of their respective temperatures, an "airflow view" showing airflow in a digital twin, etc.). In response, digital twinning system 15500 may traverse a graph database and may determine a configuration of an environment to be shown based on nodes in the graph database that are related to environmental nodes of the environment (directly or through lower level nodes) and edges defining relationships between the related nodes. In determining the configuration, the digital twin system 15500 may identify the surfaces to be shown and may present those surfaces. The digital twinning system 15500 may then render the requested digital twinning by connecting surfaces according to the configuration. The digital twinning of the presentation may then be output to a viewing device (e.g., VR headset, display, etc.). In some cases, the digital twinning system 15500 can receive real-time sensor data from the sensor system 15530 of the environment 15520 and can update the visual digital twinning based on the sensor data. For example, the digital twinning system 1550 may receive sensor data (e.g., vibration data from the vibration sensor 15536) related to the motor and its set of bearings. Based on the sensor data, the digital twinning system 15500 may update the visual digital twinning to indicate the approximate vibration characteristics of the set of bearings in the digital twinning of the motor.
Where digital twin system 15500 provides a digital twin data representation (e.g., for dynamic modeling, simulation, machine learning), digital twin system 15500 may traverse a graph database and may determine a configuration of an environment to be shown based on nodes in the graph database that are related to the environment nodes of the environment (directly or through lower level nodes) and edges defining relationships between the related nodes. In some cases, the digital twinning system 15500 can receive real-time sensor data from the sensor system 15530 of the environment 15520 and can apply one or more dynamic models to the digital twinning based on the sensor data. In other cases, a digital twin data representation may be used to perform the simulation, as discussed in more detail in this specification.
In some embodiments, the digital twinning system 15500 may perform digital ghosting of digital twinning execution with respect to an industrial environment. In these embodiments, the digital ghost may monitor one or more sensors of the sensor system 15530 of the industrial environment to detect anomalies that may indicate malicious viruses or other security issues.
As discussed, the digital twinning system 15500 can include a digital twinning management system 15502, a digital twinning I/O system 15504, a digital twinning simulation system 15506, a digital twinning dynamic model system 15508, a cognitive intelligence system 15510, and/or an environmental control system 15512.
In an embodiment, the digital twinning management system 15502 creates new digital twinning, maintains/updates existing digital twinning, and/or presents digital twinning. The digital twinning management system 15502 may receive user input, uploaded data, and/or sensor data to create and maintain existing digital twinning. Upon creation of the new digital twin, the digital twin management system 15502 may store the digital twin in the digital twin data store 15516. Digital twin creation, updating, and rendering are discussed in more detail in this disclosure.
In an embodiment, digital twin I/O system 15504 receives input from various sources and outputs data to various recipients. In an embodiment, the digital twin I/O system receives sensor data from one or more sensor systems 15530. In these embodiments, each sensor system 15530 may include one or more IoT sensors that output corresponding sensor data. Each sensor may be assigned an IP address or may have other suitable identifiers. Each sensor may output a sensor data packet including a sensor identification and sensor data. In some embodiments, the sensor data packet may also include a timestamp indicating the sensor data collection time. In some embodiments, digital twin I/O system 15504 can interface with sensor system 15530 through real-time sensor API 15514. In these embodiments, one or more devices (e.g., sensors, aggregators, edge devices) in the sensor system 15530 may send sensor data packets containing sensor data to the digital twin I/O system 15504 through an API. The digital twin I/O system may determine the sensor system 15530 that sent the sensor data package and its contents and may provide the sensor data and any other relevant data (e.g., time stamp, environment identifier/sensor system identifier, etc.) to the digital twin management system 15502.
In an embodiment, digital twin I/O system 15504 may receive imported data from one or more sources. For example, digital twinning system 15500 may provide a portal for users to create and manage their digital twinning. In these embodiments, the user may upload one or more files (e.g., image files, LIDAR scans, blueprints, etc.) related to the new digital twinning being created. In response, digital twin I/O system 15504 may provide imported data to digital twin management system 15502. Digital twin I/O system 15504 may receive other suitable types of data without departing from the scope of the present invention.
In some embodiments, digital twin analog system 15506 is used to perform the simulation using digital twin. For example, the digital twinning analog system 15506 may iteratively adjust one or more parameters of the digital twinning and/or one or more embedded digital twinning. In an embodiment, the digital twin simulation system 15506 performs a simulation based on each set of parameters and may collect simulation result data generated by the simulation. In other words, the digital twinning simulation system 15506 may collect the digital twinning used during the simulation and the properties of the digital twinning within or containing the digital twinning and any results produced by the simulation. For example, when running the simulation on digital twinning of an indoor agricultural facility, the digital twinning simulation system 15506 may change temperature, humidity, airflow, carbon dioxide, and/or other related parameters, and may perform the simulation that outputs results produced by different combinations of parameters. In another example, the digital twin simulation system 15506 can simulate the operation of a particular machine in an industrial facility that produces an output given a set of inputs. In some embodiments, the input may be changed to determine the effect of the input on the machine and its output. In another example, the digital twin simulation system 15506 can simulate vibration of a machine and/or machine components. In this example, the digital twinning of the machine may include a set of operating parameters, interfaces, and capabilities of the machine. In some embodiments, operating parameters may be changed to assess the effectiveness of the machine. The digital twin analog system 15506 is discussed in more detail in this disclosure.
In an embodiment, the digital twinning dynamic model system 15508 is used to model one or more behaviors for digital twinning of an environment. In an embodiment, the digital twin dynamic model system 15508 may receive a request to model a particular type of behavior with respect to an environment or process, and may use the dynamic model, digital twin of the environment or process, and sensor data collected from one or more sensors monitoring the environment or process to model the behavior. For example, an operator of a machine having a bearing may wish to model the vibrations of the machine and the bearing to determine whether the machine and/or the bearing can withstand an increase in output. In this example, the digital twin dynamic model system 15508 can execute a dynamic model for determining whether an increase in output can lead to adverse consequences (e.g., failure, downtime, etc.). The digital twin dynamic model system 15508 is discussed in more detail in this disclosure.
In an embodiment, the cognitive process system 15510 performs machine learning and artificial intelligence related tasks on behalf of a digital twin system. In embodiments, the cognitive process system 15510 may train any suitable type of model including, but not limited to, various types of neural networks, regression models, random forests, decision trees, hidden markov models, bayesian models, and the like. In an embodiment, the cognitive process system 15510 uses the analog output performed by the digital twin analog system 15506 to train a machine learning model. In some of these embodiments, simulation results may be used to supplement training data collected from the real world environment and/or process. In an embodiment, the cognitive process system 15510 utilizes machine learning models to predict, identify, classify, and provide decision support related to the real world environment and/or process represented by the corresponding digital twinning.
For example, a machine learning predictive model may be used to predict the cause of irregular vibration patterns (e.g., sub-optimal, critical, or alert vibration fault conditions) of engine bearings in an industrial facility. In this example, the cognitive process system 15510 may receive vibration sensor data from one or more vibration sensors disposed on or near the engine, may receive maintenance data from the industrial facility, and may generate a feature vector based on the vibration sensor data and the maintenance data. The cognitive process system 15510 may input the feature vectors to a machine learning model specifically trained for the engine (e.g., using a combination of simulated data and real world data for the cause of the irregular vibration pattern) to predict the cause of the irregular vibration pattern. In this example, the cause of the irregular vibration modes may be bearing loosening, insufficient bearing lubrication, bearing misalignment, bearing wear, bearing phase may be aligned with the engine phase, housing loosening, bolt loosening, and the like.
In another example, a machine learning model may be used to provide decision support to bring bearings of an engine in an industrial facility operating in a suboptimal vibration fault level state to a normal operating vibration fault level state. In this example, the cognitive process system 15510 may receive vibration sensor data from one or more vibration sensors disposed on or near the engine, may receive maintenance data from the industrial facility, and may generate a feature vector based on the vibration sensor data and the maintenance data. The cognitive process system 15510 may input feature vectors to a machine learning model specifically trained for the engine (e.g., a combination of simulated data and real world data for solutions using irregular vibration patterns) to provide decision support in achieving normal operating failure level conditions of the bearing. In this example, the decision support may be suggesting a tightening bearing, lubricating a bearing, realigning a bearing, ordering a new component, collecting additional vibration measurements, altering an operating speed of the engine, tightening a housing, tightening a bolt, and so forth.
In another example, a machine learning model may be used to provide decision support related to a worker collecting vibration measurements. In this example, the cognitive process system 15510 may receive vibration measurement history data from the industrial facility and may generate a feature vector based on the vibration measurement history data. The cognitive process system 15510 may input feature vectors to a machine learning model specifically trained for the engine (e.g., using a combination of simulation data and real world vibration measurement history data) to provide decision support in selecting vibration measurement locations.
In yet another example, a machine learning model may be used to identify vibration characteristics associated with a machine and/or machine component problem. In this example, the cognitive process system 15510 may receive vibration measurement history data from the industrial facility and may generate a feature vector based on the vibration measurement history data. The cognitive process system 15510 may input the feature vectors to a machine learning model specifically trained for the engine (e.g., using a combination of simulation data and real world vibration measurement history data) to identify vibration characteristics associated with the machine and/or machine components. The foregoing examples are non-limiting examples, and the cognitive process system 15510 may be used for any other suitable AI/machine learning related tasks performed for an industrial facility.
In an embodiment, the environmental control system 15512 controls one or more aspects of an industrial facility. In some of these embodiments, the environmental control system 15512 can control one or more devices in an industrial environment. For example, the environmental control system 15512 may control one or more machines in an environment, robots in an environment, HVAC systems in an environment, alarm systems in an environment, assembly lines in an environment, and the like. In an embodiment, the environmental control system 15512 may utilize the digital twinning simulation system 15506, the digital twinning dynamic model system 15508, and/or the cognitive process system 15510 to determine one or more control instructions. In an embodiment, the environmental control system 15512 can implement a rule-based method and/or a machine learning method to determine the control instructions. In response to determining the control instructions, the environmental control system 15512 can output the control instructions to the intended devices in the particular environment via the digital twin I/O system 15504.
FIG. 156 illustrates an exemplary digital twin management system 15502 according to some embodiments of the invention. In an embodiment, the digital twin management system 15502 may include, but is not limited to, a digital twin creation module 15564, a digital twin update module 15566, and a digital twin visualization module 15568.
In an embodiment, the digital twinning module 15564 may use input from a user, imported data (e.g., blueprints, specifications, etc.), image scans of the environment, 3D data from LIDAR devices and/or SLAM sensors, and other suitable data sources to create a new set of digital twinning for a set of environments. For example, a user (e.g., a user associated with an organization/client account) may provide input through the client application 15570 to create a new digital twinning of the environment. In this way, the user may upload a 2D or 3D image scan of the environment and/or a blueprint of the environment. The user may also upload 3D data, for example, photographed by a camera, LIDAR device, IR scanner, a set of SLAM sensors, radar device, EMF scanner, or the like. In response to the provided data, digital twin creation module 15564 may create a 3D representation of the environment, which may include any objects captured in the image data/any objects detected in the 3D data. In an embodiment, the cognitive process system 15572 may analyze input data (e.g., blueprints, image scans, 3D data) to classify rooms, paths, devices, etc. to assist in generating the 3D representation. In some embodiments, digital twinning module 15564 may map digital twinning to a 3D coordinate space (e.g., a cartesian space with x, y, and z axes).
In some embodiments, the digital twinning module 15564 may output a 3D representation of the environment to a Graphical User Interface (GUI). In some of these embodiments, a user may identify certain regions and/or objects and may provide input related to the identified regions and/or objects. For example, a user may mark a particular room, device, machine, etc. Additionally or alternatively, the user may provide data related to the identified objects and/or regions. For example, upon identifying a piece of equipment, a user may provide the make/model of the equipment. In some embodiments, the digital twinning module 15564 may obtain information from a manufacturer of a device, piece of equipment, or machine. The information may include one or more attributes and/or behaviors of the device, apparatus, or machine. In some embodiments, the user may identify the location of the sensor throughout the environment via the GUI. For each sensor, the user may provide the sensor type and related data (e.g., brand, model number, IP address, etc.). The digital twinning module 15564 may record the locations (e.g., x, y, z coordinates of the sensors) in the digital twinning of the environment. In an embodiment, digital twin system 15500 may employ one or more systems of automated digital twin stuffing. For example, the digital twinning system 15500 may employ a machine vision based classifier that classifies the make and model of a device, equipment, or sensor. Additionally or alternatively, the digital twinning system 15500 can iteratively ping different types of known sensors to determine whether a particular type of sensor is present in the environment. The digital twin system 15500 can extrapolate the make and model of the sensor each time the sensor responds to a ping.
In some embodiments, the manufacturer may supply or provide digital twinning (e.g., sensors, devices, machinery, equipment, raw materials, etc.) of its products. In these embodiments, the digital twinning module 15564 may import digital twinning of one or more products identified in the environment and may embed the digital twinning into the digital twinning of the environment. In an embodiment, embedding the digital twin into another digital twin may include creating a relationship between the embedded digital twin and the other digital twin. In these embodiments, the manufacturer of the digital twinning may define the behavior and/or attributes of the corresponding product. For example, digital twinning of a machine may define the manner in which the machine operates, the input/output of the machine, and so forth. In this way, the digital twinning of the machine may reflect the operation of the machine given a set of inputs.
In an embodiment, a user may define one or more processes that occur in an environment. In these embodiments, a user may define steps in a process, the machine/device performing each step in the process, process inputs, and process outputs.
In an embodiment, the digital twinning module 15564 may create a graphic database defining the relationships between a set of digital twinning. In these embodiments, digital twinning creation module 15564 may create nodes for environments, systems and subsystems of environments, devices in environments, sensors in environments, workers working in environments, processes executing in environments, and the like. In an embodiment, the digital twinning module 15564 may write a graphic database representing a set of digital twins to the digital twinning data store 15516.
In an embodiment, the digital twinning creation module 15564 may include, for each node, any data related to an entity in the node representing the entity. For example, in defining nodes representing environments, the digital twinning creation module 15564 may include dimensions, boundaries, layouts, paths, and other relevant spatial data in the nodes. Further, the digital twinning module 15564 may define a coordinate space relative to the environment. Where digital twinning may be rendered, digital twinning creation module 15564 may include references in nodes to any shape, mesh, spline, surface, etc. that may be used to render an environment. In representing a system, subsystem, device, or sensor, the digital twinning creation module 15564 may create nodes for the respective entities and may include any relevant data. For example, the digital twinning creation module 15564 may create nodes that represent machines in an environment. In this example, digital twinning module 15564 may include dimensions, behaviors, attributes, locations, and/or any other suitable data related to a machine in a node representing the machine. The digital twinning module 15564 may connect nodes and edges of related entities to create relationships between the entities. In this way, the relationships created between entities may define the relationship types characterized by the edges. During the representation, the digital twinning creation module 15564 may create nodes for the entire process, or may create nodes for each step in the process. In some of these embodiments, the digital twinning creation module 15564 may associate a process node to a node representing a machine/device that performs a step in the process. In embodiments of a machine/device in which edges relate process step nodes to steps in the execution, one of the edges or nodes may contain information indicating step inputs, step outputs, time spent in a step, nature of the process inputs producing outputs, a set of states or modes that the process may experience, etc.
In an embodiment, the digital twinning update module 15566 updates the sets of digital twinning based on the current status of one or more industrial entities. In some embodiments, the digital twinning update module 15566 receives sensor data from the sensor system 15530 of the industrial environment and updates the digital twinning of the industrial environment and/or the state of any affected systems, subsystems, devices, workers, processes, etc. As previously described, the digital twin I/O system 15504 may receive sensor data in one or more sensor data packets. The digital twin I/O system 15504 can provide sensor data to the digital twin update module 15566 and can identify the environment in which the sensor data packet is received and the sensor providing the sensor data packet. In response to the sensor data, the digital twin update module 15566 may update one or more digital twin states based on the sensor data. In some of these embodiments, the digital twin update module 15566 may update records (e.g., nodes in a graph database) corresponding to sensors providing sensor data to reflect current sensor data. In some cases, the digital twin update module 15566 may identify certain areas in the environment monitored by the sensors and may update the records (e.g., nodes in a graph database) to reflect current sensor data. For example, the digital twin update module 15566 may receive sensor data reflecting different vibration characteristics of a machine and/or machine components. In this example, the digital twin update module 15566 may update a record representing the vibration sensor providing vibration sensor data and/or a record representing the machine and/or machine components to reflect the vibration sensor data. In another example, in some cases, it may be desirable for workers in an industrial environment (e.g., manufacturing facility, industrial storage facility, mine, drilling operations, etc.) to wear wearable devices (e.g., smart watches, smart helmets, smart shoes, etc.). In these embodiments, the wearable device may collect sensor data related to the worker (e.g., location, movement, heartbeat, respiration rate, body temperature, etc.) and/or the environment surrounding the worker, and may communicate the collected sensor data to digital twinning system 15500 (e.g., through real-time sensor API 15514) directly or through an aggregation device of the sensor system. In response to receiving sensor data from the worker's wearable device, digital twin update module 15566 may update the worker's digital twin to reflect, for example, the worker's location, the worker's trajectory, the worker's health status, and the like. In some of these embodiments, the digital twin update module 15566 may update nodes representing workers and/or edges that connect nodes representing environments with collected sensor data to reflect the current state of workers.
In some embodiments, the digital twin update module 15566 may provide sensor data from one or more sensors to a digital twin dynamic model system 15508, which may model the behavior of the environment and/or one or more industrial entities to extrapolate additional state data.
In an embodiment, the digital twin visualization module 15568 receives a request to view a visual digital twin or a portion thereof. In an embodiment, the request may indicate a digital twinning (e.g., an environment identifier) to view. In response, the digital twinning visualization module 15568 may determine the requested digital twinning and any other digital twinning associated with the request. For example, upon requesting to view the digital twinning of the environment, the digital twinning visualization module 15568 may further identify the digital twinning of any industrial entity in the environment. In an embodiment, the digital twin visualization module 15568 may identify a spatial relationship between an industrial entity and an environment based on a relationship defined in, for example, a graphic database. In these embodiments, the digital twin visualization module 15568 may determine transients (e.g., objects fixed to points or object movements) that contain the relative position of an embedded digital twin within a digital twin, the relative position and/or relationship of adjacent digital twin. The digital twinning visualization module 15568 may present the requested digital twinning and any other relevant digital twinning based on the identified relationships. In some embodiments, for each digital twin, the digital twin visualization module 15568 may determine the surface of the digital twin. In some embodiments, the surface of the number may be defined or referenced in a record corresponding to the digital twinning, which may be provided by a user, determined from the imported image, or defined by the manufacturer of the industrial entity. In the case where the object may take on different poses or shapes (e.g., an industrial robot), the digital twinning visualization module 15568 may determine the pose or shape of the object for digital twinning. The digital twinning visualization module 15568 may embed the digital twinning into the requested digital twinning and may output the requested digital twinning to the client application.
In some of these embodiments, the request to view the digital twinning may further indicate a view type. As previously described, in some embodiments, digital twinning may be depicted in a number of different view types. For example, the environment or device may be viewed in the following view: a "real world" view depicting the environment or device in a typical appearance; a "hot" view depicting an environment or device in a manner that indicates the temperature of the environment or device; a "vibration" view depicting a machine and/or machine component in an industrial environment in a manner that indicates vibration characteristics of the machine and/or machine component; "filter" views that only display certain types of objects within an environment or device component (e.g., objects that need attention due to identifying fault conditions, alarms, update reports, or other factors); enhanced view, overlaying digital twin data; and/or any other suitable view type. In an embodiment, digital twinning may be depicted in a variety of different role-based view types. For example, a manufacturing facility device may look at the following view: an "operator" view showing the facility in a manner appropriate to the facility operator; a "high-rise" view showing the facility in a manner appropriate for the high Guan Cengguan manager; a "marketing" view showing facilities in a manner suitable for sales and/or marketing character workers; a "board of directors" view showing the facilities in a manner appropriate for the board of directors of the company; a "supervisory" view showing the facilities in a manner appropriate for the supervisory manager; a "human resources" view shows the facilities in a manner appropriate for human resources personnel. In response to a request indicating a view type, the digital twin visualization module 15568 can retrieve each digital twin's data corresponding to the view type. For example, if a user has requested a vibration view of the plant floor, the digital twin visualization module 15568 may retrieve vibration data of the plant floor (which may include vibration measurements taken from different machines and/or machine components and/or vibration measurements extrapolated by the digital twin dynamic model system 15508 and/or analog vibration data from the digital twin analog system 15506) as well as available vibration data of any industrial entity present at the plant floor. In this example, the digital twin visualization module 15568 can determine colors corresponding to each machine component in the plant floor that represent vibration fault level status (e.g., red for alarm, orange for critical, yellow for suboptimal, green for normal operation). The digital twinning visualization module 15568 may then present the digital twinning of the machine component in the environment based on the determined color. Additionally or alternatively, the digital twinning visualization module 15568 may use the indicator having the determined color to present digital twinning of the machine component in the environment. For example, if the vibration fault level status of the inbound bearing of the motor is suboptimal and the outbound bearing of the motor is critical, the digital twin visualization module 15568 may present the digital twin bearing of the inbound bearing with an indicator (e.g., suboptimal) with a yellow shade and the outbound bearing with an indicator (e.g., critical) with an orange shade. Note that in some embodiments, digital twin system 15500 may include an analysis system (not shown) that determines the manner in which digital twin visualization system 15500 presents information to a human user. For example, the analysis system may track results related to human interactions with real environments or objects in response to information presented in visual digital twinning. In some embodiments, the analysis system may apply the cognitive model to determine the most efficient way to display visual information (e.g., color for representing an alarm condition, type of movement or animation for noticing an alarm condition, etc.) or audio information (sound for representing an alarm condition) based on the result data. In some embodiments, the analysis system may apply a cognitive model to determine the most appropriate way to display the visual information based on the user's role. In an embodiment, the visualization may include displaying information related to the visualization digital twinning, including graphical information, graphical information depicting vibration characteristics, graphical information depicting harmonic peaks, graphical information depicting peaks, vibration severity unit data, vibration fault level status data, advice from the cognitive intelligence system 15510, predictions from the cognitive intelligence system 15510, fault probability data, maintenance history data, fault time data, downtime cost data, downtime probability data, maintenance cost data, machine replacement cost data, downtime probability data, manufacturing KPIs, and the like.
In another example, a user may request a filtered view of the digital twinning of a process, whereby the digital twinning of the process only displays components (e.g., machines or devices) involved in the process. In this example, the digital twin visualization module 15568 may retrieve the digital twin of the process and any related digital twin (e.g., digital twin affecting any machinery or devices of the process and the digital twin of the environment). The digital twinning visualization module 15568 may then render each digital twinning (e.g., of the environment and related industrial entities), and may then perform the process on the rendered digital twinning. Note that since a process may be performed over a period of time and may include moving items and/or components, the digital twin visualization module 15568 may generate a series of consecutive frames for demonstrating the process. In this case, the movements of the machine and/or device involved in the process may be determined from the actions defined in the corresponding digital twinning of the machine and/or device.
As previously described, the digital twin visualization module 15568 may output the requested digital twin to the client application 15570. In some embodiments, the client application 15570 is a virtual reality application whereby the requested digital twinning is displayed on a virtual reality headset. In some embodiments, the client application 15570 is an augmented reality application, thereby rendering the requested digital twinning in the AR-enabled device. In these embodiments, the requested digital twinning may be filtered such that visual elements and/or text are overlaid on the display of the AR-enabled device.
Note that although a graph database is discussed, digital twin system 15500 may use other suitable data structures to store information related to a set of digital twin. In these embodiments, the data structure and any associated storage system may be implemented such that the data structure provides a degree of feedback loops and/or recursion in representing iterations of the stream.
Fig. 131 illustrates an example of a digital twin I/O system 15504, where the digital twin I/O system 15504 is connected with an environment 15520, digital twin system 15500, and/or components thereof to provide bi-directional transmission of data between coupled components, according to some embodiments of the invention.
In an embodiment, the transmitted data includes signals (e.g., request signals, command signals, response signals, etc.) between the connected components, which may include software components, hardware components, physical devices, virtualization devices, analog devices, combinations thereof, and the like. The signals may define material properties (e.g., physical quantities of temperature, pressure, humidity, density, viscosity, etc.), measured values (e.g., contemporaneous or stored values obtained by a device or system), device properties (e.g., properties of a device ID or design specification of a device, materials, measurement capabilities, dimensions, absolute locations, relative locations, combinations thereof, etc.), set points (e.g., targets of material properties, device properties, system properties, combinations thereof, etc.), and/or critical points (e.g., thresholds of minimum or maximum values of material properties, device properties, system properties, etc.). The signal may be received from a system or device that obtains (e.g., directly measures or generates) or otherwise obtains (e.g., receives, calculates, looks up, filters, etc.) the data and may communicate with the digital twin I/O system 15504 at a predetermined time or in response to a request (e.g., poll) from the digital twin I/O system 15504. Communication may occur via direct or indirect connection (e.g., via intermediate modules within the circuit and/or intermediate devices between connected components). This value may correspond to real world element 157302r (e.g., an input or output of a tangible vibration sensor) or virtual element 157302v (e.g., an input or output of digital twinning 157302d and/or analog element 157302s that provide vibration data).
In an embodiment, the real world element 157302r can be an element in the industrial environment 15520. The real world objects 157302r may include, for example, non-networked objects 15522, devices 15524 (smart or non-smart), sensors 15526, and humans 15528. The real world element 151302r may be a process or non-process device in the industrial environment 15520. For example, process devices may include motors, pumps, mills, fans, painters, welders, smelters, etc., while non-process devices may include personal protective equipment, safety devices, emergency stations or equipment (e.g., safety showers, eyewash stations, fire extinguishers, sprinkler systems, etc.), warehouse features (e.g., walls, floor layouts, etc.), obstructions (e.g., personnel or other items in the environment 15520, etc.).
In an embodiment, the virtual element 157302v can be a digital representation of the simultaneous real world element 157302r or a digital representation corresponding to the simultaneous real world element 157302 r. Additionally or alternatively, the virtual element 157302v can be a digital representation of the real world element 157302r or a digital representation corresponding to the real world element 157302r that can be used for later addition and implementation into the environment 15520. The virtual elements may include, for example, analog elements 175302s and/or digital twins 157302d. In an embodiment, the analog element 157302s can be a digital representation of the real world element 157302s that is not present in the industrial environment 15520. The simulation element 157302s can simulate desired physical properties that can then be integrated in the environment 15520 as the real world element 157302r (e.g., a "black box" to simulate the size of the real world element 157302 r). The analog element 157302s may include digital twinning of the existing object (e.g., a single analog element 151302s may include one or more digital twinning 151302d for an existing sensor). For example, information related to the simulated element 157302s may be obtained from a library (e.g., a physical library, a chemical library, etc.) that defines information and behavior of the simulated element 131302s by evaluating the behavior of the corresponding real world element 157302r using a mathematical model or algorithm.
In an embodiment, the digital twinning 157302d can be a digital representation of one or more real world elements 157302 r. The digital twinning 157302d is used to simulate, replicate, and/or model the behavior and response of the real world element 157302r in response to inputs, outputs, and/or conditions of the surrounding or external environment. For example, data related to physical characteristics and responses of the real world element 157302r may be obtained through user input, sensor input, and/or physical modeling (e.g., thermodynamic model, electric model, mechanical kinetic model, etc.). The information of the digital twinning 157302d may correspond to and be obtained from one or more real world elements 157302r corresponding to the digital twinning 157302d, and from the one or more real world elements 157302 r. For example, in some embodiments, the digital twinning 131302d can correspond to one real world element 157302r that is a fixed digital vibration sensor 15536 on the machine component, and the vibration data of the digital twinning 131302d can be obtained by polling or acquiring vibration data measured by the fixed digital vibration sensor on the machine component. In another example, the digital twinning 157302d can correspond to a plurality of real world elements 157302r such that each element can be a fixed digital vibration sensor on a machine component, and the vibration data of the digital twinning 157302d can be obtained by polling or acquiring vibration data measured by each fixed digital vibration sensor on the plurality of real world elements 157302 r. Additionally or alternatively, the vibration data of the first digital twin 157302d can be obtained by acquiring vibration data of a second digital twin 157302d embedded within the first digital twin 157302d, and the vibration data of the first digital twin 157302d can include vibration data of the second digital twin 157302d or be derived from vibration data of the second digital twin 157302 d. For example, the first digital twin may be digital twin 157302d of the environment 15520 (alternatively referred to as "environment digital twin"), and the second digital twin 157302d may be digital twin 157302d corresponding to the vibration sensor disposed in the environment 15520, such that vibration data of the first digital twin 157302d is obtained from or calculated based on data including vibration data of the second digital twin 157302 d.
In an embodiment, digital twinning system 15500 uses sensors 15526 in respective environments 15520 to monitor properties of real world element 157302r, environment 15520 is an output of a model of digital twinning 157302d and/or one or more analog elements 157302s or may be represented by an output of a model of digital twinning 157302d and/or one or more analog elements 157302 s. In an embodiment, the digital twinning system 15500 may perform simulation (e.g., by the digital twinning simulation system 15506) by extending the polling interval and/or minimizing data transmission of sensors corresponding to the affected real world elements 157302r, and during the extended interval using data obtained from other sources (e.g., physically proximate to the affected real world elements 157302r or sensors having an effect on the affected real world elements 157302 r), while minimizing network congestion while maintaining efficient monitoring of the process. Additionally or alternatively, error checking may be performed by comparing the collected sensor data with data obtained from the digital twin simulation system 15506. For example, consistent deviations or fluctuations between the sensor data obtained from the real world element 157302r and the analog element 157302s may indicate a fault or another fault condition of the respective sensor.
In an embodiment, the digital twinning system 15500 may optimize characteristics of the environment by using one or more analog elements 157302 s. For example, the digital twinning system 15500 may evaluate the effects of the digital twinning simulation elements 157302s of the environment to quickly and efficiently determine the cost and/or benefit generated by including, excluding, or replacing the real world elements 157302r in the environment 15520. Costs and benefits may include, for example, increasing mechanical costs (e.g., capital investment and maintenance), increasing efficiency (e.g., reducing waste or increasing throughput through process optimization), reducing or changing floor space in the environment 15520, extending or optimizing service life, minimizing component failures, minimizing component downtime, etc.
In an embodiment, the digital twin I/O system 15504 may include one or more software modules that are executed by one or more controllers of one or more devices (e.g., server devices, user devices, and/or distributed devices) to affect the described functionality. Digital twin I/O system 15504 may include, for example, an input module 157304, an output module 157306, and an adapter module 157308.
In an embodiment, the input module 157304 may obtain or import data from a data source in communication with the digital twin I/O system 15504 (e.g., the sensor system 15530 and the digital twin analog system 15506). The data may be used directly by or stored in digital twin system 15500. Imported data may be obtained from a data stream, a data batch, in response to a trigger event, a combination thereof, or the like. The input module 157304 may receive data in a format suitable for transmitting, reading, and/or writing information within the digital twin system 15500.
In an embodiment, the output module 157306 can output or export data to other system components (e.g., digital twin data store 15516, digital twin simulation system 15506, cognitive intelligence system 15510, etc.), devices 15524, and/or client applications 15570. The data may be output in a data stream, a data batch, in response to a trigger event (e.g., a request), a combination thereof, or the like. The output module 157306 may output data in a format suitable for use or storage by the target element (e.g., one protocol for output to the client application and another protocol for digital twin data store 15516).
In an embodiment, adapter module 157308 may process and/or convert data between input module 157304 and output module 157306. In embodiments, the adapter module 157308 may automatically convert and/or route data (e.g., based on data type) as well as convert and/or route data in response to a received request (e.g., in response to information in the data).
In an embodiment, the digital twinning system 15500 may represent a set of industrial workpiece elements in digital twinning, and the digital twinning simulation system 15506 simulates a set of physical interactions of a worker with the workpiece elements.
In an embodiment, the digital twin simulation system 15506 may determine process results for simulated physical interactions that take into account simulation artifacts. For example, changes in workpiece throughput may be modeled by digital twin system 15500, including, for example, worker response time to events, worker fatigue, discontinuities within worker action (e.g., natural changes in human body movement speed, different positioning times, etc.), effects of discontinuities on downstream processes, and the like. In an embodiment, personalized worker interactions may be modeled using historical data collected, acquired, and/or stored by digital twin system 15500. The simulation may begin based on an estimated number (e.g., worker age, industry average level, workplace expectations, etc.). The simulation may also personalize the data for each worker (e.g., compare the estimated number to the collected worker-specific results).
In an embodiment, information related to a worker (e.g., fatigue rate, efficiency, etc.) may be determined by analyzing the performance of a particular worker over time and modeling the performance.
In an embodiment, the digital twinning system 15500 includes a plurality of proximity sensors in the sensor system 15530. The proximity sensor is or may be used to detect elements of the environment 15520 within a predetermined area. For example, the proximity sensor may include an electromagnetic sensor, a light sensor, and/or an acoustic sensor.
Electromagnetic sensors are or may be used to sense objects or interactions via one or more electromagnetic fields (e.g., emitted electromagnetic radiation or received electromagnetic radiation). In embodiments, electromagnetic sensors include inductive sensors (e.g., radio frequency identification sensors), capacitive sensors (e.g., contact and non-contact capacitive sensors), combinations thereof, and the like.
The light sensor is or may be used to sense objects or interactions via electromagnetic radiation in, for example, the far infrared, near infrared, optical and/or ultraviolet spectra. In embodiments, the light sensor may include image sensors (e.g., charge coupled devices and CMOS active pixel sensors), photosensors (e.g., beam-passing sensors, retro-reflective sensors, and diffuse sensors), combinations thereof, and the like. Furthermore, the light sensor may be implemented as part of a system or subsystem, such as a light detection and ranging ("LIDAR") sensor.
The acoustic sensor is or may be used to sense objects or interactions via acoustic waves transmitted and/or received by the acoustic sensor. In embodiments, the acoustic sensor can include an infrasonic, sonic, and/or ultrasonic sensor. Furthermore, the acoustic sensors may be grouped as part of a system or subsystem, such as a sound navigation and ranging ("SONAR") sensor.
In an embodiment, the digital twinning system 15500 stores and collects data from a set of proximity sensors in the environment 15520 or portion thereof. The collected data may be stored, for example, in digital twin data memory 15516 for use by components of digital twin system 15500 and/or for visualization by a user. Such use and/or visualization may be performed concurrently with or subsequent to data collection (e.g., during subsequent analysis and/or process optimization).
In an embodiment, data collection may occur in response to a trigger condition. These trigger conditions may include, for example, expiration of a static or dynamic predetermined interval, obtaining a value that is less than or exceeds a static or dynamic value, receiving an automatically generated request or instruction from digital twinning system 15500 or components thereof, interaction of an element with a corresponding sensor (e.g., in response to a worker or machine interrupting a light beam or reaching within a predetermined distance from a proximity sensor), interaction of a user with digital twinning (e.g., selecting environmental digital twinning, sensor array digital twinning, or sensor digital twinning), combinations thereof, and the like.
In some embodiments, the digital twinning system 15500 collects and/or stores RFID data in response to worker interactions with the real world element 157302 r. For example, in response to a worker's interaction with a real environment, digital twinning will collect and/or store RFID data from RFID sensors in or associated with the corresponding environment 15520. Additionally or alternatively, worker interaction with a sensor array that is digitally twinned will collect and/or store RFID data from RFID sensors in or associated with the corresponding sensor array. Similarly, worker interaction with sensor digital twinning will collect and/or store RFID data from the corresponding sensor. The RFID data may include suitable data available to the RFID sensor, including proximity RFID tags, RFID tag locations, authorized RFID tags, unauthorized RFID tags, unidentified RFID tags, RFID types (e.g., active or passive), error codes, combinations thereof, and the like.
In an embodiment, digital twinning system 15500 may further embed output from one or more devices into a corresponding digital twinning. In an embodiment, the digital twinning system 15500 embeds the output from a set of individual related devices into an industrial digital twinning. For example, digital twin I/O system 15504 may receive information output from one or more wearable devices 15554 or mobile devices (not shown) associated with individuals in an industrial environment. The wearable device may include an image capturing device (e.g., a personal camera or an augmented reality headset), a navigation device (e.g., a GPS device, an inertial guidance system), a motion tracker, an acoustic capturing device (e.g., a microphone), a radiation detector, combinations thereof, and the like.
In an embodiment, upon receiving the output information, digital twin I/O system 15504 routes the information to digital twin creation module 15564 to check and/or update the environmental digital twin and/or associated digital twin in the environment (e.g., digital twin of a worker, machine, or robot location at a given time). Further, the digital twinning system 15500 may use the embedded output to determine characteristics of the environment 15520.
In an embodiment, the digital twinning system 15500 embeds the output from the LIDAR point cloud system into an industrial digital twinning. For example, the digital twin I/O system 15504 may receive information output from one or more Lidar devices 15538 in an industrial environment. Lidar device 15538 is used to provide a plurality of points with associated position data (e.g., coordinates of absolute or relative x, y, and z values). Each of the plurality of points may include other LIDAR attributes such as intensity, number of returns, total returns, laser color data, return color data, scan angle, scan direction, etc. The Lidar device 15538 may provide a point cloud comprising a plurality of points to a digital twin system 15500 via, for example, a digital twin I/O system 15504. Additionally or alternatively, the digital twinning system 15500 may receive and aggregate point streams into point clouds, or may receive and combine the received point clouds with existing point cloud data, map data, or three-dimensional (3D) model data.
In an embodiment, upon receiving the output information, the digital twin I/O system 15504 routes the point cloud information to the digital twin creation module 15564 to check and/or update the environmental digital twin and/or associated digital twin in the environment (e.g., digital twin of worker, machine, or robot location at a given time). In some embodiments, the digital twinning system 15500 is also used to determine a closed shape object in the received LIDAR data. For example, the digital twinning system 15500 may group a plurality of points within a point cloud as objects and, if desired, estimate a obstructed surface of the objects (e.g., a surface of the objects that is in contact with or adjacent to a floor or another object such as another device). The system may use such closed-shape objects to narrow down the digitally twinned search space, thereby improving the efficiency of the matching algorithm (e.g., shape matching algorithm).
In an embodiment, digital twinning system 15500 embeds the output from a simultaneous localization and mapping ("SLAM") system in an ambient digital twinning. For example, the digital twinning I/O system 15504 may receive information output from a SLAM system, such as SLAM sensor 15562, and embed the received information into an ambient digital twinning corresponding to a location determined by the SLAM system. In an embodiment, upon receiving output information from the SLAM system, the digital twin I/O system 15504 routes the information to the digital twin creation module 15564 to inspect and/or update the environment digital twin and/or associated digital twin in the environment (e.g., digital twin of a work piece, furniture, movable object, or autonomous object). Such updates automatically provide digital twinning of non-connected elements (e.g., furniture or personnel) without requiring the user to interact with digital twinning system 15500.
In an embodiment, the digital twinning system 15500 may utilize known digital twinning to reduce the computational requirements of the SLAM sensor 15562 by using a suboptimal map building algorithm. For example, a suboptimal map construction algorithm may enable higher uncertainty tolerance to be achieved using a simple bounded region representation and identifying possible digital twinning. Additionally or alternatively, the digital twinning system 15500 may use the bounded region representation to limit the number of digital twins, analyze the potential digital twinning groups to distinguish features, then perform a higher accuracy analysis on the distinguishing features to identify and/or eliminate categories, groups, or individuals of digital twinning, and perform an accurate scan only of the remaining region to be scanned in the event that no matching digital twinning is found.
In an embodiment, the digital twinning system 15500 may further reduce the computations required to construct a location map by: an initial map construction process (e.g., a simple bounded area map or other suitable photogrammetry method) is performed with data captured from other sensors in the environment (e.g., captured images or videos, radio images, etc.), digital twins of known environmental objects are associated with features of the simple bounded area map to refine the simple bounded area map, and more accurate scans are performed on the remaining simple bounded areas to further refine the map. In some embodiments, digital twinning system 15500 may detect objects within the received mapping information and, for each detected object, determine whether the detected object corresponds to an existing digital twinning of the real world element. In response to determining that the detected object does not correspond to an existing real-world element digital twin, the digital twin system 15500 may generate a new digital twin (e.g., a detected object digital twin) corresponding to the detected object and add the detected object digital twin to the real-world element digital twin in the digital twin data store using, for example, the digital twin creation module 15564. Additionally or alternatively, in response to determining that the detected object corresponds to an existing real-world element digital twinning, digital twinning system 15500 may update the real-world element digital twinning to include new information detected by simultaneous localization and mapping sensors (if any).
In an embodiment, the digital twinning system 15500 represents the locations of autonomous or remotely movable elements in an industrial digital twinning and their attributes. Such movable elements may include, for example, workers, personnel, vehicles, automated vehicles, robots, and the like. The position of the movable element may be updated in response to a trigger condition. These trigger conditions may include, for example, expiration of a static or dynamic predetermined interval, receipt of an automatically generated request or instruction from digital twinning system 15500 or components thereof, interaction of an element with a corresponding sensor (e.g., in response to a worker or machine interrupting a light beam or coming within a predetermined distance from a proximity sensor), interaction of a user with digital twinning (e.g., selection of ambient digital twinning, sensor array digital twinning, or sensor digital twinning), combinations thereof, and the like.
In an embodiment, the time interval may be based on a probability that the respective movable element moves within the time period. For example, for workers intended to move frequently (e.g., those responsible for handling items in the environment 15520 and through the environment 15520), the time interval for updating the worker's location may be relatively short; for workers that are expected to move infrequently (e.g., those responsible for monitoring the process), the time interval may be relatively long. Additionally or alternatively, the time interval may be dynamically adjusted based on applicable conditions, such as increasing the time interval when no movable elements are detected, decreasing the time interval when the number of movable elements in the environment increases (e.g., increasing the number of worker and worker interactions), increasing the time interval during reduced environmental activities (e.g., rest time such as lunch), decreasing the time interval during abnormal environmental activities (e.g., inspection, or maintenance), decreasing the time interval when unexpected or non-characteristic movements are detected (e.g., frequent movement of elements that are typically stationary or coordinated movement of workers approaching an exit or handling large objects by cooperative movement, etc.), combinations thereof, and the like. Furthermore, the time interval may also include additional semi-random acquisitions. For example, occasional intermediate interval positions may be acquired by digital twinning system 15500 to enhance or evaluate the validity of a particular time interval.
In an embodiment, the digital twin system 15500 may analyze the data received from the digital twin I/O system 15504 to refine, remove, or add conditions. For example, the digital twinning system 15500 may optimize the data collection time for mobile elements that are updated more frequently than is needed (e.g., multiple consecutive receiving locations are the same or within a predetermined error margin).
In an embodiment, the digital twinning system 15500 may receive, identify, and/or store a set of states 15840a-n related to the environment 15520. The states 15840a-n can be, for example, a data structure including a plurality of attributes 158404a-n and a set of identification criteria 158406a-n to uniquely identify each respective state 15840a-n. In an embodiment, the states 15840a-n may correspond to states where the digital twinning system 15500 is desired to set or change conditions (e.g., increase/decrease monitoring intervals, change operating conditions, etc.) of the real world element 157302r and/or the environment 15520.
In an embodiment, the set of states 15840a-n may also include, for example, a minimum monitored attribute for each state 15840a-n, a set of identification criteria 158406a-n for each state 15840a-n, and/or actions that may be taken or suggested in response to each state 15840a-n. Such information may be stored by, for example, digital twin data store 15516 or another data store. The states 15840a-n, or portions thereof, may be provided to the digital twinning system 15500, determined by the digital twinning system 15500, or changed by the digital twinning system 15500. Further, the set of states 15840a-n may include data from different sources. For example, the detailed information for identifying and/or responding to the occurrence of the first state may be provided to the digital twin system 15500 via user input, the detailed information for identifying and/or responding to the occurrence of the second state may be provided to the digital twin system 15500 via an external system, the detailed information for identifying and/or responding to the occurrence of the third state may be determined by the digital twin system 15500 (e.g., via simulation or analysis of process data), and the detailed information for identifying and/or responding to the occurrence of the fourth state may be stored by the digital twin system 15500 and changed as desired (e.g., responding to the simulated occurrence of the state or responding to data collected during the analysis of the state).
In an embodiment, the plurality of attributes 158404a-n includes at least the attributes 158404a-n required to identify the respective state 15840a-n. The plurality of attributes 158404a-n may further include additional attributes that are or may be monitored in determining the respective state 15840a-n, but these attributes are not required to identify the respective state 15840a-n. For example, the plurality of attributes 158404a-n for the first state can include rotational speed, fuel level, energy input, linear speed, acceleration, temperature, strain, torque, volume, weight, and the like related information.
The set of identification criteria 158406a-n may include information for each of the set of attributes 158404a-n to uniquely identify the respective state. Identification criteria 158406a-n may include, for example, rules, thresholds, limits, ranges, logical values, conditions, comparisons, combinations thereof, and the like.
The operating conditions or monitored changes may be any suitable changes. For example, after identifying that the respective states 158406a-n occur, the digital twinning system 15500 can increase or decrease the monitoring interval of the device (e.g., decrease the monitoring interval in response to a measured parameter that is different from nominal operation) without changing the operation of the device. Additionally or alternatively, the digital twin system 15500 may change the operation of the device (e.g., reduce speed or power input) without changing the monitoring of the device. In other embodiments, the digital twin system 15500 may change the operation of the device (e.g., reduce speed or power input) and change the monitoring interval of the device (e.g., reduce the monitoring interval).
FIG. 158 illustrates an exemplary set of identification states 15840a-n that are related to an industrial environment that the digital twin system 15500 can identify and/or store for access by a smart system (e.g., cognitive smart system 15510) or a user of the digital twin system 15500 according to some embodiments of the invention. The states 15840a-n may include operational states (e.g., suboptimal, normal, optimal, critical, or alarm operation of one or more components), excess or shortage states (e.g., supply side or output side numbers), combinations thereof, and the like.
In an embodiment, the digital twinning system 15500 may monitor the properties 158404a-n of the real world element 157302r and/or the digital twinning 157302d to determine the respective states 15840a-n. The attributes 158404a-n can be, for example, operating conditions, set points, critical points, status indicators, other sensed information, combinations thereof, and the like. For example, attributes 158404a-n can include power input 158404a, operating speed 158404b, critical speed 158404c, and operating temperature 158404d of the monitored element. Although the illustrated example shows uniform monitoring properties, the monitoring properties may differ from target device to target device (e.g., the digital twin system 15500 will not monitor the rotational speed of an object without a rotatable component).
Each state 15840a-n includes a set of identification criteria 158406a-n that meets a particular criteria that is unique among the set of monitored states 13240 a-n. Digital twinning system 15500 can identify overspeed state 15540a, for example, in response to monitored attributes 158404a-n meeting a first set of identification criteria 158406a (e.g., operating speed 158404b is above critical speed 158404c, and operating temperature 158404d is at nominal speed).
In response to determining that one or more states 15840a-n exist or have occurred, digital twin system 15500 may update the trigger conditions of one or more monitoring protocols, issue an alarm or notification, or trigger actions of a sub-component of digital twin system 15500. For example, a sub-component of digital twinning system 15500 may take action to mitigate and/or evaluate the effects of detected states 15540 a-n. When attempting to take action to mitigate the effect of the detected states 15540a-n on the real world element 157302r, the digital twinning system 15500 can determine whether an instruction is present (e.g., stored in the digital twinning data store 15516) or should be developed (e.g., developed through analog and cognitive intelligence or through user or worker input). Further, digital twin system 15500 can evaluate the impact of detected states 15540a-n, e.g., concurrently with the mitigating action or in response to determining that digital twin system 15500 does not have stored mitigating instructions for detected states 15540 a-n.
In an embodiment, digital twin system 15500 employs digital twin simulation system 15506 to simulate the effects of one or more identified states, such as immediate, upstream, downstream, and/or persistent effects. The digital twinning simulation system 15506 may collect and/or have values associated with the evaluation states 15540 a-n. In simulating the effects of one or more states 15540a-n, the digital twin simulation system 15506 can recursively evaluate the performance characteristics of the affected digital twin 157302d until convergence is reached. The digital twinning simulation system 15506 may, for example, work in conjunction with the cognitive intelligence system 15510 to determine that the responsive actions of one or more of the states 15540a-n are to be alleviated, mitigated, inhibited, and/or prevented from occurring. For example, the digital twinning simulation system 15506 may recursively simulate the effects of one or more of the states 15540a-n until a desired fit is achieved (e.g., convergence is achieved), provide simulation values for evaluating and determining potential actions to the cognitive intelligence system 15510, receive potential actions, evaluate the effects of each potential action against a corresponding desired fit (e.g., cost functions for minimizing production disturbances, maintaining critical components, minimizing maintenance and/or downtime, optimizing systems, worker, user or personal safety, etc.).
In an embodiment, the digital twin simulation system 15506 and the cognitive intelligence system 15510 may repeatedly share and update the simulation values and response actions for each desired result until the desired condition (e.g., convergence of each evaluation cost function for each evaluation action) is met. The digital twinning system 15500 may store the results in the digital twinning data store 15516 in response to determining that one or more of the states 15540a-n have occurred. Further, the digital twin simulation system 15506 and/or the cognitive intelligence system 15510 may perform simulation and evaluation in response to the occurrence or detection of an event.
In an embodiment, simulation and evaluation are triggered only when there are no related actions within the digital twinning system 15500. In other embodiments, the simulation and evaluation is performed concurrently with the use of the stored actions to evaluate the effectiveness or efficiency of the actions in real time and/or to evaluate whether further actions should be taken or whether unidentified conditions may occur. In an embodiment, the cognitive intelligence system 15510 may also be provided with notifications that illustrate instances of undesired actions with or without data regarding undesired aspects or results of such actions to optimize subsequent evaluations.
In an embodiment, digital twin system 15500 evaluates and/or represents the impact of machine downtime in a digital twin system of a manufacturing facility. For example, the digital twinning system 15500 may employ a digital twinning simulation system 15506 to simulate the immediate, upstream, downstream, and/or sustained effects of the machine shutdown state 15540 b. The digital twinning simulation system 15506 may collect or have performance-related values, such as optimal, sub-optimal, and minimum performance requirements for elements in the affected digital twinning 157302d (e.g., the real world element 157302r and/or the nested digital twinning 157302 d), and/or characteristics of elements that may be used in the affected digital twinning 157302d, the nested digital twinning 157302d, redundant systems within the affected digital twinning 157302d, combinations thereof, and the like.
In an embodiment, digital twinning system 15500 is used for: simulating one or more operating parameters of the real-world element in response to providing the given characteristic to the industrial environment using the real-world element digital twinning; responsive to providing the contemporaneous characteristic, computing a mitigation action to be taken by the one or more real-world elements; and in response to detecting the contemporaneous characteristic, initiating a mitigation action. The calculations may be performed in response to detection of contemporaneous characteristics or operating parameters that are outside of the respective design parameters, or the calculations may be determined through simulation prior to detection of such characteristics.
Additionally or alternatively, the digital twinning system 15500 can provide an alert to one or more users or system elements in response to detecting a status.
In an embodiment, the digital twin I/O system 15504 includes a path control module 157310. The path control module 157310 can obtain navigation data from the element 157302, provide and/or request navigation data to components of the digital twinning system 15500 (e.g., the digital twinning simulation system 15506, the digital twinning behavior system, and/or the cognitive intelligent system 15510), and/or output navigation data to the element 157302 (e.g., to the wearable device 15554). The navigation data may be collected or estimated using, for example, historical data, guidance data provided to the element 157302, combinations thereof, and the like.
For example, the navigation data may be collected or estimated using historical data stored by the digital twin system 15500. The historical data may include or be processed to provide information of acquisition time, associated elements 157302, polling intervals, tasks performed, loaded or unloaded conditions, whether to provide and/or follow previous boot data, conditions of environment 15520, other elements 157302 in environment 15520, combinations thereof, and the like. The estimation data may be determined using one or more suitable path control algorithms. For example, the estimate data may be calculated using an appropriate order picking algorithm, an appropriate path searching algorithm, combinations thereof, and the like. For example, the order picking algorithm may be a maximum gap algorithm, an S-shaped algorithm, a lane-by-lane algorithm, a combination algorithm, combinations thereof, and the like. For example, the path search algorithm may be Dijkstra algorithm, a-x algorithm, hierarchical path search algorithm, incremental path search algorithm, arbitrary angle path search algorithm, flow field algorithm, combinations thereof, and the like.
Additionally or alternatively, the navigation data may be collected or estimated using guidance data of the worker. For example, the guidance data may include a calculated route for an apparatus (e.g., a mobile device or a wearable device 15554) provided to the worker. In another example, the guidance data may include a calculated route for a device provided to the worker that instructs the worker to collect vibration measurements from one or more locations on one or more machines along the route. The collected and/or estimated navigation data may be provided to a user of digital twin system 15500 for visualization, used by other components of digital twin system 15500 for analysis, optimization, and/or modification, provided to one or more elements 157302, combinations thereof, and the like.
In an embodiment, digital twin system 15500 obtains navigation data for a set of workers for representation in the digital twin system. Additionally or alternatively, the digital twinning system 15500 inputs navigation data for a set of mobile device assets of an industrial environment into the digital twinning.
In an embodiment, the digital twinning system 15500 obtains a system for modeling traffic for mobile elements in industrial digital twinning. For example, the digital twinning system 15500 may model traffic patterns for workers or personnel, mobile device assets, combinations thereof, and the like in the environment 15520. Traffic patterns may be estimated based on modeling traffic patterns based on historical data and contemporaneous acquisition data. Further, the traffic pattern may be continuously or intermittently updated according to conditions in the environment 15520 (e.g., multiple autonomous mobile device assets may provide information to the digital twinning system 15500 at an update interval slower than the environment 15520 (including both human and mobile device assets)).
The digital twinning system 15500 can change the mode of transportation (e.g., by providing updated navigation data to one or more mobile elements) to achieve one or more predetermined criteria. For example, the predetermined criteria may include improving processing efficiency, reducing interactions between loaded workers and mobile device assets, minimizing worker path length, routing the mobile device around a person's path or potential path, combinations thereof, and the like.
In an embodiment, the digital twinning system 15500 may provide traffic data and/or navigation information to mobile elements in industrial digital twinning. The navigation information may be provided as an instruction or rule set, displayed path data, or selective actuation of the device. For example, the digital twinning system 15500 may provide a set of instructions to the robot to direct the robot to a desired route and/or to direct the robot along a desired route to collect vibration data from one or more specified locations on one or more specified machines along the route using vibration sensors. Robots may communicate updated information to the system including obstructions, route diversions, unexpected interactions with other assets in the environment 15520, and the like.
In some embodiments, ant-based system 15574 enables an industrial entity (including a robot) to use one or more messages to set trajectories that other industrial entities (including themselves) track during a later trip. In an embodiment, the message includes information related to vibration measurement collection. In an embodiment, the message includes information related to the vibration sensor measurement location. In some embodiments, the trajectory may be used to fade over time. In some embodiments, the ant-based trajectory may be experienced via an augmented reality system. In some embodiments, the ant-based trajectory may be experienced via a virtual reality system. In some embodiments, the ant-based trajectory may be experienced via a mixed reality system. In some embodiments, ant-based zone markers may trigger pain responses and/or accumulate into a warning signal. In an embodiment, the ant-based path may be used to generate an information filtering response. In some embodiments, the ant-based path may be used to generate an information filtering response, where the information filtering response is an enhanced visual perception. In some embodiments, the ant-based path may be used to generate an information filtering response, where the information filtering response is an enhanced acoustic perception. In some embodiments, the message includes vectorized data.
In an embodiment, the digital twinning system 15500 includes design specification information for representing the real world element 157302r using digital twinning 157302 d. The numbers may correspond to existing real world elements 157302r or potential real world elements 157302r. Design specification information may be received from one or more sources. For example, the design specification information may include design parameters set by user input, design parameters determined by digital twinning system 15500 (e.g., by digital twinning simulation system 15506), design parameters optimized by a user or digital twinning simulation system 15506, combinations thereof, and the like. The digital twinning simulation system 15506 may present design specification information of the components to a user, for example, via a display device or a wearable device. The design specification information may be displayed schematically (e.g., as part of a process map or information table) or as part of an augmented reality or virtual reality display. For example, the design specification information may be displayed in response to a user interaction with the digital twinning system 15500 (e.g., via a user selection element or user selection typically including the design specification information within a display). Additionally or alternatively, the design specification information may be automatically displayed, for example, when an element enters into a view of an augmented reality or virtual reality device. In an embodiment, the displayed design specification information may further include indicia of information sources (e.g., different display colors indicating user input and digital twin system 15500 determination), non-matching indicia (e.g., between design specification information and operational information), combinations thereof, and the like.
In an embodiment, the digital twinning system 15500 embeds a set of control instructions for the wearable device in the industrial digital twinning such that the control instructions are provided to the wearable device to induce an experience for a wearer of the wearable device when interacting with elements of the industrial digital twinning. The induced experience may be, for example, an augmented reality experience or a virtual reality experience. Wearable devices (e.g., headphones) may be used to output video, audio, and/or haptic feedback to the wearer to induce an experience. For example, the wearable device may include a display device and the experience may include displaying information related to the respective digital twinning. The displayed information may include maintenance data associated with digital twinning, vibration measurement location data associated with digital twinning, financial data associated with digital twinning, such as a profit and loss associated with operation of digital twinning, manufacturing KPIs associated with digital twinning, information related to an occlusion element (e.g., a sub-assembly) that is at least partially occluded by a foreground element (e.g., a housing), a virtual model of the occlusion element overlaid on and visible to the foreground element, an operating parameter of the occlusion element, a comparison of design parameters corresponding to the displayed operating parameter, combinations thereof, and the like. For example, the comparison may include altering the display of the operating parameter to alter the color, size, and/or display period of the operating parameter.
In some embodiments, the displayed information may include indicia for removable elements that are or may be used to provide access to the occlusion element, wherein each indicia is displayed proximate to or overlaying a corresponding removable element. Further, the indicia may also be displayed sequentially such that a first indicia corresponding to a first removable element (e.g., a housing) is displayed and a second indicia corresponding to a second removable element (e.g., an access panel within the housing) is displayed in response to a worker removing the first removable element. In some embodiments, the induced experience allows the wearer to see one or more locations on the machine for optimal vibration measurement collection. In an example, the digital twinning system 15500 can provide an augmented reality view that includes vibration measurement collection locations highlighted on the machine and/or instructions related to collecting vibration measurements. Further, in this example, the digital twinning system 15500 can provide an augmented reality view that includes instructions related to the timing of vibration measurement collection. Information for displaying the highlighted placement location may be obtained using information stored by digital twinning system 15500. In some embodiments, the mobile element may be tracked by the digital twinning system 15500 (e.g., via an observation element in the environment 15520 and/or via path control information communicated to the digital twinning system 15500) and continuously displayed by the wearable device within the occluded view of the worker. This optimizes the movement of the element in the environment 15520, improves worker safety, and minimizes element downtime due to damage.
In some embodiments, the digital twinning system 15500 may provide an augmented reality view that displays to the wearer a mismatch between the design parameters and the expected parameters of the real world element 157302 r. The displayed information may correspond to a real world element 157302r that is not in the line of sight of the wearer (e.g., an element in another room or an element that is mechanically obscured). This enables a worker to quickly and accurately exclude a mismatch to determine one or more sources of the mismatch. The cause of the mismatch may then be determined, for example, by the digital twinning system 15500 and the commanded corrective action. In an exemplary embodiment, the wearer may be able to view the malfunctioning sub-assembly of the machine without removing the shielding element (e.g., the housing or shroud). Additionally or alternatively, the wearer may be provided with an indication for servicing the device, for example, including a display of the removal process (e.g., the location of the fastener to be removed), components or subassemblies that should be transported to other areas for servicing (e.g., dust sensitive components), components or subassemblies that require lubrication, and the location of the object for reassembly (e.g., storing the location where the wearer placed the removed object and guiding the wearer or another wearer to the stored location to expedite reassembly and minimize further disassembly or missing components in the reassembled element). This may expedite maintenance work, minimize process impact, allow workers to disassemble and reassemble the device (e.g., by coordinating disassembly without direct communication between workers), improve device life and reliability (e.g., by ensuring that all components are properly replaced before being re-used), combinations of such components, and the like.
In some embodiments, the evoked experience includes a virtual reality view or an augmented reality view that allows the wearer to view information related to existing or planned elements. The information may be independent of the actual performance of the element (e.g., asset value, energy cost, input material cost, output material value, legal compliance, and financial performance of corporate operations, etc.). One or more wearers may perform virtual roaming or enhanced roaming of the industrial environment 15520.
For example, the wearable device displays compliance information that expedites work inspection or execution.
In other examples, the wearable device displays financial information for identifying the change or optimization objective. For example, a manager or senior manager may check whether the environment 15520 complies with updated regulations, including compliance with previous regulations, "exempt from new regulations," and/or information regarding exception elements. This may be used to reduce unnecessary downtime (e.g., schedule upgrades to a minimum impact time, such as during a planned maintenance period), prevent unnecessary upgrades (e.g., replace older or exceptional equipment), and reduce capital investment.
Referring back to fig. 155, in an embodiment, digital twin system 15500 may include, integrate, manage, manipulate digital twin dynamic model system 15508, link to digital twin dynamic model system 15508, obtain input from digital twin dynamic model system 15508, provide output to digital twin dynamic model system 15508, control digital twin dynamic model system 15508, coordinate digital twin dynamic model system 15508, or otherwise interact with digital twin dynamic model system 15508. The digital twinning dynamic model system 15508 may update a set of digital twinning attributes of a set of industrial entities and/or environments, including physical industrial assets, workers, processes, manufacturing facilities, warehouses, etc. (or any other type of entity or environment described in the present invention or in the documents incorporated by reference herein) such that digital twinning may represent industrial entities and environments and their real-time or very near real-time characteristics or attributes. In some embodiments, the digital twin dynamic model system 15508 can obtain sensor data received from the sensor system 15530 and can determine one or more attributes of an industrial environment or an industrial entity in the environment based on the sensor data and based on one or more dynamic models.
In an embodiment, the digital twinning dynamic model system 15508 can update/assign values of various attributes in the digital twinning and/or the one or more embedded digital twinning, including, but not limited to, vibration values, vibration fault level states, fault probability values, shutdown cost values, shutdown probability values, financial values, KPI values, temperature values, humidity values, heat values, fluid flow values, radiation values, material concentration values, velocity values, acceleration values, position values, pressure values, stress values, strain values, light intensity values, sound level values, volume values, shape characteristics, material characteristics, and dimensions.
In embodiments, digital twinning may include (e.g., by reference) other embedded digital twinning. For example, digital twinning of a manufacturing facility may include embedded digital twinning of a machine and one or more embedded digital twinning of one or more corresponding motors enclosed within the machine. For example, digital twinning may be embedded in a memory of an industrial machine having an on-board IT system (e.g., a memory of an on-board diagnostic system, a control system (e.g., a SCADA system), etc.). Other non-limiting examples of embeddable digital twinning include: on a wearable device of a worker; in memory on local network assets (e.g., switches, routers, access points, etc.); in cloud computing resources provided for an environment or entity; and asset tags or other memory structures specific to the entity.
In one example, the digital twin dynamic model system 15508 can update the vibration characteristics in the entire industrial environment digital twin based on captured vibration sensor data measured at one or more locations in the industrial environment and one or more dynamic models modeling vibrations in the industrial environment digital twin. Prior to updating, the industrial digital twin may already contain information about properties of the industrial entity and/or environment that may be used to feed the dynamic model, such as information of material, shape/volume (e.g. of the catheter), location, connection/interface, etc., so that vibration characteristics may be represented for the entity and/or environment in the digital twin. Alternatively, the dynamic model may be configured using this information.
In an embodiment, the digital twinning dynamic model system 15508 may update digital twinning and/or one or more embedded digital twinning attributes on behalf of the client application 15570. In an embodiment, the client application 15570 may be an application related to an industrial component or environment (e.g., monitoring an industrial facility or component therein, simulating an industrial environment, etc.). In an embodiment, the client application 15570 may be used in conjunction with fixed and mobile data collection systems. In an embodiment, the client application 15570 may be used in conjunction with an industrial internet of things sensor system 15530.
In an embodiment, the digital twin dynamic model system 15508 utilizes the digital twin dynamic model 155100 to model the behavior of an industrial entity and/or environment. For example, the dynamic model 155100 may ensure that digital twinning can represent physical reality (including interactions of industrial entities) using a limited number of measurements based on scientific principles to enrich the digital representation of the industrial entities and/or environments. In an embodiment, the dynamic model 155100 is a formula or mathematical model. In an embodiment, the dynamic model 155100 follows scientific laws, natural laws, and formulas (e.g., newton's law of motion, second law of thermodynamics, bernoulli's principle, ideal gas law, dalton partial pressure law, hooke's law of elasticity, fourier heat conduction law, archimedes' buoyancy principle, etc.). In an embodiment, the dynamic model is a machine learning model.
In an embodiment, the digital twinning system 15500 may have a digital twinning dynamic model data store 155102 for storing dynamic models 155100 that may be represented in digital twinning. In an embodiment, the digital twin dynamic model data store may be searchable and/or discoverable. In an embodiment, the digital twin dynamic model data store may contain metadata that allows a user to understand the features that a given dynamic model may handle, what inputs are required, what outputs are provided, and so on. In some embodiments, the digital twin dynamic model data store 155102 may be hierarchical (e.g., the model may be deepened or made simpler depending on the range of available data and/or inputs, granularity of inputs, and/or contextual factors (e.g., accessing high-level interests and higher fidelity models over a period of time).
In an embodiment, a digital twin or digital representation of an industrial entity or facility may include a set of data structures that collectively define a set of attributes of the represented physical industrial asset, device, worker, process, facility, and/or environment and/or possible behavior thereof. In an embodiment, the digital twinning dynamic model system 15508 may utilize the dynamic model 155100 to inform the set of data structures that collectively define digital twinning using real-time data values. The digital twin dynamic model 155100 may receive as input one or more sensor measurements, industrial internet of things device data, and/or other suitable data and calculate one or more outputs based on the received data and the one or more dynamic models 155100. The digital twin dynamic model system 15508 then uses one or more outputs to update the digital twin data structure.
In one example, the set of properties of the digital twin of the industrial asset that may be updated by the digital twin dynamic model system 15508 using the dynamic model 155100 may include vibration characteristics of the asset, one or more temperatures of the asset, a state of the asset (e.g., solid, liquid, or gaseous), a position of the asset, a displacement of the asset, a velocity of the asset, an acceleration of the asset, a shutdown probability value associated with the asset, a shutdown cost value associated with the asset, a shutdown probability value associated with the asset, a manufacturing KPI associated with the asset, financial information associated with the asset, heat flow characteristics associated with the asset, fluid flow rates associated with the asset (e.g., fluid flow rates of fluids flowing through a pipeline), identifiers of other digital twin embedded in the digital twin of the asset, and/or identifiers of digital twin embedded digital twin of the asset, and/or other suitable properties. The dynamic model 155100 associated with digital twinning of an asset may be used to calculate, interpolate, extrapolate, and/or output values for such asset digital twinning attributes based on input data and/or other suitable data collected from sensors and/or devices disposed in an industrial setting, and then populate the asset digital twinning with the calculated values.
In some embodiments, the set of properties of the digital twinning of the industrial device that may be updated by the digital twinning dynamic model system 15508 using the dynamic model 155100 may include the status of the device, the location of the device, one or more temperatures of the device, the trajectory of the device, the digital twinning of the apparatus, other digital twinning identifiers embedded, linked, included, integrated, input obtained therefrom, output provided thereto and/or interacted with, etc. The dynamic model 155100 associated with digital twinning of devices may be used to calculate values or output values for these device digital twinning attributes based on the input data and then use the calculated values to update the device digital twinning.
In some embodiments, the set of properties of the digital twinning of the industrial worker that may be updated by the digital twinning dynamic model system 15508 using the dynamic model 155100 may include the status of the worker, the location of the worker, pressure measurements of the worker, tasks performed by the worker, performance measurements of the worker, and the like. The dynamic model associated with the industrial worker's digital twinning may be used to calculate or output values of these attributes based on the input data, which calculated values may then be used to populate the industrial worker's digital twinning. In embodiments, an industrial worker dynamic model (e.g., a psychometric model) may be used to predict responses to stimuli (e.g., give a prompt to workers) to instruct them to perform tasks and/or alarms or warnings intended to induce safety behavior. In an embodiment, the industrial worker dynamic model may be a workflow model (Gantt chart, etc.), a failure mode impact analysis model (FMEA), a biophysical model (e.g., modeling worker fatigue, energy utilization, hunger), etc.
Exemplary properties of the digital twin dynamic model of the industrial environment that may be updated by the digital twin dynamic model system 15508 using the dynamic model 155100 may include the scale of the industrial environment, one or more temperatures of the industrial environment, one or more humidity values of the industrial environment, fluid flow characteristics in the industrial environment, heat flow characteristics of the industrial environment, lighting characteristics of the industrial environment, acoustic characteristics of the industrial environment, physical objects in the environment, processes occurring in the industrial environment, water flow (if a body of water) of the industrial environment, and the like. The dynamic model associated with digital twinning of an industrial environment may be used to calculate or output these attributes based on input data collected from sensors and/or devices disposed in the industrial environment and/or other suitable data, and then populate the industrial environment digital twinning with the calculated values.
In an embodiment, the dynamic model 155100 may follow physical constraints that define boundary conditions, constants, or variables for digital twin modeling. For example, the digital twinned physical characteristics of an industrial entity or industrial environment may include a gravitational constant (e.g., 9.8m/s 2), a surface coefficient of friction, a material thermal coefficient, a maximum temperature of an asset, a maximum flow rate, and the like. Additionally or alternatively, the dynamic model may also follow natural laws. For example, the dynamic model may follow the laws of thermodynamics, motion, fluid dynamics, buoyancy, heat transfer, radiation, quantum dynamics, etc. In some embodiments, the dynamic model may follow biological aging theory or mechanical aging theory. Thus, when the digital twinned dynamic model system 15508 facilitates a real-time digital representation, the digital representation may conform to the dynamic model such that the digital representation simulates real world conditions. In some embodiments, one or more outputs from the dynamic model may be presented to a human user and/or compared to real world data to ensure convergence of the dynamic model with the real world. Further, since the dynamic model is based in part on assumptions, the properties of digital twinning may be improved and/or corrected when the behavior of the real world is different from that of digital twinning. In an embodiment, additional data collection and/or instrumentation may be recommended based on the following awareness: the expected lack of input to the dynamic model, the model in operation does not work as intended (possibly due to lack and/or erroneous sensor information), the need for different results (e.g., due to contextual factors that make something highly interesting), etc.
The dynamic model may be obtained from a number of different sources. In some embodiments, the user may upload a model created by the user or a third party. Additionally or alternatively, a model may be created on the digital twinning system using a graphical user interface. The dynamic model may include a custom model configured for a particular environment and/or a set of industrial entities and/or agnostic models adapted for similar types of digital twinning. The dynamic model may be a machine learning model.
Fig. 159 illustrates an exemplary embodiment of a method for updating a set of attributes of a digital twinning and/or one or more embedded digital twinning on behalf of a client application 15570. In an embodiment, digital twin dynamic model system 15508 utilizes one or more dynamic models 155100 to update a set of properties of digital twin and/or one or more embedded digital twin on behalf of client application 15570 based on sensor data collected from sensor system 15530, data collected from an internet-of-things connection device 15524, and/or the effects of other suitable data in the set of dynamic models 155100 for implementing industrial digital twin. In an embodiment, the digital twinned dynamic model system 15508 may be instructed to run a particular dynamic model using one or more digital twins that represent physical industrial assets, devices, workers, processes, and/or industrial environments managed, maintained, and/or monitored by the client application 15570.
In an embodiment, the digital twin dynamic model system 15508 may obtain data from other types of external data sources that are not necessarily industrial related data sources, but may provide data that may be used as input data for a dynamic model. For example, weather data, news events, social media data, etc. may be collected, crawled, subscribed to, to supplement sensor data, industrial internet of things device data, and/or other data used by the dynamic model. In an embodiment, the digital twin dynamic model system 15508 may obtain data from a machine vision system. The machine vision system may use video and/or still images to provide measurements (e.g., position, status, etc.) that may be used as input by the dynamic model.
In an embodiment, the digital twin dynamic model system 15508 may feed this data into one or more of the dynamic models described above to obtain one or more outputs. These outputs may include calculated vibration fault level states, vibration severity unit values, vibration characteristics, fault probability values, shutdown cost values, time to failure values, temperature values, pressure values, humidity values, precipitation values, visibility values, air quality values, strain values, stress values, displacement values, velocity values, acceleration values, position values, performance values, financial values, manufacturing KPI values, electrodynamic values, thermodynamic values, fluid flow rate values, and the like. The client application 15570 may then use the results obtained by the digital twin dynamic model system 15508 to initiate a digital twin visualization event. In an embodiment, the visualization may be a heat map visualization.
In an embodiment, the digital twinning dynamic model system 15508 may receive a request to update one or more attributes of the digital twinning of the industrial entity and/or environment such that the digital twinning represents the industrial entity and/or environment in real-time. At 159100, the digital twinning dynamic model system 15508 receives a request to update one or more attributes of one or more digital twinning of an industrial entity and/or environment. For example, the digital twinning dynamic model system 15508 may receive a request from the client application 15570 or from another process (e.g., a predictive maintenance process) performed by the digital twinning system 15500. The request may indicate one or more attributes and a digital twin or digital twin associated with the request. In step 159102, the digital twinning dynamic model system 15508 determines the one or more digital twinning required to fulfill the request and retrieves the required one or more digital twinning, including any embedded digital twinning, from the digital twinning data store 15516. At 159104, the digital twin dynamic model system 15508 determines the one or more dynamic models required to satisfy the request and retrieves the required one or more dynamic models from the digital twin dynamic model data store 155102. At 159106, the digital twin dynamic model system 15508 selects one or more sensors in the sensor system 15530, data collected from the physical networking connection device 15524, and/or other data sources in the digital twin I/O system 15504 based on the available data sources and one or more desired inputs of one or more dynamic models. In embodiments, the data sources may be defined in the inputs required for one or more dynamic models, or may be selected using a lookup table. At 159108, the digital twin dynamic model system 15508 retrieves selected data from the digital twin I/O system 15504. At 159110, the digital twin dynamic model system 15508 uses the retrieved input data (e.g., vibration sensor data, industrial internet of things device data, etc.) as input to run one or more dynamic models and determine one or more output values based on the one or more dynamic models and the input data. At 159112, the digital twin dynamic model system 15508 updates values of one or more attributes of one or more digital twin based on one or more outputs of one or more dynamic models.
In an exemplary embodiment, the client application 15570 can be used to provide a digital representation and/or visualization of digital twinning of an industrial entity. In an embodiment, the client application 15570 may include one or more software modules executed by one or more server devices. These software modules may be used to quantify properties of digital twinning, model properties of digital twinning, and/or to visualize digital twinning behavior. In an embodiment, these software modules may enable a user to select a particular digital twinning behavior visualization to view. In an embodiment, these software modules may enable a user to choose to view a digital twinning behavior visualization. In some embodiments, the client application 15570 can provide the selected behavior visualization to the digital twin dynamic model system 15508.
In an embodiment, the digital twinning dynamic model system 15508 can receive a request from the client application 15570 to update properties of the digital twinning to enable a digital representation of an industrial entity and/or environment, wherein the real-time digital representation is a visualization of the digital twinning. In an embodiment, digital twinning may be presented by a computing device such that a human user may view a digital representation of a real-world industrial asset, apparatus, worker, process, and/or environment. For example, digital twinning may be presented and output to a display device. In an embodiment, the dynamic model output and/or related data may be superimposed on a digitally twinned presentation. In an embodiment, dynamic model output and/or related information may appear as digital twinning is presented in a display interface. In an embodiment, the related information may include a real-time video clip associated with a real-world entity represented by a digital twin. In an embodiment, the relevant information may include a sum of each vibration fault level state in the machine. In an embodiment, the related information may be graphical information. In an embodiment, the graphical information may describe the motion and/or describe the motion as a function of the frequency of the individual machine components. In an embodiment, the graphical information may describe the motion and/or describe the motion as a function of the frequency of the individual machine components, wherein the user is able to select views of the graphical information in the x, y and z dimensions. In an embodiment, the graphical information may describe the motion and/or describe the motion as a function of frequency of the individual machine components, wherein the graphical information includes harmonic peaks and peaks. In an embodiment, the relevant information may be cost data, including outage cost data, maintenance cost data, new component cost data, new machine cost data, etc. per day. In an embodiment, the relevant information may be outage probability data, failure probability data, or the like. In an embodiment, the relevant information may be time of failure data.
In an embodiment, the relevant information may be advice and/or insight. For example, advice or insight obtained from cognitive intelligence systems associated with the machine may be presented in a display interface as a digital twin of the machine.
In embodiments, clicking, touching, or otherwise interacting with digital twins presented in a display interface may allow a user to "see in depth" and view the underlying subsystem or process and/or embedded digital twins. For example, in response to a user clicking on a machine bearing presented in a digital twin of the machine, the display interface may allow the user to learn and view information about the bearing, view a 3D visualization of the bearing vibration, and/or view the digital twin of the bearing.
In embodiments, clicking, touching, or otherwise interacting with information related to digital twinning presented in a display interface may allow a user to "learn" about and view basic information.
FIG. 160 illustrates an exemplary embodiment of a display interface presenting digital twinning of a dryer centrifuge and other information related to the dryer centrifuge.
In some embodiments, the digital twinning may be presented and output in a virtual reality display. For example, a user may view a 3D presentation of the environment (e.g., using a display or virtual reality headset). The user may also check and/or interact with the digital twinning of the industrial entity. In an embodiment, a user may view a process being performed for one or more digital twins (e.g., collecting measurements, moving, interacting, inventorying, loading, packaging, transporting, etc.). In an embodiment, a user may provide input through a graphical user interface that controls one or more properties of a digital twin.
In some embodiments, the digital twinning dynamic model system 15508 can receive a request from the client application 15570 to update properties of the digital twinning to enable a digital representation of an industrial entity and/or environment, wherein the digital representation is a digital twinning heatmap visualization. In an embodiment, a platform is provided with a heat map showing data collected from sensor system 15530, internet of things connection device 15524, and data output from dynamic model 155100 for providing input to a display interface. In an embodiment, the heat map interface is provided as an output of digital twinning data, e.g., for processing and providing visual information of various sensor data, dynamic model output data, and other data (e.g., map data, analog sensor data, and other data) to another system, e.g., a mobile device, tablet, control panel, computer, AR/VR device, etc. A digital twin representation, such as a representation of a map that includes analog sensor data, a level indicator of digital sensor data, and output values from a dynamic model (e.g., data indicative of vibration fault level status, vibration severity unit values, shutdown probability values, shutdown cost values, shutdown probability values, failure time values, failure probability values, manufacturing KPIs, temperatures, rotation levels, vibration characteristics, fluid flow, heating or cooling, pressure, substance concentration, and many other output values) may be provided in a form factor suitable for transmitting visual input to a user (e.g., a user device, a VR enabled device, an AR enabled device, etc.). In an embodiment, signals (or selective combinations, permutations, blends, etc.) from various sensors or input sources and data determined by the digital twin dynamic model system 15508 may provide input data to a heatmap. The coordinates may include real world location coordinates (e.g., geographic location or location on an environmental map) as well as other coordinates, such as time-based coordinates, frequency-based coordinates, or other coordinates that allow for the representation of analog sensor signals, digital signals, dynamic model outputs, input source information, and various combinations in a map-based visualization, such that the colors may represent different input levels along the relevant dimensions. For example, in many other possibilities, if an industrial machine component is in a critical vibration fault level state, the heat map interface may alert a user by displaying the machine component in orange. In a heat map example, clicking, touching, or otherwise interacting with the heat map may allow a user to gain insight into and view the underlying sensors, dynamic model outputs, or other input data used as heat map display inputs. In other examples, such as examples where digital twinning is displayed in VR or AR environments, if vibration of an industrial machine component exceeds normal operation (e.g., at a suboptimal vibration, critical vibration, or alarm vibration failure level), when a user contacts a representation of the machine component, or if the machine component is operating in an unsafe manner, the haptic interface may induce vibration, and the directional sound signal may direct the user's attention to the machine in digital twinning, such as by playing in a particular speaker of a headset or other sound system.
In an embodiment, the digital twinning dynamic model system 15508 may obtain a set of ambient environmental data and/or other data and automatically update a set of attributes of the digital twinning of the industrial entity or facility based on the impact of the environmental data and/or other data in the dynamic model set 155100 for enabling digital twinning. The ambient data may include temperature data, pressure data, humidity data, wind data, rainfall data, tide data, storm tide data, cloud cover data, snowfall data, visibility data, water level data, and the like. Additionally or alternatively, the digital twinning dynamic model system 15508 may use a set of environmental data measurements collected by a set of internet of things connection devices 15524 disposed in an industrial environment as input to the set of dynamic models 155100 for enabling digital twinning. For example, digital twin dynamic model system 15508 may feed data collected, processed, or exchanged by internet of things connection device 15524, such as cameras, displays, embedded sensors, mobile devices, diagnostic devices and systems, instrumentation systems, telematics systems, etc., for example, for monitoring machines, devices, components, parts, operations, functions, conditions, states, events, workflows, and other elements of an industrial environment (collectively, "states"). Other examples of internet of things connection devices include intelligent fire alarms, intelligent security systems, intelligent air quality monitors, intelligent/learning thermostats, and intelligent lighting systems.
FIG. 161 illustrates an exemplary embodiment of a method for updating a set of vibration fault level states for a set of bearings in a digital twin of a machine. In this example, the client application 15570 interfacing with the digital twinning dynamic model system 15508 can be used to provide visualization of the fault level status of the bearings in digital twinning of the machine.
In this example, the digital twinned dynamic model system 15508 can receive a request from the client application 15570 to update the vibration fault level state of the machine digital twinning. At 161200, the digital twinning dynamic model system 15508 receives a request from the client application 15570 to update one or more vibration fault level states of the machine digital twinning. Next, in step 161202, the digital twinning dynamic model system 15508 determines the one or more digital twinning required to fulfill the request and retrieves the required one or more digital twinning from the digital twinning data store 15516. In this example, the digital twin dynamic model system 15508 can retrieve digital twinning of the machine and any embedded digital twinning (e.g., any embedded motor digital twinning and bearing digital twinning) as well as any digital twinning embedded into the machine digital twinning (e.g., manufacturing facility digital twinning). At 161204, the digital twin dynamic model system 15508 determines the one or more dynamic models required to satisfy the request and retrieves the required one or more dynamic models from the digital twin dynamic model data store 155102. At 161206, digital twin dynamic model system 15508 selects a dynamic model input data source (e.g., one or more sensors from sensor system 15530, data from internet of things connection device 15524, and any other suitable data) via digital twin I/O system 15504 based on available data sources (e.g., available sensors from a set of sensors of sensor system 15530) and one or more desired inputs of one or more dynamic models. In this example, the retrieved one or more dynamic models 155100 can take as input to the dynamic model one or more vibration sensor measurements from vibration sensor 15536. In an embodiment, vibration sensor 15536 may be an optical vibration sensor, a uniaxial vibration sensor, a triaxial vibration sensor, or the like. At 161208, the digital twin dynamic model system 15508 retrieves one or more measurements from each selected data source via the digital twin I/O system 15504. Next, in 161210, the digital twin dynamic model system 15508 uses the retrieved vibration sensor measurements as input to run one or more dynamic models and calculate one or more outputs representing bearing vibration fault level states. Next, at 161212, the digital twinning dynamic model system 15508 updates one or more bearing fault level states of the manufacturing facility digital twinning, machine digital twinning, motor digital twinning, and/or bearing digital twinning based on one or more outputs of the one or more dynamic models. The client application 15570 can obtain the vibration fault level status of the bearings and can display the obtained vibration fault level status associated with each bearing and/or display color associated with the fault level severity when one or more digital twins are presented on a display interface (e.g., red for alarm, critical, yellow for suboptimal, green for normal operation).
In another example, the client application 15570 can be an augmented reality application. In some embodiments of this example, the client application 15570 may obtain the vibration fault level state of the bearing in the field of view of the AR-enabled device (e.g., smart glasses) from digital twinning of the industrial environment (e.g., through the API of the digital twinning system 15500), and may display the obtained vibration fault level state on a display of the AR-enabled device such that the displayed vibration fault level state corresponds to a location in the field of view of the AR-enabled device. In this way, even if there is no vibration sensor within the field of view of the AR-enabled device, a vibration fault level status may be displayed.
FIG. 162 illustrates an exemplary embodiment of a method for updating a set of vibration severity unit values for a digital twin center bearing of a machine. The vibration severity unit may be measured as displacement, velocity, and acceleration.
In this example, the client application 15570 interfacing with the digital twinning dynamic model system 15508 can be used to provide visualization of the three-dimensional vibration characteristics of the bearing in digital twinning of the machine.
In this example, the digital twin dynamic model system 15508 can receive a request from the client application 15570 to update the vibration severity unit value of the bearing in digital twin of the machine. At 162300, the digital twinning dynamic model system 15508 receives a request from the client application 15570 to update one or more vibration severity unit values of the manufacturing facility digital twinning. Next, in step 162302, the digital twinning dynamic model system 15508 determines the one or more digital twinning required to fulfill the request and retrieves the required one or more digital twinning from the digital twinning data store 15516. In this example, the digital twin dynamic model system 15508 can retrieve the digital twin of the machine and any embedded digital twin (e.g., of bearings and other components). At 162304, the digital twin dynamic model system 15508 determines the one or more dynamic models required to satisfy the request and retrieves the required one or more dynamic models from the dynamic model data store 155102. At 162306, digital twin dynamic model system 15508 selects a dynamic model input data source (e.g., one or more sensors from sensor system 15530, data from internet of things connection device 15524, and any other suitable data) via digital twin I/O system 15504 based on available data sources (e.g., available sensors from a set of sensors of sensor system 15530) and one or more desired inputs of one or more dynamic models. In this example, the retrieved dynamic model may be used to take as input one or more vibration sensor measurements and provide a severity unit value for the bearing in the machine. At 162308, the digital twin dynamic model system 15508 retrieves one or more measurements from each selected sensor. In this example, the digital twin dynamic model system 15508 retrieves measurements from the vibration sensor 15536 via the digital twin I/O system 15504. At 162310, the digital twin dynamic model system 15508 runs one or more dynamic models using the retrieved vibration measurements as input and calculates one or more output values that represent the vibration severity unit values for the bearings in the machine. Next, at 162312, the digital twin dynamic model system 15508 updates one or more vibration severity unit values of the machine digital twin and all other embedded digital twin or digital twin in-bearing of the embedded machine digital twin based on one or more values of the one or more dynamic model outputs.
FIG. 163 illustrates an exemplary embodiment of a method for updating a set of fault probability values for a machine component in a digital twin of a machine.
In this example, the digital twinning dynamic model system 15508 can receive a request from the client application 15570 to update the failure probability values of the components in the machine digital twinning. At 156400, the digital twinning dynamic model system 15508 receives a request from the client application 15570 to update one or more fault probability values for machine digital twinning, any embedded component digital twinning, and any digital twinning (e.g., manufacturing facility digital twinning) embedded with the machine digital twinning. Next, in step 163402, the digital twinning dynamic model system 15508 determines the digital twinning or twinning required to fulfill the request and retrieves the required digital twinning or twinning. In this example, the digital twin dynamic model system 15508 can retrieve digital twin of the manufacturing facility, digital twin of the machine, and digital twin of the machine components from the digital twin data store 15516. At 163404, the digital twin dynamic model system 15508 determines the one or more dynamic models required to satisfy the request and retrieves the required one or more dynamic models from the dynamic model data store 155102. At 163406, digital twin dynamic model system 15508 selects a dynamic model input data source (e.g., one or more sensors from sensor system 15530, data from internet of things connection device 15524, and any other suitable data) via digital twin I/O system 15504 based on available data sources (e.g., available sensors from a set of sensors of sensor system 15530) and one or more desired inputs of one or more dynamic models. In this example, the retrieved dynamic model may input one or more vibration measurements from vibration sensor 15536 and historical fault data as a dynamic model and output a fault probability value for a machine component in digital twinning of the machine. At 163408, the digital twin dynamic model system 15508 retrieves data from each selected sensor and/or internet of things connection device via the digital twin I/O system 15504. At 163410, the digital twin dynamic model system 15508 uses the retrieved vibration data and historical fault data as inputs to run one or more dynamic models and calculate one or more outputs representing fault probability values for bearings in the machine digital twin. Next, at 163412, the digital twin dynamic model system 15508 updates one or more failure probabilities of the machine digital twin, all embedded digital twin, and all digital twin in which the machine digital twin is embedded based on the output of the one or more dynamic models.
FIG. 164 illustrates an exemplary embodiment of a method for updating a set of outage probabilities for a machine in a digital twin of a manufacturing facility.
In this example, the client application 15570 interfacing with the digital twinning dynamic model system 15508 can be used to provide visualization of outage probability values for a manufacturing facility in digital twinning of the manufacturing facility.
In this example, the digital twinning dynamic model system 15508 can receive a request from the client application 15570 to assign a shutdown probability value to a machine in the manufacturing facility digital twinning. At 164500, digital twinning dynamic model system 16208 receives a request from client application 15570 to update one or more outage probability values for machines in manufacturing facility digital twinning and any embedded digital twinning (e.g., single machine digital twinning). Next, in step 164502, the digital twinning dynamic model system 15508 determines the one or more digital twinning required to fulfill the request and retrieves the required one or more digital twinning from the digital twinning data store 15516. In this example, the digital twin dynamic model system 15508 can retrieve the digital twin and any embedded digital twin of the manufacturing facility from the digital twin data store 15516. At 164504, the digital twin dynamic model system 15508 determines the one or more dynamic models required to satisfy the request and retrieves the required one or more dynamic models from the dynamic model data store 155102. At 164506, digital twin dynamic model system 15508 selects a dynamic model input data source (e.g., one or more sensors from sensor system 15530, data from internet of things connection device 15524, and any other suitable data) via digital twin I/O system 15504 based on available data sources (e.g., available sensors from a set of sensors of sensor system 15530) and one or more desired inputs of one or more dynamic models. In this example, one or more dynamic models may be used to take as input vibration measurements from vibration sensors and historical shutdown data and output shutdown probability values for different machines throughout the manufacturing facility. At 164508, the digital twin dynamic model system 15508 retrieves one or more measurements from each selected sensor via the digital twin I/O system 15504. At 164510, the digital twin dynamic model system 15508 uses the retrieved vibration measurements and historical shutdown data as inputs to run one or more dynamic models and calculate one or more outputs representing shutdown probability values for machines in the manufacturing facility. Next, at 164512, the digital twin dynamic model system 15508 updates one or more outage probability values for the manufacturing facility digital twin and all embedded digital twin machines based on one or more outputs of the dynamic model.
FIG. 165 illustrates an exemplary embodiment of a method for updating one or more shutdown probability values for digital twinning of an enterprise having a set of manufacturing facilities.
In this example, digital twinning dynamic model system 15508 may receive a request from client application 15570 to update downtime probability values for a set of manufacturing facilities in an enterprise digital twinning. At 165600, digital twinning dynamic model system 15508 receives a request from client application 15570 to update one or more shutdown probability values for enterprise digital twinning and any embedded digital twinning. Next, in step 165602, the digital twinning dynamic model system 15508 determines the one or more digital twinning required to fulfill the request and retrieves the required one or more digital twinning from the digital twinning data store 15516. In this example, the digital twinning dynamic model system 15508 can retrieve enterprise digital twinning and any embedded digital twinning. At 165604, the digital twin dynamic model system 15508 determines the one or more dynamic models required to satisfy the request and retrieves the required one or more dynamic models from the dynamic model data store 155102. At 165606, digital twin dynamic model system 15508 selects a dynamic model input data source (e.g., one or more sensors from sensor system 15530, data from internet of things connection device 15524, and any other suitable data) via digital twin I/O system 15504 based on available data sources (e.g., available sensors from a set of sensors of sensor system 15530) and one or more desired inputs of one or more dynamic models. In this example, the retrieved dynamic model may be used to take as input one or more vibration measurements from vibration sensor 15536 and/or other suitable data and output shutdown probability values for each manufacturing entity in the enterprise digital twin. At 165608, the digital twin dynamic model system 15508 retrieves one or more vibration measurements from each selected vibration sensor 15536 via the digital twin I/O system 15504. At 165610, the digital twin dynamic model system 15508 uses the retrieved vibration measurements and historical shutdown data as inputs to run one or more dynamic models and calculate one or more outputs representing shutdown probability values for the manufacturing facility in the enterprise digital twin. Next, at 165612, the digital twin dynamic model system 15508 updates one or more shutdown probability values for the enterprise digital twin and all embedded digital twin based on one or more outputs of the one or more dynamic models.
FIG. 159 illustrates an exemplary embodiment of a method for updating a set of downtime cost values for a machine in a digital twin of a manufacturing facility. In an embodiment, manufacture
In this example, the digital twinning dynamic model system 15508 can receive a request from the client application 15570 to populate real-time downtime cost values associated with machines in the manufacturing facility digital twinning. At 159700, digital twinning dynamic model system 15508 receives a request from client application 15570 to update manufacturing facility digital twinning and one or more downtime cost values for any embedded digital twinning (e.g., machine component, etc.) from client application 15570. Next, in step 159702, the digital twinning dynamic model system 15508 determines the digital twinning or twinning required to fulfill the request and retrieves the required digital twinning or twinning. In this example, the digital twin dynamic model system 15508 can retrieve digital twin of the manufacturing facility, machine component, and any other embedded digital twin from the digital twin data store 15516. At 159704, the digital twin dynamic model system 15508 determines the one or more dynamic models required to satisfy the request and retrieves the required one or more dynamic models from the dynamic model data store 155102. At 159706, digital twin dynamic model system 15508 selects a dynamic model input data source (e.g., one or more sensors from sensor system 15530, data from internet of things connection device 15524, and any other suitable data) via digital twin I/O system 15504 based on available data sources (e.g., available sensors from a set of sensors of sensor system 15530) and one or more desired inputs of one or more dynamic models. In this example, one or more retrieved dynamic models may be used to take as input historical downtime data and operational data, and output data representing daily downtime costs for machines in a manufacturing facility. At 159708, the digital twin dynamic model system 15508 retrieves historical shutdown data and operational data from the digital twin I/O system 15504. At 159710, the digital twin dynamic model system 15508 uses the retrieved data as input to run one or more dynamic models and calculate one or more outputs representing the daily downtime costs of the machine in the manufacturing facility. Next, at 159712, the digital twin dynamic model system 15508 updates one or more downtime cost values for the manufacturing facility digital twin and the machine digital twin based on one or more outputs of the one or more dynamic models.
FIG. 167 illustrates an exemplary embodiment of a method for updating a digitally twinned set of manufacturing KPI values for a manufacturing facility. In an embodiment, the manufacturing KPI is selected from the group consisting of: normal operation time, capacity utilization, standard operation efficiency, overall plant availability, machine downtime, unplanned downtime, machine setup time, inventory turnover, inventory accuracy, quality (e.g., failure rate), primary pass rate, rework, discard, number of audit failures, on-time delivery, customer returns, number of training hours, employee flow rate, reportable health and safety incidents, employee average revenue, employee average profit, planned completion, total cycle time, throughput, conversion time, profitability, planned maintenance percentage, availability, and customer return rate.
In this example, the digital twinning dynamic model system 15508 can receive a request from the client application 15570 to populate the real-time manufacturing KPI values of the manufacturing facility digital twinning. At 167800, the digital twinning dynamic model system 15508 receives a request from the client application 15570 to update one or more KPI values of the manufacturing facility digital twinning and any embedded digital twinning (e.g., machines, machine components, etc.) from the client application 15570. Next, in step 167802, the digital twinning dynamic model system 15508 determines the digital twinning or twinning required to fulfill the request and retrieves the required digital twinning or twinning. In this example, the digital twin dynamic model system 15508 can retrieve digital twin of the manufacturing facility, machine component, and any other embedded digital twin from the digital twin data store 15516. At 167804, the digital twin dynamic model system 15508 determines the one or more dynamic models required to satisfy the request and retrieves the required one or more dynamic models from the dynamic model data store 155102. At 167806, digital twin dynamic model system 15508 selects a dynamic model input data source (e.g., one or more sensors from sensor system 15530, data from internet of things connection device 15524, and any other suitable data) via digital twin I/O system 15504 based on available data sources (e.g., available sensors from a set of sensors of sensor system 15530) and one or more desired inputs of one or more dynamic models. In this example, the one or more retrieved dynamic models may be used to take as input one or more vibration measurements and other operational data obtained from vibration sensor 15536 and output one or more manufacturing KPIs for the facility. At 167808, the digital twin dynamic model system 15508 retrieves one or more vibration measurements from each selected vibration sensor 15536 and operational data from the digital twin I/O system 15504. At 167810, the digital twin dynamic model system 15508 uses the retrieved vibration measurements and operational data as inputs to run one or more dynamic models and calculate one or more outputs representing manufacturing KPIs for the manufacturing facility. Next, at 167812, the digital twinning dynamic model system 15508 updates one or more KPI values of the manufacturing facility digital twinning, the machine component digital twinning, and all other embedded digital twinning based on one or more outputs of the one or more dynamic models.
Further embodiments may include the following examples. Fig. 155 illustrates an exemplary environment for a digital twinning system 15500. In an embodiment, the digital twinning system 15500 generates a set of digital twinning of the set of industrial environments 15520 and/or the industrial entities in the set of industrial environments. In an embodiment, the digital twinning system 15500 uses sensor data or the like acquired from the respective sensor system 15530 monitoring the industrial environment 15520 to maintain a set of states of the respective industrial environment 15520. In an embodiment, the digital twinning system 15500 can include a digital twinning management system 15502, a digital twinning I/O system 15504, a digital twinning simulation system 15506, a digital twinning dynamic model system 15508, a cognitive intelligence system 15510, and/or an environmental control module 15512. In an embodiment, digital twinning system 15500 can provide a real-time sensor API that provides a set of capabilities for enabling a set of interfaces for sensors of a respective sensor system 15530. In embodiments, digital twin system 15500 may include and/or employ other suitable APIs, agents, connectors, bridges, gateways, hubs, ports, routers, switches, data integration systems, peer systems, etc. to facilitate the transfer of data to and from digital twin system 15500. In these embodiments, these connection components may allow IoT sensors or intermediate devices (e.g., relays, edge devices, switches, etc.) in sensor system 15530 to transmit data to digital twin system 155300 and/or receive data (e.g., configuration data, control data, etc.) from digital twin system 15500 or other external systems. In an embodiment, the digital twinning system 15500 can also include a digital twinning data store 15516 that stores digital twinning 15518 of various industrial environments 15520, as well as objects 15522, devices 15524, sensors 15526, and/or humans 15528 in the environment 15520.
Digital twinning may refer to a digital representation of one or more industrial entities, such as industrial environment 15520, physical object 15522, device 15524, sensor 15526, human 15528, or any combination thereof. Examples of industrial environments 15520 include, but are not limited to, factories, power plants, food production facilities (which may include inspection facilities), commercial kitchens, indoor planting facilities, natural resource excavation sites (e.g., mines, oil fields, etc.), and the like. The types of objects, devices and sensors found in an environment will also vary depending on the type of environment. Non-limiting examples of physical objects 15522 include raw materials, manufactured products, excavated material, containers (e.g., boxes, garbage cans, cooling towers, vats, trays, drums, boxes, etc.), furniture (e.g., tables, counters, workstations, shelves, etc.), and the like. Non-limiting examples of the device 15524 include robots, computers, vehicles (e.g., automobiles, trucks, tank trucks, trains, forklifts, cranes, etc.), machines/devices (e.g., tractors, tillers, drills, presses, assembly lines, conveyors, etc.), and the like. The sensor 15526 can be any sensor device and/or sensor aggregation device found in the sensor system 15530 in the environment. Non-limiting examples of sensors 15526 that may be implemented in the sensor system 15530 may include temperature sensors 15532, humidity sensors 15534, vibration sensors 15536, LIDAR sensors 15538, motion sensors 15540, chemical sensors 15542, audio sensors 15544, pressure sensors 15546, weight sensors 15548, radiation sensors 15550, video sensors 15552, wearable devices 15554, relays 15556, edge devices 15558, cross-point switches 15560, and/or any other suitable sensors. Examples of different types of physical objects 15522, devices 15524, sensors 15526, and environments 15520 are referenced herein.
In some embodiments, device-in-sensor fusion and data storage for industrial IoT devices is supported, including device-in-sensor fusion and data storage for industrial IoT devices, wherein data from multiple sensors is multiplexed in a device for storing a fused data stream. For example, in a byte-like structure (where time, pressure, and temperature are bytes in a data structure such that pressure and temperature remain associated in time without requiring separate processing of the streams by an external system), or by addition, division, multiplication, subtraction, etc., pressure and temperature data may be multiplexed into a data stream that combines pressure and temperature in a time series such that the fused data may be stored on the device. Any of the sensor data types described throughout the present disclosure (including vibration data) may be fused in this manner and stored in a local data pool, memory, or on an IoT device, such as a data collector, machine component, or the like.
In some embodiments, a set of digital twins may represent an entire organization, such as an energy production organization, an oil and gas organization, a renewable energy production organization, an aerospace manufacturer, a vehicle manufacturer, a heavy equipment manufacturer, a mining organization, a drilling organization, an offshore platform organization, and the like. In these examples, digital twinning may include digital twinning of one or more industrial facilities of the organization.
In an embodiment, digital twin management system 15502 generates digital twin. Digital twinning may include (e.g., by reference) other digital twinning. In this way, discrete digital twinning may include a set of other discrete digital twinning. For example, digital twinning of a machine may include digital twinning of sensors on the machine, digital twinning of components that make up the machine, digital twinning of other devices incorporated in or integrated with the machine (e.g., a system that provides input to or takes output from the machine), and/or digital twinning of products or other items manufactured by the machine. Still further to this example, digital twinning of an industrial facility (e.g., a plant) may include digital twinning that represents the layout of the industrial facility, including the placement of physical assets and systems within or around the facility, as well as digital assets of assets within the facility (e.g., digital twinning of machines), as well as digital twinning of storage areas within the facility, digital twinning of humans collecting vibration measurements from machines of the entire facility, and so forth. In this second example, digital twinning of an industrial facility may refer to embedded digital twinning, and then other digital twinning embedded in these digital twinning may be referred to.
In some embodiments, digital twinning may represent an abstract entity, such as a workflow and/or process, including inputs, outputs, sequences of steps, decision points, processing loops, etc., that make up such a workflow and process. For example, digital twinning may be a digital representation of a manufacturing process, a logistics workflow, an agricultural process, or a mineral extraction process, among others. In these embodiments, the digital twinning may include a reference to an industrial entity contained in a workflow or process. Digital twinning of a manufacturing process may reflect various stages of the process. In some of these embodiments, the digital twinning system 15500 receives real-time data from an industrial facility (e.g., from the sensor system 15530 of the environment 15520) where the manufacturing process occurs and reflects the current (or substantially current) state of the process in real-time.
In an embodiment, the digital representation may include a set of data structures (e.g., categories) that collectively define a set of attributes of the physical object 15522, device 15524, sensor 15526, or environment 15520 of the representation and/or its possible behavior. For example, the set of properties of the physical object 15522 may include a type of the physical object, a size of the object, a mass of the object, a density of the object, one or more materials of the object, physical characteristics of the one or more materials, a surface of the physical object, a state of the physical object, a location of the physical object, an identifier of other digital twins contained in the object, and/or other suitable properties. Examples of behavior of a physical object may include a state of the physical object (e.g., solid, liquid, or gas), a melting point of the physical object, a density of the physical object when in a liquid state, a viscosity of the physical object when in a liquid state, a freezing point of the physical object, a density of the physical object when in a solid state, a hardness of the physical object when in a solid state, a ductility of the physical object, a buoyancy of the physical object, a conductivity of the physical object, a burning point of the physical object, a manner of influence of humidity on the physical object, a manner of influence of water or other liquid on the physical object, a terminal velocity of the physical object, and so forth. In another example, a set of properties of a device may include a type of the device, a size of the device, a mass of the device, a density of the device, one or more materials of the device, a physical property of the one or more materials, a surface of the device, an output of the device, a state of the device, a location of the device, a trajectory of the device, a vibration characteristic of the device, an identifier of the device connection and/or other digital twinning involved, and so forth. Examples of behavior of a device may include maximum acceleration of the device, maximum velocity of the device, range of motion of the device, heating profile of the device, cooling profile of the device, processes performed by the device, operations performed by the device, and so forth. Exemplary properties of an environment may include the size of the environment, the boundaries of the environment, the temperature of the environment, the humidity of the environment, the airflow of the environment, physical objects in the environment, the flow of water (if a body of water) of the environment, and so forth. Examples of the behavior of an environment may include scientific laws governing the environment, processes performed in the environment, rules or regulations that must be complied with in the environment, and the like.
In an embodiment, the properties of digital twinning may be adjusted. For example, the temperature of the digital twin, the humidity of the digital twin, the shape of the digital twin, the material of the digital twin, the dimensions of the digital twin, or any other suitable parameter may be adjusted. As the properties of the digital twinning are adjusted, other properties may also be affected. For example, if the temperature of the environment 15520 increases, the pressure in the environment may also increase, such as a gas pressure according to the ideal gas law. In another example, if the digital twinning of a sub-zero environment is warmed to an above-zero temperature, the embedded twinning properties of solid water (i.e., ice) may become liquid over time.
Digital twinning can take many different forms. In an embodiment, the digital twinning may be visual digital twinning presented by a computing device such that a human user may view the environment 15520 and/or a digital representation of physical objects 15522, devices 15524, and/or sensors 15526 in the environment. In an embodiment, the digital twinning may be presented and output to a display device. In some of these embodiments, the digital twinning may be presented in a graphical user interface so that a user may interact with the digital twinning. For example, a user may "get in-depth" of a particular element (e.g., a physical object or device) to view additional information for the element (e.g., a state of the physical object or device, properties of the physical object or device, etc.). In some embodiments, the digital twinning may be presented and output in a virtual reality display. For example, a user may view a 3D presentation of the environment (e.g., using a display or virtual reality headset). While doing so, the user may view/check the digital twinning of physical assets or devices in the environment.
In some embodiments, the data structure of the visual digital twinning (i.e., digital twinning configured to be displayed in 2D or 3D) may include a surface (e.g., spline, mesh, polygonal mesh, etc.). In some embodiments, the surface may include texture data, shading information, and/or reflection data. In this way, the surface can be displayed in a more realistic manner. In some embodiments, such surfaces may be presented by a visualization engine (not shown) when the digital twinning is within the field of view and/or when present in a larger digital twinning (e.g., a digital twinning of an industrial environment). In these embodiments, digital twinning system 15500 may present surfaces of a digital object, whereby the digital twinning presented may be described as a set of adjacent surfaces.
In an embodiment, a user may provide input through a graphical user interface that controls one or more properties of a digital twin. For example, a user may provide input that alters the properties of a digital twin. In response, digital twinning system 15500 may calculate the effect of the changed attribute and may update the digital twinning and any other digital twinning affected by the attribute change.
In an embodiment, a user may view a process performed for one or more digital twins (e.g., manufacturing a product, extracting minerals from a mine or well, livestock inspection line, etc.). In these embodiments, the user may view the entire process or specific steps in the process.
In some embodiments, digital twinning (and any digital twinning embedded therein) may be represented in a non-visual representation (or "data representation"). In these embodiments, the digital twinning and any embedded digital twinning exist in a binary representation, but the relationship between the digital twinning is maintained unchanged. For example, in an embodiment, each digital twin and/or component thereof may be represented by a set of physical dimensions defining the shape of the digital twin (or component thereof). Further, the data structure embodying the digital twin may include a location of the digital twin. In some embodiments, the digitally twinned position may be provided using a set of coordinates. For example, digital twinning of an industrial environment can be defined for a coordinate space (e.g., cartesian coordinate space, polar coordinate space, etc.). In an embodiment, the embedded digital twinning may be represented as a set of one or more ordered triples (e.g., [ x-coordinate, y-coordinate, z-coordinate ] or other vector-based representation). In some of these embodiments, each ordered triplet may represent a location of a particular point (e.g., a center point, a vertex, a nadir, etc.) on an industrial entity (e.g., an object, device, or sensor) relative to an environment in which the industrial entity is located. In some embodiments, the data structure of the digital twin may include a vector that indicates the motion of the digital twin relative to the environment. For example, a fluid (e.g., liquid or gas) or solid may be represented by a vector that indicates the velocity (e.g., direction and magnitude of velocity) of an entity represented by a digital twin. In an embodiment, the vector in twinning may represent a microscopic sub-assembly, such as a particle in a fluid; digital twinning may represent physical properties such as displacement, velocity, acceleration, momentum, kinetic energy, vibration properties, thermal properties, electromagnetic properties, etc.
In some embodiments, a set of two or more digital twins may be represented by a graph database that includes nodes and edges connecting the nodes. In some implementations, edges may represent spatial relationships (e.g., "adjoining," "attached," "including," etc.). In these embodiments, each node in the graph database represents a digital twin of an entity (e.g., an industrial entity) and may include a data structure defining the digital twin. In these embodiments, each edge in the graph database may represent a relationship between two entities represented by connected nodes. In some embodiments, edges may represent spatial relationships (e.g., "adjoining," "attached," "joined," "having," "including," etc.). In embodiments, various types of data may be stored in nodes or edges. In an embodiment, a node may store attribute data, status data, and/or metadata related to a facility, system, subsystem, and/or component. The types of attribute data and state data may vary based on the entity represented by the node. For example, a node representing a robot may include attribute data indicating the material of the robot, the size of the robot (or its components), the mass of the robot, etc. In this example, the state data of the robot may include a current pose of the robot, a position of the robot, and the like. In an embodiment, an edge may store relationship data and metadata related to a relationship between two nodes. Examples of relationship data may include the nature of the relationship, whether the relationship is a permanent relationship (e.g., a fixed component will have a permanent relationship with the structure to which it is attached or attached), and so forth. In an embodiment, an edge may include metadata about a relationship between two entities. For example, if a product is produced on an assembly line, one relationship between product digital twinning that can be recorded and the assembly line may be "creation mode". In these embodiments, an exemplary edge representing a "creation style" relationship may include a timestamp indicating the date and time of product creation. In another example, a sensor may make measurements related to the status of a device, whereby one relationship between the sensor and the device may include "measured" and may define the type of measurement measured by the sensor. In this example, the metadata stored in the edge may include a list of N measurements taken and a timestamp for each respective measurement. In this way, temporal data relating to the nature of the relationship between two entities may be maintained, allowing an analysis engine, machine learning engine, and/or visualization engine to utilize such temporal relationship data, such as by aligning different data sets with a series of points in time, for example, to facilitate causal analysis for a predictive system.
In some embodiments, the graphic database may be implemented in a hierarchical manner such that the graphic database relates to a set of facilities, systems, and components. For example, a digital twin of a manufacturing environment may include nodes representing the manufacturing environment. The graphic database may also include nodes representing various systems in the manufacturing environment, such as nodes representing HVAC systems, lighting systems, manufacturing systems, etc., all of which may be connected to nodes representing the manufacturing systems. In this example, each system may also be connected to various subsystems and/or components of the system. For example, in an HVAC system, the HVAC system may be connected to a subsystem node representing a cooling system of a facility, a second subsystem node representing a heating system of the facility, a third subsystem node representing a fan system of the facility, and one or more nodes representing a thermostat (or thermostats) of the facility. Further implementing the example, the subsystem node and/or the component node may be connected to a lower level node, which may include the subsystem node and/or the component node. For example, a subsystem node representing a cooling subsystem may be connected to a component node representing an air conditioning unit. Similarly, a component node representing a thermostat device may be connected to one or more component nodes representing various sensors (e.g., temperature sensors, humidity sensors, etc.).
In embodiments implementing a graphic database, the graphic database may involve a single environment or may represent a larger enterprise. In the latter case, the company may have various manufacturing and distribution facilities. In these embodiments, an enterprise node representing an enterprise may be connected to an environmental node of each respective facility. In this way, digital twinning system 15500 can maintain digital twinning for multiple industrial facilities of an enterprise.
In an embodiment, digital twin system 15500 may use a graphic database to generate digital twin that may be presented and displayed and/or may be represented in a data representation. In the former case, digital twinning system 15500 may receive a request to present digital twinning, whereby the request includes one or more parameters indicating the view to be shown. For example, the one or more parameters may indicate an industrial environment and a presentation type to be shown (e.g., a "real world view" showing the environment in a manner that is viewable by humans, an "infrared view" showing objects as a function of their respective temperatures, an "airflow view" showing airflow in a digital twin, etc.). In response, digital twinning system 15500 may traverse a graph database and may determine a configuration of an environment to be shown based on nodes in the graph database that are related to environmental nodes of the environment (directly or through lower level nodes) and edges defining relationships between the related nodes. In determining the configuration, the digital twin system 15500 may identify the surfaces to be shown and may present those surfaces. The digital twinning system 15500 may then render the requested digital twinning by connecting surfaces according to the configuration. The digital twinning of the presentation may then be output to a viewing device (e.g., VR headset, display, etc.). In some cases, the digital twinning system 15500 can receive real-time sensor data from the sensor system 15530 of the environment 15520 and can update the visual digital twinning based on the sensor data. For example, the digital twin system 15500 can receive sensor data related to the motor and its set of bearings (e.g., vibration data from the vibration sensor 15536). Based on the sensor data, the digital twinning system 15500 may update the visual digital twinning to indicate the approximate vibration characteristics of the set of bearings in the digital twinning of the motor.
Where digital twin system 15500 provides a digital twin data representation (e.g., for dynamic modeling, simulation, machine learning), digital twin system 15500 may traverse a graph database and may determine a configuration of an environment to be shown based on nodes in the graph database that are related to the environment nodes of the environment (directly or through lower level nodes) and edges defining relationships between the related nodes. In some cases, the digital twinning system 15500 can receive real-time sensor data from the sensor system 15530 of the environment 15520 and can apply one or more dynamic models to the digital twinning based on the sensor data. In other cases, a digital twin data representation may be used to perform the simulation, as discussed in more detail in this specification.
In some embodiments, the digital twinning system 15500 may perform digital ghosting of digital twinning execution with respect to an industrial environment. In these embodiments, the digital ghost may monitor one or more sensors of the sensor system 15530 of the industrial environment to detect anomalies that may indicate malicious viruses or other security issues.
As discussed, the digital twinning system 15500 can include a digital twinning management system 15502, a digital twinning I/O system 15504, a digital twinning simulation system 15506, a digital twinning dynamic model system 15508, a cognitive intelligence system 15510, and/or an environmental control module 15512.
In an embodiment, the digital twinning management system 15502 creates new digital twinning, maintains/updates existing digital twinning, and/or presents digital twinning. The digital twinning management system 15502 may receive user input, uploaded data, and/or sensor data to create and maintain existing digital twinning. Upon creation of the new digital twin, the digital twin management system 15502 may store the digital twin in the digital twin data store 15516. Digital twin creation, updating, and rendering are discussed in more detail in this disclosure.
In an embodiment, digital twin I/O system 15504 receives input from various sources and outputs data to various recipients. In an embodiment, the digital twin I/O system receives sensor data from one or more sensor systems 15530. In these embodiments, each sensor system 15530 may include one or more IoT sensors that output corresponding sensor data. Each sensor may be assigned an IP address or may have other suitable identifiers. Each sensor may output a sensor data packet including a sensor identification and sensor data. In some embodiments, the sensor data packet may also include a timestamp indicating the sensor data collection time. In some embodiments, digital twin I/O system 15504 can interface with sensor system 15530 through real-time sensor API 15514. In these embodiments, one or more devices (e.g., sensors, aggregators, edge devices) in the sensor system 15530 may send sensor data packets containing sensor data to the digital twin I/O system 15504 through an API. The digital twin I/O system may determine the sensor system 15530 that sent the sensor data package and its contents and may provide the sensor data and any other relevant data (e.g., time stamp, environment identifier/sensor system identifier, etc.) to the digital twin management system 15502.
In an embodiment, digital twin I/O system 15504 may receive imported data from one or more sources. For example, digital twinning system 15500 may provide a portal for users to create and manage their digital twinning. In these embodiments, the user may upload one or more files (e.g., image files, LIDAR scans, blueprints, etc.) related to the new digital twinning being created. In response, digital twin I/O system 15504 may provide imported data to digital twin management system 15502. Digital twin I/O system 15504 may receive other suitable types of data without departing from the scope of the present invention.
In some embodiments, digital twin analog system 15506 is used to perform the simulation using digital twin. For example, the digital twinning analog system 15506 may iteratively adjust one or more parameters of the digital twinning and/or one or more embedded digital twinning. In an embodiment, the digital twin simulation system 15506 performs a simulation based on each set of parameters and may collect simulation result data generated by the simulation. In other words, the digital twinning simulation system 15506 may collect the digital twinning used during the simulation and the properties of the digital twinning within or containing the digital twinning and any results produced by the simulation. For example, when running the simulation on digital twinning of an indoor agricultural facility, the digital twinning simulation system 15506 may change temperature, humidity, airflow, carbon dioxide, and/or other related parameters, and may perform the simulation that outputs results produced by different combinations of parameters. In another example, the digital twin simulation system 15506 can simulate the operation of a particular machine in an industrial facility that produces an output given a set of inputs. In some embodiments, the input may be changed to determine the effect of the input on the machine and its output. In another example, the digital twin simulation system 15506 can simulate vibration of a machine and/or machine components. In this example, the digital twinning of the machine may include a set of operating parameters, interfaces, and capabilities of the machine. In some embodiments, operating parameters may be changed to assess the effectiveness of the machine. The digital twin analog system 15506 is discussed in more detail in this disclosure.
In an embodiment, the digital twinning dynamic model system 15508 is used to model one or more behaviors for digital twinning of an environment. In an embodiment, the digital twin dynamic model system 15508 may receive a request to model a particular type of behavior with respect to an environment or process, and may use the dynamic model, digital twin of the environment or process, and sensor data collected from one or more sensors monitoring the environment or process to model the behavior. For example, an operator of a machine having a bearing may wish to model the vibrations of the machine and the bearing to determine whether the machine and/or the bearing can withstand an increase in output. In this example, the digital twin dynamic model system 15508 can execute a dynamic model for determining whether an increase in output can lead to adverse consequences (e.g., failure, downtime, etc.). The digital twin dynamic model system 15508 is discussed in more detail in this disclosure.
In an embodiment, the cognitive process system 15510 performs machine learning and artificial intelligence related tasks on behalf of a digital twin system. In embodiments, the cognitive process system 15510 may train any suitable type of model including, but not limited to, various types of neural networks, regression models, random forests, decision trees, hidden markov models, bayesian models, and the like. In an embodiment, the cognitive process system 15510 uses the analog output performed by the digital twin analog system 15506 to train a machine learning model. In some of these embodiments, simulation results may be used to supplement training data collected from the real world environment and/or process. In an embodiment, the cognitive process system 15510 utilizes machine learning models to predict, identify, classify, and provide decision support related to the real world environment and/or process represented by the corresponding digital twinning.
For example, a machine learning predictive model may be used to predict the cause of irregular vibration patterns (e.g., sub-optimal, critical, or alert vibration fault conditions) of engine bearings in an industrial facility. In this example, the cognitive process system 15510 may receive vibration sensor data from one or more vibration sensors disposed on or near the engine, may receive maintenance data from the industrial facility, and may generate a feature vector based on the vibration sensor data and the maintenance data. The cognitive process system 15510 may input the feature vectors to a machine learning model specifically trained for the engine (e.g., using a combination of simulated data and real world data for the cause of the irregular vibration pattern) to predict the cause of the irregular vibration pattern. In this example, the cause of the irregular vibration modes may be bearing loosening, insufficient bearing lubrication, bearing misalignment, bearing wear, bearing phase may be aligned with the engine phase, housing loosening, bolt loosening, and the like.
In another example, a machine learning model may be used to provide decision support to bring engine bearings in an industrial facility operating in a suboptimal vibration fault level state to a normal operating vibration fault level state. In this example, the cognitive process system 15510 may receive vibration sensor data from one or more vibration sensors disposed on or near the engine, may receive maintenance data from the industrial facility, and may generate a feature vector based on the vibration sensor data and the maintenance data. The cognitive process system 15510 may input feature vectors to a machine learning model specifically trained for the engine (e.g., a combination of simulated data and real world data for solutions using irregular vibration patterns) to provide decision support in achieving normal operating failure level conditions of the bearing. In this example, the decision support may be suggesting a tightening bearing, lubricating a bearing, realigning a bearing, ordering a new component, collecting additional vibration measurements, altering an operating speed of the engine, tightening a housing, tightening a bolt, and so forth.
In another example, a machine learning model may be used to provide decision support related to a worker collecting vibration measurements. In this example, the cognitive process system 15510 may receive vibration measurement history data from the industrial facility and may generate a feature vector based on the vibration measurement history data. The cognitive process system 15510 may input feature vectors to a machine learning model specifically trained for the engine (e.g., using a combination of simulation data and real world vibration measurement history data) to provide decision support in selecting vibration measurement locations.
In yet another example, a machine learning model may be used to identify vibration characteristics associated with a machine and/or machine component problem. In this example, the cognitive process system 15510 may receive vibration measurement history data from the industrial facility and may generate a feature vector based on the vibration measurement history data. The cognitive process system 15510 may input the feature vectors to a machine learning model specifically trained for the engine (e.g., using a combination of simulation data and real world vibration measurement history data) to identify vibration characteristics associated with the machine and/or machine components. The foregoing examples are non-limiting examples, and the cognitive process system 15510 may be used for any other suitable AI/machine learning related tasks performed for an industrial facility.
In an embodiment, the environmental control system 15512 controls one or more aspects of an industrial facility. In some of these embodiments, the environmental control system 15512 can control one or more devices in an industrial environment. For example, the environmental control system 15512 may control one or more machines in an environment, robots in an environment, HVAC systems in an environment, alarm systems in an environment, assembly lines in an environment, and the like. In an embodiment, the environmental control system 15512 may utilize the digital twinning simulation system 15506, the digital twinning dynamic model system 15508, and/or the cognitive process system 15510 to determine one or more control instructions. In an embodiment, the environmental control system 15512 can implement a rule-based method and/or a machine learning method to determine the control instructions. In response to determining the control instructions, the environmental control system 15512 can output the control instructions to the intended devices in the particular environment via the digital twin I/O system 15504.
FIG. 156 illustrates an exemplary digital twin management system 15502 according to some embodiments of the invention. In an embodiment, the digital twin management system 15502 may include, but is not limited to, a digital twin creation module 15564, a digital twin update module 15566, and a digital twin visualization module 15568.
In an embodiment, the digital twinning module 15564 may use input from a user, imported data (e.g., blueprints, specifications, etc.), image scans of the environment, 3D data from LIDAR devices and/or SLAM sensors, and other suitable data sources to create a new set of digital twinning for a set of environments. For example, a user (e.g., a user associated with an organization/client account) may provide input through the client application 15570 to create a new digital twinning of the environment. In this way, the user may upload a 2D or 3D image scan of the environment and/or a blueprint of the environment. The user may also upload 3D data, for example, photographed by a camera, LIDAR device, IR scanner, a set of SLAM sensors, radar device, EMF scanner, or the like. In response to the provided data, digital twin creation module 15564 may create a 3D representation of the environment, which may include any objects captured in the image data/any objects detected in the 3D data. In an embodiment, the cognitive process system 15572 may analyze input data (e.g., blueprints, image scans, 3D data) to classify rooms, paths, devices, etc. to assist in generating the 3D representation. In some embodiments, digital twinning module 15564 may map digital twinning to a 3D coordinate space (e.g., a cartesian space with x, y, and z axes).
In some embodiments, the digital twinning module 15564 may output a 3D representation of the environment to a Graphical User Interface (GUI). In some of these embodiments, a user may identify certain regions and/or objects and may provide input related to the identified regions and/or objects. For example, a user may mark a particular room, device, machine, etc. Additionally or alternatively, the user may provide data related to the identified objects and/or regions. For example, upon identifying a piece of equipment, a user may provide the make/model of the equipment. In some embodiments, the digital twinning module 15564 may obtain information from a manufacturer of a device, piece of equipment, or machine. The information may include one or more attributes and/or behaviors of the device, apparatus, or machine. In some embodiments, the user may identify the location of the sensor throughout the environment via the GUI. For each sensor, the user may provide the sensor type and related data (e.g., brand, model number, IP address, etc.). The digital twinning module 15564 may record the locations (e.g., x, y, z coordinates of the sensors) in the digital twinning of the environment. In an embodiment, digital twin system 15500 may employ one or more systems of automated digital twin stuffing. For example, the digital twinning system 15500 may employ a machine vision based classifier that classifies the make and model of a device, equipment, or sensor. Additionally or alternatively, the digital twinning system 15500 can iteratively ping different types of known sensors to determine whether a particular type of sensor is present in the environment. The digital twin system 15500 can extrapolate the make and model of the sensor each time the sensor responds to a ping.
In some embodiments, the manufacturer may supply or provide digital twinning (e.g., sensors, devices, machinery, equipment, raw materials, etc.) of its products. In these embodiments, the digital twinning module 15564 may import digital twinning of one or more products identified in the environment and may embed the digital twinning into the digital twinning of the environment. In an embodiment, embedding the digital twin into another digital twin may include creating a relationship between the embedded digital twin and the other digital twin. In these embodiments, the manufacturer of the digital twinning may define the behavior and/or attributes of the corresponding product. For example, digital twinning of a machine may define the manner in which the machine operates, the input/output of the machine, and so forth. In this way, the digital twinning of the machine may reflect the operation of the machine given a set of inputs.
In an embodiment, a user may define one or more processes that occur in an environment. In these embodiments, a user may define steps in a process, the machine/device performing each step in the process, process inputs, and process outputs.
In an embodiment, the digital twinning module 15564 may create a graphic database defining the relationships between a set of digital twinning. In these embodiments, digital twinning creation module 15564 may create nodes for environments, systems and subsystems of environments, devices in environments, sensors in environments, workers working in environments, processes executing in environments, and the like. In an embodiment, the digital twinning module 15564 may write a graphic database representing a set of digital twins to the digital twinning data store 15516.
In an embodiment, the digital twinning creation module 15564 may include, for each node, any data related to an entity in the node representing the entity. For example, in defining nodes representing environments, the digital twinning creation module 15564 may include dimensions, boundaries, layouts, paths, and other relevant spatial data in the nodes. Further, the digital twinning module 15564 may define a coordinate space relative to the environment. Where digital twinning may be rendered, digital twinning creation module 15564 may include references in nodes to any shape, mesh, spline, surface, etc. that may be used to render an environment. In representing a system, subsystem, device, or sensor, the digital twinning creation module 15564 may create nodes for the respective entities and may include any relevant data. For example, the digital twinning creation module 15564 may create nodes that represent machines in an environment. In this example, digital twinning module 15564 may include dimensions, behaviors, attributes, locations, and/or any other suitable data related to a machine in a node representing the machine. The digital twinning module 15564 may connect nodes and edges of related entities to create relationships between the entities. In this way, the relationships created between entities may define the relationship types characterized by the edges. During the representation, the digital twinning creation module 15564 may create nodes for the entire process, or may create nodes for each step in the process. In some of these embodiments, the digital twinning creation module 15564 may associate a process node to a node representing a machine/device that performs a step in the process. In embodiments of a machine/device in which edges relate process step nodes to steps in the execution, one of the edges or nodes may contain information indicating step inputs, step outputs, time spent in a step, nature of the process inputs producing outputs, a set of states or modes that the process may experience, etc.
In an embodiment, the digital twinning update module 15566 updates the sets of digital twinning based on the current status of one or more industrial entities. In some embodiments, the digital twinning update module 15566 receives sensor data from the sensor system 15530 of the industrial environment and updates the digital twinning of the industrial environment and/or the state of any affected systems, subsystems, devices, workers, processes, etc. As previously described, the digital twin I/O system 15504 may receive sensor data in one or more sensor data packets. The digital twin I/O system 15504 can provide sensor data to the digital twin update module 15566 and can identify the environment in which the sensor data packet is received and the sensor providing the sensor data packet. In response to the sensor data, the digital twin update module 15566 may update one or more digital twin states based on the sensor data. In some of these embodiments, the digital twin update module 15566 may update records (e.g., nodes in a graph database) corresponding to sensors providing sensor data to reflect current sensor data. In some cases, the digital twin update module 15566 may identify certain areas in the environment monitored by the sensors and may update the records (e.g., nodes in a graph database) to reflect current sensor data. For example, the digital twin update module 15566 may receive sensor data reflecting different vibration characteristics of a machine and/or machine components. In this example, the digital twin update module 15566 may update a record representing the vibration sensor providing vibration sensor data and/or a record representing the machine and/or machine components to reflect the vibration sensor data. In another example, in some cases, it may be desirable for workers in an industrial environment (e.g., manufacturing facility, industrial storage facility, mine, drilling operations, etc.) to wear wearable devices (e.g., smart watches, smart helmets, smart shoes, etc.). In these embodiments, the wearable device may collect sensor data related to the worker (e.g., location, movement, heartbeat, respiration rate, body temperature, etc.) and/or the environment surrounding the worker, and may communicate the collected sensor data to digital twinning system 15500 (e.g., through real-time sensor API 15514) directly or through an aggregation device of the sensor system. In response to receiving sensor data from the worker's wearable device, digital twin update module 15566 may update the worker's digital twin to reflect, for example, the worker's location, the worker's trajectory, the worker's health status, and the like. In some of these embodiments, the digital twin update module 15566 may update nodes representing workers and/or edges that connect nodes representing environments with collected sensor data to reflect the current state of workers.
In some embodiments, the digital twin update module 15566 may provide sensor data from one or more sensors to a digital twin dynamic model system 15508, which may model the behavior of the environment and/or one or more industrial entities to extrapolate additional state data.
In an embodiment, the digital twin visualization module 15568 receives a request to view a visual digital twin or a portion thereof. In an embodiment, the request may indicate a digital twinning (e.g., an environment identifier) to view. In response, the digital twinning visualization module 15568 may determine the requested digital twinning and any other digital twinning associated with the request. For example, upon requesting to view the digital twinning of the environment, the digital twinning visualization module 15568 may further identify the digital twinning of any industrial entity in the environment. In an embodiment, the digital twin visualization module 15568 may identify a spatial relationship between an industrial entity and an environment based on a relationship defined in, for example, a graphic database. In these embodiments, the digital twin visualization module 15568 may determine transients (e.g., objects fixed to points or object movements) that contain the relative position of an embedded digital twin within a digital twin, the relative position and/or relationship of adjacent digital twin. The digital twinning visualization module 15568 may present the requested digital twinning and any other relevant digital twinning based on the identified relationships. In some embodiments, for each digital twin, the digital twin visualization module 15568 may determine the surface of the digital twin. In some embodiments, the surface of the number may be defined or referenced in a record corresponding to the digital twinning, which may be provided by a user, determined from the imported image, or defined by the manufacturer of the industrial entity. In the case where the object may take on different poses or shapes (e.g., an industrial robot), the digital twinning visualization module 15568 may determine the pose or shape of the object for digital twinning. The digital twinning visualization module 15568 may embed the digital twinning into the requested digital twinning and may output the requested digital twinning to the client application.
In some of these embodiments, the request to view the digital twinning may further indicate a view type. As previously described, in some embodiments, digital twinning may be depicted in a number of different view types. For example, the environment or device may be viewed in the following view: a "real world" view depicting the environment or device in a typical appearance; a "hot" view depicting an environment or device in a manner that indicates the temperature of the environment or device; a "vibration" view depicting a machine and/or machine component in an industrial environment in a manner that indicates vibration characteristics of the machine and/or machine component; "filter" views that only display certain types of objects within an environment or device component (e.g., objects that need attention due to identifying fault conditions, alarms, update reports, or other factors); enhanced view, overlaying digital twin data; and/or any other suitable view type. In an embodiment, digital twinning may be depicted in a variety of different role-based view types. For example, a manufacturing facility device may look at the following view: an "operator" view showing the facility in a manner appropriate to the facility operator; a "high-rise" view showing the facility in a manner appropriate for the high Guan Cengguan manager; a "marketing" view showing facilities in a manner suitable for sales and/or marketing character workers; a "board of directors" view showing the facilities in a manner appropriate for the board of directors of the company; a "supervisory" view showing the facilities in a manner appropriate for the supervisory manager; a "human resources" view shows the facilities in a manner appropriate for human resources personnel. In response to a request indicating a view type, the digital twin visualization module 15568 can retrieve each digital twin's data corresponding to the view type. For example, if a user has requested a vibration view of the plant floor, the digital twin visualization module 15568 may retrieve vibration data of the plant floor (which may include vibration measurements taken from different machines and/or machine components and/or vibration measurements extrapolated by the digital twin dynamic model system 15508 and/or analog vibration data from the digital twin analog system 15506) as well as available vibration data of any industrial entity present at the plant floor. In this example, the digital twin visualization module 15568 can determine colors corresponding to each machine component in the plant floor that represent vibration fault level status (e.g., red for alarm, orange for critical, yellow for suboptimal, green for normal operation). The digital twinning visualization module 15568 may then present the digital twinning of the machine component in the environment based on the determined color. Additionally or alternatively, the digital twinning visualization module 15568 may use the indicator having the determined color to present digital twinning of the machine component in the environment. For example, if the vibration fault level status of the inbound bearing of the motor is suboptimal and the outbound bearing of the motor is critical, the digital twin visualization module 15568 may present the digital twin bearing of the inbound bearing with an indicator (e.g., suboptimal) with a yellow shade and the outbound bearing with an indicator (e.g., critical) with an orange shade. Note that in some embodiments, digital twin system 15500 may include an analysis system (not shown) that determines the manner in which digital twin visualization system 15568 presents information to a human user. For example, the analysis system may track results related to human interactions with real environments or objects in response to information presented in visual digital twinning. In some embodiments, the analysis system may apply the cognitive model to determine the most efficient way to display visual information (e.g., color for representing an alarm condition, type of movement or animation for drawing attention to an alarm condition, etc.) or audio information (sound for representing an alarm condition) based on the result data. In some embodiments, the analysis system may apply a cognitive model to determine the most appropriate way to display the visual information based on the user's role. In an embodiment, the visualization may include displaying information related to the visualization digital twinning, including graphical information, graphical information depicting vibration characteristics, graphical information depicting harmonic peaks, graphical information depicting peaks, vibration severity unit data, vibration fault level status data, advice from the cognitive intelligence system 15510, predictions from the cognitive intelligence system 15510, fault probability data, maintenance history data, fault time data, downtime cost data, downtime probability data, maintenance cost data, machine replacement cost data, downtime probability data, manufacturing KPIs, and the like.
In another example, a user may request a filtered view of the digital twinning of a process, whereby the digital twinning of the process only displays components (e.g., machines or devices) involved in the process. In this example, the digital twin visualization module 15568 may retrieve the digital twin of the process and any related digital twin (e.g., digital twin affecting any machinery or devices of the process and the digital twin of the environment). The digital twinning visualization module 15568 may then render each digital twinning (e.g., of the environment and related industrial entities), and may then perform the process on the rendered digital twinning. Note that since a process may be performed over a period of time and may include moving items and/or components, the digital twin visualization module 15568 may generate a series of consecutive frames for demonstrating the process. In this case, the movements of the machine and/or device involved in the process may be determined from the actions defined in the corresponding digital twinning of the machine and/or device.
As previously described, the digital twin visualization module 15568 may output the requested digital twin to the client application 15570. In some embodiments, the client application 15570 is a virtual reality application whereby the requested digital twinning is displayed on a virtual reality headset. In some embodiments, the client application 1.5570 is an augmented reality application, thereby rendering the requested digital twinning in the AR-enabled device. In these embodiments, the requested digital twinning may be filtered such that visual elements and/or text are overlaid on the display of the AR-enabled device.
Note that although a graph database is discussed, digital twin system 15500 may use other suitable data structures to store information related to a set of digital twin. In these embodiments, the data structure and any associated storage system may be implemented such that the data structure provides a degree of feedback loops and/or recursion in representing iterations of the stream.
Fig. 131 illustrates an example of a digital twin I/O system 15504, where the digital twin I/O system 15504 is connected with an environment 15520, digital twin system 15500, and/or components thereof to provide bi-directional transmission of data between coupled components, according to some embodiments of the invention.
In an embodiment, the transmitted data includes signals (e.g., request signals, command signals, response signals, etc.) between the connected components, which may include software components, hardware components, physical devices, virtualization devices, analog devices, combinations thereof, and the like. The signals may define material properties (e.g., physical quantities of temperature, pressure, humidity, density, viscosity, etc.), measured values (e.g., contemporaneous or stored values obtained by a device or system), device properties (e.g., properties of a device ID or design specification of a device, materials, measurement capabilities, dimensions, absolute locations, relative locations, combinations thereof, etc.), set points (e.g., targets of material properties, device properties, system properties, combinations thereof, etc.), and/or critical points (e.g., thresholds of minimum or maximum values of material properties, device properties, system properties, etc.). The signal may be received from a system or device that obtains (e.g., directly measures or generates) or otherwise obtains (e.g., receives, calculates, looks up, filters, etc.) the data and may communicate with the digital twin I/O system 15504 at a predetermined time or in response to a request (e.g., poll) from the digital twin I/O system 15504. Communication may occur via direct or indirect connection (e.g., via intermediate modules within the circuit and/or intermediate devices between connected components). This value may correspond to real world element 131302r (e.g., an input or output of a tangible vibration sensor) or virtual element 131302v (e.g., an input or output of digital twinning 131302d and/or analog element 131302s that provide vibration data).
In an embodiment, the real world element 131302r can be an element in the industrial environment 15520. The real world elements 131302r can include, for example, non-networked objects 15522, devices 15524 (smart or non-smart), sensors 15526, and humans 15528. The real world element 131302r may be a process or non-process device in the industrial environment 15520. For example, process devices may include motors, pumps, mills, fans, painters, welders, smelters, etc., while non-process devices may include personal protective equipment, safety devices, emergency stations or equipment (e.g., safety showers, eyewash stations, fire extinguishers, sprinkler systems, etc.), warehouse features (e.g., walls, floor layouts, etc.), obstructions (e.g., personnel or other items in the environment 15520, etc.).
In an embodiment, the virtual element 131302v can be a digital representation of the simultaneous real world element 131302r or a digital representation corresponding to the simultaneous real world element 131302 r. Additionally or alternatively, the virtual element 131302v can be a digital representation of the real world element 131302r or a digital representation corresponding to the real world element 131302r that can be used for later addition and implementation into the environment 15520. The virtual elements may include, for example, analog elements 131302s and/or digital twins 131302d. In an embodiment, the analog element 131302s can be a digital representation of the real world element 131302s that is not present in the industrial environment 15520. The simulation element 131302s can simulate desired physical properties that can then be integrated in the environment 15520 as the real world element 1313021r (e.g., a "black box" to simulate the size of the real world element 131302 r). The analog element 131302s may include digital twinning of the existing object (e.g., a single analog element 131302s may include one or more digital twinning 131302d for an existing sensor). For example, information related to the simulated element 131302s may be obtained from a library (e.g., a physical library, a chemical library, etc.) that defines information and behavior of the simulated element 131302s by evaluating the behavior of the corresponding real world element 131302r using a mathematical model or algorithm.
In an embodiment, the digital twinning 131302d can be a digital representation of one or more real world elements 131302 r. The digital twinning 131302d is used to simulate, replicate, and/or model the behavior and response of the real world element 131302r in response to inputs, outputs, and/or conditions of the surrounding or external environment. For example, data related to physical characteristics and responses of the real world element 131302r may be obtained through user input, sensor input, and/or physical modeling (e.g., thermodynamic model, electric model, mechanical kinetic model, etc.). The information of the digital twinning 131302d may correspond to and be obtained from one or more real world elements 131302r corresponding to the digital twinning 131302d, and from the one or more real world elements 131302 r. For example, in some embodiments, the digital twinning 131302d can correspond to one real world element 131302r that is a fixed digital vibration sensor 15536 on the machine component, and the vibration data of the digital twinning 131302d can be obtained by polling or acquiring vibration data measured by the fixed digital vibration sensor on the machine component. In another example, the digital twinning 131302d can correspond to a plurality of real world elements 131302r such that each element can be a fixed digital vibration sensor on a machine component, and the vibration data of the digital twinning 131302d can be obtained by polling or acquiring vibration data measured by each fixed digital vibration sensor on the plurality of real world elements 131302 r. Additionally or alternatively, the vibration data of the first digital twin 131302d can be obtained by acquiring vibration data of a second digital twin 157302d embedded within the first digital twin 157302d, and the vibration data of the first digital twin 157302d can include vibration data of the second digital twin 157302d or be derived from vibration data of the second digital twin 157302 d. For example, the first digital twin may be digital twin 157302d of the environment 15520 (alternatively referred to as "environment digital twin"), and the second digital twin 157302d may be digital twin 157302d corresponding to the vibration sensor disposed in the environment 15520, such that vibration data of the first digital twin 157302d is obtained from or calculated based on data including vibration data of the second digital twin 157302 d.
In an embodiment, digital twinning system 15500 uses sensors 15526 in respective environments 15520 to monitor properties of real world element 157302r, environment 15520 is an output of a model of digital twinning 157302d and/or one or more analog elements 157302s or may be represented by an output of a model of digital twinning 157302d and/or one or more analog elements 157302 s. In an embodiment, the digital twinning system 15500 may perform simulation (e.g., by the digital twinning simulation system 15506) by extending the polling interval and/or minimizing data transmission of sensors corresponding to the affected real world elements 157302r, and during the extended interval using data obtained from other sources (e.g., physically proximate to the affected real world elements 157302r or sensors having an effect on the affected real world elements 157302 r), while minimizing network congestion while maintaining efficient monitoring of the process. Additionally or alternatively, error checking may be performed by comparing the collected sensor data with data obtained from the digital twin simulation system 15506. For example, consistent deviations or fluctuations between the sensor data obtained from the real world element 157302r and the analog element 157302s may indicate a fault or another fault condition of the respective sensor.
In an embodiment, the digital twinning system 15500 may optimize characteristics of the environment by using one or more analog elements 157302 s. For example, the digital twinning system 15500 may evaluate the effects of the digital twinning simulation elements 157302s of the environment to quickly and efficiently determine the cost and/or benefit generated by including, excluding, or replacing the real world elements 157302r in the environment 15520. Costs and benefits may include, for example, increasing mechanical costs (e.g., capital investment and maintenance), increasing efficiency (e.g., reducing waste or increasing throughput through process optimization), reducing or changing floor space in the environment 15520, extending or optimizing service life, minimizing component failures, minimizing component downtime, etc.
In an embodiment, the digital twin I/O system 15504 may include one or more software modules that are executed by one or more controllers of one or more devices (e.g., server devices, user devices, and/or distributed devices) to affect the described functionality. Digital twin I/O system 15504 may include, for example, an input module 157304, an output module 157306, and an adapter module 157308.
In an embodiment, the input module 157304 may obtain or import data from a data source in communication with the digital twin I/O system 15504 (e.g., the sensor system 15530 and the digital twin analog system 15506). The data may be used directly by or stored in digital twin system 15500. Imported data may be obtained from a data stream, a data batch, in response to a trigger event, a combination thereof, or the like. The input module 157304 may receive data in a format suitable for transmitting, reading, and/or writing information within the digital twin system 15500.
In an embodiment, the output module 157306 can output or export data to other system components (e.g., digital twin data store 15516, digital twin simulation system 15506, cognitive intelligence system 15510, etc.), devices 15524, and/or client applications 15570. The data may be output in a data stream, a data batch, in response to a trigger event (e.g., a request), a combination thereof, or the like. The output module 157306 may output data in a format suitable for use or storage by the target element (e.g., one protocol for output to the client application and another protocol for digital twin data store 15516).
In an embodiment, adapter module 157308 may process and/or convert data between input module 157304 and output module 157306. In embodiments, the adapter module 157308 may convert and/or route data automatically (e.g., based on data type) or in response to a received request (e.g., in response to information in the data).
In an embodiment, the digital twinning system 15500 may represent a set of industrial workpiece elements in digital twinning, and the digital twinning simulation system 15506 simulates a set of physical interactions of a worker with the workpiece elements.
In an embodiment, the digital twin simulation system 15506 may determine process results for simulated physical interactions that take into account simulation artifacts. For example, changes in workpiece throughput may be modeled by digital twin system 15500, including, for example, worker response time to events, worker fatigue, discontinuities within worker action (e.g., natural changes in human body movement speed, different positioning times, etc.), effects of discontinuities on downstream processes, and the like. In an embodiment, personalized worker interactions may be modeled using historical data collected, acquired, and/or stored by digital twin system 15500. The simulation may begin based on an estimated number (e.g., worker age, industry average level, workplace expectations, etc.). The simulation may also personalize the data for each worker (e.g., compare the estimated number to the collected worker-specific results).
In an embodiment, information related to a worker (e.g., fatigue rate, efficiency, etc.) may be determined by analyzing the performance of a particular worker over time and modeling the performance.
In an embodiment, the digital twinning system 15500 includes a plurality of proximity sensors in the sensor system 15530. The proximity sensor is or may be used to detect elements of the environment 15520 within a predetermined area. For example, the proximity sensor may include an electromagnetic sensor, a light sensor, and/or an acoustic sensor.
Electromagnetic sensors are or may be used to sense objects or interactions via one or more electromagnetic fields (e.g., emitted electromagnetic radiation or received electromagnetic radiation). In embodiments, electromagnetic sensors include inductive sensors (e.g., radio frequency identification sensors), capacitive sensors (e.g., contact and non-contact capacitive sensors), combinations thereof, and the like.
The light sensor is or may be used to sense objects or interactions via electromagnetic radiation in, for example, the far infrared, near infrared, optical and/or ultraviolet spectra. In embodiments, the light sensor may include image sensors (e.g., charge coupled devices and CMOS active pixel sensors), photosensors (e.g., beam-passing sensors, retro-reflective sensors, and diffuse sensors), combinations thereof, and the like. Furthermore, the light sensor may be implemented as part of a system or subsystem, such as a light detection and ranging ("LIDAR") sensor.
The acoustic sensor is or may be used to sense objects or interactions via acoustic waves transmitted and/or received by the acoustic sensor. In embodiments, the acoustic sensor can include an infrasonic, sonic, and/or ultrasonic sensor. Furthermore, the acoustic sensors may be grouped as part of a system or subsystem, such as a sound navigation and ranging ("SONAR") sensor.
In an embodiment, the digital twinning system 15500 stores and collects data from a set of proximity sensors in the environment 15520 or portion thereof. The collected data may be stored, for example, in digital twin data memory 15516 for use by components of digital twin system 15500 and/or for visualization by a user. Such use and/or visualization may be performed concurrently with or subsequent to data collection (e.g., during subsequent analysis and/or process optimization).
In an embodiment, data collection may occur in response to a trigger condition. These trigger conditions may include, for example, expiration of a static or dynamic predetermined interval, obtaining a value that is less than or exceeds a static or dynamic value, receiving an automatically generated request or instruction from digital twinning system 15500 or components thereof, interaction of an element with a corresponding sensor (e.g., in response to a worker or machine interrupting a light beam or reaching within a predetermined distance from a proximity sensor), interaction of a user with digital twinning (e.g., selecting environmental digital twinning, sensor array digital twinning, or sensor digital twinning), combinations thereof, and the like.
In some embodiments, the digital twinning system 15500 collects and/or stores RFID data in response to worker interactions with the real world element 157302 r. For example, in response to a worker's interaction with a real environment, digital twinning will collect and/or store RFID data from RFID sensors in or associated with the corresponding environment 15520. Additionally or alternatively, worker interaction with a sensor array that is digitally twinned will collect and/or store RFID data from RFID sensors in or associated with the corresponding sensor array. Similarly, worker interaction with sensor digital twinning will collect and/or store RFID data from the corresponding sensor. The RFID data may include suitable data available to the RFID sensor, including proximity RFID tags, RFID tag locations, authorized RFID tags, unauthorized RFID tags, unidentified RFID tags, RFID types (e.g., active or passive), error codes, combinations thereof, and the like.
In an embodiment, digital twinning system 15500 may further embed output from one or more devices into a corresponding digital twinning. In an embodiment, the digital twinning system 15500 embeds the output from a set of individual related devices into an industrial digital twinning. For example, digital twin I/O system 15504 may receive information output from one or more wearable devices 15554 or mobile devices (not shown) associated with individuals in an industrial environment. The wearable device may include an image capturing device (e.g., a personal camera or an augmented reality headset), a navigation device (e.g., a GPS device, an inertial guidance system), a motion tracker, an acoustic capturing device (e.g., a microphone), a radiation detector, combinations thereof, and the like.
In an embodiment, upon receiving the output information, digital twin I/O system 15504 routes the information to digital twin creation module 15564 to check and/or update the environmental digital twin and/or associated digital twin in the environment (e.g., digital twin of a worker, machine, or robot location at a given time). Further, the digital twinning system 15500 may use the embedded output to determine characteristics of the environment 15520.
In an embodiment, the digital twinning system 15500 embeds the output from the LIDAR point cloud system into an industrial digital twinning. For example, the digital twin I/O system 15504 may receive information output from one or more Lidar devices 15538 in an industrial environment. Lidar device 15538 is used to provide a plurality of points with associated position data (e.g., coordinates of absolute or relative x, y, and z values). Each of the plurality of points may include other LIDAR attributes such as intensity, number of returns, total returns, laser color data, return color data, scan angle, scan direction, etc. The Lidar device 15538 may provide a point cloud comprising a plurality of points to a digital twin system 15500 via, for example, a digital twin I/O system 15504. Additionally or alternatively, the digital twinning system 15500 may receive and aggregate point streams into point clouds, or may receive and combine the received point clouds with existing point cloud data, map data, or three-dimensional (3D) model data.
In an embodiment, upon receiving the output information, the digital twin I/O system 15504 routes the point cloud information to the digital twin creation module 15564 to check and/or update the environmental digital twin and/or associated digital twin in the environment (e.g., digital twin of worker, machine, or robot location at a given time). In some embodiments, the digital twinning system 15500 is also used to determine a closed shape object in the received LIDAR data. For example, the digital twinning system 15500 may group a plurality of points within a point cloud as objects and, if desired, estimate a obstructed surface of the objects (e.g., a surface of the objects that is in contact with or adjacent to a floor or another object such as another device). The system may use such closed shape objects to narrow down the digitally twinned search space, thereby improving the efficiency of the matching algorithm (e.g., shape matching algorithm).
In an embodiment, digital twinning system 15500 embeds the output from a simultaneous localization and mapping ("SLAM") system in an ambient digital twinning. For example, the digital twinning I/O system 15504 may receive information output from a SLAM system, such as SLAM sensor 15562, and embed the received information into an ambient digital twinning corresponding to a location determined by the SLAM system. In an embodiment, upon receiving output information from the SLAM system, the digital twin I/O system 15504 routes the information to the digital twin creation module 15564 to inspect and/or update the environment digital twin and/or associated digital twin in the environment (e.g., digital twin of a work piece, furniture, movable object, or autonomous object). Such updates automatically provide digital twinning of non-connected elements (e.g., furniture or personnel) without requiring the user to interact with digital twinning system 15500.
In an embodiment, the digital twinning system 15500 may utilize known digital twinning to reduce the computational requirements of the SLAM sensor 15562 by using a suboptimal map building algorithm. For example, a suboptimal map construction algorithm may enable higher uncertainty tolerance to be achieved using a simple bounded region representation and identifying possible digital twinning. Additionally or alternatively, the digital twinning system 15500 may use the bounded region representation to limit the number of digital twins, analyze the potential digital twinning groups to distinguish features, then perform a higher accuracy analysis on the distinguishing features to identify and/or eliminate categories, groups, or individuals of digital twinning, and perform an accurate scan only of the remaining region to be scanned in the event that no matching digital twinning is found.
In an embodiment, the digital twinning system 15500 may further reduce the computations required to construct a location map by: an initial map construction process (e.g., a simple bounded area map or other suitable photogrammetry method) is performed with data captured from other sensors in the environment (e.g., captured images or videos, radio images, etc.), digital twins of known environmental objects are associated with features of the simple bounded area map to refine the simple bounded area map, and more accurate scans are performed on the remaining simple bounded areas to further refine the map. In some embodiments, digital twinning system 15500 may detect objects within the received mapping information and, for each detected object, determine whether the detected object corresponds to an existing digital twinning of the real world element. In response to determining that the detected object does not correspond to an existing real-world element digital twin, the digital twin system 15500 may generate a new digital twin (e.g., a detected object digital twin) corresponding to the detected object and add the detected object digital twin to the real-world element digital twin in the digital twin data store using, for example, the digital twin creation module 15564. Additionally or alternatively, in response to determining that the detected object corresponds to an existing real-world element digital twinning, digital twinning system 15500 may update the real-world element digital twinning to include new information detected by simultaneous localization and mapping sensors (if any).
In an embodiment, the digital twinning system 15500 represents the locations of autonomous or remotely movable elements in an industrial digital twinning and their attributes. Such movable elements may include, for example, workers, personnel, vehicles, automated vehicles, robots, and the like. The position of the movable element may be updated in response to a trigger condition. These trigger conditions may include, for example, expiration of a static or dynamic predetermined interval, receipt of an automatically generated request or instruction from digital twinning system 15500 or components thereof, interaction of an element with a corresponding sensor (e.g., in response to a worker or machine interrupting a light beam or coming within a predetermined distance from a proximity sensor), interaction of a user with digital twinning (e.g., selection of ambient digital twinning, sensor array digital twinning, or sensor digital twinning), combinations thereof, and the like.
In an embodiment, the time interval may be based on a probability that the respective movable element moves within the time period. For example, for workers intended to move frequently (e.g., those responsible for handling items in the environment 15520 and through the environment 15520), the time interval for updating the worker's location may be relatively short; for workers that are expected to move infrequently (e.g., those responsible for monitoring the process), the time interval may be relatively long. Additionally or alternatively, the time interval may be dynamically adjusted based on applicable conditions, such as increasing the time interval when no movable elements are detected, decreasing the time interval when the number of movable elements in the environment increases (e.g., increasing the number of worker and worker interactions), increasing the time interval during reduced environmental activities (e.g., rest time such as lunch), decreasing the time interval during abnormal environmental activities (e.g., inspection, or maintenance), decreasing the time interval when unexpected or non-characteristic movements are detected (e.g., frequent movement of elements that are typically stationary or coordinated movement of workers approaching an exit or handling large objects by cooperative movement, etc.), combinations thereof, and the like. Furthermore, the time interval may also include additional semi-random acquisitions. For example, occasional intermediate interval positions may be acquired by digital twinning system 15500 to enhance or evaluate the validity of a particular time interval.
In an embodiment, the digital twin system 15500 may analyze the data received from the digital twin I/O system 15504 to refine, remove, or add conditions. For example, the digital twinning system 15500 may optimize the data collection time for mobile elements that are updated more frequently than is needed (e.g., multiple consecutive receiving locations are the same or within a predetermined error margin).
In an embodiment, the digital twinning system 15500 may receive, identify, and/or store a set of states 15540a-n related to the environment 15520. The states 15540a-n may be, for example, a data structure including a plurality of attributes 158404a-n and a set of identification criteria 158406a-n to uniquely identify each respective state 15540a-n. In an embodiment, the states 15540a-n may correspond to states where the digital twinning system 15500 is desired to set or change conditions (e.g., increase/decrease monitoring intervals, change operating conditions, etc.) of the real world element 157302r and/or the environment 15520.
In an embodiment, the set of states 15540a-n may also include, for example, a minimum monitored attribute for each state 15540a-n, a set of identification criteria 158406a-n for each state 15540a-n, and/or actions that may be taken or suggested in response to each state 15540a-n. Such information may be stored by, for example, digital twin data store 15516 or another data store. The states 15540a-n, or portions thereof, may be provided to the digital twinning system 15500, determined by the digital twinning system 15500, or changed by the digital twinning system 15500. Further, the set of states 15540a-n may include data from different sources. For example, the detailed information for identifying and/or responding to the occurrence of the first state may be provided to the digital twin system 15500 via user input, the detailed information for identifying and/or responding to the occurrence of the second state may be provided to the digital twin system 15500 via an external system, the detailed information for identifying and/or responding to the occurrence of the third state may be determined by the digital twin system 15500 (e.g., via simulation or analysis of process data), and the detailed information for identifying and/or responding to the occurrence of the fourth state may be stored by the digital twin system 15500 and changed as desired (e.g., responding to the simulated occurrence of the state or responding to data collected during the analysis of the state).
In an embodiment, the plurality of attributes 158404a-n includes at least the attributes 158404a-n required to identify the respective state 15540a-n. The plurality of attributes 158404a-n may further include additional attributes that are or may be monitored in determining the respective state 15540a-n, but these attributes are not required to identify the respective state 15540a-n. For example, the plurality of attributes 158404a-n for the first state can include rotational speed, fuel level, energy input, linear speed, acceleration, temperature, strain, torque, volume, weight, and the like related information.
The set of identification criteria 158406a-n may include information for each of the set of attributes 158404a-n to uniquely identify the respective state. Identification criteria 158406a-n may include, for example, rules, thresholds, limits, ranges, logical values, conditions, comparisons, combinations thereof, and the like.
The operating conditions or monitored changes may be any suitable changes. For example, after identifying that the respective states 158406a-n occur, the digital twinning system 15500 can increase or decrease the monitoring interval of the device (e.g., decrease the monitoring interval in response to a measured parameter that is different from nominal operation) without changing the operation of the device. Additionally or alternatively, the digital twin system 15500 may change the operation of the device (e.g., reduce speed or power input) without changing the monitoring of the device. In other embodiments, the digital twin system 15500 may change the operation of the device (e.g., reduce speed or power input) and change the monitoring interval of the device (e.g., reduce the monitoring interval).
FIG. 151 illustrates an exemplary set of identification states 15540a-n that are related to an industrial environment that the digital twin system 15500 can identify and/or store for access by a smart system (e.g., cognitive smart system 15510) or user of the digital twin system 15500 according to some embodiments of the invention. States 15540a-n may include operational states (e.g., sub-optimal, normal, optimal, critical, or alarm operation of one or more components), excess or shortage states (e.g., supply side or output side numbers), combinations thereof, and the like.
In an embodiment, the digital twinning system 15500 may monitor the properties 151404a-n of the real world element 157302r and/or the digital twinning 157302d to determine the corresponding state 15540a-n. The attributes 151404a-n can be, for example, operating conditions, set points, critical points, status indicators, other sensed information, combinations thereof, and the like. For example, attributes 151404a-n can include power input 151404a, operating speed 151404b, critical speed 151404c, and operating temperature 151404d of the monitored element. Although the illustrated example shows uniform monitoring properties, the monitoring properties may differ from target device to target device (e.g., the digital twin system 15500 will not monitor the rotational speed of an object without a rotatable component).
Each of the states 15540a-n includes a set of identification criteria 151406a-n that meets a particular criteria that is unique among the set of monitored states 15540 a-n. Digital twinning system 15500 can identify overspeed state 15540a, for example, in response to monitored attributes 151404a-n meeting a first set of identification criteria 151406a (e.g., operating speed 151404b being above critical speed 151404c and operating temperature 151404d being a nominal speed).
In response to determining that one or more states 15540a-n exist or have occurred, digital twin system 15500 can update trigger conditions for one or more monitoring protocols, issue an alarm or notification, or trigger actions of a sub-component of digital twin system 15500. For example, a sub-component of digital twinning system 15500 may take action to mitigate and/or evaluate the effects of detected states 15540 a-n. When attempting to take action to mitigate the effect of the detected states 15540a-n on the real world element 157302r, the digital twinning system 15500 can determine whether an instruction is present (e.g., stored in the digital twinning data store 15516) or should be developed (e.g., developed through analog and cognitive intelligence or through user or worker input). Further, digital twin system 15500 can evaluate the impact of detected states 15540a-n, e.g., concurrently with the mitigating action or in response to determining that digital twin system 15500 does not have stored mitigating instructions for detected states 15540 a-n.
In an embodiment, digital twin system 15500 employs digital twin simulation system 15506 to simulate the effects of one or more identified states, such as immediate, upstream, downstream, and/or persistent effects. The digital twinning simulation system 15506 may collect and/or have values associated with the evaluation states 15540 a-n. In simulating the effects of one or more states 15540a-n, the digital twin simulation system 15506 can recursively evaluate the performance characteristics of the affected digital twin 157302d until convergence is reached. The digital twinning simulation system 15506 may, for example, work in conjunction with the cognitive intelligence system 15510 to determine that the responsive actions of one or more of the states 15540a-n are to be alleviated, mitigated, inhibited, and/or prevented from occurring. For example, the digital twinning simulation system 15506 may recursively simulate the effects of one or more of the states 15540a-n until a desired fit is achieved (e.g., convergence is achieved), provide simulation values for evaluating and determining potential actions to the cognitive intelligence system 15510, receive potential actions, evaluate the effects of each potential action against a corresponding desired fit (e.g., cost functions for minimizing production disturbances, maintaining critical components, minimizing maintenance and/or downtime, optimizing systems, worker, user or personal safety, etc.).
In an embodiment, the digital twin simulation system 15506 and the cognitive intelligence system 15510 may repeatedly share and update the simulation values and response actions for each desired result until the desired condition (e.g., convergence of each evaluation cost function for each evaluation action) is met. The digital twinning system 15500 may store the results in the digital twinning data store 15516 in response to determining that one or more of the states 15540a-n have occurred. Further, the digital twin simulation system 15506 and/or the cognitive intelligence system 15510 may perform simulation and evaluation in response to the occurrence or detection of an event.
In an embodiment, simulation and evaluation are triggered only when there are no related actions within the digital twinning system 15500. In other embodiments, the simulation and evaluation is performed concurrently with the use of the stored actions to evaluate the effectiveness or efficiency of the actions in real time and/or to evaluate whether further actions should be taken or whether unidentified conditions may occur. In an embodiment, the cognitive intelligence system 15510 may also be provided with notifications that illustrate instances of undesired actions with or without data regarding undesired aspects or results of such actions to optimize subsequent evaluations.
In an embodiment, digital twin system 15500 evaluates and/or represents the impact of machine downtime in a digital twin system of a manufacturing facility. For example, the digital twinning system 15500 may employ a digital twinning simulation system 15506 to simulate the immediate, upstream, downstream, and/or sustained effects of the machine shutdown state 15540 b. The digital twinning simulation system 15506 may collect or have performance-related values, such as optimal, sub-optimal, and minimum performance requirements for elements in the affected digital twinning 157302d (e.g., the real world element 157302r and/or the nested digital twinning 157302 d), and/or characteristics of elements that may be used in the affected digital twinning 157302d, the nested digital twinning 157302d, redundant systems within the affected digital twinning 157302d, combinations thereof, and the like.
In an embodiment, digital twinning system 15500 is used for: simulating one or more operating parameters of the real-world element in response to providing the given characteristic to the industrial environment using the real-world element digital twinning; responsive to providing the contemporaneous characteristic, computing a mitigation action to be taken by the one or more real-world elements; and in response to detecting the contemporaneous characteristic, initiating a mitigation action. The calculations may be performed in response to detection of contemporaneous characteristics or operating parameters that are outside of the respective design parameters, or the calculations may be determined through simulation prior to detection of such characteristics.
Additionally or alternatively, the digital twinning system 15500 can provide an alert to one or more users or system elements in response to detecting a status.
In an embodiment, the digital twin I/O system 15504 includes a path control module 157310. The path control module 157310 can obtain navigation data from the element 157302, provide and/or request navigation data to components of the digital twinning system 15500 (e.g., the digital twinning simulation system 15506, the digital twinning behavior system, and/or the cognitive intelligent system 15510), and/or output navigation data to the element 157302 (e.g., to the wearable device 15554). The navigation data may be collected or estimated using, for example, historical data, guidance data provided to the element 157302, combinations thereof, and the like.
For example, the navigation data may be collected or estimated using historical data stored by the digital twin system 15500. The historical data may include or be processed to provide information of acquisition time, associated elements 157302, polling intervals, tasks performed, loaded or unloaded conditions, whether to provide and/or follow previous boot data, conditions of environment 15520, other elements 157302 in environment 15520, combinations thereof, and the like. The estimation data may be determined using one or more suitable path control algorithms. For example, the estimate data may be calculated using an appropriate order picking algorithm, an appropriate path searching algorithm, combinations thereof, and the like. For example, the order picking algorithm may be a maximum gap algorithm, an S-shaped algorithm, a lane-by-lane algorithm, a combination algorithm, combinations thereof, and the like. For example, the path search algorithm may be Dijkstra algorithm, a-x algorithm, hierarchical path search algorithm, incremental path search algorithm, arbitrary angle path search algorithm, flow field algorithm, combinations thereof, and the like.
Additionally or alternatively, the navigation data may be collected or estimated using guidance data of the worker. For example, the guidance data may include a calculated route for an apparatus (e.g., a mobile device or a wearable device 15554) provided to the worker. In another example, the guidance data may include a calculated route for a device provided to the worker that instructs the worker to collect vibration measurements from one or more locations on one or more machines along the route. The collected and/or estimated navigation data may be provided to a user of digital twin system 15500 for visualization, used by other components of digital twin system 15500 for analysis, optimization, and/or modification, provided to one or more elements 157302, combinations thereof, and the like.
In an embodiment, digital twin system 15500 obtains navigation data for a set of workers for representation in the digital twin system. Additionally or alternatively, the digital twinning system 15500 inputs navigation data for a set of mobile device assets of an industrial environment into the digital twinning.
In an embodiment, the digital twinning system 15500 obtains a system for modeling traffic for mobile elements in industrial digital twinning. For example, the digital twinning system 15500 may model traffic patterns for workers or personnel, mobile device assets, combinations thereof, and the like in the environment 15520. Traffic patterns may be estimated based on modeling traffic patterns based on historical data and contemporaneous acquisition data. Further, the traffic pattern may be continuously or intermittently updated according to conditions in the environment 15520 (e.g., multiple autonomous mobile device assets may provide information to the digital twinning system 15500 at an update interval slower than the environment 15520 (including both human and mobile device assets)).
The digital twinning system 15500 can change the mode of transportation (e.g., by providing updated navigation data to one or more mobile elements) to achieve one or more predetermined criteria. For example, the predetermined criteria may include improving processing efficiency, reducing interactions between loaded workers and mobile device assets, minimizing worker path length, routing the mobile device around a person's path or potential path, combinations thereof, and the like.
In an embodiment, the digital twinning system 15500 may provide traffic data and/or navigation information to mobile elements in industrial digital twinning. The navigation information may be provided as an instruction or rule set, displayed path data, or selective actuation of the device. For example, the digital twinning system 15500 may provide a set of instructions to the robot to direct the robot to a desired route and/or to direct the robot along a desired route to collect vibration data from one or more specified locations on one or more specified machines along the route using vibration sensors. Robots may communicate updated information to the system including obstructions, route diversions, unexpected interactions with other assets in the environment 15520, and the like.
In some embodiments, ant-based system 15574 enables an industrial entity (including a robot) to use one or more messages to set trajectories that other industrial entities (including themselves) track during a later trip. In an embodiment, the message includes information related to vibration measurement collection. In an embodiment, the message includes information related to the vibration sensor measurement location. In some embodiments, the trajectory may be used to fade over time. In some embodiments, the ant-based trajectory may be experienced via an augmented reality system. In some embodiments, the ant-based trajectory may be experienced via a virtual reality system. In some embodiments, the ant-based trajectory may be experienced via a mixed reality system. In some embodiments, ant-based zone markers may trigger pain responses and/or accumulate into a warning signal. In an embodiment, the ant-based path may be used to generate an information filtering response. In some embodiments, the ant-based path may be used to generate an information filtering response, where the information filtering response is an enhanced visual perception. In some embodiments, the ant-based path may be used to generate an information filtering response, where the information filtering response is an enhanced acoustic perception. In some embodiments, the message includes vectorized data.
In an embodiment, the digital twinning system 15500 includes design specification information for representing the real world element 157302r using digital twinning 157302 d. The numbers may correspond to existing real world elements 157302r or potential real world elements 157302r. Design specification information may be received from one or more sources. For example, the design specification information may include design parameters set by user input, design parameters determined by digital twinning system 15500 (e.g., by digital twinning simulation system 15506), design parameters optimized by a user or digital twinning simulation system 15506, combinations thereof, and the like. The digital twinning simulation system 15506 may present design specification information of the components to a user, for example, via a display device or a wearable device. The design specification information may be displayed schematically (e.g., as part of a process map or information table) or as part of an augmented reality or virtual reality display. For example, the design specification information may be displayed in response to a user interaction with the digital twinning system 15500 (e.g., via a user selection element or user selection typically including the design specification information within a display). Additionally or alternatively, the design specification information may be automatically displayed, for example, when an element enters into a view of an augmented reality or virtual reality device. In an embodiment, the displayed design specification information may further include indicia of information sources (e.g., different display colors indicating user input and digital twin system 15500 determination), non-matching indicia (e.g., between design specification information and operational information), combinations thereof, and the like.
In an embodiment, the digital twinning system 15500 embeds a set of control instructions for the wearable device in the industrial digital twinning such that the control instructions are provided to the wearable device to induce an experience for a wearer of the wearable device when interacting with elements of the industrial digital twinning. The induced experience may be, for example, an augmented reality experience or a virtual reality experience. Wearable devices (e.g., headphones) may be used to output video, audio, and/or haptic feedback to the wearer to induce an experience. For example, the wearable device may include a display device and the experience may include displaying information related to the respective digital twinning. The displayed information may include maintenance data associated with digital twinning, vibration measurement location data associated with digital twinning, financial data associated with digital twinning, such as a profit and loss associated with operation of digital twinning, manufacturing KPIs associated with digital twinning, information related to an occlusion element (e.g., a sub-assembly) that is at least partially occluded by a foreground element (e.g., a housing), a virtual model of the occlusion element overlaid on and visible to the foreground element, an operating parameter of the occlusion element, a comparison of design parameters corresponding to the displayed operating parameter, combinations thereof, and the like. For example, the comparison may include altering the display of the operating parameter to alter the color, size, and/or display period of the operating parameter.
In some embodiments, the displayed information may include indicia for removable elements that are or may be used to provide access to the occlusion element, wherein each indicia is displayed proximate to or overlaying a corresponding removable element.
Further, the indicia may also be displayed sequentially such that a first indicia corresponding to a first removable element (e.g., a housing) is displayed and a second indicia corresponding to a second removable element (e.g., an access panel within the housing) is displayed in response to a worker removing the first removable element. In some embodiments, the induced experience allows the wearer to see one or more locations on the machine for optimal vibration measurement collection. In an example, the digital twinning system 15500 can provide an augmented reality view that includes vibration measurement collection locations highlighted on the machine and/or instructions related to collecting vibration measurements.
Further, in this example, the digital twinning system 15500 can provide an augmented reality view that includes instructions related to the timing of vibration measurement collection. Information for displaying the highlighted placement location may be obtained using information stored by digital twinning system 15500. In some embodiments, the mobile element may be tracked by the digital twinning system 15500 (e.g., via an observation element in the environment 15520 and/or via path control information communicated to the digital twinning system 15500) and continuously displayed by the wearable device within the occluded view of the worker. This optimizes the movement of the element in the environment 15520, improves worker safety, and minimizes element downtime due to damage.
In some embodiments, the digital twinning system 15500 may provide an augmented reality view that displays to the wearer a mismatch between the design parameters and the expected parameters of the real world element 157302 r. The displayed information may correspond to a real world element 157302r that is not in the line of sight of the wearer (e.g., an element in another room or an element that is mechanically obscured). This enables a worker to quickly and accurately exclude a mismatch to determine one or more sources of the mismatch. The cause of the mismatch may then be determined, for example, by the digital twinning system 15500 and the commanded corrective action. In an exemplary embodiment, the wearer may be able to view the malfunctioning sub-assembly of the machine without removing the shielding element (e.g., the housing or shroud). Additionally or alternatively, the wearer may be provided with an indication for servicing the device, for example, including a display of the removal process (e.g., the location of the fastener to be removed), components or subassemblies that should be transported to other areas for servicing (e.g., dust sensitive components), components or subassemblies that require lubrication, and the location of the object for reassembly (e.g., storing the location where the wearer placed the removed object and guiding the wearer or another wearer to the stored location to expedite reassembly and minimize further disassembly or missing components in the reassembled element). This may expedite maintenance work, minimize process impact, allow workers to disassemble and reassemble the device (e.g., by coordinating disassembly without direct communication between workers), improve device life and reliability (e.g., by ensuring that all components are properly replaced before being re-used), combinations of such components, and the like.
In some embodiments, the evoked experience includes a virtual reality view or an augmented reality view that allows the wearer to view information related to existing or planned elements. The information may be independent of the actual performance of the element (e.g., asset value, energy cost, input material cost, output material value, legal compliance, and financial performance of corporate operations, etc.). One or more wearers may perform virtual roaming or enhanced roaming of the industrial environment 15520.
For example, the wearable device displays compliance information that expedites work inspection or execution.
In other examples, the wearable device displays financial information for identifying the change or optimization objective. For example, a manager or senior manager may check whether the environment 15520 complies with updated regulations, including compliance with previous regulations, "exempt from new regulations," and/or information regarding exception elements. This may be used to reduce unnecessary downtime (e.g., schedule upgrades to a minimum impact time, such as during a planned maintenance period), prevent unnecessary upgrades (e.g., replace older or exceptional equipment), and reduce capital investment.
Referring back to fig. 155, in an embodiment, digital twin system 15500 may include, integrate, manage, manipulate digital twin dynamic model system 15508, link to digital twin dynamic model system 15508, obtain input from digital twin dynamic model system 15508, provide output to digital twin dynamic model system 15508, control digital twin dynamic model system 15508, coordinate digital twin dynamic model system 15508, or otherwise interact with digital twin dynamic model system 15508. The digital twinning dynamic model system 15508 may update a set of digital twinning attributes of a set of industrial entities and/or environments, including physical industrial assets, workers, processes, manufacturing facilities, warehouses, etc. (or any other type of entity or environment described in the present invention or in the documents incorporated by reference herein) such that digital twinning may represent industrial entities and environments and their real-time or very near real-time characteristics or attributes. In some embodiments, the digital twin dynamic model system 15508 can obtain sensor data received from the sensor system 15530 and can determine one or more attributes of an industrial environment or an industrial entity in the environment based on the sensor data and based on one or more dynamic models.
In an embodiment, the digital twinning dynamic model system 15508 can update/assign values of various attributes in the digital twinning and/or the one or more embedded digital twinning, including, but not limited to, vibration values, vibration fault level states, fault probability values, shutdown cost values, shutdown probability values, financial values, KPI values, temperature values, humidity values, heat values, fluid flow values, radiation values, material concentration values, velocity values, acceleration values, position values, pressure values, stress values, strain values, light intensity values, sound level values, volume values, shape characteristics, material characteristics, and dimensions.
In embodiments, digital twinning may include (e.g., by reference) other embedded digital twinning. For example, digital twinning of a manufacturing facility may include embedded digital twinning of a machine and one or more embedded digital twinning of one or more corresponding motors enclosed within the machine. For example, digital twinning may be embedded in a memory of an industrial machine having an on-board IT system (e.g., a memory of an on-board diagnostic system, a control system (e.g., a SCADA system), etc.). Other non-limiting examples of embeddable digital twinning include: on a wearable device of a worker; in memory on local network assets (e.g., switches, routers, access points, etc.); in cloud computing resources provided for an environment or entity; and asset tags or other memory structures specific to the entity.
In one example, the digital twin dynamic model system 15508 can update the vibration characteristics in the entire industrial environment digital twin based on captured vibration sensor data measured at one or more locations in the industrial environment and one or more dynamic models modeling vibrations in the industrial environment digital twin. Prior to updating, the industrial digital twin may already contain information about properties of the industrial entity and/or environment that may be used to feed the dynamic model, such as information of material, shape/volume (e.g. of the catheter), location, connection/interface, etc., so that vibration characteristics may be represented for the entity and/or environment in the digital twin. Alternatively, the dynamic model may be configured using this information.
In an embodiment, the digital twinning dynamic model system 15508 may update digital twinning and/or one or more embedded digital twinning attributes on behalf of the client application 15570. In an embodiment, the client application 15570 may be an application related to an industrial component or environment (e.g., monitoring an industrial facility or component therein, simulating an industrial environment, etc.). In an embodiment, the client application 15570 may be used in conjunction with fixed and mobile data collection systems. In an embodiment, the client application 15570 may be used in conjunction with an industrial internet of things sensor system 15530.
In an embodiment, the digital twin dynamic model system 15508 utilizes the digital twin dynamic model 155100 to model the behavior of an industrial entity and/or environment. For example, the dynamic model 155100 may ensure that digital twinning can represent physical reality (including interactions of industrial entities) using a limited number of measurements based on scientific principles to enrich the digital representation of the industrial entities and/or environments. In an embodiment, the dynamic model 155100 is a formula or mathematical model. In an embodiment, the dynamic model 155100 follows scientific laws, natural laws, and formulas (e.g., newton's law of motion, second law of thermodynamics, bernoulli's principle, ideal gas law, dalton partial pressure law, hooke's law of elasticity, fourier heat conduction law, archimedes' buoyancy principle, etc.). In an embodiment, the dynamic model is a machine learning model.
In an embodiment, the digital twinning system 15500 may have a digital twinning dynamic model data store 155102 for storing dynamic models 155100 that may be represented in digital twinning. In an embodiment, the digital twin dynamic model data store may be searchable and/or discoverable. In an embodiment, the digital twin dynamic model data store may contain metadata that allows a user to understand the features that a given dynamic model may handle, what inputs are required, what outputs are provided, and so on. In some embodiments, the digital twin dynamic model data store 155102 may be hierarchical (e.g., the model may be deepened or made simpler depending on the range of available data and/or inputs, granularity of inputs, and/or contextual factors (e.g., accessing high-level interests and higher fidelity models over a period of time).
In an embodiment, a digital twin or digital representation of an industrial entity or facility may include a set of data structures that collectively define a set of attributes of the represented physical industrial asset, device, worker, process, facility, and/or environment and/or possible behavior thereof. In an embodiment, the digital twinning dynamic model system 15508 may utilize the dynamic model 155100 to inform the set of data structures that collectively define digital twinning using real-time data values. The digital twin dynamic model 155100 may receive as input one or more sensor measurements, industrial internet of things device data, and/or other suitable data and calculate one or more outputs based on the received data and the one or more dynamic models 155100. The digital twin dynamic model system 15508 then uses one or more outputs to update the digital twin data structure.
In one example, the set of properties of the digital twin of the industrial asset that may be updated by the digital twin dynamic model system 15508 using the dynamic model 155100 may include vibration characteristics of the asset, one or more temperatures of the asset, a state of the asset (e.g., solid, liquid, or gaseous), a position of the asset, a displacement of the asset, a velocity of the asset, an acceleration of the asset, a shutdown probability value associated with the asset, a shutdown cost value associated with the asset, a shutdown probability value associated with the asset, a manufacturing KPI associated with the asset, financial information associated with the asset, heat flow characteristics associated with the asset, fluid flow rates associated with the asset (e.g., fluid flow rates of fluids flowing through a pipeline), identifiers of other digital twin embedded in the digital twin of the asset, and/or identifiers of digital twin embedded digital twin of the asset, and/or other suitable properties. The dynamic model 155100 associated with digital twinning of an asset may be used to calculate, interpolate, extrapolate, and/or output values for such asset digital twinning attributes based on input data and/or other suitable data collected from sensors and/or devices disposed in an industrial setting, and then populate the asset digital twinning with the calculated values.
In some embodiments, the set of properties of the digital twinning of the industrial device that may be updated by the digital twinning dynamic model system 15508 using the dynamic model 155100 may include the status of the device, the location of the device, one or more temperatures of the device, the trajectory of the device, the digital twinning of the apparatus, other digital twinning identifiers embedded, linked, included, integrated, input obtained therefrom, output provided thereto and/or interacted with, etc. The dynamic model 155100 associated with digital twinning of devices may be used to calculate values or output values for these device digital twinning attributes based on the input data and then use the calculated values to update the device digital twinning.
In some embodiments, the set of properties of the digital twinning of the industrial worker that may be updated by the digital twinning dynamic model system 15508 using the dynamic model 155100 may include the status of the worker, the location of the worker, pressure measurements of the worker, tasks performed by the worker, performance measurements of the worker, and the like. The dynamic model associated with the industrial worker's digital twinning may be used to calculate or output values of these attributes based on the input data, which calculated values may then be used to populate the industrial worker's digital twinning. In embodiments, an industrial worker dynamic model (e.g., a psychometric model) may be used to predict responses to stimuli (e.g., give a prompt to workers) to instruct them to perform tasks and/or alarms or warnings intended to induce safety behavior. In an embodiment, the industrial worker dynamic model may be a workflow model (Gantt chart, etc.), a failure mode impact analysis model (FMEA), a biophysical model (e.g., modeling worker fatigue, energy utilization, hunger), etc.
Exemplary properties of the digital twin dynamic model of the industrial environment that may be updated by the digital twin dynamic model system 15508 using the dynamic model 155100 may include the scale of the industrial environment, one or more temperatures of the industrial environment, one or more humidity values of the industrial environment, fluid flow characteristics in the industrial environment, heat flow characteristics of the industrial environment, lighting characteristics of the industrial environment, acoustic characteristics of the industrial environment, physical objects in the environment, processes occurring in the industrial environment, water flow (if a body of water) of the industrial environment, and the like. The dynamic model associated with digital twinning of an industrial environment may be used to calculate or output these attributes based on input data collected from sensors and/or devices disposed in the industrial environment and/or other suitable data, and then populate the industrial environment digital twinning with the calculated values.
In an embodiment, the dynamic model 155100 may follow physical constraints that define boundary conditions, constants, or variables for digital twin modeling. For example, the digital twinned physical characteristics of an industrial entity or industrial environment may include a gravitational constant (e.g., 9.8m/s 2), a surface coefficient of friction, a material thermal coefficient, a maximum temperature of an asset, a maximum flow rate, and the like. Additionally or alternatively, the dynamic model may also follow natural laws. For example, the dynamic model may follow the laws of thermodynamics, motion, fluid dynamics, buoyancy, heat transfer, radiation, quantum dynamics, etc. In some embodiments, the dynamic model may follow biological aging theory or mechanical aging theory. Thus, when the digital twinned dynamic model system 15508 facilitates a real-time digital representation, the digital representation may conform to the dynamic model such that the digital representation simulates real world conditions. In some embodiments, one or more outputs from the dynamic model may be presented to a human user and/or compared to real world data to ensure convergence of the dynamic model with the real world. Further, since the dynamic model is based in part on assumptions, the properties of digital twinning may be improved and/or corrected when the behavior of the real world is different from that of digital twinning. In an embodiment, additional data collection and/or instrumentation may be recommended based on the following awareness: the expected lack of input to the dynamic model, the model in operation does not work as intended (possibly due to lack and/or erroneous sensor information), the need for different results (e.g., due to contextual factors that make something highly interesting), etc.
The dynamic model may be obtained from a number of different sources. In some embodiments, the user may upload a model created by the user or a third party. Additionally or alternatively, a model may be created on the digital twinning system using a graphical user interface. The dynamic model may include a custom model configured for a particular environment and/or a set of industrial entities and/or agnostic models adapted for similar types of digital twinning. The dynamic model may be a machine learning model.
Fig. 159 illustrates an exemplary embodiment of a method for updating a set of attributes of a digital twinning and/or one or more embedded digital twinning on behalf of a client application 15570. In an embodiment, digital twin dynamic model system 15508 utilizes one or more dynamic models 155100 to update a set of properties of digital twin and/or one or more embedded digital twin on behalf of client application 15570 based on sensor data collected from sensor system 15530, data collected from an internet-of-things connection device 15524, and/or the effects of other suitable data in the set of dynamic models 155100 for implementing industrial digital twin. In an embodiment, the digital twinned dynamic model system 15508 may be instructed to run a particular dynamic model using one or more digital twins that represent physical industrial assets, devices, workers, processes, and/or industrial environments managed, maintained, and/or monitored by the client application 15570.
In an embodiment, the digital twin dynamic model system 15508 may obtain data from other types of external data sources that are not necessarily industrial related data sources, but may provide data that may be used as input data for a dynamic model. For example, weather data, news events, social media data, etc. may be collected, crawled, subscribed to, to supplement sensor data, industrial internet of things device data, and/or other data used by the dynamic model. In an embodiment, the digital twin dynamic model system 15508 may obtain data from a machine vision system. The machine vision system may use video and/or still images to provide measurements (e.g., position, status, etc.) that may be used as input by the dynamic model.
In an embodiment, the digital twin dynamic model system 15508 may feed this data into one or more of the dynamic models described above to obtain one or more outputs.
These outputs may include calculated vibration fault level states, vibration severity unit values, vibration characteristics, fault probability values, shutdown cost values, time to failure values, temperature values, pressure values, humidity values, precipitation values, visibility values, air quality values, strain values, stress values, displacement values, velocity values, acceleration values, position values, performance values, financial values, manufacturing KPI values, electrodynamic values, thermodynamic values, fluid flow rate values, and the like. The client application 15570 may then use the results obtained by the digital twin dynamic model system 15508 to initiate a digital twin visualization event. In an embodiment, the visualization may be a heat map visualization.
In an embodiment, the digital twinning dynamic model system 15508 may receive a request to update one or more attributes of the digital twinning of the industrial entity and/or environment such that the digital twinning represents the industrial entity and/or environment in real-time. At 159100, the digital twinning dynamic model system 15508 receives a request to update one or more attributes of one or more digital twinning of an industrial entity and/or environment. For example, the digital twinning dynamic model system 15508 may receive a request from the client application 15570 or from another process (e.g., a predictive maintenance process) performed by the digital twinning system 15500. The request may indicate one or more attributes and a digital twin or digital twin associated with the request. In step 159102, the digital twinning dynamic model system 15508 determines the one or more digital twinning required to fulfill the request and retrieves the required one or more digital twinning, including any embedded digital twinning, from the digital twinning data store 15516. At 159104, the digital twin dynamic model system 15508 determines the one or more dynamic models required to satisfy the request and retrieves the required one or more dynamic models from the digital twin dynamic model memory 155102. At 159106, the digital twin dynamic model system 15508 selects one or more sensors in the sensor system 15530, data collected from the physical networking connection device 15524, and/or other data sources in the digital twin I/O system 15504 based on the available data sources and one or more desired inputs of one or more dynamic models. In embodiments, the data sources may be defined in the inputs required for one or more dynamic models, or may be selected using a lookup table. At 159108, the digital twin dynamic model system 15508 retrieves selected data from the digital twin I/O system 15504. At 159110, the digital twin dynamic model system 15508 uses the retrieved input data (e.g., vibration sensor data, industrial internet of things device data, etc.) as input to run one or more dynamic models and determine one or more output values based on the one or more dynamic models and the input data. At 159112, the digital twin dynamic model system 15508 updates values of one or more attributes of one or more digital twin based on one or more outputs of one or more dynamic models.
In an exemplary embodiment, the client application 15570 can be used to provide a digital representation and/or visualization of digital twinning of an industrial entity. In an embodiment, the client application 15570 may include one or more software modules executed by one or more server devices. These software modules may be used to quantify properties of digital twinning, model properties of digital twinning, and/or to visualize digital twinning behavior. In an embodiment, these software modules may enable a user to select a particular digital twinning behavior visualization to view. In an embodiment, these software modules may enable a user to choose to view a digital twinning behavior visualization. In some embodiments, the client application 15570 can provide the selected behavior visualization to the digital twin dynamic model system 15508.
In an embodiment, the digital twinning dynamic model system 15508 can receive a request from the client application 15570 to update properties of the digital twinning to enable a digital representation of an industrial entity and/or environment, wherein the real-time digital representation is a visualization of the digital twinning. In an embodiment, digital twinning may be presented by a computing device such that a human user may view a digital representation of a real-world industrial asset, apparatus, worker, process, and/or environment. For example, digital twinning may be presented and output to a display device. In an embodiment, the dynamic model output and/or related data may be superimposed on a digitally twinned presentation. In an embodiment, dynamic model output and/or related information may appear as digital twinning is presented in a display interface. In an embodiment, the related information may include a real-time video clip associated with a real-world entity represented by a digital twin. In an embodiment, the relevant information may include a sum of each vibration fault level state in the machine. In an embodiment, the related information may be graphical information. In an embodiment, the graphical information may describe the motion and/or describe the motion as a function of the frequency of the individual machine components. In an embodiment, the graphical information may describe the motion and/or describe the motion as a function of the frequency of the individual machine components, wherein the user is able to select views of the graphical information in the x, y and z dimensions. In an embodiment, the graphical information may describe the motion and/or describe the motion as a function of frequency of the individual machine components, wherein the graphical information includes harmonic peaks and peaks. In an embodiment, the relevant information may be cost data, including outage cost data, maintenance cost data, new component cost data, new machine cost data, etc. per day. In an embodiment, the relevant information may be outage probability data, failure probability data, or the like. In an embodiment, the relevant information may be time of failure data.
In an embodiment, the relevant information may be advice and/or insight. For example, advice or insight obtained from cognitive intelligence systems associated with the machine may be presented in a display interface as a digital twin of the machine.
In embodiments, clicking, touching, or otherwise interacting with digital twins presented in a display interface may allow a user to "see in depth" and view the underlying subsystem or process and/or embedded digital twins. For example, in response to a user clicking on a machine bearing presented in a digital twin of the machine, the display interface may allow the user to learn and view information about the bearing, view a 3D visualization of the bearing vibration, and/or view the digital twin of the bearing.
In embodiments, clicking, touching, or otherwise interacting with information related to digital twinning presented in a display interface may allow a user to "see in depth" and view the underlying information.
FIG. 160 illustrates an exemplary embodiment of a display interface presenting digital twinning of a dryer centrifuge and other information related to the dryer centrifuge.
In some embodiments, the digital twinning may be presented and output in a virtual reality display. For example, a user may view a 3D presentation of the environment (e.g., using a display or virtual reality headset). The user may also check and/or interact with the digital twinning of the industrial entity. In an embodiment, a user may view a process being performed for one or more digital twins (e.g., collecting measurements, moving, interacting, inventorying, loading, packaging, transporting, etc.). In an embodiment, a user may provide input through a graphical user interface that controls one or more properties of a digital twin.
In some embodiments, the digital twinning dynamic model system 15508 can receive a request from the client application 15570 to update properties of the digital twinning to enable a digital representation of an industrial entity and/or environment, wherein the digital representation is a digital twinning heatmap visualization. In an embodiment, a platform is provided with a heat map showing data collected from sensor system 15530, internet of things connection device 15524, and data output from dynamic model 155100 for providing input to a display interface. In an embodiment, the heat map interface is provided as an output of digital twinning data, e.g., for processing and providing visual information of various sensor data, dynamic model output data, and other data (e.g., map data, analog sensor data, and other data) to another system, e.g., a mobile device, tablet, control panel, computer, AR/VR device, etc. A digital twin representation, such as a representation of a map that includes analog sensor data, a level indicator of digital sensor data, and output values from a dynamic model (e.g., data indicative of vibration fault level status, vibration severity unit values, shutdown probability values, shutdown cost values, shutdown probability values, failure time values, failure probability values, manufacturing KPIs, temperatures, rotation levels, vibration characteristics, fluid flow, heating or cooling, pressure, substance concentration, and many other output values) may be provided in a form factor suitable for transmitting visual input to a user (e.g., a user device, a VR enabled device, an AR enabled device, etc.). In an embodiment, signals (or selective combinations, permutations, blends, etc.) from various sensors or input sources and data determined by the digital twin dynamic model system 15508 may provide input data to a heatmap. The coordinates may include real world location coordinates (e.g., geographic location or location on an environmental map) as well as other coordinates, such as time-based coordinates, frequency-based coordinates, or other coordinates that allow for the representation of analog sensor signals, digital signals, dynamic model outputs, input source information, and various combinations in a map-based visualization, such that the colors may represent different input levels along the relevant dimensions. For example, in many other possibilities, if an industrial machine component is in a critical vibration fault level state, the heat map interface may alert a user by displaying the machine component in orange. In a heat map example, clicking, touching, or otherwise interacting with the heat map may allow a user to gain insight into and view the underlying sensors, dynamic model outputs, or other input data used as heat map display inputs. In other examples, such as examples where digital twinning is displayed in VR or AR environments, if vibration of an industrial machine component exceeds normal operation (e.g., at a suboptimal vibration, critical vibration, or alarm vibration failure level), when a user contacts a representation of the machine component, or if the machine component is operating in an unsafe manner, the haptic interface may induce vibration, and the directional sound signal may direct the user's attention to the machine in digital twinning, such as by playing in a particular speaker of a headset or other sound system.
In an embodiment, the digital twinning dynamic model system 15508 may obtain a set of ambient environmental data and/or other data and automatically update a set of attributes of the digital twinning of the industrial entity or facility based on the impact of the environmental data and/or other data in the dynamic model set 155100 for enabling digital twinning. The ambient data may include temperature data, pressure data, humidity data, wind data, rainfall data, tide data, storm tide data, cloud cover data, snowfall data, visibility data, water level data, and the like. Additionally or alternatively, the digital twinning dynamic model system 15508 may use a set of environmental data measurements collected by a set of internet of things connection devices 15524 disposed in an industrial environment as input to the set of dynamic models 155100 for enabling digital twinning. For example, digital twin dynamic model system 15508 may feed data collected, processed, or exchanged by internet of things connection device 15524, such as cameras, displays, embedded sensors, mobile devices, diagnostic devices and systems, instrumentation systems, telematics systems, etc., for example, for monitoring machines, devices, components, parts, operations, functions, conditions, states, events, workflows, and other elements of an industrial environment (collectively, "states"). Other examples of internet of things connection devices include intelligent fire alarms, intelligent security systems, intelligent air quality monitors, intelligent/learning thermostats, and intelligent lighting systems.
FIG. 161 illustrates an exemplary embodiment of a method for updating a set of vibration fault level states for a set of bearings in a digital twin of a machine. In this example, the client application 15570 interfacing with the digital twinning dynamic model system 15508 can be used to provide visualization of the fault level status of the bearings in digital twinning of the machine.
In this example, the digital twinned dynamic model system 15508 can receive a request from the client application 15570 to update the vibration fault level state of the machine digital twinning. At 161200, the digital twinning dynamic model system 15508 receives a request from the client application 15570 to update one or more vibration fault level states of the machine digital twinning. Next, in step 161202, the digital twinning dynamic model system 15508 determines the one or more digital twinning required to fulfill the request and retrieves the required one or more digital twinning from the digital twinning data store 15516. In this example, the digital twin dynamic model system 15508 can retrieve digital twinning of the machine and any embedded digital twinning (e.g., any embedded motor digital twinning and bearing digital twinning) as well as any digital twinning embedded into the machine digital twinning (e.g., manufacturing facility digital twinning). At 161204, the digital twin dynamic model system 15508 determines the one or more dynamic models required to satisfy the request and retrieves the required one or more dynamic models from the digital twin dynamic model data store 155102. At 161206, digital twin dynamic model system 15508 selects a dynamic model input data source (e.g., one or more sensors from sensor system 15530, data from internet of things connection device 15524, and any other suitable data) via digital twin I/O system 15504 based on available data sources (e.g., available sensors from a set of sensors of sensor system 15530) and one or more desired inputs of one or more dynamic models. In this example, the retrieved one or more dynamic models 155100 can take as input to the dynamic model one or more vibration sensor measurements from vibration sensor 15536. In an embodiment, vibration sensor 15536 may be an optical vibration sensor, a uniaxial vibration sensor, a triaxial vibration sensor, or the like. At 161208, the digital twin dynamic model system 15508 retrieves one or more measurements from each selected data source via the digital twin I/O system 15504. Next, in 161210, the digital twin dynamic model system 15508 uses the retrieved vibration sensor measurements as input to run one or more dynamic models and calculate one or more outputs representing bearing vibration fault level states. Next, at 161212, the digital twinning dynamic model system 15508 updates one or more bearing fault level states of the manufacturing facility digital twinning, machine digital twinning, motor digital twinning, and/or bearing digital twinning based on one or more outputs of the one or more dynamic models. The client application 15570 can obtain the vibration fault level status of the bearings and can display the obtained vibration fault level status associated with each bearing and/or display color associated with the fault level severity when one or more digital twins are presented on a display interface (e.g., red for alarm, critical, yellow for suboptimal, green for normal operation).
In another example, the client application 15570 can be an augmented reality application. In some embodiments of this example, the client application 15570 may obtain the vibration fault level state of the bearing in the field of view of the AR-enabled device (e.g., smart glasses) from digital twinning of the industrial environment (e.g., through the API of the digital twinning system 15500), and may display the obtained vibration fault level state on a display of the AR-enabled device such that the displayed vibration fault level state corresponds to a location in the field of view of the AR-enabled device. In this way, even if there is no vibration sensor within the field of view of the AR-enabled device, a vibration fault level status may be displayed.
FIG. 155 illustrates an exemplary embodiment of a method for updating a set of vibration severity unit values for a digital twin center bearing of a machine. The vibration severity unit may be measured as displacement, velocity, and acceleration.
In this example, the client application 15570 interfacing with the digital twinning dynamic model system 15508 can be used to provide visualization of the three-dimensional vibration characteristics of the bearing in digital twinning of the machine.
In this example, the digital twin dynamic model system 15508 can receive a request from the client application 15570 to update the vibration severity unit value of the bearing in digital twin of the machine. At 155300, digital twinning dynamic model system 15508 receives a request from client application 15570 to update one or more vibration severity unit values of a manufacturing facility digital twinning. Next, in step 155302, the digital twinning dynamic model system 15508 determines the one or more digital twinning required to fulfill the request and retrieves the required one or more digital twinning from the digital twinning data store 15516. In this example, the digital twin dynamic model system 15508 can retrieve the digital twin of the machine and any embedded digital twin (e.g., of bearings and other components). At 155304, the digital twin dynamic model system 15508 determines the one or more dynamic models required to satisfy the request and retrieves the required one or more dynamic models from the dynamic model data store 155102. At 155306, digital twin dynamic model system 15508 selects a dynamic model input data source (e.g., one or more sensors from sensor system 15530, data from internet of things connection device 15524, and any other suitable data) via digital twin I/O system 15504 based on available data sources (e.g., available sensors from a set of sensors of sensor system 15530) and one or more desired inputs of one or more dynamic models. In this example, the retrieved dynamic model may be used to take as input one or more vibration sensor measurements and provide a severity unit value for the bearing in the machine. At 155308, the digital twin dynamic model system 15508 retrieves one or more measurements from each selected sensor. In this example, the digital twin dynamic model system 15508 retrieves measurements from the vibration sensor 15536 via the digital twin I/O system 15504. At 155310, the digital twin dynamic model system 15508 uses the retrieved vibration measurements as input to run one or more dynamic models and calculate one or more output values representing vibration severity unit values for bearings in the machine. Next, at 155312, the digital twin dynamic model system 15508 updates one or more vibration severity unit values of the machine digital twin and all other embedded digital twin or digital twin in-bearing of the embedded machine digital twin based on one or more values of the one or more dynamic model outputs.
FIG. 163 illustrates an exemplary embodiment of a method for updating a set of fault probability values for a machine component in a digital twin of a machine.
In this example, the digital twinning dynamic model system 15508 can receive a request from the client application 15570 to update the failure probability values of the components in the machine digital twinning. At 163400, the digital twinning dynamic model system 15508 receives a request from the client application 15570 to update one or more fault probability values for the machine digital twinning, any embedded component digital twinning, and any digital twinning (e.g., manufacturing facility digital twinning) that embeds the machine digital twinning. Next, in step 163402, the digital twinning dynamic model system 15508 determines the digital twinning or twinning required to fulfill the request and retrieves the required digital twinning or twinning. In this example, the digital twin dynamic model system 15508 can retrieve digital twin of the manufacturing facility, digital twin of the machine, and digital twin of the machine components from the digital twin data store 15516. At 163404, the digital twin dynamic model system 15508 determines the one or more dynamic models required to satisfy the request and retrieves the required one or more dynamic models from the dynamic model data store 155102. At 163406, digital twin dynamic model system 15508 selects a dynamic model input data source (e.g., one or more sensors from sensor system 15530, data from internet of things connection device 15524, and any other suitable data) via digital twin I/O system 15504 based on available data sources (e.g., available sensors from a set of sensors of sensor system 15530) and one or more desired inputs of one or more dynamic models. In this example, the retrieved dynamic model may input one or more vibration measurements from vibration sensor 15536 and historical fault data as a dynamic model and output a fault probability value for a machine component in digital twinning of the machine. At 163408, the digital twin dynamic model system 15508 retrieves data from each selected sensor and/or internet of things connection device via the digital twin I/O system 15504. At 163410, the digital twin dynamic model system 15508 uses the retrieved vibration data and historical fault data as inputs to run one or more dynamic models and calculate one or more outputs representing fault probability values for bearings in the machine digital twin. Next, at 163412, the digital twin dynamic model system 15508 updates one or more failure probabilities of the machine digital twin, all embedded digital twin, and all digital twin in which the machine digital twin is embedded based on the output of the one or more dynamic models.
FIG. 164 illustrates an exemplary embodiment of a method for updating a set of outage probabilities for a machine in a digital twin of a manufacturing facility.
In this example, the client application 15570 interfacing with the digital twinning dynamic model system 15508 can be used to provide visualization of outage probability values for a manufacturing facility in digital twinning of the manufacturing facility.
In this example, the digital twinning dynamic model system 15508 can receive a request from the client application 15570 to assign a shutdown probability value to a machine in the manufacturing facility digital twinning. At 164500, the digital twinning dynamic model system 15508 receives a request from the client application 15570 to update one or more outage probability values for machines in the manufacturing facility digital twinning and any embedded digital twinning (e.g., single machine digital twinning). Next, in step 164502, the digital twinning dynamic model system 15508 determines the one or more digital twinning required to fulfill the request and retrieves the required one or more digital twinning from the digital twinning data store 15516. In this example, the digital twin dynamic model system 15508 can retrieve the digital twin and any embedded digital twin of the manufacturing facility from the digital twin data store 15516. At 164504, the digital twin dynamic model system 15508 determines the one or more dynamic models required to satisfy the request and retrieves the required one or more dynamic models from the dynamic model data store 155102. At 164506, the digital twin dynamic model system 15508 selects a dynamic model input, a data source based on available data sources (e.g., available sensors from a set of sensors of the sensor system 15530) (e.g., one or more sensors from the sensor system 15530, data from the internet of things connection device 15524, and any other suitable data), and one or more desired inputs of the digital twin I/O system 15504. In this example, one or more dynamic models may be used to take as input vibration measurements from vibration sensors and historical shutdown data and output shutdown probability values for different machines throughout the manufacturing facility. At 164508, the digital twin dynamic model system 15508 retrieves one or more measurements from each selected sensor via the digital twin I/O system 15504. At 164510, the digital twin dynamic model system 15508 uses the retrieved vibration measurements and historical shutdown data as inputs to run one or more dynamic models and calculate one or more outputs representing shutdown probability values for machines in the manufacturing facility. Next, at 164512, the digital twin dynamic model system 15508 updates one or more outage probability values for the manufacturing facility digital twin and all embedded digital twin machines based on one or more outputs of the dynamic model.
FIG. 165 illustrates an exemplary embodiment of a method for updating one or more shutdown probability values for digital twinning of an enterprise having a set of manufacturing facilities.
In this example, digital twinning dynamic model system 15508 may receive a request from client application 15570 to update downtime probability values for a set of manufacturing facilities in an enterprise digital twinning. At 165600, digital twinning dynamic model system 15508 receives a request from client application 15570 to update one or more shutdown probability values for enterprise digital twinning and any embedded digital twinning. Next, in step 165602, the digital twinning dynamic model system 15508 determines the one or more digital twinning required to fulfill the request and retrieves the required one or more digital twinning from the digital twinning data store 15516. In this example, the digital twinning dynamic model system 15508 can retrieve enterprise digital twinning and any embedded digital twinning. At 165604, the digital twin dynamic model system 15508 determines the one or more dynamic models required to satisfy the request and retrieves the required one or more dynamic models from the dynamic model data store 155102. At 165606, the digital twin dynamic model system 15508 selects a dynamic model input, a data source based on available data sources (e.g., available sensors from a set of sensors of the sensor system 15530) (e.g., one or more sensors from the sensor system 15530, data from the internet of things connection device 15524, and any other suitable data), and one or more desired inputs of the digital twin I/O system 15504. In this example, the retrieved dynamic model may be used to take as input one or more vibration measurements from vibration sensor 15536 and/or other suitable data and output shutdown probability values for each manufacturing entity in the enterprise digital twin. At 165608, the digital twin dynamic model system 15508 retrieves one or more vibration measurements from each selected vibration sensor 15536 via the digital twin I/O system 15504. At 165610, the digital twin dynamic model system 15508 uses the retrieved vibration measurements and historical shutdown data as inputs to run one or more dynamic models and calculate one or more outputs representing shutdown probability values for the manufacturing facility in the enterprise digital twin. Next, at 165612, the digital twin dynamic model system 15508 updates one or more shutdown probability values for the enterprise digital twin and all embedded digital twin based on one or more outputs of the one or more dynamic models.
FIG. 159 illustrates an exemplary embodiment of a method for updating a set of downtime cost values for a machine in a digital twin of a manufacturing facility. In an embodiment, manufacture
In this example, the digital twinning dynamic model system 15508 can receive a request from the client application 15570 to populate real-time downtime cost values associated with machines in the manufacturing facility digital twinning. At 159700, digital twinning dynamic model system 15508 receives a request from client application 15570 to update manufacturing facility digital twinning and one or more downtime cost values for any embedded digital twinning (e.g., machine component, etc.) from client application 15570. Next, in step 159702, the digital twinning dynamic model system 15508 determines the digital twinning or twinning required to fulfill the request and retrieves the required digital twinning or twinning. In this example, the digital twin dynamic model system 15508 can retrieve digital twin of the manufacturing facility, machine component, and any other embedded digital twin from the digital twin data store 15516. At 159704, the digital twin dynamic model system 15508 determines the one or more dynamic models required to satisfy the request and retrieves the required one or more dynamic models from the dynamic model data store 155102. At 159706, the digital twin dynamic model system 15508 selects a dynamic model input, a data source based on available data sources (e.g., available sensors from a set of sensors of the sensor system 15530) (e.g., one or more sensors from the sensor system 15530, data from the internet of things connection device 15524, and any other suitable data), and one or more desired inputs of the digital twin I/O system 15504. In this example, one or more retrieved dynamic models may be used to take as input historical downtime data and operational data, and output data representing daily downtime costs for machines in a manufacturing facility. At 159708, the digital twin dynamic model system 15508 retrieves historical shutdown data and operational data from the digital twin I/O system 15504. At 159710, the digital twin dynamic model system 15508 uses the retrieved data as input to run one or more dynamic models and calculate one or more outputs representing the daily downtime costs of the machine in the manufacturing facility. Next, at 159712, the digital twin dynamic model system 15508 updates one or more downtime cost values for the manufacturing facility digital twin and the machine digital twin based on one or more outputs of the one or more dynamic models.
FIG. 160 illustrates an exemplary embodiment of a method for updating a digitally twinned set of manufacturing KPI values for a manufacturing facility. In an embodiment, the manufacturing KPI is selected from the group consisting of: normal operation time, capacity utilization, standard operation efficiency, overall plant availability, machine downtime, unplanned downtime, machine setup time, inventory turnover, inventory accuracy, quality (e.g., failure rate), primary pass rate, rework, discard, number of audit failures, on-time delivery, customer returns, number of training hours, employee flow rate, reportable health and safety incidents, employee average revenue, employee average profit, planned completion, total cycle time, throughput, conversion time, profitability, planned maintenance percentage, availability, and customer return rate.
In this example, the digital twinning dynamic model system 15508 can receive a request from the client application 15570 to populate the real-time manufacturing KPI values of the manufacturing facility digital twinning. At 159700, the digital twinning dynamic model system 15508 receives a request from the client application 15570 to update one or more KPI values of the manufacturing facility digital twinning and any embedded digital twinning (e.g., machines, machine components, etc.) from the client application 15570. Next, in step 159702, the digital twinning dynamic model system 15508 determines the digital twinning or twinning required to fulfill the request and retrieves the required digital twinning or twinning. In this example, the digital twin dynamic model system 15508 can retrieve digital twin of the manufacturing facility, machine component, and any other embedded digital twin from the digital twin data store 15516. At 159704, the digital twin dynamic model system 15508 determines the one or more dynamic models required to satisfy the request and retrieves the required one or more dynamic models from the dynamic model data store 155102. At 159706, the digital twin dynamic model system 15508 selects a dynamic model input, a data source based on available data sources (e.g., available sensors from a set of sensors of the sensor system 15530) (e.g., one or more sensors from the sensor system 15530, data from the internet of things connection device 15524, and any other suitable data), and one or more desired inputs of the digital twin I/O system 15504. In this example, the one or more retrieved dynamic models may be used to take as input one or more vibration measurements and other operational data obtained from vibration sensor 15536 and output one or more manufacturing KPIs for the facility. At 167708, the digital twin dynamic model system 15508 retrieves one or more vibration measurements from each selected vibration sensor 15536 and operational data from the digital twin I/O system 15504. At 159710, the digital twin dynamic model system 15508 uses the retrieved vibration measurements and operational data as inputs to run one or more dynamic models and calculate one or more outputs representing manufacturing KPIs for the manufacturing facility. Next, at 159712, the digital twinning dynamic model system 15508 updates one or more KPI values of the manufacturing facility digital twinning, the machine component digital twinning, and all other embedded digital twinning based on one or more outputs of the one or more dynamic models.
With the popularity of vibration sensors and other industrial internet of things (IIoT) sensors, a large amount of industrial environment-related data may be acquired. These data help to predict maintenance needs and categorize potential problems in an industrial environment. However, vibration sensor data and other IIoT sensor data have many undeveloped uses that can improve the operation and uptime of an industrial environment and provide industrial entities with the ability to flexibly respond to problems before they become catastrophic.
Industrial enterprises that rely on industrial experts have difficulty acquiring knowledge of these experts as they jump to other enterprises or leave work stations. There is a need in the art to acquire industrial expertise and use the acquired industrial expertise to guide new workers or mobile electronic industry entities to perform industry related tasks.
The knowledge distribution process and related techniques are described more fully below in connection with the accompanying drawings, in which illustrative embodiments are shown. However, knowledge distribution processes and techniques may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. The knowledge distribution process may use a knowledge distribution platform or system that stores digital knowledge using blockchain techniques and provides convenient security control over such digital knowledge.
In the case where digital knowledge can be protected by encryption, there may be many practical barriers to knowledge sharing, such as lack of trust between parties that may benefit from knowledge sharing. For example, a manufacturer may benefit from having a provider acquire the manufacturer's trade secrets to make components or materials on behalf of the manufacturer, but sharing the trade secrets creates a risk that the provider may use the trade secrets on behalf of itself or on behalf of a competitor. Similarly, engineers may wish to share valuable code or instruction sets with others, but fear that the code is abused. There is a need for a digital knowledge distribution system that facilitates orchestration of knowledge sharing by providing a high degree of control over the extent to which a transacting adversary has access to shared knowledge.
Even with knowledge security and good control, certain types of knowledge are so sensitive that owners may be reluctant to share the entire knowledge set with a single adversary. For example, a proprietary process may be divided between different suppliers to prevent any one supplier from deriving or reverse engineering the entire process. However, partitioning knowledge presents operational challenges because owners may coordinate a series of secure interactions with all interested parties to ensure that the entire set of knowledge can be maintained and accurately implemented. There is a need for a digital knowledge distribution system that facilitates processing and control of knowledge subsets, including automatic processing of knowledge aggregation or related output due to knowledge subset partitioning.
Referring to fig. 168, a knowledge distribution system 16802 is used to facilitate management of digital knowledge 16804 by one or more users through a distributed ledger 16808. Digital knowledge 16804 may include any suitable knowledge that may be transferred from one party to another, such as knowledge transferred in a digital format. The user and/or principal may include one or more knowledge providers 16806 and/or one or more knowledge recipients 16818. Knowledge provider 16806 is a principal that provides knowledge to be managed through knowledge distribution system 16802, such as by uploading one or more instances of digital knowledge 16804 to knowledge distribution system 16802 and/or distributed ledgers 16808. Uploading one or more instances of digital knowledge 16804 to and/or hosting one or more instances of digital knowledge 16804 on distributed ledger 16808 may include uploading instances of digital knowledge 16804 itself to distributed ledger 16808 (e.g., may be tokenized, contained in a smart contract, and/or stored in a relational database) and/or providing a reference to an accessible location of the instance of digital knowledge 16804 and any other information needed to retrieve the digital knowledge from the accessible location. When digital knowledge 16804 is received by reference, digital knowledge 16804 itself or a reference thereto may be received. The term knowledge receiver 16848 may refer to a principal that receives knowledge from knowledge provider 16806 through knowledge distribution system 16802 (including by reference or linking to digital knowledge 16804) and/or through a distributed ledger 16508 that stores digital knowledge 16804. In some embodiments, knowledge distribution system 16802 can facilitate management of digital knowledge 16804 by facilitating the work, ownership, and/or ownership chain of establishing one or more instances of digital knowledge, such as by acting as an ownership log of knowledge instances. The ownership log may include a chain of log entries including an indication of a set of owners and/or contributors. For example, in some embodiments, knowledge distribution system 16802 can facilitate establishing a work chain, ownership chain, and/or ownership chain corresponding to a 3D print instruction set for a 3D print object (e.g., custom designed component, replacement component, toy, medical device, tool, etc.). In some embodiments, the knowledge distribution system 16802 can facilitate building a vendor database in which schematics can be stored prior to one or more sales, transferred to a buyer database, entered into terms of the smart contract 16840 a serial number of a customized component, and printed by a buyer-owned 3D printer.
In some embodiments, knowledge distribution system 16802 can facilitate management of digital knowledge 16804 by managing an aggregation of instances of digital knowledge 16804, e.g., component instances of digital knowledge 16804 are aggregated to form larger digital knowledge instances (e.g., chapter instances are concatenated in series to form book instances, component instances are linked schematically to form a system, element instances are linked graphically to generate a workflow, partial instances are coupled to form an entire instance (e.g., a necessary portion of a formula), related instances are linked locally to form clusters, and by many other forms of aggregation).
In some embodiments, knowledge distribution system 16802 can facilitate management of digital knowledge 16804 by facilitating verification of one or more sources of digital knowledge and/or providing chains of origin for digital or physical items with related knowledge. For example, in an embodiment, the knowledge distribution system 16802 may record a digital signature of a steel manufacturer in a distributed ledger, certify the quality level of steel the steel manufacturer provides to a plant owner, and may link the digital signature to a serial number of each component produced by a plant owned by the plant owner, wherein the plant uses the steel manufacturer provided steel.
In some embodiments, knowledge distribution system 16802 may facilitate control of digital knowledge 16804 through collaboration of multiple knowledge providers 16806 such that information related to one or more instances of digital knowledge from one or more different principals, different knowledge providers 16806, and different distributed ledgers 16508 may be tracked and/or incorporated into one or more integrated distributed ledgers.
In some embodiments, examples of digital knowledge 16804 may include, for example, instruction sets (e.g., food production, process steps in transit, and other methods), executable algorithm logic (e.g., computer programs), firmware programs, instruction sets of Field Programmable Gate Arrays (FPGAs), server-less code logic, crystal manufacturing systems/processes, polymer production processes, chemical synthesis processes, biological production processes, component schematics, and/or production records (e.g., production records for aircraft parts, spacecraft parts, nuclear engine parts, etc.), process and/or instruction sets for semiconductor manufacturing (e.g., silicon etching and/or doping), instruction sets for 3D printers (e.g., for printing medical devices, automobile parts, aircraft parts, a piece of furniture or components thereof, replacement parts for industrial robots or machines), algorithm logic (e.g., instruction sets used in applications), AI logic and/or definitions, machine learning logic and/or definitions, encryption logic, server-less code logic, trade secrets and/or other intellectual property (e.g., proprietary technology, proprietary materials and author works), food preparation instructions (e.g., for industrial food preparation), coating process instructions, bio-production process instructions, chemical synthesis instructions, polymer production instructions, smart contract instructions, defining and/or populating a set of digitally twinned data sets and/or sensor information (e.g. digital twinning embodying digital knowledge about one or more physical entities, comprising the relevant configuration, knowledge of operating mode, instruction set, capability, defects, performance parameters, etc.), and/or any other suitable type of digitally transmittable knowledge. In these embodiments, the instruction set may be used/utilized by a computing device, a dedicated device, or a combination of devices (e.g., factory devices). In some embodiments, examples of digital knowledge 16804 can include personal and/or professional knowledge, such as professional resume and/or professional history tracking information, associated with one or more organizations and/or individuals. In some embodiments, the personal and/or expertise may include one or more records of a professional certificate (e.g., a degree and/or a certificate). In some embodiments, the person and/or expertise may include one or more verifications of the specialized positions held by one or more persons. In some embodiments, the personal and/or expertise may include specialized feedback and/or verification of work performed for and/or by one or more third parties. The personal and/or professional knowledge may include personal and/or business financial histories, personal life achievements verified by one or more third parties. Knowledge provider 16806 may be any principal that provides, at least in part, one or more instances of digital knowledge 16804, such as a manufacturer, seller, customer, wholesaler, user, manager, notary, factory owner, maintenance worker, or any other suitable provider of digital knowledge 16804.
In an embodiment, the distributed ledgers 16508 may be any suitable type of electronic ledgers 16508, such as blockchains (e.g., hyperledger, solidity, ethereum, etc.). The distributed ledger 16808 may be a centralized, decentralized, or hybrid configuration in which the knowledge distribution system 16802 stores copies of the distributed ledger 16808 in addition to any number of participant nodes 16916. When referring to distributed ledgers 16508, the term "distributed ledgers" (and/or any logs, records, smart contracts, blocks, tokens, and/or data stored thereon) may refer to specific instances of copies of distributed ledgers 16108 (and/or any logs, records, smart contracts, blocks, tokens, and/or data stored thereon). And/or a set of local copies of distributed ledgers 16508-L, stored on any number of nodes (which may include knowledge distribution system 16802), unless specifically indicated otherwise.
In some embodiments, a private network of authorized participants, such as one or more knowledge providers and/or nodes, may establish an encryption-based consensus regarding one or more items such that the knowledge distribution system 16802 may provide security, transparency, auditability, invariance, and non-repudiation to transactions of digital knowledge. In some embodiments, a trusted authority (e.g., knowledge distribution system 16802 or other suitable authority) may issue private and public key pairs to each registered user of knowledge distribution system 16802. These private and public key pairs may be used to encrypt and decrypt data (e.g., messages, files, documents, etc.) and/or perform operations with respect to distributed ledgers 16508. In some embodiments, knowledge distribution system 16802 (or other trusted authority) may provide users with two or more levels of access rights. In some embodiments, knowledge distribution system 16802 can define one or more user categories, wherein each user category is granted a respective level of access rights. In some of these embodiments, knowledge distribution system 16802 can issue one or more access keys to one or more user categories, wherein the one or more access keys each correspond to a respective access rights level, thereby providing users with different levels of access rights through their respective issued access keys. In an embodiment, possession of certain access keys may be used to determine the level of access to distributed ledgers 16508. For example, in some embodiments, a first type of user may be granted full view access to a block, while a second type of user may be granted view access to a block and the ability to verify and/or authenticate one or more instances of digital knowledge contained in the block, while a third type of user may be granted view access to a block, the ability to verify and/or authenticate one or more instances of digital knowledge contained in the block, and the ability to modify the one or more instances of digital knowledge contained in the block. In some embodiments, a class of users may be authenticated as legitimate users of one or more roles of distributed ledgers 16508 and given relevant rights to the distributed ledgers and content stored therein. For example, a user may be authenticated as a true knowledge provider 16806 using knowledge provider device 1689, a knowledge receiver 16848 using knowledge receiver device 16894, and/or a crowdsourcer 16846 using crowdsourcer device 16892. There may be any number of devices 16880, 16892, 16894 per device. As shown in fig. 168, there is one knowledge provider device 16890, two crowdsourcing devices 16892, and one knowledge receiver device 16894. In other examples, as understood, there may be any combination of one, two, three, or more of any of the device types 16890, 16892, 16894. In other examples, there may be one of each device type (e.g., one knowledge provider device 16890, one crowdsourcing device 16892, and one knowledge receiver device 16894). In other embodiments, these devices 16880, 16892, 16894 may be implemented as one or more computing devices and/or server devices (e.g., as part of a server farm).
In some embodiments, knowledge distribution system 16802 may include ledger management system 16910. In some embodiments, ledger management system 16910 manages one or more distributed ledgers (also referred to as "ledgers"). In some embodiments, ledger management system 16910 may instantiate a distributed ledger for a particular knowledge provider 16806 or a group of knowledge providers 16806, such as by instantiating a distributed ledger 16508 that stores instances of digital knowledge 16804 provided by knowledge provider 16806 or a group of knowledge providers 16806. The knowledge distribution system 16802 may only allow a particular knowledge provider 16806 or a particular set of knowledge providers 16806 to keep instances of digital knowledge 16804 on the associated distributed ledger 16806 and/or for each instance of digital knowledge 16804 (e.g., through use of the knowledge provider device 16890) such that each distributed ledger 16806 is specific to the respective knowledge provider 16806 and/or instance of digital knowledge 16804. In some embodiments, ledger management system 16910 may instantiate a plurality of distributed ledgers 16508, wherein one or more of the distributed ledgers 16508 are used to facilitate custody, sharing, purchasing, selling, licensing, or otherwise managing a class of digital knowledge 16804. The category of digital knowledge may relate to, for example, one or more industries, such as automotive and/or financial, or one or more types of digital knowledge, such as 3D printed schematics. In some embodiments, ledger management system 16910 may maintain a distributed ledger that facilitates management of some or all instances of digital knowledge 16508 and/or knowledge provider 16806 that store relevant data by knowledge distribution system 16802.
In some embodiments, distributed ledger 16508 is any suitable type of blockchain. However, any other suitable type of distributed ledger may be used without departing from the scope of the invention. The distributed ledger may be a public distributed ledger or a private distributed ledger. In embodiments, where the distributed ledger is a private distributed ledger, a user such as knowledge provider 16806 (e.g., using knowledge provider device 1689) may be limited to an invitee, a user with one or more accounts/passwords, or any other suitable method of restricting access to distributed ledgers 1688 to read and/or verify privileges from the ledger. In some embodiments, distributed ledgers 16508 may be at least partially centralized distributed ledgers such that multiple nodes of the distributed ledgers are stored by knowledge distribution system 16802. In some embodiments, the distributed ledgers are federated distributed ledgers in that the distributed ledgers may be stored on preselected or pre-approved nodes associated with principals that manage digital knowledge 16804 through knowledge distribution system 16802. However, the techniques described herein may also be applied to a common distributed ledger. In a public distributed ledger, any suitably configured computing device (personal computer, user device, server) or set of devices (e.g., server farm) may act as node 16916 and may store a local copy of distributed ledgers 16808-L, whether or not the owner of the node is involved in transactions facilitated by knowledge distribution system 16802. In these embodiments, such node 16916 may add authentication/rejection new blocks, save the new blocks to distributed ledgers 16508 (if authenticated) to maintain a complete copy (or near complete copy) of the transaction history associated with distributed ledgers 16508, and broadcast the transaction history to other participating nodes 16916.
In some embodiments, ledger management system 16910 (and/or a collection of participant nodes 16916) may be used to create an immutable log using distributed ledgers 16508 to establish a chain of work, ownership, and/or ownership of one or more instances of digital knowledge 16804, and establishing verification of 16810 may use distributed ledgers to manage a set of rights keys that provide access to one or more instances of digital knowledge 16804 and/or services associated with knowledge distribution system 16802. In some embodiments, distributed ledger 16508 provides provable access to digital knowledge 16804, such as by one or more cryptographic credentials and/or techniques. In some embodiments, distributed ledger 16508 may provide provable access to digital knowledge 16804 through one or more zero knowledge proof techniques. In some embodiments, ledger management system 16910 may manage distributed ledgers to facilitate collaboration and/or collaboration between two or more knowledge providers 16806 with respect to one or more instances of digital knowledge 16804.
Fig. 169 illustrates an exemplary embodiment of a distributed ledger 16508, wherein the distributed ledger 16508 is distributed across a ledger network 16970. Ledger network 16970 may include a distributed ledger 16808 and a set of node computing devices 16916-1, 16916-2, 16916-3, 16916-N that communicate over one or more communication networks 16814. In some embodiments, communication network 16414 may include the internet, private networks, cellular networks, and the like. In an embodiment, node 16916 may keep a copy of distributed ledger 16508 (or a portion thereof) in its entirety. For example, ledger network 16970 may include first node 16916-1, second node 16916-2, third node 16916-3, … …, nth node 16916-N in communication with knowledge distribution system 16802 and other nodes 16916 in ledger network 16970. In some embodiments, knowledge distribution system 16802 is used to execute ledger management system 16910 and may store and manage local copies of distributed ledgers 16808 that are used to facilitate management of one or more instances of digital knowledge 16804 by knowledge distribution system 16802. In some embodiments, knowledge distribution system 16802 (or ledger management system 16910 executing thereon) may also be considered and referred to as a node of ledger network 16970. In some embodiments, ledger administration system 16910 may also generate and assign private and public key pairs to users, such as one or more knowledge providers 16806 and/or one or more knowledge recipients 16846 (also referred to as "knowledge recipients") of digital knowledge 16804, and/or to each node 16916 in ledger network 16970, such that these private and public key pairs are used to encrypt data transmitted between nodes 16916 in ledger network 16970.
In some embodiments, each node 16916 of ledger network 16970 (except knowledge distribution system 16802) may be associated with knowledge provider 16806 and/or knowledge receiver 1688. In some embodiments, node 16916 may include computing devices that are not involved in providing or receiving knowledge (e.g., that are not associated with knowledge provider 16806 nor any knowledge receiver 1688). In some embodiments, each of nodes 16916 may store a local copy 16508-L of the respective distributed ledger 16808. In some embodiments, one or more nodes may store a partial copy of distributed ledger 16508. In some embodiments, each of the nodes 16916, 16916-1, 16916-2, 16916-3, 16916-N may execute a respective agent 16920, 16920-1, 16920-2, 16920-3, 16920-N. The agent 16920 may be used to perform one or more of the following: manage a local copy 16508-L of the distributed ledger 16508 associated with the node 16916 of the execution agent 16920; helping to verify blocks previously stored on ledger 16508; helping to validate requests from other nodes 16916 to store new blocks on ledgers 16508; rights to perform operations related to digital knowledge or management thereof on behalf of a user associated with the node 16916 at which the agent resides; and/or facilitate collaboration between one or more of the knowledge providers 16806 and/or one or more of the knowledge recipients 16888 (e.g., using knowledge provider device 16880 and/or knowledge recipient device 16894, respectively), such as by facilitating verification and/or transmission of one or more instances of digital knowledge 16804 and/or execution of one or more terms of one or more intelligent contracts 16840. It should be understood that the node may perform additional or alternative tasks without departing from the scope of the invention.
In some embodiments, knowledge receiver 168418 may receive one or more slave distributed ledgers 16508 and/or any device using digital knowledge 16804, such as a computing device, in knowledge receiver device 16894, and/or may be a device for using digital knowledge 16804, such as a 3D printer, manufacturing device, or system, etc. In some scenarios, knowledge receiver 168418 may use a plurality of knowledge receiver devices 16894, such as servers or computing devices, for downloading one or more instances of digital knowledge 16804 from distributed ledgers 16508 and transmitting the one or more instances of digital knowledge to a 3D printer, a factory machine, a manufacturing system, or some other suitable device for using the one or more instances of digital knowledge 16804. For example, knowledge provider 16806 may upload a link (e.g., using knowledge provider device 1689) to a distributed ledger 1688 for a Computer Aided Design (CAD) file of 3D printable aircraft parts. In an embodiment, knowledge provider 16806 may define or otherwise provide a smart contract using, for example, knowledge provider device 16880, which controls the use of digital knowledge (e.g., design files for aircraft components), including the cost of using CAD files for aircraft components. Knowledge receiver 16848 may transfer funds (e.g., using knowledge receiver device 16894) to knowledge provider 16806 (e.g., knowledge provider device 16890) (e.g., via a smart contract) in exchange for accessing CAD files via distributed ledgers 16808. The knowledge receiver device 16894 may then download a CAD file, which may then be used to 3D print the part. For example, the knowledge receiver device 16894 may be a commercial computer in communication with a 3D printer or the intelligent 3D printer itself. In the former scenario, the commercial computer may transmit the CAD file to the 3D printer. After receiving the CAD file, the 3D printer may 3D print the aircraft component. In some embodiments, the digital knowledge itself (e.g., CAD files) may be included in the smart contract such that the smart contract provides the digital knowledge to the knowledge receiver device 16894 upon verifying that the knowledge receiver 16848 satisfies the condition to release the digital knowledge 16804 (e.g., deposit the necessary amount of money). In some embodiments, each time knowledge receiver 1688 uses an instance of knowledge, intelligent contract, knowledge distribution system 16802, agent 16920, and/or knowledge receiver device 16894 may update distributed ledger 16508 with blocks indicating that knowledge receiver used an instance of digital knowledge 16804.
In some embodiments, knowledge distribution system 16802 can be used to facilitate the participation of one or more crowdsourcing devices 16846 in the management of digital knowledge 16804, such as by allowing crowdsourcing devices 16846 to verify one or more aspects of an instance of digital knowledge 16804 (e.g., using crowdsourcing devices 16892). In an embodiment, crowdsourcer 16846 may be granted crowdsourcing rights such that crowdsourcer 16836 may view/examine digital knowledge and provide validation votes 16926 and/or opinions. In embodiments, non-limiting examples of crowdsourcing rights may include one or more of the following: examine an instance of digital knowledge 16804; signing an instance of digital knowledge 16804; verify instances of digital knowledge 16804, and the like. Examples of crowdsourcers 16846 include authentication entities, domain experts, customers, manufacturers, wholesalers, and any other suitable party capable of verifying digital knowledge instances. In an embodiment, an authentication entity or domain expert may prove that an instance of digital knowledge 16804 is authentic, accurate, and/or reliable, and/or from an authentic, accurate, and/or reliable source. In an embodiment, a customer may review an instance of digital knowledge 16804, for example, to indicate that digital knowledge 16804 is in an operational state and/or has a desired quality. In an embodiment, the manufacturer and/or wholesaler may sign an instance of digital knowledge 16804, for example, by applying a serial number to a piece of digital knowledge 16804 before the piece of digital knowledge 16804 can be transmitted to knowledge recipient 16848 (e.g., through knowledge recipient device 16894). Authentication, review, signature, and/or any other verification indicia by crowdsourcing 16846 may be recorded in distributed ledger 16508, such as by adding one or more new blocks 16922 to distributed ledger 16108 indicating the authentication, review, signature, or other verification indicia. In some embodiments, the new block 16922 may include data (e.g., crowdsourced identifiers, timestamps, locations, etc.) related to authentication, auditing, signing, or other validation tagging by one or more crowdsourcing devices 16846 using crowdsourcing devices 16892, etc. In some examples, knowledge distribution system 16802 can be paired with a crowdsourcing system (e.g., crowdsourcing device 16892). In particular, in an example, the crowdsourcing system (e.g., crowdsourcing device 16892) can communicate with and engage with the smart contract 16840 such that digital knowledge 16804 can be embodied (e.g., recorded) in the distributed ledger 16808 when crowdsourcing elements of digital knowledge 16804 through the smart contract 16840. Knowledge distribution system 16802 can use smart contracts 16840 to facilitate management of digital knowledge 16804, such as by allowing smart contracts 16840 and crowdsourcers 16846 to verify (and/or contribute) one or more aspects of an instance of digital knowledge 16804. For example, a software developer may provide crowd-sourced requests for modules or functions in a smart contract 16840. The crowdsourcing request may be embedded in the open source code as a request for a code element of the product (e.g., a first provider of work code may obtain a benefit (or an amount of money, or a token, etc.). For this example, crowd-sourcer 16846 may respond to the crowd-sourcing request using the crowd-sourcing system (e.g., crowd-sourcing device 16892) by looking at/checking digital knowledge (e.g., open source code), and may provide collaboration in the form of verification, opinion, correction, and/or contribution to the open source code, which may be related to improvements in the open source code (e.g., improving accuracy and/or reliability of software). These verification, opinion, correction, and/or contribution indicia provided by crowdsourcing 16846 may be recorded in distributed ledger 16508 by adding one or more new blocks 16922 to distributed ledger 16108 indicating these indicia. The crowdsourcers 16846 may be compensated for the percentage contribution to the open source code (e.g., through smart contracts 16840) such that the original software developer may share the revenue (or monetary amount, or tokens, etc.) of the software product with the crowdsourcers. The contribution percentage may be based on the amount of code written and/or the contribution of each crowdsourcer to the final open source code functionality.
In some embodiments, digital knowledge 16804 may be tagged (e.g., at least partially converted to/packaged in knowledge tag 17038). In embodiments, tagging digital knowledge 16804 may include packaging the digital knowledge into knowledge tags 17038 and/or packaging access rights, permissions, ownership, and/or other suitable rights associated with the digital knowledge 16804 such that the access rights, permissions, ownership, and/or other suitable rights are managed by one or more of the knowledge tags 17038. By tagging digital knowledge 16804, digital knowledge 16804 may reside in distributed ledgers 16508 and smart contracts 16840 and be distributed through distributed ledgers 16108 and smart contracts 16840. In some embodiments, knowledge distribution system 16802 can define rights and/or operations associated with knowledge tab 17038. For example, knowledge tab 17038 may allow digital knowledge 16804 of a tab to be viewed, edited, copied, purchased, sold, and/or licensed based on permissions set by knowledge distribution system 16802 at the time of the tab. In an embodiment, knowledge distribution system 16802 can provide for orchestration of a marketplace or exchange of digital knowledge 16804, such as can exchange a body or instance of digital knowledge 16804, such as, but not limited to, through sets of knowledge tabs 17038, which can optionally be managed by smart contracts that can be configured by knowledge exchange or marketplace custody and/or by knowledge provider 16806 (e.g., using knowledge provider device 1689) or knowledge receiver 16848 (e.g., using knowledge receiver device 16894). For example, an exchange or marketplace may keep an exchange of specific classes of proprietary technologies, expertise, instruction sets, trade secrets, insights, or other knowledge elements described or referenced herein, wherein: classifying knowledge according to the interested subject matter; the terms of the transaction are predefined and/or configurable (e.g., through configurable smart contracts that support various transaction models including a buying and selling model, an auction model, a donation model, a reverse auction model, a fixed price model, a variable price model, or a priced model, etc.); collecting and/or representing metadata about knowledge exchange categories; relevant content is presented, including market pricing data, knowledge domain substantive content, provider content, and the like. Such exchanges may facilitate monetization of the tag knowledge represented in knowledge tag 17038. In embodiments, the knowledge exchange may be integrated with or in another exchange, such as an exchange of a particular domain, an exchange of a particular geographic location, etc., as described herein, wherein the knowledge exchange may facilitate an exchange of valuable or sensitive knowledge related to the subject matter of the other exchange. The other exchange may be a securities exchange, a commodity exchange, a derivative exchange, a futures exchange, an advertising exchange, an energy exchange, a renewable energy credit exchange, a cryptocurrency exchange, a bond exchange, a currency exchange, a precious metal exchange, a petroleum exchange, a commodity exchange, a service exchange, or various other exchanges. This may include integration through APIs, connectors, ports, agents, and other interfaces, as well as integration through Extraction Translation Load (ETL) techniques, smart contracts, wrappers, containers, or other functions.
In some embodiments, the knowledge distribution system 16802 may be used to create and issue one or more monetary tokens associated with a distributed ledger 16808. The monetary tokens may be digital objects of encrypted tokens, encrypted money, etc., that may be purchased, mined, distributed, and/or distributed to users of distributed ledgers 16508. In some embodiments, a monetary token may represent legal currency (e.g., dollars, pounds, euros, etc.), such that the value of the token hooks with the legal currency. In embodiments, monetary tokens may be used to transact digital knowledge. For example, in an embodiment, a smart contract may be used to receive and verify that knowledge recipient 16848 has paid the necessary amount of funds before digital knowledge 16804 is released to knowledge recipient device 16894.
Additionally or alternatively, knowledge receiver 1688 can use traditional payment methods (e.g., credit card payments) to transact knowledge instances. In some embodiments, the currency tokens may act as digital currency. For example, a currency token may be paid by a knowledge recipient to a knowledge provider in exchange for digital knowledge 16804 and/or to a crowdsourcer (e.g., an authenticator or expert) to verify one or more aspects of digital knowledge 16804. In some embodiments, one or more users may be awarded monetary tokens as rewards for discovering or "mining" one or more new blocks 16922 of the distributed ledger 16508. In some embodiments, the monetary tokens may be asset-vouched-for tokens, such as tokens vouched-for by one or more other currencies (e.g., legal currencies), securities, property ownership, intellectual property ownership, property and/or intellectual property licensing rights, and the like. In some embodiments, the knowledge distribution system 16802 may be used to track access rights and/or ownership rights for one or more monetary tokens, such as by recording the contents and/or balance of a user's digital wallet. In some embodiments, the knowledge distribution system 16802 can be used to issue a wallet password to a user that is needed when accessing, viewing, transferring, and otherwise managing money tokens owned (or at least partially owned) by the user that has issued the wallet password.
In some embodiments, knowledge distribution system 16802 may include a smart contract system 16868 for generating smart contracts 16840 and deploying smart contracts 16840 to distributed ledgers 16508. In an embodiment, smart contract 16840 may refer to software stored on distributed ledger 16508 for managing one or more rights associated with one or more instances of digital knowledge 16804 and/or one or more knowledge tags 17038. In an embodiment, the smart contract may be a computer protocol (e.g., distributed over a blockchain such as the etherum blockchain) that assists in negotiating and/or fulfilling terms in the protocol. Intelligent contracts may be used for banking services, government, administration, supply chains, automobiles, real estate, healthcare, insurance, and the like. In some embodiments, the smart contract 16840 may be contained and/or executed in a virtual machine or container (e.g., a Docker container). In some embodiments, one or more of nodes 16916 of ledger network 16970 may provide an execution environment for smart contracts 16840. In an embodiment, smart contracts 16840 may include information, data, and/or logic related to an instance of digital knowledge 16804, one or more trigger events, one or more smart contract actions performed in response to detecting one or more of these trigger events, and the like. In an embodiment, a triggering event may define a condition that may be met by an event that may be performed by one or more users (e.g., knowledge provider 16806, knowledge receiver 16818, and/or crowdsourcer 16836, or one or more third parties). Examples of trigger events include: payment by one party to another party; one or more parties adhere to or do not adhere to one or more terms of a sale, license, insurance or other agreement; one or more thresholds or ranges of attributes satisfying one or more digital knowledge 16804, such as value, user rating, throughput, or any other suitable attribute; elapsed time; or any other suitable triggering event. Additionally or alternatively, the triggering event defined in the smart contract 16840 may include a condition that may be satisfied independently of a person's presence or absence. For example, the trigger event may be: when a certain date is reached; when the stock price reaches a certain threshold value; at the expiration of the patent rights; when the copyright expires; when a natural event occurs (e.g., hurricane, tornado, drought, etc.), and the like. Trigger events may be defined as different types of triggers. For example, a trigger or trigger event may refer to a change state (e.g., a state change event), such as intelligence being effective at about a set of data states (e.g., state change events). In other examples, triggers or triggering events may refer to events that occur such that a user may need to passively wait for such events to occur, while the knowledge distribution system 16802 may need to monitor such events.
Referring to FIG. 170, knowledge distribution system 16802 includes details of smart contracts 16840 and smart contract system 17068. In an embodiment, the smart contract actions 17086 may include, for example: monitoring events from defined data sources; verifying that one or more users and/or third parties fulfill obligations according to one or more conditions 17084 defined in the smart contract 16840; verifying payment and/or transfer of tokens, property, other goods or services between one or more users and/or third parties; transmitting digital knowledge 16804 between principals or to one or more users; recording one or more transactions in a digital ledger 16508; performing one or more operations on the digital ledger 16508; one or more new blocks 16922 are created in digital ledgers 16508, etc. In some embodiments, the smart contract 16840 can include an event listener 17080 for monitoring one or more data sources (e.g., databases, data feeds, data lakes, public data sources, etc.) to detect events to determine whether one or more conditions 17084 are met. For example, event listener 17080 may listen to an Application Programming Interface (API) that provides a connection between knowledge distribution system 16802 and a printer, such that a smart contract may trigger an obligation for a user to pay when printing an item using a set of printing instructions managed by the knowledge distribution set (e.g., a set of instructions marked in knowledge marking 17038). Thus, when a predefined set of conditions 17084 is met, a smart contract action 17086 may be triggered. This may include triggering a payment process (e.g., initiating credit card payment authorization), ending a contract (e.g., when the number of prepaid uses of the knowledge set is reached), determining a price (e.g., by initiating a reference to current pricing data in the marketplace or exchange), reporting a result (e.g., reporting a workflow or event), etc. In response to being triggered, the smart contract may automatically perform smart contract actions 17086. In some embodiments, the smart contract is an Etherum smart contract and may be defined in accordance with the Etherum Specification, which may be accessed through https:// githmub. In other embodiments, the smart contract system 17068 may include an event listener 17080.
In some embodiments, smart contracts 16840 can be used to "package" one or more instances of digital knowledge 16804 in a smart contract wrapper (e.g., a "smart wrapper"). After being packaged, the digital knowledge instances may be processed and/or accessed in a different manner than when unpackaged, such as being readable, editable, and/or transferable only in accordance with the terms, conditions, and/or operations of the smart contract 16840. The smart contract 16840 can package the digital knowledge 16804 such that the digital knowledge 16804 must first be "unpacked" (e.g., restored to a pre-package form) in order to be accessed by the knowledge receiver 16848. In some embodiments, the pre-package form may be in the form of a label. The smart contract 16840, the distributed ledger 16508, and/or the knowledge distribution system 16802 may unpack one or more labels and/or instances of the digital knowledge 16804 in response to one or more trigger events. In some embodiments, knowledge distribution system 16802 or other suitable system may store a plurality of smart contract templates from which smart contracts 16840 may be generated. In some embodiments, the smart contract system 17068 may include a Smart Contract (SC) generator 17082 that may parameterize at least one smart contract template (from the plurality of smart contract templates) based on user-provided information and any user-defined conditions 17084 and/or actions 17086. For example, the smart contract template may correspond to a digital knowledge to be tagged. The contract template may include parameters based on the type of digital knowledge. These parameters may include: financial parameters for using tagged digital knowledge (e.g., financial parameters), license rate parameters for intellectual property (e.g., license fee parameters), number of times an instruction set may be used (e.g., usage parameters), output parameters that may be generated using an instruction set (e.g., yield parameters), price allocation parameters between a principal of an intelligent contract and a specified beneficiary of an intelligent contract (e.g., price allocation parameters), identity parameters that may have access to distributed ledgers 16508 and/or digital knowledge (e.g., identity parameters), and/or access condition parameters for distributed ledgers 16508 and/or digital knowledge (e.g., access condition parameters). In some embodiments, the smart contract 16840 may be used to manage the labeling of packages based on a set of aggregated instructions defined in the smart contract 16840.
In some embodiments, distributed ledger 16508 may store smart contracts 16840 for facilitating licensing of one or more intellectual property rights corresponding to digital knowledge instances, such as proprietary technology, proprietary material, branding, author work (e.g., copyrights), and/or trade secrets. In an embodiment, knowledge distribution system 16802 can be used to allow one or more of knowledge providers 16806 to sign a licensing agreement with one or more of knowledge recipients 168418 (e.g., using one or more knowledge provider devices 1689 and/or one or more knowledge recipient devices 16894) via smart contracts 16840. In an embodiment, the smart contract 16840 may be used to embed licensing terms of intellectual property rights, including usage scope, exemption, reimbursement, usage restrictions, geographic restrictions, and the like, in one or more of the blocks 16922 of the distributed ledger 16508. In an embodiment, one or more copies of and/or references to one or more intellectual property rights may be stored in distributed ledger 16508 and access to one or more intellectual property rights may be constrained by the terms of smart contract 16840. Upon execution of the smart contract 16840, the knowledge distribution system 16802 can automatically transfer access rights and licensing rights for intellectual property rights to knowledge recipients 168418 (e.g., knowledge recipient devices 16894 of knowledge recipients 168418) in accordance with the terms and/or operations specified in the smart contract 16840. In some embodiments, the knowledge distribution system may be used to verify assignee rights using resources such as public patent assignee journals prior to transferring access rights and/or permissions. In an embodiment, the smart contract 16840 may contain one or more operations to be performed with respect to the distributed ledger 16508 to facilitate execution of the smart contract 16840 definitions. In some embodiments, the smart contract 16840 can be used to automatically allocate licensing fees (e.g., using the knowledge provider device 1689 and the knowledge receiver device 16894) in a transfer between one or more knowledge providers 16806 and knowledge receivers 16818 that involves transfer of access rights, ownership, and/or licensing rights for the intellectual property rights. For example, if the owner of the digital knowledge pays a licensing fee to a third party patent owner of one or more aspects of the digital knowledge (e.g., the inventor of a particular product design), the smart contract may allocate a set percentage or amount of the transaction price of the digital knowledge 16804 to the licensor such that the license to manufacture, sell, use, and/or otherwise transact is transferred to the recipient of the digital knowledge 16104. In an embodiment, the operation for assigning a licensing fee may be performed in accordance with one or more terms of one or more of the smart contracts 16840 and may have an associated smart contract action 17086.
In some embodiments, the knowledge distribution system 16802 may be used to aggregate intellectual property licensing terms. The distributed ledger 16508 may be used to store an aggregated stack of instances of digital knowledge 16804, where one or more aspects of digital knowledge 16804 are limited according to a principal's intellectual property (e.g., a knowledge provider or any other principal's patent, copyright, trademark, or trade secret). In an embodiment, ledger management system 16910 may facilitate adding one or more instances of intellectual property to an aggregation stack, thereby associating the added intellectual property instance to the intellectual property stack to which the intellectual property instance was added. The knowledge distribution system 16802 may perform operations such as transfer of control rights, editing rights, viewing rights, ownership and/or licensing rights to the entire intellectual property stack according to the terms of one or more intelligent contracts 16840. Access, ownership, and/or re-licensing rights to the intellectual property aggregation stack may be transferred from one or more of the knowledge providers 16806 to one or more of the knowledge receivers 1688 through the knowledge distribution system 16802 (e.g., using the knowledge provider device 16880 and the knowledge receiver device 16894). In some embodiments, the smart contracts may be used to transfer rights to an intellectual property aggregation stack associated with the digital knowledge instance (e.g., rights to use, sell, offer to sell, export, import products or processes associated with the intellectual property stack) or to transfer the intellectual property stack as a whole to the digital knowledge recipient. In the latter scenario, a smart contract may be used to facilitate transfer of an intellectual property stack to a digital knowledge recipient (e.g., filling out a transfer form of a patent, trademark, or rights bureau submitted to one or more jurisdictions, and electronically archiving a transfer file). In some embodiments, the transfer of intellectual property rights may also be recorded in distributed ledgers 16508.
In some embodiments, ledger administration system 16910 may define one or more operations that may manipulate or process one or more principals' commitments to smart contracts 16840 and/or terms thereof. When a set of parties (e.g., knowledge provider 16806, knowledge receiver 16818, crowdsourcer 16846, and/or third party) promises to adhere to the terms of smart contract 16840 and the terms of the smart contract that manage the transfer of digital knowledge 16804, knowledge distribution system 16802 (and/or smart contract 16840 itself) may manipulate or process the commitment of the party and/or the party's identity to one or more portions (e.g., terms) of smart contract 16840. In an embodiment, after a set of principals promises to adhere to the smart contract 16840, the smart contract 16840 and/or the knowledge distribution system 16802 can link one or more of the principals to one or more of the trigger events defined in the smart contract 16840, begin monitoring one or more data sources to determine whether any of the trigger events defined by the conditions 17084 are met, and/or automatically perform operations/actions defined in the smart contract (e.g., in response to the occurrence of the trigger event). For example, knowledge provider 16806 may upload smart contracts 16840 (e.g., using knowledge provider device 16880) to distributed ledgers 16508 and/or customize smart contracts 16840 using smart contract templates associated with instances of uploaded digital knowledge 16804. In an embodiment, knowledge provider 16806, knowledge receiver 16848, or other parties may indicate (e.g., through knowledge distribution system 16802, distributed ledger 16808, and/or smart contract 16840) terms of a agreement between knowledge provider 16806 and knowledge receiver 16848 when an agreement is reached between knowledge provider 16806 and knowledge receiver 16848. In some embodiments, the smart contract 16840 can include one or more rights, terms, and/or obligations provided by the knowledge provider 16806 and/or third party prior to identifying and/or processing the knowledge receiver 1688. After agreeing to receive digital knowledge 16804 (e.g., using knowledge receiver device 16894), knowledge receiver 16848 may agree to be constrained by rights, terms, and/or obligations defined by smart contract 16840. Knowledge receiver 16848 may be a user willing to trade digital knowledge 16804 (e.g., purchase, license, or otherwise trade with knowledge provider 16806). In response to receiving the indication through knowledge distribution system 16802 and/or distributed ledger 16508, intelligent contract 16840 can cause knowledge provider 16806, knowledge recipient 1688, and/or other parties to commit to or otherwise subject to the terms and/or conditions 17084 of intelligent contract 16840.
In some embodiments, knowledge distribution system 16802 may include an account management system. In an embodiment, account management system 16846 may facilitate creation and/or storage of user accounts associated with users of knowledge distribution system 16802, and/or distributed ledgers 16508. For example, account management system 16846 may be used to facilitate registration of one or more of knowledge provider 16806, knowledge receiver 16848, crowdsourcer 16836, and/or other third parties that may be associated with knowledge distribution system 16802, and/or distributed ledger 16808. In some embodiments, account management system 16846 may be used to facilitate, in conjunction with ledger management system 16910, obtaining data from registered users of distributed ledgers 16808, such as name, address, corporate home, financial account information (e.g., bank account numbers and/or routing numbers), numeric identifiers (e.g., IP addresses, MAC addresses, etc.), and any other suitable information related to registered users.
The account management system 16846 may update the user account of the registered user with the received data associated with the registered user. In some embodiments, the account management system may facilitate the generation and/or distribution of one or more of the rights keys 16932 to one or more of the registered users. Rights keys 16932, 16932-1, 16932-2, 16932-3, 16932-N may provide registered users with access to one or more instances of digital knowledge 16804 and/or services associated with knowledge distribution system 16802.
In some embodiments, knowledge distribution system 16802 can include a user interface system 168450 for presenting a user interface. The user interface may be used to facilitate the uploading of digital knowledge 16804, the generation and/or uploading of smart contracts 16840, and the viewing of digital knowledge 16804 and/or smart contracts 16840 (and their status). The user interface may be a graphical user interface. The information presented to the user of knowledge distribution system 16802 via the user interface may include: descriptions of one or more instances of digital knowledge 16804; ownership and/or licensing information related to one or more instances of digital knowledge 16804; information related to the user viewing the user interface and/or other users of the knowledge distribution system 16802; price information related to one or more instances of digital knowledge 16804; statistics and/or metrics related to the distributed ledger 16508 and/or its content, such as node count, expense for generating additional nodes; and any other suitable information. In some embodiments, the user may view the contents of his digital wallet, such as the balance of one or more types of currency tokens, through the user interface.
In some embodiments, the user interface may be used to enable one or more users to perform one or more of the operations related to digital knowledge 16804 and/or distributed ledger 16808, such as purchasing, selling, verifying, and/or reviewing digital knowledge 16804 and/or performing other operations related to distributed ledger 16508 discussed herein. For example, the knowledge provider 16806 can select a computer file (e.g., a 3D printer schematic file) to upload to the distributed ledger 16808 (e.g., using the knowledge provider device 16880) via the user interface. The user interface may present knowledge provider 16806 with one or more options related to uploading digital knowledge 16804, such as the ability to configure smart contract 16840 and related terms for packaging and/or tagging digital knowledge 16804. Other options may include privacy options, such as options related to one or more users or one or more types of users that may or may not view, purchase, sell, license, evaluate, verify, review, or otherwise manage or interact with digital knowledge 16804.
In some embodiments, the user interface system 168450 can include a marketplace system 16854 for establishing and maintaining a digital marketplace 16856. In an embodiment, digital marketplace 16856 provides an environment that allows knowledge providers and potential recipients to engage in commerce related to the transfer of digital knowledge 16804. For example, a digital marketplace may be used to enable one or more users and/or third parties to: searching for one or more digital knowledge 16804 similar to the digital storefront; one or more of the transaction digital knowledge 16804 (e.g., purchase, sell, license, lease, bid, and/or relinquish digital knowledge); receiving a recommendation for digital knowledge 16804; review one or more of digital knowledge 16804; validating source information and/or other information related to one or more of digital knowledge 16804; one or more of transaction digital knowledge 16804 (e.g., one or more of purchase, license, bid digital knowledge 16804); and/or perform any other suitable market interaction with one or more of digital knowledge 16804, knowledge provider 16806, digital ledger 16508, one or more of knowledge receiver 16888, one or more of crowdsourcing people 16846, or any other user or third party. In some embodiments, the digital marketplace 16856 may be used to allow users to edit user accounts associated with themselves and view user accounts associated with other users. In some embodiments, the digital marketplace 16856 user interface may allow users to comment on and/or rate other users.
In an embodiment, the knowledge distribution system 16802 can include one or more data stores 16858. Fig. 171 illustrates an exemplary set of data stores 16858 for a knowledge distribution system 16802. In some embodiments, knowledge distribution system 16802 may include one or more data stores 16858 for storing data related to digital knowledge 16804, distributed ledgers 16508, knowledge providers 16806, knowledge recipients 1688, crowdsourcers 16846, knowledge tags 17038, smart contracts 16840, account management systems 16846, marketing systems 16854, or any other suitable type of data. The data store may store folders, files, documents, databases, data lakes, structured data, unstructured data, or any other suitable data.
In some embodiments, the data store 16858 can include a knowledge data store 17160 for storing data. The knowledge data store 17160 may be in communication with a user interface system 168450. The user interface system 168450 can be used to populate a user interface with data stored in the knowledge data store 17160. In some embodiments, the data stored in knowledge data store 17160 may include knowledge related to digital knowledge 16804, such as sources, reviews, prices, ownership, permissions, related knowledge provider 16806, related knowledge receiver 16518, serial numbers, related crowdsourcers 16846, or any other suitable information. For example, the knowledge data store 17160 may contain information related to the 3D printer schematic such as source, creation date, one or more contributing individuals, groups, and/or companies' names, pricing, market trends for related schematic, serial numbers, and/or component identifiers, as well as any other suitable type of data related to the 3D printer schematic.
In some embodiments, data store 16858 may include a client data store 17162 (e.g., may include a user data store), client data store 17162 for storing data related to users of knowledge distribution system 16802. The client data store 17162 may be in communication with the account management system 16846 and may be populated with user accounts related to one or more of the user accounts, data contained in one or more of the user accounts, data related to one or more user accounts, and/or combinations thereof.
In some embodiments, as shown in fig. 170 and 171, the data store 16858 may include a smart contract data store 17064. In an embodiment, the smart contract data store 17064 is used to store data related to one or more of the smart contracts 16840 and/or smart contract templates from which the smart contracts 16840 can be parameterized and instantiated. In an embodiment, the smart contract data store 17064 may be in communication with a ledger management system 16910. The data stored in the smart contract data store may include, for example, smart contract templates, one or more smart contracts 16840, data related to instances of digital knowledge 16804 related to one or more of the smart contracts 16840, data related to principals of one or more of the smart contracts 16840, and any other suitable data. The smart contract data store 17064 may be used to store completed smart contracts that have been executed. The smart contract data store 17064 may be used to store smart contracts that have not been uploaded to the distributed ledger 16508.
Referring to fig. 168, in some embodiments, knowledge distribution system 16802 may include an analysis system 16866 for analyzing instances of one or more tags of digital knowledge 16804, such as knowledge tag 17038, and reporting the analysis results. The analysis system may analyze the marked instances of digital knowledge 16804 to determine one or more attributes and/or metrics of the marked instances of digital knowledge 16804. The attributes of the tagged digital knowledge 16804 can include, for example, sources, reviews, prices, ownership, permissions, related knowledge providers 16806, related knowledge recipients 16518, serial numbers, related crowdsourcers 16846, or any other suitable information. The analysis system 16866 may be used to determine one or more trends, metrics, and/or predictions related to the attributes. In some embodiments, analysis system 16866 can include a machine learning module for performing predictions and/or analyses on attributes associated with digital knowledge 16804 via one or more machine learning techniques.
In some embodiments, the attributes of the tagged intellectual property may be analyzed by an analysis system 16866. For example, analysis system 16866 can be used to analyze digital knowledge 16804 of a tag that includes intellectual property. The analyzed property of the tagged intellectual property may include, for example: transaction history, including changes and/or transfer of ownership and/or licensing rights of intellectual property rights; litigation history, including litigation involving intellectual property and litigation-related data; information extracted from one or more intellectual property databases related to intellectual property rights; and any other indicia of the intellectual property of the indicia or any other suitable attribute of any other indicia or suitable instance of digital knowledge 16804. Metrics related to tagged intellectual property may include, for example, value, age, strength, efficacy, or any other suitable metric related to tagged intellectual property or any other suitable instance of digital knowledge 16804 or 17038.
In some embodiments, knowledge distribution system 16802 can be used to perform an aggregation operation that aggregates a set of operations and/or instructions included in one or more instances of digital knowledge 16804. Aggregation may be employed to aggregate component instances of digital knowledge 16804 to form larger digital knowledge instances. In an embodiment, aggregation occurs where component instances are concatenated to form a larger instance, for example by adding component instances as additional (optionally tagged) blocks into a chain, or by adding references or links to component knowledge blocks. Examples of concatenation aggregation include concatenating chapter instances to form book instances, concatenating sentence instances to form paragraphs, concatenating word instances to form sentence instances, and the like. In an embodiment, aggregation occurs where component instances are schematically linked to form a system, e.g., knowledge of physical component parts representing a machine are linked to form a machine, component steps in a process are linked to form a complete process, etc. In an embodiment, the aggregation of knowledge involves linking elements in a stream, such as graphically linking instances to generate a workflow, such as linking components and process steps to generate a recipe or recipe process, wherein workflow steps and materials are linked to describe a work process (e.g., a process involving expertise or proprietary technology), and the like. In an embodiment, aggregation of knowledge involves coupling partial instances to form an entire instance, e.g., joining two or more sub-portions of a formula to form a complete formula (e.g., chemical, pharmaceutical, biological, material science, physical, or other formula), joining two or more sub-portions of an instruction set (e.g., computer code) to form an entire instruction set, etc. In embodiments, aggregation may involve linking related knowledge instances in a cluster, such as linking knowledge instances locally to form a cluster of related topics in a knowledge domain (e.g., scientific domain, humane domain, social science domain, business domain, or many other domains). In an embodiment, the aggregation may involve hierarchical aggregation of knowledge, such as by representing the knowledge according to one or more defined hierarchies, such as an organizational hierarchy (e.g., an organizational structure or a reporting structure), an industry hierarchy, a topic hierarchy, a physical hierarchy, and so forth. Many other examples of aggregation are contemplated. Ledger administration system 16910 may be used to perform aggregation operations. The aggregation operation adds at least one instruction to a pre-existing instruction set, resulting in a modified instruction set. The modified instruction set may then be stored in distributed ledgers 16508 and may be marked, manipulated, and/or managed similarly to any instance of digital knowledge 16804. In some embodiments, the aggregation operation may be performed by the smart contract 16840 in accordance with one or more terms of the smart contract 16840 and/or in response to triggering of one or more triggering events of the smart contract 16840. In some embodiments, the aggregation operation may be performed at the request of a user of knowledge distribution system 16802.
In some embodiments, ledger network 16970 is a federated network such that ledger management system 16910 of knowledge distribution system 16802 may act as an arbiter to simplify the consensus mechanism. For the management of blocks of distributed ledgers 16508 and/or data contained therein, such as one or more instances of digital knowledge 16804, some or all of nodes 16916 may be pre-selected or pre-approved to act as nodes 16916. Ledger administration system 16910 may reduce the computational burden on other nodes 16916 in ledger network 16970. In some embodiments, distributed ledgers 16508 are distributed such that participating nodes 16916 may each store a local copy 1608-L of the respective distributed ledgers 1608, wherein each local copy 1608-L may include the entire distributed ledger 1608 or a portion thereof.
In the illustrated example, knowledge distribution system 16802 stores a copy of distributed ledger 16808, the copy of distributed ledger 16808 being local to knowledge distribution system 16802, and each node 16916 stores a distributed local copy 16808-L of distributed ledger 16808. However, in some embodiments, knowledge distribution system 16802 does not store a local copy of distributed ledger 16808 such that distributed ledger 16808 is maintained entirely by participant node 16916. The distributed copies of the distributed ledger 16508 (e.g., copies 16808-L) may contain the entire ledger 16508 or only a portion of the ledger 16508. Typically, each copy of ledger 16508 stores a set of blocks 16922. In some embodiments, each respective chunk may store information related to the respective state change event as a hash value, and may also store a chunk identifier of a "parent chunk" added to ledger 16508 prior to the respective chunk. In some embodiments, ledger administration system 16910 may select the block most recently added to the ledger as the parent block, whereby ledger administration system 16910 includes the block identifier of the block most recently added to the state change event record.
A state change event may refer to any state change associated with digital knowledge 16804 and/or management of one or more instances of digital knowledge 16804. Non-exhaustive examples of state change events may include: creating a new instance of digital knowledge 16804; a new user of the registered knowledge distribution system 16802, such as a new knowledge provider 16806 (and/or a registered new knowledge provider device 16890) or a new knowledge receiver 16848 (and/or a registered new knowledge receiver device 16894); granting the new user the right to perform a specific operation; authentication and/or verification of one or more instances of modified digital knowledge 16804 upon request by a user; transmitting one or more instances of digital knowledge 16804 to one or more knowledge recipients 16816 (e.g., knowledge recipient device 16894); recording the use of instances of digital knowledge 16804 by the knowledge receiver, and the like. In some embodiments, ledger administration system 16910 may create, for each state change event that occurs, a state change event record indicating the state change event (e.g., the operation performed). The state change event record may also be information/metadata related to the event, such as one or more user identifiers of one or more respective users associated with the state change event, a timestamp corresponding to the state change event, a device identifier of a device requesting or performing the operation, an IP address corresponding to the device requesting or performing the operation, and/or any other related data. In some embodiments, ledger management system 16910 may include in the state change event record the block identifier of the previous block previously stored on ledger 16508 such that the previous block may be the "parent block" of the new block to be generated based on the state change event record because the state change event record references the previously stored block, but the previously stored block will not reference the new block. In some embodiments, the block identifier may be a hash value of a previously generated block.
In some embodiments, one or more of ledger management system 16910 and/or nodes 16916 may be configured to: a status change event record is generated by the knowledge distribution system 16802 for each event that occurs in the administrative aspects of the digital knowledge 16804. In an embodiment, one or more of ledger administration system 16910 and/or nodes 16916 may generate new blocks 16922 corresponding to state change event records by: a cryptographic hash (or "hash value") of the state change event record is generated by inputting the state change event record into a hash function to obtain the cryptographic hash. The generated hash value is a unique value representing the contents of the state change event record (or a basic unique value with very low likelihood of collision) such that the generated hash value is a unique identifier that identifies the new block and also encodes its contents, including the block identifier of the parent block. Thus, when a new block is "broken" (breaking a block in this context may refer to the process of determining the original content of the state change event record encoded in the hash value), the solution of the new block displays the content of the state change event record, including the block identifier of the parent block. In this way, the authenticity of the current state change event record can be verified by verification using the hash value of the last state change event record. While the above describes blocks storing only one state change event record, in some embodiments, one or more of ledger administration system 16910 and/or node 16916 may encode two or more state change event records in a single block. One or more of ledger administration system 16910 and/or nodes 16916 may include two or more state change event records in the body of the new block data structure and may include the block identifier of the previous block in the block header of the new block data structure. One or more of ledger administration system 16910 and/or nodes 16916 may then input the new chunk data structure into a hash function that outputs new chunk 16922. In these embodiments, the new block 16922 may be a cryptographic hash of the block identifier representing two or more state change event records and the previous block (i.e., the parent block of the new block). In this way, when a new block is broken, the block's solution is two or more state change event records and the block identifier of the parent block, where the block identifier of the parent block may be used to verify the authenticity and accuracy of the new block.
In an embodiment, one or more of ledger administration system 16910 and/or node 16916 may request verification of block 16922. In some embodiments, verifying block 16922 may include broadcasting request 16924 to other nodes 16916 in ledger network 16970 (which may include ledger management system 16910 provided that request 16924 is not ledger management system 16910) to verify block 16922 (referred to as "block 16922 to be verified"). In some embodiments, request 16924 may include or be broadcast with block 16922 to be authenticated. Validation may also include one of the other nodes 16916 (or possibly ledger management system 16910) receiving the request 16924 cracking the block 16922 to be validated. Node 16916 may determine that it has broken block 16922 under the following conditions: the solution of a block includes a valid block identifier, i.e., a block identifier that references one of the other blocks 16922 stored on ledger 16508. Once the solver determines the solution of block 16922, the solver broadcasts a "proof of work" 16928 to the other nodes 16916. In some embodiments, the job ticket 16928 may be a block identifier of the previous block 16922. In some embodiments, each non-solving node 16916 (which may include ledger management system 16910) may receive the proof of work and may verify the proof of work based on a copy of the distributed ledgers 16508 stored in node 16916. In these embodiments, each node 16916 may determine whether the block identifier contained in the job proof corresponds to (e.g., matches) the block identifier of the block stored in the local copy of ledger 16808.
In some embodiments, ledger administration system 16910, along with other nodes 16916 in ledger network 16970, uses knowledge distribution system 16802 to maintain an immutable record of any operations performed by knowledge distribution system 16802 in terms of the administration of digital knowledge 16804. In these embodiments, ledger administration system 16910 may: generating a new event state record corresponding to the operation; encoding the new event status record into a new block data structure and encoding a block identifier of a previous block (e.g., a recently added block) into a block header of the new block data structure; the new block data structure is hashed using a hash function to obtain a new block. Further, in some embodiments ledger administration system 16910 may transmit request 169240 to verify new block 16922 to other nodes 16922 in network 16814. In some embodiments, one of nodes 16916 may attempt to determine a solution for new block 16922. If a valid solution is determined, the solver node 16916 may transmit the job proof 16928 to other nodes 16916 in the ledger network 16970 and the other nodes 16916 may attempt to verify the job proof 16928.
In some embodiments, ledger management system 16910 utilizes distributed ledgers 16508 to manage the permissions of different users of knowledge distribution system 16802, such as one or more of knowledge providers 16806 (and/or one or more knowledge provider devices 1689) and/or one or more of knowledge recipients 16848 (and/or one or more knowledge recipient devices 16894). In some embodiments, rights may be granted for one instance of digital knowledge 16804 or a set of instances of digital knowledge 16804. For example, the rights may include: allowing a user to upload rights for one instance of digital knowledge 16804 or a set of instances of digital knowledge 16804; allowing a user to view rights for one instance of digital knowledge 16804 or a set of instances of digital knowledge 16804; allowing a user to edit rights for an instance of digital knowledge 16804 or a set of instances of digital knowledge 16804; allowing the user to delete rights for one instance of digital knowledge 16804 or a group of instances of digital knowledge 16804; allowing a user to download rights for one instance of digital knowledge 16804 or a set of instances of digital knowledge 16804; allowing a user to print (e.g., onto paper or 3D print) an instance of digital knowledge 16804 or a set of rights for an instance of digital knowledge 16804, etc. Additionally or alternatively, the rights may relate to services 16930 provided by the knowledge distribution system 16802. For example, the rights may include: permission to allow a user to access the full text search function of knowledge distribution system 16802; allowing the user to use the rights of the virus scanner provided by knowledge distribution system 16802; allowing a user to have knowledge distribution system 16802 generate one instance of machine-generated digital knowledge 16804 or rights for one instance of digital knowledge 16804, etc. Rights may also include rights to perform operations granted to one or more other users. Rights may be applied by default to one or more users, to one or more classes of users (e.g., users holding one or more of rights keys 16932), automatically to one or more classes of users (e.g., knowledge provider 16806, knowledge receiver 16818, and/or crowdsourcer 16846), and/or may be manually granted to one or more users by an administrator and/or manager of the knowledge distribution system.
In some embodiments, ledger administration system 16910 may manage access rights of each participant to a respective service 16930 by: one or more unique service-specific rights keys 16932 are generated for the respective service 16930 and each respective unique service-specific rights key 16932 is issued to a respective participant that has been granted access to the respective service 16930. In some of these embodiments, ledger management system 16910 may utilize distributed ledgers 16508 to store proof of service-specific rights keys 16932 and manage rights to services 16930. In these embodiments, ledger administration system 16910 may: receiving an instruction for granting a user access to a specific service; generating a service specific authority key 16932 corresponding to the specific service and distributing the key 16932 to the user; encoding a user ID of the user and a service specific authority key 16932 in a state change event record; generating a new block from the state change event record and a block identifier of a previously stored block; the new block is stored in its local copy of the distributed ledger 16508 and broadcast to other nodes 16916 in the network to be stored on the respective copies of the ledgers 1608 stored at those nodes. In some embodiments, ledger administration system 16910 may verify a new block before storing and transferring the block for storage at other nodes 16916. Ledger administration system 16910 may also transmit the new blocks to computing devices (which may or may not be participating nodes) associated with the user, whereby agents 16920 on the computing devices may store the new blocks. In this way, when a user attempts to access a particular service 16930 from a computing device, the agent 16920 can use the new block to gain access to the particular service. When a user attempts to access a particular service 16930, the agent may communicate a block containing the rights key 16932 corresponding to the particular service 16930 to ledger management system 16910. Ledger administration system 16910 may parse received blocks and/or validate received blocks in the manner described above. If ledger administration system 16910 is able to verify the received blocks, ledger administration system 16910 grants the user's computing device access to service 16930, whereby the user can begin using service 16930. In some embodiments, rights and access to an instance of digital knowledge 16804 or a set of instances of digital knowledge 16804 in a stored organization may be managed in a similar manner, wherein a user is granted rights keys 16932, wherein these rights keys 16932 correspond to particular operations that a user may perform on an instance of digital knowledge 16804 or a set of instances of digital knowledge 16804 associated with rights keys 16932.
In some embodiments, ledger management system 16910 and/or one or more computing node computing devices 16916 with necessary processing resources may generate an immutable log of transactions based on distributed ledgers 16508. In these embodiments, ledger administration system 16910 and/or node 16916 (collectively, solving node 16916) may begin to solve for the latest block 16922 in distributed ledger 16508. Each time a block is broken, the solving node 16916 may transmit the proof of work 16928 to the other nodes 16916, which may then verify the accuracy of the solution. The solving node 16916 may iteratively solve each block 16922 in the distributed ledger 16508 in this manner until the entire ledger 16508 is solved, thereby generating a managed oplog of digital knowledge 16804 through the knowledge distribution system 16802. The oplog may define actions or operations performed using the knowledge distribution system 16802. Ledger 16508 is generated in a transparent, secure manner by creating, verifying, and solving block 16922 of distributed ledger 16508 in the manner described above. The generated oplog is stored in an encrypted manner until broken, once broken, the oplog is auditable and immutable. The oplog may indicate: each time a user is allowed to access distributed ledgers 16808 and/or manage digital knowledge 16804 through knowledge distribution system 16802; rights granted to each user; each user initiated request to perform an operation or use a service 16930; an operation performed; a user performing an operation, etc.
In some embodiments, solving node 16916 may optimize the solving of ledgers 16508 by solving different blocks 16922 in a distributed manner. For example, if distributed ledger 16508 includes one or more branches (e.g., when multiple child blocks 16922 point to the same parent block 16922), distributed ledger 1608 may be considered to branch at parent block 16922. In this example, each chain originating from a bifurcation may have a final block 16922 (or leaf block 16922). In this case, different solving nodes 16916 may begin to solve ledgers 16508 at different leaf blocks 16922 in a breadth-first or depth-first manner, thereby increasing the speed of solving ledgers 16508.
In some embodiments, ledger administration system 16910 may be configured to: collaboration between one or more of the knowledge providers 16806, one or more of the knowledge recipients 16818, or a combination thereof is facilitated by facilitating the execution of management of digital knowledge 16804 by way of knowledge distribution system 16802 using, for example, smart contracts. In these embodiments, ledger administration system 16910 may provide for the administration of digital knowledge 16804 through knowledge distribution system 16802, knowledge distribution system 16802 being defined to facilitate the administration of corresponding types of digital knowledge 16804. For example, to transfer one or more instances of digital knowledge to knowledge receiver 16848 (e.g., using knowledge receiver device 16894) to exchange funds transferred to knowledge provider 16806 (e.g., knowledge provider device 16890) in accordance with sales, licensing, or lease agreements and/or smart contracts, ledger management system 16910 defines various tasks that must be completed before the next step in selling, licensing, or leasing of digital knowledge 16804 can be performed. In this example, knowledge distribution system 16802 may require that an instance of digital knowledge 16804, or a link and/or reference thereto, must be uploaded to distributed ledger 16808 prior to transferring funds from knowledge receiver 168418 to knowledge provider 16806. Another condition may be that one or more parties with sufficient rights to sign the document must electronically execute the document before engaging in the transfer of one or more instances of digital knowledge 16804. Knowledge distribution system 16802, ledger management system 16910, and/or distributed ledgers 16808 may be preconfigured based on the type of management of digital knowledge 16804 to be performed by knowledge distribution system 16802, and/or may be customized by one or more principals associated with the management of digital knowledge 16804 by knowledge distribution system 16802. In some embodiments, each operation and/or management of digital knowledge 16804 by knowledge distribution system 16802 may be encoded in a smart contract, whereby the smart contract may manage various stages of a workflow when the smart contract determines that one or more desired conditions are met. In some embodiments, copies of the smart contract are stored and executed by the agents 16920 of one or more respective nodes 16916. The agent 16920 may facilitate performance of operations defined in the smart contract (including verifying authority to perform operations using the distributed ledger 16508), reporting and logging performance of operations (e.g., by generating blocks or request generation blocks from the ledger administration system 16910), and/or verifying satisfaction of one or more conditions defined in the smart contract. After consensus is reached on one or more desired conditions, management of the digital knowledge 16804 by the knowledge distribution system 16802 may proceed to a next stage of the workflow. In this way, ledger network 16970 (e.g., ledger management system 16910 and participating node 16916) may facilitate collaboration between principals in managing digital knowledge 16804 through knowledge distribution system 16802 by: the framework is provided for facilitating execution of workflows related to managing digital knowledge 16804 by knowledge distribution system 16802, verifying pre-shutdown and shutdown tasks, and/or managing digital knowledge 16804 by intelligent approximation by knowledge distribution system 16802.
FIG. 172 illustrates a method 17200 of deploying knowledge markup 17038 and related smart contracts 16840 through a knowledge distribution system 16802.
At 17210, knowledge distribution system 16802 receives an instance of digital knowledge 16804, e.g., from a user. In embodiments, the user may be affiliated with an organization (e.g., an organization that owns digital knowledge) or may be an independent individual (e.g., an individual who creates digital knowledge by himself or in collaboration with other independent individuals). The user may provide an instance of digital knowledge 16804 through a graphical user interface. For example, a user may upload digital knowledge through a graphical user interface. In an embodiment, the digital knowledge may be a set of instructions executable by a device or a group of devices. The user may upload the digital knowledge by providing the knowledge itself or a reference to the digital knowledge (e.g., an address from which the digital knowledge may be electronically accessed/retrieved).
In an embodiment, the user may provide additional information such as the type of digital knowledge, a description of the digital knowledge, the price to charge to access the digital knowledge, and the like. In some embodiments, the user may provide licensing data, such as any patent, trademark, copyright that is licensed or otherwise assigned to the knowledge recipient, licensing terms (e.g., when each license expires), the scope of the license (e.g., usage/sales/transferability restrictions or geographic restrictions), and so forth. In an embodiment, the user may define authentication information, such as authentication/verification of digital knowledge. In embodiments, the user may also define distribution limits for digital knowledge (e.g., the total number of knowledge tokens that may be generated).
In an embodiment, a user may define a set of conditions and/or actions for generating an intelligent contract for managing digital knowledge transactions. Examples of conditions may include: a period of time for which the smart contract is valid; requirements of the recipient device that must be verified before releasing the digital knowledge (e.g., certain specifications of the device, such as type of 3D printer, minimum processing power, machinery required to perform certain processes, etc.); requirements of the knowledge receiver (e.g., definitions of certain types of data must be provided to ensure that the knowledge receiver is eligible to receive digital knowledge) or any other suitable condition. In some embodiments, a user may define a set of actions that may be performed in response to certain conditions being triggered. Some actions performed by the smart contract may be default conditions, such as writing transaction records to a distributed ledger or releasing digital knowledge. In some embodiments, the user may define custom actions, such as defining a funding allocation for a third party rights holder, generating a serial number for a digital knowledge generated product, digitally signing a digital knowledge generated product, disclosing an API to a knowledge recipient, and the like.
At 17212, the knowledge distribution system tags digital knowledge 16804, thereby creating knowledge tag 17038. In an embodiment, the ledger administration system may tag digital knowledge by wrapping intelligent contracts around the digital knowledge to obtain knowledge tags. In some embodiments, the ledger administration system may retrieve the smart contract template from the smart contract data store such that the smart contract template corresponds to the type of digital knowledge to be tagged. In some of these embodiments, the ledger administration system may parameterize the smart contract template based on user-provided information and any user-defined conditions and/or actions. For example, the smart contract may be parameterized for an instance of digital knowledge (or a reference to digital knowledge), any permissions granted, prices to be paid, any conditions to be met, and any actions to be performed. In some embodiments, the ledger administration system may include any libraries in the smart contracts that are needed to support any function defined in the smart contracts. In some embodiments, the ledger administration system may configure one or more event listeners that allow the intelligent contract to monitor one or more data sources. In these embodiments, the ledger administration system may define the data sources to be monitored, whereby the event listener retrieves and/or processes data from the data sources and then uses the data to determine whether a certain condition or set of conditions is met. Other examples of tags may be found in the Etherum Specification, which may be accessed through https:// gitsub.com/Etherum. In an embodiment, the knowledge distribution system may generate a number of knowledge tokens, whereby each knowledge token may be used to facilitate a different transaction of a digital knowledge instance.
At 17214, the knowledge distribution system stores knowledge tag 17038. In an embodiment, ledger management system may store knowledge tags by deploying knowledge tags to distributed ledgers 16508. In an embodiment, the ledger administration system may initially assign ownership of the knowledge tag to the knowledge provider. In an embodiment, the knowledge distribution system may also store information related to the digital knowledge instances in a knowledge data store, which may be used to populate a marketplace site where potential knowledge recipients may view the information related to the digital knowledge.
Fig. 173 shows a method 17300 of performing a brief flow of a smart contract that distributes digital knowledge. In an embodiment, the smart contract may be a knowledge tag stored in the distributed ledger and executed by one or more nodes hosting the distributed ledger. In some of these embodiments, the smart contract may be executed in a virtual machine or container.
At 17310, the smart contract monitors one or more of the conditions defined in the smart contract. In some embodiments, the event listener obtains data (either passive or active) from one or more data sources defined in the smart contract 16840. When the event listener obtains data from one or more data sources, the smart contract may determine whether certain conditions are met and, if so, may perform actions triggered by the met conditions.
At 17312, the smart contract verifies conditions of the digital knowledge transaction; at 17314, the smart contract initiates transfer of digital knowledge 16804. In an embodiment, the smart contract may include an event listener that determines whether the necessary amount of funds have been deposited into the smart contract. Once the principal deposits the necessary amount of funds (e.g., a predefined amount of cryptocurrency or legal currency), the smart contract can begin transferring digital knowledge to the knowledge recipient (e.g., the principal depositing the necessary amount of funds). In an embodiment, this may include: updating the distributed ledger using a block that indicates ownership changes of the tag to the knowledge recipient; any required keys are provided to the knowledge receiver. Once ownership of the knowledge tag changes, the knowledge recipient can access the digital knowledge contained therein (and according to any restrictions defined in the smart contract, e.g., using a particular type of device).
As discussed, the techniques described herein may be used to facilitate transactions of different types of instruction sets. In some embodiments, the knowledge distribution system may be used to distribute instruction sets for 3D printing of particular products (e.g., replacement parts, medical devices, custom products, manufacturing parts, etc.). In operation, knowledge distribution system 16802 can present a graphical user interface to a user whereby the user can provide an instance of digital knowledge and a user provider (e.g., knowledge provider) can upload an instruction set for printing 3D items to knowledge distribution system 16802. In an embodiment, the 3D printing instruction set may include a file (e.g., a CAD file and/or an STL file) and any accompanying instructions for printing the product defined in the file. In some embodiments, the user may also define a transaction price defining the monetary (legal and/or crypto) amount that must be paid to purchase the knowledge tag containing the 3D printed instruction set. In addition, the user may provide a description of the product and any printing requirements (e.g., the required materials and/or device type or minimum specification required for a 3D printed product). The user may also provide additional information such as a photograph of the printed product, authentication with respect to the product, etc.
In an embodiment, a user may define any intellectual property rights (also referred to as an intellectual property stack) that are licensed or otherwise transferred to an intellectual receiver along with digital knowledge in a transaction of a 3D printing instruction set. In some embodiments, a user may define an allocation plan that defines how to divide licensing fees among one or more licensing parties. For example, if a product printed from a digital knowledge instance is licensed according to one or more patents, design patents, copyrights, and/or trademarks, a portion of the transaction price for each printed product may be assigned to each licensor as a licensing fee payment. In this example, the user may identify the licensor charging the licensing fee and may assign a percentage or amount of the licensing fee to each respective licensor. In embodiments, the user may define any geographic limitation of digital knowledge. For example, a user may define a country, region, jurisdiction, or other geographic region to which digital knowledge may or may not be distributed. In embodiments, the user may also define other types of rights or restrictions, including 3D printer requirements (e.g., a set of 3D printer types, make and model numbers of products that can be printed, serial numbers of 3D printers that can print products, types of materials that must be used to print 3D products, etc.), time periods during which the item can be 3D printed, whether a digital label can be transferred to a downstream recipient, etc. In an embodiment, a user may define actions to be performed when 3D printing an object, such as assigning a serial number to a product (which may or may not be printable onto the object), and so on. In embodiments, the user may also define any guarantees, disclaimers, reimbursements, etc. associated with the 3D printed product.
In an embodiment, the smart contract system 17068 may generate a knowledge tag 17038 that contains digital knowledge (or a reference thereto). In some embodiments, the smart contract system 17068 may tag digital knowledge by wrapping the digital knowledge (e.g., 3D print instruction set or reference to instruction set) with a smart contract wrapper. In some embodiments, the smart contract system 17068 may obtain a smart contract template and may use some information entered by the user to parameterize the smart contract, such as price, licensing fee allocation, geographic restrictions, other restrictions, custom operations (e.g., assigning serial numbers), whether or not a flag will expire/when it expires, 3D printer requirements, and so forth. In some embodiments, each knowledge tag generated for the 3D printer instruction set may be assigned a different serial number, such that each 3D printed product may be identified by its serial number and associated with the tag on which it was printed. In this way, the product may be verified and associated with a particular record in the distributed ledger. In an embodiment, the smart contract system 17068 may output the generated knowledge tag to the ledger management system 16910.
In an embodiment, ledger management system 16910 may upload knowledge tags to distributed ledgers. In some embodiments, ledger management system 16910 may generate blocks containing knowledge tokens and may broadcast the blocks to distributed ledgers 16508, whereby knowledge recipients may then transact for one or more of the knowledge tokens (e.g., print one or more corresponding products using a 3D printing instruction set). In some embodiments, one or more of the recipient nodes may execute a smart contract that wraps the digital signature, whereby the smart contract listens for one or more trigger conditions (e.g., receives a monetary amount equal to the transaction price of the knowledge signature). Additionally or alternatively, ledger management system 16910 may execute smart contracts (e.g., in containers) and may record knowledge tagged transactions to distributed ledgers.
In an embodiment, knowledge distribution system 16802 can provide or connect to a digital knowledge marketplace whereby potential recipients can purchase knowledge indicia corresponding to various 3D printing instruction sets. For example, markets may present items that may be 3D printed, such as aircraft parts, automotive parts, mechanical parts, other types of replacement parts, toys, medical devices, and the like. The potential recipients may develop transactions for a particular 3D printing instruction set. In an embodiment, the potential recipient may select one of the items. In response, the knowledge distribution system may provide the potential recipients with the price of each tag, restrictions associated with the knowledge tag (e.g., any device requirements, geographic restrictions, usage restrictions, etc.), guarantees, disclaimers, reimbursements, certificates, and the like. The potential recipient may then choose to accept the terms of the transaction (e.g., agree to a purchase indicia). The potential recipient may then submit a defined amount of currency to the transaction. In response, the smart contract may listen for additional conditions (if defined) before completing the transaction and/or releasing the digital knowledge. For example, the smart contract may require the potential recipient to verify whether the printer requirements are met or may connect to a 3D printer to verify the requirements. If all conditions required to complete the transaction are met, the smart contract may provide money to the knowledge provider (and any other licensor) and may perform any other action, such as releasing digital knowledge to a 3D printer (or other device), broadcasting blocks to distributed ledgers that verify the transaction, and/or recording serial numbers in the distributed ledgers. The 3D printer may receive the 3D printing instructions and may print the product according to the 3D printing instruction set.
In embodiments, knowledge distribution system 16802 can be deployed on or integrated with a set of infrastructure capabilities, such as cloud computing infrastructure, platform-as-a-service infrastructure, internet of things platform capabilities, distributed database capabilities, data management platform infrastructure, enterprise database resources (including cloud and local resources), and the like. In embodiments, the knowledge distribution system 16802 may use or be integrated with or in various services, such as identity management services, information management services, digital rights management services, information rights management services, encryption services, key management services, distributed database services, and the like.
Referring to FIG. 174, in an embodiment, the knowledge distribution system 16802 can provide one or more collaboration APIs 17474 for facilitating collaboration between users. These collaboration APIs may be used to allow users to provide and share information to establish a set of shared data resources for performing collaboration, such as: providing a shared "ground truth" about ground facts; establishing an alternative set of perspectives about ground facts (e.g., determining where there may be divergence about ground truth or lack of information needed to establish a common understanding); facilitating management of a set of scenarios requiring collaboration; facilitating a set of simulations related to topics of interest to the collaborators; facilitating controlled access to shared and non-shared knowledge elements; and/or allow users to provide, verify, and/or share information outside of an enterprise firewall. Collaboration API 17474 may be used to enable users and/or principals to provide, receive, share, and/or verify information, such as digital knowledge 16804, information related to transactions performed through distributed ledgers 16808, through one or more smart contracts 16840, through marketplace system 17454, and the like. These APIs may be used to enable sharing of information privately, publicly, or a combination thereof. Information shared through these APIs or events or transactions related thereto may be stored on distributed ledgers 16508 and thus distributed on nodes 16916 of the distributed ledgers. The users may include knowledge providers 16806, knowledge receivers 16848, crowdsourcers 16836, distributed ledgers 16808, and/or users and/or parties to the digital marketplace 17456, among others.
In some embodiments, collaboration API 17474 may include operational and/or contextual knowledge that may be captured by knowledge distribution system 16802. Collaboration API 17474 may be used to process the contextual knowledge and transmit the contextual knowledge and/or an interpretation of the contextual knowledge to ledger management system 16910. Ledger management system 16910 may be used to store contextual knowledge and/or interpretations of contextual knowledge on distributed ledgers 16508. Examples of contextual knowledge are data regarding the current status, state, and/or location of a mortgage associated with an instance of digital knowledge 16804 and/or associated with smart contract 16840. Another example of situational knowledge is the completion status of the work-in-process, the payment due, and/or the completion of an item (e.g., an instance of digital knowledge 16804) to a lending triggered by a certain completion stage.
In an embodiment, the smart contract system 16868 may include one or more transaction frameworks 17476 for facilitating the management of transactions through the smart contracts 16840. The transaction framework 17476 can include one or more data structures, routines, subroutines, and the like for facilitating management of transactions, such as by automatically importing, exporting, ordering, configuring, processing, or otherwise processing data related to transactions processed through the knowledge distribution system 16802. The smart contract system 17068 may be used to include one or more transaction frameworks 17476 related to billing, payment, reporting, auditing, reconciliation, and the like.
In an embodiment, each of the transaction frames 17476 may be used to facilitate the management of one or more specific types of transactions. Examples of transaction types and related data that one or more of the transaction frameworks 17476 may use to facilitate management include: purchase/sell, lend/lease, permit, resource/time share, service contract, repair/repair, guarantee, insurance, profit/revenue share, manufacture (optionally layered), resale/distribution (optionally layered), demand aggregate, long-term market/futures transaction, and conditional/contractual, etc., including any of the various types described in the documents of the present invention and incorporated by reference herein. For example, in a hierarchical allocation contract framework, the transaction framework 17476 may be used to allocate one or more of payments, commissions, and costs in a granular manner using distributed ledgers 16508 and smart contracts 16840. In another example, in or with a contract framework, the trading framework 17476 can be used to manage one or more of options, futures, emergency events, and the like using distributed ledgers 16508 and smart contracts 16840. Other examples of the smart contract framework 17476 include a framework for managing commissions, rewards payments, milestone payments (e.g., partial work, supply chain intermediate delivery, etc.), and escrow funds.
In an embodiment, one or more of the transaction frameworks 17476 may be used to facilitate management of the smart contract 16840 in the event that one or more parties to the agreement have a fulfillment problem. Fulfillment issues may include, for example, default, failure to pay, overdue payment, poor performance, poor quality of merchandise, failure to fulfill a service, and so forth. The remedies for the questions that may be encoded in the transaction framework 17476 may include an extraction function, a loss of permissions, a dramatic drop in performance, an economic penalty (e.g., loss of tokens or money), and the like.
In an embodiment, the transaction framework 17476 may facilitate assigning risk and liability in a granular manner using distributed ledgers 16508 and smart contracts 16840. The knowledge distribution system 16802 can be used to import sensor data from one or more sensors (e.g., ioT sensors). The sensor data may include single sensor data, multiple sensor data, fused sensor data (e.g., results from two or more sensors are combined, e.g., by multiplexing, by calculation, etc.), raw sensor data, normalized sensor data (e.g., allowing comparison to a scale, e.g., quality scale, condition scale, etc.), calibrated sensor data (e.g., allowing accuracy comparisons to other sensors), etc. In an embodiment, the sensor data may indicate a state or condition of a physical item or its environment at a point in time or over a period of time, such as its temperature, the ambient temperature of its environment, the ambient humidity, movement of an item (e.g., due to impact, vibration, transportation, etc.), exposure to heat, exposure to radiation, exposure to chemicals (including particulates, toxins, etc.), load bearing, weight bearing, stress exposure, strain exposure, impact exposure, damage (e.g., pitting, deformation, flexing, breaking, fracturing, cracking, tearing, etc.), exposure to biological factors (including pathogens), progressive damage (e.g., tarnishing), etc. factors. In an embodiment, the sensor data may indicate the presence or absence of activity or workflow related to the item, e.g., the sensor data indicates a liquid level (e.g., oil or other lubricant, fuel, antifreeze, and other liquids, which indicate whether a desired maintenance, e.g., oil change, is performed in time), a level of particulate matter or other matter (e.g., dirt, grime, sand, etc., which indicate whether necessary cleaning is performed), a degree of rust, etc. In various embodiments, the imported sensor data may allow the smart contract system 17068 to assign, through the transaction framework 17476, other relevant factors related to performance, performance inadequacies, utilization, degradation, wear, damage, maintenance activities, or possibly attributed to various components of the layered manufacturing system (including various machines, equipment items, equipment, component parts, etc.) to particular interested parties. As one of many examples, a series of parties owning an item may assign their devaluation, worsening or other decreasing responsibilities of value, such as environmental conditions in which the item is stored and the presence or absence of required maintenance activities, such as liquid changes, cleaning, etc., based on their measured activities in taking care of the item. For example, a party storing the item in the original condition at a specified temperature and changing the liquid according to a defined schedule may assign a medium number of points (or other metrics), while a party storing the item in the severe outdoor conditions may assign a higher number of points to automatically assign responsibility for changing the item by the smart contract. Similarly, a principal's role or not may be directly measured as causing damage (e.g., the item falls and dents when owned by the principal), then it may be automatically assigned responsibility for the damage. Thus, a sensor-enabled smart contract may track and distribute responsibility for conditions and activities involving a physical item throughout the life cycle of the item, including among parties sharing or transferring possession of the item, sharing use of the item, etc. Shared use or possession throughout the life cycle may include cases of layered manufacturing, such as component parts being gradually deployed into the overall system by a group of parties. In this case, the smart contract may use the sensor data collected throughout the manufacturing process to determine responsibility for an item failure (e.g., a manufacturing defect) based on which portion of the item failed and/or the cause of the item failure (e.g., due to a problem in the manufacturing chain). Shared use or possession may also include "shared economy" situations, such as shared use property (including rooms, office spaces, houses, apartments, real estate, vehicles, electronic equipment, etc.), where smart contracts may allocate responsibility for damage, maintenance (or lack of maintenance), cleaning, and other factors based on sensor data collected over the life cycle of the shared item. In an embodiment, the smart contract framework 17476 may also specify more complex reasons including reimbursement terms and allocation responsibilities and/or limitations thereof (including exceptions to responsibilities and/or limitations thereof), which may include factors related to the sensor data collected over the above-described time. For example, a smart contract may limit the manufacturer's responsibility for defects for a period of time (e.g., ninety days, one year, etc.), but a smart contract may include exceptions that hide defects (e.g., defects that exist but do not occur during warranty). The sensor data may indicate whether a defect occurred within a base warranty period and automatically determine whether a warranty claim raised after the warranty period is valid. In embodiments, such intelligent contracts may also allocate and optionally perform value (e.g., money) transfers when final responsibility between parties is determined, such as when one party has paid for another party for one type of responsibility. In an embodiment, the smart contracts may be used to perform the calculation and distribution of net liabilities among multiple parties involved in contracts where one or more parties make an reimbursement for another party. In embodiments, such smart contracts may use sensor data for determining the scope of responsibility assigned to each principal (e.g., the principal's perceived change as or without possibly resulting in an item condition, possibly causing greater reimbursement responsibilities for other principals). In embodiments, such smart contracts may automatically credit or debit accounts, trigger value transfers, and the like.
In an embodiment, the transaction framework 17476 may facilitate the use of distributed ledgers 16508 and smart contracts 16840 to allocate payments, commissions, costs, and the like in a granular manner. The transaction framework 17476 may facilitate the inclusion and management of one or more principals of a set of component-marketing agreements, value-added dealer agreements, manufacturer agreements, sub-distributor agreements, sub-licensee agreements, payment agreements, service agreements, maintenance agreements, update agreements, upgrade agreements, leasing agreements, resource sharing agreements, item sharing agreements, warranty agreements, insurance agreements, lending agreements, reimbursement agreements, vouchers agreements, and the like in the smart contract 16840.
In an embodiment, the transaction framework 17476 may facilitate management and/or execution or contract with the distributed ledger 16508 and the smart contract 16840. Or a contract may include terms that specify that the provision of goods, services, payments, etc. is dependent on the occurrence of one or more triggering events. For example, tickets for a sporting event may be sold to fans of multiple sports teams, the effectiveness of the tickets and/or related transactions depending on the team supported by the fan being eligible to participate in the sporting event. In an embodiment, the transaction framework 17476 may have one or more data collection facilities, such as crawlers, spider programs, clustering systems, sensor data collection systems, services, APIs, etc., that collect data indicating the presence or absence or triggering conditions of a contract, e.g., in the example of an event-triggered contract, the system searches for the presence or absence of an event involving a particular performer, player, or team (among others) of the event, and the smart contract may automatically handle the distribution of rights triggered by the occurrence of the event. For example, the right to attend a superbowl (or other game) is triggered by the presence of a particular team in the game (or in a similar example, the right to attend is triggered by the occurrence or achievement of a desired instance of an event), the smart contract transaction framework 17476 may automatically determine (e.g., via a search engine or other capability to operate a news data source) the triggering event (e.g., a team wins a league champion game, thereby becoming a participant of the league champion, or other similar example, or a particular performer or group has announced the date and place of a tour or other performance). Further, the smart contract transaction framework 17476 may trigger a set of actions upon automatically determining an instance of a trigger event, such as: transferring ticket rights to a party to whom the rights belong in the event; notifying other parties that their contracts have been closed (i.e., for those parties that an event is still unlikely to occur under specified conditions, such as a rights holder associated with a team failing to enter a champion game); issuing a placebo prize for the losing party; triggering other smart contracts (e.g., smart contracts that distribute relevant goods and services offerings, such as travel and transportation services (e.g., automatically locking tickets, automatically locking rentals, etc., based on the location of the ticket holder and the location of the race), hospitality services (e.g., automatically locking hotel reservations in a city for fans not hosting the city in the race, automatically locking restaurant reservations, etc.)).
In embodiments, smart contracts for events that include triggers that automatically detect incidents related to the occurrence or implementation of event instances and that include automatically assigning rights (e.g., attendance, travel, and hospitality, etc.) based on those triggers may include, acquire input from, use, connect to, link to, and/or integrate with a set of smart agents, e.g., using any of the artificial intelligence, machine learning, deep learning, and other techniques described herein or in documents incorporated by reference, including robotic process automation trained and/or supervised by human experts. The set of intelligent agents may include a trained set of intelligent agents, such as, (a) determining and managing a set of possible events (e.g., in terms of a series of tournaments, sports, venues, etc., which teams may participate in what games at what venues and at what points in time), including expanding or curtailing a list based on game results and other factors (e.g., a team's competition out of a post-season competition seat, such that a prior possible competition becomes impossible); (b) Predicting a probability of likelihood of an event instance (e.g., likelihood of playing a game between two particular teams, likelihood of a performer holding a concert at a given location (or geofence) over a date range, etc.) based on current and historical data; (c) Generating and configuring smart contracts, managing rights allocation affected by or with events, including setting parameters for the start of a market or other venue (e.g., by way of auctions, drawing, "drop" etc.), through which a principal can contract for or with events; (d) Predicting demand for contract instances based on a given type or event (e.g., if a kenta university is racing, new Orleans demand for four-strength tickets, or demand for EltonJohn tickets in Paris in the third quarter of a given year, etc.), such as based on historical demand for similar events (optionally operating historical attendance data, aftermarket ticket data, and other data sets using various clusters and similar techniques), expressed demand (including demand expressed in demand aggregation contracts, such as where some users purchased options for events or similar events), historical data for similar projects or services or intelligent contracts, auxiliary indicators of demand (e.g., search engine metrics, social media metrics, etc.), etc.; (e) Setting initial pricing for the event, including historical pricing data based on predicted demand and base events (e.g., ticket price, aftermarket price and activity level, time required for the ticket to be sold-out, etc.) and other or event contracts; (f) managing distribution of smart contracts, such as batch publishing; (g) Collect and manage specific party factors and user profiles, such as knowing location factors (e.g., residence, workplace), affinity and loyalty factors (favorite team, favorite restaurant, favorite airline, favorite chain hotel, favorite food type, etc.), etc.; (h) Managing matches of particular principal factors and user profiles with or with events (e.g., finding and presenting smart contract opportunities that match the user profile, e.g., opportunities related to activities that may be engaged in favorite teams, players, or performers); (i) Managing discovery and presentation of smart contracts and parameter configuration, including other goods and services that may be paired with or have event smart contracts (e.g., automatically locating and matching appropriate airline flights, train reservations, bus tickets, etc. and configuring or having smart contracts for these items between transport providers and potential event ticket holders; automatically locating and matching appropriate hotel reservations and configuring smart contract hotel reservations between potential ticket holders and hotel providers, or creating similar or intelligent contracts between providers and potential ticket holders for other travel, accommodation, and hospitality packages (e.g., restaurant reservations, rentals, etc.); etc. Thus, the set of AI-enabled intelligent agents may provide automation of various capabilities to support or have market creation, custody, provisioning, and resolution of event intelligent contracts.
In an embodiment, the transaction framework 17476 may facilitate demand aggregation through distributed ledgers 16508 and smart contracts 16840. The transaction framework 17476 may aggregate the demand for one or more products and/or services accumulated by analysis, commitment, options, or any other suitable source. When demand accumulates, for example, by meeting a demand metric for a demand threshold, the smart contract 16840 can trigger to begin design, manufacture, distribution, etc. of the associated product and/or service. In an embodiment, a set of intelligent agents using the various AI capabilities described above may be used to facilitate demand aggregation, including agents with the following capabilities: (a) Predicting demand for an instance or product type, such as various auxiliary metrics based on demand, such as search engine metrics, chat activity (e.g., in a related forum), event information (e.g., attending a related industry event), social media information (e.g., number of posts), product sales, historical sales time (e.g., time from product posting to product sold out), etc.; (b) Aggregating demand, such as by configuring a set of smart contracts, to have a principal promise to purchase an item after the item has been instantiated, such as within a given time window within a given price range, and determining total demand; (c) Predicting the cost of demand aggregate products/services, e.g., based on models (optionally managed and/or created by intelligent agents) indicating the projected cost of the item at various throughputs, optionally based on predictions or models of possible component parts and their costs, and other costs, e.g., assembly, shipping, financing, warranty, etc.; (d) Predicting prices of demand aggregate items at various supply amounts, e.g., based on predicted demand and historical pricing; (e) Predicting may be associated with providing profits associated with demand aggregate items at various throughput and/or various points in time, such as using predicted demand, cost, and pricing information; etc. Thus, the automation capability provided by the set of intelligent agents may enable and/or support a demand syndication marketplace.
In an embodiment, the smart contract system 16868 may be used to import implementation patterns and/or system build knowledge into one or more of the transaction frameworks 17476. Implementation patterns and/or system build knowledge may include, for example, knowledge systems, workflows, product management, support calls, human interaction, social media, redundant systems, data storage, and large-scale implementation patterns. The smart contract system 16868 may automatically configure the smart contracts 16840 to implement imported implementation patterns and/or system build knowledge. Imported implementation patterns and/or system build knowledge may be stored in the data store 16858.
In some embodiments, the knowledge distribution system 16802 may include an Artificial Intelligence (AI) system 17480 in communication with the ledger administration system 16910 and/or the smart contract system 17468, the AI system 17480 for performing AI-related tasks according to a machine learning model. The AI system 17480 can be utilized to perform actions on the knowledge distribution system 16802 to manage digital knowledge 16804. The AI system 17480 can have similar rights and access to manage, use, and interact with the systems of the knowledge distribution system 16802 as the user.
In an embodiment, the AI system 17480 can be trained by one or more transaction specialists to develop the machine learning model from which the AI system 17480 operates to perform AI-related functions. Examples of trading experts that may at least partially train the AI system 17480 include agents, brokers, traders, lawyers, financial advisors, auditors, accountants, banners, marketers, advertisers, exchange operators, buyers, sellers, distributors, and manufacturers/developers. The AI system 17480 can be trained via any suitable machine learning algorithm and any suitable training data set. Examples of machine learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, self-learning, feature learning, sparse dictionary learning, anomaly detection, robotics learning, and association rules. The machine learning model may be any suitable type of model, such as an artificial neural network, a decision tree, a support vector machine, a regression analysis model, a bayesian network, or a genetic algorithm.
In an embodiment, the AI system 17480 can be trained to identify opportunities for smart contracts. Examples of opportunities include a switch opportunity and a arbitrage opportunity.
In an embodiment, the AI system 17480 can be trained to configure market contract terms and conditions.
In an embodiment, the AI system 17480 can be trained to monitor market conditions.
In an embodiment, the AI system 17480 can be trained to monitor and manage contract terms and conditions. Monitoring and managing contract terms and conditions may include monitoring goods and/or viewing services.
In an embodiment, the AI system 17480 can be trained to monitor and manage the transaction process. In an embodiment, the AI system 17480 may be trained to recognize the release of funds from a escrow account.
In an embodiment, the AI system 17480 may be trained to monitor counterpart information. Examples of opponent information include repayment capability, performance status, and performance quality.
In an embodiment, the AI system 17480 can be trained to identify transaction opportunities. Examples of transaction opportunities include instances of exchange and arbitration opportunities.
In an embodiment, the AI system 17480 can be trained to negotiate transactions involving digital knowledge on behalf of the principal.
In an embodiment, the AI system 17480 can be used to configure and execute an auction. The AI system 17480 can perform actions related to the auction, such as selecting an auction type appropriate for the transaction and/or setting rules and parameters for the auction to proceed at least in part on the distributed ledger 16508. The selected auction may be any suitable type of auction for at least partially conducting on distributed ledger 16508, such as a netherlands auction or a reverse auction.
In an embodiment, the AI system 17480 can be used to distribute digital knowledge 16804 of the monetary tokens and/or indicia.
In an embodiment, the AI system 17480 can be used to configure and manage the exchange of digital knowledge 16804 between different markets and exchanges. The AI system 17480 can set an exchange rate between exchange local currencies and/or can tag the digital knowledge 16804 and set an exchange rate between instances of the tagged digital knowledge 16804.
In an embodiment, the AI system 17480 can be utilized to establish, monitor, and/or negotiate payment, lease, and/or loan options related to the management of digital knowledge 16804. The lending options may include payment plans, untrusted scenarios, and/or non-untrusted scenarios.
In an embodiment, the knowledge distribution system 16802 may include a Robotic Process Automation (RPA) system 17482 in communication with the AI system 17480, the RPA system 17482 for improving one or more functions of the AI system 17480. The RPA system 17482 may use robotic process automation technology such that the AI system 17480 may interface with one or more of the systems of the knowledge distribution system 16802, the distributed ledgers 16508, and systems, markets, and/or exchanges external to the knowledge distribution system 16802 by: by performing actions in one or more graphical user interfaces of the knowledge distribution system 16802, the distributed ledgers 16508, and systems, markets, exchanges, etc., external to the knowledge distribution system 16802.
In an embodiment, the knowledge distribution system 16802 may include a rights management system 17484 for managing rights of users other than exchanges or markets.
In an embodiment, the knowledge distribution system 16802 can include a market management system 17486 for establishing a market for selling and/or reselling monetary tokens and/or instances of digital knowledge 16804. The marketplace may be configured such that instances of packages and/or labels of digital knowledge 16804 may be reselled without being unpacked. The market may be established and configured as a spot market, an aftermarket, and/or a futures/derivative market. Futures and derivatives that are reselled in the futures/derivatives market may include options, futures, and other derivatives. In addition to the resale market for digital currency and instances of digital knowledge 16804, knowledge distribution system 16802 can also establish and/or monitor secondary markets, auxiliary markets, forward markets, and the like.
In an embodiment, the marketplace management system 17486 may be used to monitor metrics of users, buyers and/or sellers participating in one or more marketplaces established by the marketplace management system 17486. The metrics may include, for example, metrics indicating how, where, or how long to use an instance of digital knowledge 16804. Alternatively or additionally, the metrics may include metrics regarding creation of digital knowledge 16804, duration of time that a given type of digital knowledge 16804 remains relevant or valuable, and/or metrics regarding transaction patterns. Examples of transaction patterns include transaction size, transaction pricing and trends thereof, profile information for buyers, sellers, consumers, users and/or creators of digital knowledge 16804, and the like. Additionally or alternatively, other metrics monitored by the marketplace system may include metrics indicative of gambling and/or improper behavior of the marketplace user.
In an embodiment, digital knowledge 16804 may include a set of instructions, for example: process steps or food preparation instructions in food production (e.g., for industrial food preparation), additive manufacturing/3D printing instructions, instruction sets typically used for surgical robots and human/robot interfaces, crystal manufacturing system instructions, crystal manufacturing process instructions, polymer production process instructions, chemical synthesis process instructions, coating process instructions, semiconductor manufacturing process instructions, silicon etching instructions, doping instructions, chemical vapor deposition instructions, bio-production process instructions, smart contract instructions, and/or instructions for setup, update and/or validation work, ownership chains, and the like.
In an embodiment, digital knowledge 16804 may include code, software, and/or logic, for example: algorithm logic, instruction set for use in an application, executable algorithm logic, computer program, firmware program, instruction set for a field programmable gate array, instruction set for a complex programmable logic device, serverless code logic, encryption logic, AI definition, machine learning logic, and/or definition and/or quantum algorithm.
In an embodiment, digital knowledge 16804 may include digital documents, such as digital documents related to: component schematics, production records (e.g., production records for aircraft components, spacecraft components, nuclear engine components, and any other suitable components), automotive components, aircraft components, furniture or assemblies thereof, replacement components for industrial robots or machines, trade secrets, and/or other intellectual property rights, such as proprietary technology, proprietary materials, and author works.
In an embodiment, digital knowledge 16804 may include 3D printed schematics, such as schematics for printing medical devices, automotive parts, aircraft parts, furniture assemblies, and/or replacement parts for industrial robots or machines.
In an embodiment, digital knowledge 16804 may include personal and/or professional knowledge related to one or more organizations and/or individuals. Personal and/or expertise may include: professional resume, professional history tracking information, professional certification records, academic circles, professional certificates, proof of professional positions that one or more individuals are in, professional feedback, proof of work that one or more individuals and/or one or more parties do, personal financial history, corporate financial history, and/or personal life achievements that are proof of one or more third parties.
In an embodiment, digital knowledge 16804 may include data sets and/or sensor information defining and/or populating a set of digital twins. Digital twinning may embody one or more instances of digital knowledge 16804 associated with one or more physical entities. One or more examples of digital knowledge 16804 may include knowledge related to one or more of configuration, operation mode, instruction set, capabilities, defects, performance parameters, and the like.
In an embodiment, knowledge distribution system 16802 can be used to transmit and/or receive instances of digital knowledge 16804 to and/or from one or more external knowledge exchanges and/or knowledge databases. The external knowledge exchange and/or knowledge database includes domain-specific exchanges, geographic location-specific exchanges, and the like. Knowledge distribution system 16802 can be used to facilitate exchange of valuable or sensitive instances of digital knowledge 16804 related to external knowledge exchanges and/or topics of knowledge databases. Additional or alternative examples of external knowledge exchanges and/or databases may include securities exchanges, commodity exchanges, derivative exchanges, futures exchanges, advertising exchanges, energy exchanges, renewable energy credit exchanges, cryptocurrency exchanges, bond exchanges, currency exchanges, precious metal exchanges, petroleum exchanges, commodity exchanges, service exchanges, or any other suitable type of exchanges and/or databases. The knowledge distribution system 16802 may integrate and/or communicate with external knowledge exchanges and/or interfaces of databases, such as API connectors, ports, agents. The integration and/or communication may be facilitated by one or more of an Extract Transform Load (ETL) technique, a smart contract, a wrapper, a token, a container, and the like.
In an embodiment, the knowledge distribution system 16802 can be deployed on or integrated with or in a set of infrastructure capabilities. Examples of infrastructure capabilities include cloud computing infrastructure, platform-as-a-service infrastructure, internet of things platform capabilities, distributed database capabilities, data management platform infrastructure, enterprise database resources (including cloud and local resources), and the like.
In embodiments, the knowledge distribution system 16802 may use or be integrated with or in various services. Examples of services with which or in which the knowledge distribution system 16802 may integrate include identity management services, information management services, digital rights management services, information rights management services, encryption services, key management services, distributed database services, and the like.
Referring to fig. 175, a knowledge distribution system 17500 for controlling digital knowledge-related rights is shown. Knowledge distribution system 17500 can include input system 17802, tagging system 17812, ledger management system 17818, and smart contract system 17824. In some embodiments, knowledge distribution system 17500 may also include smart contract generator 17858, execution system 17510, reporting system 17514, and crowd-sourcing module 17516.
The input system 17802 receives digital knowledge 17808 from the user 17502 and the tagging system 17812 can tag the received digital knowledge 17808 to generate tagged/tagged digital knowledge 17814 that operates as a tag.
Ledger management system 17818 creates and manages one or more distributed ledgers 17820, where a distributed ledger may include multiple encrypted link blocks distributed across multiple nodes of network 17848, as described elsewhere herein. Ledger management system 17818 may then store intelligent contracts 17822 and tagged digital knowledge 17814 in distributed ledger 17820.
Smart contract system 17824 may implement and manage smart contracts 17822, which may include tagged digital knowledge 17814, trigger events 17828, and smart contract actions 17830. When a triggering event 17828 occurs, the smart contract system 17824 can perform smart contract actions 17830. The smart contract system 17824 may handle the principal's 17532 commitment 17832 to the smart contract 17822. The smart contract system 17824 can manage rights 17540, including control rights 17840 to the tagged digital knowledge 17814 and access rights 17842 to who can view, edit, access, or use the tagged digital knowledge 17814. The smart contract 17822 also includes a smart contract wrapper 17503. Knowledge distribution system 17500 further comprises an account management system 17505, a user interface system 17507, and a marketplace system 17509.
As shown in fig. 180, the digital knowledge 17814 of the tag may include executable algorithm logic 18002, a 3D printer instruction set 18004, an instruction set 18008 for a coating process, an instruction set 18010 for a semiconductor manufacturing process, a firmware program 18012, an instruction set 18014 for a Field Programmable Gate Array (FPGA), serverless code logic 18018, an instruction set 18020 for a crystal manufacturing system, an instruction set 18022 for a food preparation process, an instruction set 18024 for a polymer production process, an instruction set 18030 for a bio-production process, a data set 18032 for digital twinning, an instruction set 18034 for performing a trade secret, intellectual property 18038, an instruction set 18040, and the like. In an embodiment, where the tagged digital intellectual property 17814 includes intellectual property 18038, the smart contract system 17824 may embed the intellectual property licensing terms 18802 of the intellectual property 18038 into a distributed ledger and, in response to a triggering event 17828, update the access rights 17842 to provide access to the intellectual property 18038 or to handle the commitment 17832 of the principal 17532 to the smart contract 17822 and corresponding intellectual property licensing terms 18802.
In an embodiment, the smart contract 17822 may include a smart contract wrapper 17503, which may: intellectual property 18038 is added to an intellectual property stack, which may be on a distributed ledger 17820; adding one or more principals 17532 promises 17832 to: licensing fees are apportioned for the added intellectual property 18804. The smart contract wrapper 17503 may record in the distributed ledger a commitment 17832 of one or more principals 17532 to: licensing fees are allocated for intellectual property aggregation stack 18804 or contract terms 18810.
In an embodiment, ledger management system 17818 may include a logging system 17512 to store logged data in distributed ledgers 17820. In an embodiment, digital knowledge 17808 may be an instruction set that ledger administration system 17818 may provide provable access to and execute on the system. Providing provable access may include logging or recording data in at least one of the plurality of encrypted link blocks. Provable access may include: aggregating a view of the trade secrets into a chain that records which knowledge recipients have viewed the trade secrets on the distributed ledger 18814; recording the principal contributing to the digital knowledge by logging data related to the principal, the log recording access transactions to instances of the digital knowledge 18830; the source of the digital information instance is recorded by storing data associated with the source.
Knowledge distribution system 17500 may include reporting system 17514 for reporting analysis data or analysis responses 18834 based on a number of operations performed on the digital knowledge of the distributed ledgers or tags. Reporting system 17514 may also analyze the digital knowledge of the markers 17814 and report the analysis results 18832.
In one embodiment, the smart contract system 17824 may aggregate instruction sets into instruction sets 18040, add instructions to pre-existing instruction sets to provide modified instruction sets 18040, manage allocation of instruction subsets 18042 to distributed ledgers, manage access to instructions in the instruction sets based on access rights 17842, and the like.
In an embodiment, ledger management system 17818 may include a crowdsourcing module 17516 to obtain crowdsourcing information 18602, which crowdsourcing information 18602 may then be stored in a distributed ledger, which crowdsourcing information 18602 may include: an audit 18824 of an instance of digital knowledge, a signature 18826 related to an instance of crowd-sourced information, verification 18828 of an instance of digital knowledge, and the like.
In an embodiment, knowledge distribution system 17500 may comprise a private network system to enable a private network and allow an authorized party to establish encryption-based consensus requirements to verify a new encrypted link block to be added to the plurality of encrypted link blocks. In an embodiment, ledger administration may establish cryptocurrency tokens for transactions between users of distributed ledgers.
In an embodiment, knowledge distribution system 17500 can include an account management system 17505 to facilitate creation and management of a plurality of user accounts 19094 corresponding to a plurality of users 17502, 19004 of knowledge distribution system 17500. The user account data may be stored in a distributed ledger.
In an embodiment, the knowledge distribution system may include a user interface system 17507, 19074 to present a user interface 19093 to one or more users 17502, 19004 that enables the users to view data related to an instance of digital knowledge.
In an embodiment, the knowledge distribution system may include a marketplace system 17509 to establish and maintain a digital marketplace 19090 and visually present data corresponding to instances of digital knowledge to users of the knowledge distribution system.
In an embodiment, the knowledge distribution system may include a data store in communication with the distributed ledger, wherein the data store may include: a knowledge data store for storing data related to digital knowledge; a client data store for storing data relating to a plurality of users of the knowledge distribution system; and the intelligent contract data storage is used for storing data related to intelligent contracts and the like.
In an embodiment, knowledge distribution system 17500 may include smart contract generator 17858 to generate smart contracts using parameterizable smart contract templates. The smart contract parameters may be based on the type of digital knowledge to be tagged and may include financial parameters, license fee parameters, usage parameters, yield parameters, price allocation parameters, identity parameters, access condition parameters, and the like.
Referring to FIG. 176, a computer-implemented method for controlling digital knowledge dependent rights is shown. In an embodiment, a distributed ledger is created and managed (step 17602), where the distributed ledger may include a plurality of encrypted link blocks distributed across a plurality of nodes of a network, as shown elsewhere herein. The smart contract may be implemented and managed (step 17604). The smart contracts may include trigger events, corresponding smart contract actions, and the like. The smart contracts may be stored in a distributed ledger. An instance of digital knowledge may be received (step 17608). The digital knowledge may be tagged (step 17610) and the resulting tagged digital knowledge stored by a distributed ledger (step 17612). Commitments of the smart contracts by multiple principals may be processed (step 17610) and control or access to the marked digital knowledge may be managed in accordance with the smart contracts (step 17218). In response to the occurrence of the trigger event, a corresponding smart contract action may be performed with respect to the marked digital knowledge (step 17620).
Referring to FIG. 177, an embodiment of a computer-implemented method for controlling digital knowledge-related rights is shown. The computer-implemented method may further include orchestrating an exchange of new digital knowledge for the marked digital knowledge based on the smart contract (step 17702). The method may further include integrating the knowledge exchange with a separate exchange, wherein the knowledge exchange facilitates exchange of at least one of valuable knowledge and sensitive knowledge related to a topic of the separate exchange (step 17704).
Referring to fig. 178, a knowledge distribution system 17800 for controlling digital knowledge related rights is shown. In an embodiment, knowledge distribution system 17800 may include an input system 17802, a tagging system 17812, a ledger management system 17818, an intelligent contract system 17824, an event monitoring module 17850, and an intelligent contract generator 17858. The input system 17802 receives the information 17862 and the digital knowledge 17808 from the knowledge provider device 17804, and the tagging system 17812 can tag the digital knowledge 17808 to produce tagged/tagged digital knowledge 17814 that operates as a tag.
As shown in fig. 180, the digital knowledge 17814 of the tag may include executable algorithm logic 18002, a 3D printer instruction set 18004, an instruction set 18008 for a coating process, an instruction set 18010 for a semiconductor manufacturing process, a firmware program 18012, an instruction set 18014 for a Field Programmable Gate Array (FPGA), serverless code logic 18018, an instruction set 18020 for a crystal manufacturing system, an instruction set 18022 for a food preparation process, an instruction set 18024 for a polymer production process, an instruction set 18030 for a bio-production process, a data set 18032 for digital twinning, an instruction set 18034 for performing a trade secret, intellectual property 18038, an instruction set 18040, and the like.
In some embodiments, the digital knowledge may include a 3D printer instruction set for a 3D printed object (e.g., custom part, custom product, manufactured part, replacement part, toy, medical device, tool, etc.). As shown in fig. 179, the 3D printer instruction set 17810 for the 3D print object may include a 3D print schematic 17902, a source 17904, a creation date 17908, a name of a contributing person 17910, a name of a contributing group 17912, a name of a contributing company 17914, a price 17918, a market trend 17920 of the related schematic, a serial number 17922, a component identifier 17924, and the like.
Ledger management system 17818 creates and manages one or more distributed ledgers 17820, where a distributed ledger may include multiple series of encrypted link blocks distributed across multiple nodes of network 17848, as described elsewhere herein. Ledger management system 17818 may then store intelligent contracts 17822 and tagged digital knowledge 17814 in distributed ledger 17820.
The smart contract system 17824 may implement and manage smart contracts 17822, where the smart contracts 17822 may include one or more trigger events 17828 and corresponding smart contract actions 17830. The smart contract system may manage rights 17861, such as control rights 17840 and access rights 17842, to the tagged digital knowledge 17814 in accordance with smart contract 17822. The smart contract system may handle commitments of knowledge provider 17834 and knowledge receiver 17838 to 3D printer instruction set 17810 for 3D print objects.
In response to the occurrence of trigger event 17828, smart contract system 17824 can perform a corresponding smart contract action 17830 and manage smart contract actions 17830 according to conditions 17844 and trigger event 17828. The triggering event may be the transmission of a 3D printer instruction or the use of a 3D printer instruction, and the smart contract action may be to modify the distributed ledger based on the control right 17840 and the access right 17842 when purchasing, downloading, or using the 3D printer instruction set. As shown in fig. 181, the smart contract actions 17830 may include: assigning a serial number 18108 to the 3D printed object; monitoring for a trigger event 18110; verifying the performance of the obligation according to the condition 18112; transmission 18114 of digital knowledge to verify payment or indicia; recording one or more transactions in the distributed ledger 18118; performing one or more operations 18120 on the distributed ledger; creating one or more new blocks in the distributed ledger 18122; verifying whether condition 18124 is satisfied; generating a payment request 18128 of the knowledge recipient; the distributed ledger 18130, etc. is modified when the 3D printer instruction set is purchased, downloaded, or used. The smart contract actions 17830 may include: receiving a purchase request 18102 from a knowledge receiver device; fulfilling the purchase request 18104 from the knowledge receiver device; verifying that the condition is met when the condition is a printer requirement, a payment received, money transferred from the knowledge receiver device or knowledge receiver, transferring the marked digital knowledge to the knowledge receiver device, etc. As shown in fig. 182, the condition 17844 may include: printer requirements 18202; received payment 18204; money 18208 transferred from the knowledge receiver or knowledge receiver device; the marked digital knowledge is transferred to a knowledge receiver or knowledge receiver device 18210, etc.
Referring to figure 183, possible control and access rights 17861 to the marked digital knowledge may include at least one of: rights 18302 for 3D printing by the user using multiple instances of the 3D printer instruction set; 3D printer requirements 18304; a time period 18308 in which the object may be 3D printed; whether the tagged digital knowledge is transferred to a downstream knowledge receiver 18310; guarantee 18312; disclaimers 18314; compensation 18318; or authentication 18320 with respect to the object.
Referring to fig. 184, a possible trigger event 17828 may include transmission 18402 of a 3D printer instruction or use 18404 of a 3D printer instruction.
Referring to FIG. 185, a computer-implemented method 18500 for controlling digital knowledge dependent rights is shown. In an embodiment, the method may include creating and managing a distributed ledger, wherein the distributed ledger includes a plurality of encrypted link blocks distributed across a plurality of nodes of a network (step 18502). The smart contract may be implemented and then managed (step 18502). The smart contract may include a triggering event and may be stored in a distributed ledger. Responsive to the occurrence of the trigger event, a smart contract action may be performed with respect to the digital knowledge (step 18506). The method 18500 may further comprise: receiving an instance of digital knowledge from a knowledge provider device, the digital knowledge including a 3D printer instruction set for a three-dimensional (3D) print object (step 18508); marking the digital knowledge (step 18510); the digital knowledge of the tag is stored by the distributed ledger (step 18512). The method 18500 may further comprise: processing commitments of the smart contract by the knowledge provider and the knowledge receiver of the 3D printer instruction set (step 18514); managing control and access to the digital knowledge of the tags according to the smart contracts (step 18516); smart contract actions are managed based on the conditions and trigger events (step 18518).
In an embodiment, referring to fig. 186 and 187, a computer-implemented method 18600 for controlling digital knowledge-related rights is shown. The computer-implemented method 18600 may include crowdsourcing information about the digital knowledge (step 18602), wherein the crowdsourcing information may include: element 18702 of an instance of digital knowledge; information 18704 about elements of an instance of digital knowledge; information about knowledge provider 18708; information on knowledge recipients 18710, etc. The computer-implemented method 18600 may also include updating the smart contract (step 18604) or updating the conditions (step 18608) in response to the crowd-sourced information.
Referring to fig. 190, a knowledge distribution system 19000 for controlling digital knowledge-related rights is shown. In an embodiment, knowledge distribution system 19000 may include input system 19002, tagging system 19012, ledger management system 19018, and smart contract system 19024. The input system 19020 receives the digital knowledge 19008 and the tagging system 19012 can tag the digital knowledge 19008 to produce tagged digital knowledge 19014 that operates as a tag.
Ledger management system 19018 may create and manage a distributed ledger 19020, where the distributed ledger may include multiple encrypted link blocks distributed across multiple nodes of a network, as described elsewhere herein. Ledger management system 19018 may store intelligent contracts 19002 and tagged digital knowledge 19014 in distributed ledgers 19020. Ledger administration system 19018 may provide provable access to digital knowledge 19048 by recording and storing access transactions 19008 in a distributed ledger. Other methods of providing provable access are described elsewhere herein.
The smart contract system 19024 may implement and manage a smart contract 1902, which may include tagged digital knowledge 19014 and trigger events 19028. Upon occurrence of the triggering event 19028, the smart contract system 19024 can execute the rights 19062, including the control rights 19040 to the tagged digital knowledge 19014 and the access rights 19042 to who can view, edit, access, or use the digital knowledge 19008. The smart contract 19024 may handle the principal's promise 19034 to the smart contract 19032.
In an embodiment, the smart contract 19022 may include a smart contract wrapper 19064 that may perform operations on a distributed ledger to: adding intellectual property 18038; adding intellectual property 18038 to the intellectual property stack; adding one or more principals 17532 promises 17832 to: licensing fees are apportioned for the added intellectual property 18804.
In an embodiment, knowledge distribution system 19000 may include an account management system 19072 in communication with a distributed ledger to facilitate creation and management of a plurality of user accounts 19094 corresponding to a plurality of users 19004 of knowledge distribution system 19500. Knowledge distribution system 19000 can include user interface system 19074 to present user interface 19093 to one or more users 19004 that enable users to view data related to instances of digital knowledge 19008.
In an embodiment, knowledge distribution system 19000 can include a marketplace system 19078 to establish and maintain a digital marketplace 19090 and visually present data corresponding to instances of digital knowledge 19008 to users 19004 of knowledge distribution system 19000.
In an embodiment, knowledge distribution system 19000 may include a data store in communication with a distributed ledger, where the data store may include: a knowledge data memory 19082 for storing data related to the digital knowledge 19008; a client data store 19084 for storing data related to a plurality of users 19004 of the knowledge distribution system; smart contract data store 19088 for storing data and the like relating to smart contracts 19022.
The knowledge distribution system 19002 may include a reporting system 19080 for analyzing the tagged digital knowledge 19014 and reporting analysis results 19098.
In an embodiment, the knowledge distribution system 19000 may include a smart contract generator 19088 to generate smart contracts 19060 using parameterizable smart contract templates 19022. The smart contract parameters may be based on the type of digital knowledge to be tagged and may include financial parameters, license fee parameters, usage parameters, yield parameters, price allocation parameters, identity parameters, access condition parameters, and the like.
Referring to FIG. 191, an illustrative and non-limiting exemplary method for controlling digital knowledge related rights is shown. The method may include: creating and managing a distributed ledger (19102), wherein the distributed ledger may include a plurality of encrypted link blocks distributed over a plurality of nodes of a network; marking the digital knowledge (19104); storing the tagged digital knowledge by a distributed ledger (19108); implementing and managing smart contracts (19110), wherein the smart contracts include triggering events, tagged knowledge, and corresponding smart contract actions, and are stored in a distributed ledger; receiving an instance of digital knowledge (19112); processing commitments of the smart contracts by the plurality of parties (19114); managing control and access rights to the marked digital knowledge according to the smart contract (19118); responsive to the occurrence of the trigger event, performing a corresponding smart contract action for the marked digital knowledge (19120); the smart contract actions are managed in response to the trigger event (19122). In some embodiments, referring to fig. 192, the method may further comprise: crowd sourcing information about elements of the instance of digital knowledge (19224); the smart contract is updated responsive to the crowd-sourced information (19228). In some embodiments, referring to fig. 193, the method may further comprise: adding intellectual property to the distributed ledger (19324); causing the principal to promise to apportion license fees for the added intellectual property rights (19328); the principal's promise to agree to the terms of the agreement is processed (19330). In some embodiments, referring to fig. 194, the method may further comprise: creating a user account (19424); receiving a request from a user account to display data related to an instance of digital knowledge (19428); confirming an instance that the user account is allowed to access the digital knowledge (19430); a user interface is presented for displaying data related to an instance of digital knowledge (19432). In some embodiments, referring to fig. 195, the method may further comprise: digital knowledge is purchased or sold (19524). In some embodiments, referring to fig. 196, the method may further comprise: a monetary token associated with the distributed ledger is created and issued (19624).
Referring to fig. 190, a knowledge distribution system 19000 for controlling digital knowledge-related rights is shown. In an embodiment, knowledge distribution system 19000 may include input system 19002, tagging system 19012, ledger management system 19018, and smart contract system 19024. The input system 19020 receives the digital knowledge 19008 and the tagging system 19012 can tag the digital knowledge 19008 to produce tagged digital knowledge 19014 that operates as a tag.
Ledger management system 19018 may create and manage a distributed ledger 19020, where the distributed ledger may include multiple encrypted link blocks distributed across multiple nodes of a network, as described elsewhere herein. Ledger management system 19018 may store intelligent contracts 19002 and tagged digital knowledge 19014 in distributed ledgers 19020. Ledger administration system 19018 may provide provable access to digital knowledge 19048 by recording and storing access transactions 19008 in a distributed ledger. Other methods of providing provable access are described elsewhere herein.
The smart contract system 19024 may implement and manage a smart contract 1902, which may include tagged digital knowledge 19014 and trigger events 19028. Upon occurrence of the triggering event 19028, the smart contract system 19024 can execute the rights 19062, including the control rights 19040 to the tagged digital knowledge 19014 and the access rights 19042 to who can view, edit, access, or use the digital knowledge 19008. The smart contract 19024 may handle the principal's promise 19034 to the smart contract 19032.
In an embodiment, the smart contract 19022 may include a smart contract wrapper 19064 that may perform operations on a distributed ledger to: adding intellectual property 18038; adding intellectual property 18038 to the intellectual property stack; adding one or more principals 17532 promises 17832 to: licensing fees are apportioned for the added intellectual property 18804.
In an embodiment, knowledge distribution system 19000 may include an account management system 19072 in communication with a distributed ledger to facilitate creation and management of a plurality of user accounts 19094 corresponding to a plurality of users 19004 of knowledge distribution system 19500. Knowledge distribution system 19000 can include user interface system 19074 to present user interface 19093 to one or more users 19004 that enable users to view data related to instances of digital knowledge 19008.
In an embodiment, knowledge distribution system 19000 can include a marketplace system 19078 to establish and maintain a digital marketplace 19090 and visually present data corresponding to instances of digital knowledge 19008 to users 19004 of knowledge distribution system 19000.
In an embodiment, knowledge distribution system 19000 may include a data store in communication with a distributed ledger, where the data store may include: a knowledge data memory 19082 for storing data related to the digital knowledge 19008; a client data store 19084 for storing data related to a plurality of users 19004 of the knowledge distribution system; smart contract data store 19088 for storing data and the like relating to smart contracts 19022.
The knowledge distribution system 19002 may include a reporting system 19080 for analyzing the tagged digital knowledge 19014 and reporting analysis results 19098.
In an embodiment, the knowledge distribution system 19000 may include a smart contract generator 19088 to generate smart contracts 19060 using parameterizable smart contract templates 19022. The smart contract parameters may be based on the type of digital knowledge to be tagged and may include financial parameters, license fee parameters, usage parameters, yield parameters, price allocation parameters, identity parameters, access condition parameters, and the like.
Workflow management system
In an embodiment, the workflow management system may support various workflows associated with the facility, such as an interface including a platform through which a facility manager may view various analysis results, status information, and the like. In an embodiment, the workflow management system tracks the operation of the action follow-up module to ensure that the use platform sends the correct follow-up message to the appropriate person, system and/or service, either automatically or under the control of a facility agent.
In various embodiments, the workflow for each of the energy item, the computing item (e.g., cryptocurrency and/or AI), and the blending item includes various elements. In an embodiment, provided herein is an information technology system for providing data to an intelligent energy and computing facility resource management system, the system having a system for learning a training set of facility results, facility parameters, and data collected from a data source to train an artificial intelligence/machine learning system to perform at least one of: predicting a likelihood of a facility production outcome; predicting facility production results; optimizing the supply and allocation of energy and computing resources to produce a favorable facility resource utilization profile in a set of available profiles; optimizing the supply and allocation of energy and computing resources to produce a favorable facility resource output selection among a set of available outputs; optimizing the solicitation and provision of available energy and computing resources to produce a favorable facility input resource profile in a set of available profiles; optimizing the configuration of available energy and computing resources to produce a favorable facility resource configuration profile in a set of available profiles; optimizing selection and configuration of the artificial intelligence system to produce a favorable facility output profile in a set of available artificial intelligence systems and configurations; or generate an indication that the facility should provide output to contact the current or potential customer.
Management application platform
Referring to fig. 33, a transaction, finance, and marketing support system 3300 is shown that includes a set of systems, applications, processes, modules, services, layers, devices, components, machines, products, subsystems, interfaces, connections, and other elements that work cooperatively to enable intelligent management of a set of finance and transaction entities 3330 that may occur, operate, transact, etc. within, integrated with, linked to, or operating on one or more platforms operating markets 3327, external markets 3390, or portions of a platform 3300; or own, operate, support, or enable such markets or platforms. The platform market 3327 and the external market 3390 may include various markets and exchanges for physical goods, services, virtual goods, digital content, advertisements, credits (e.g., renewable energy credits, pollution abatement credits, etc.), money, commodities, crypto-currencies, loyalty points, physical resources, human resources, attention resources, information technology resources, storage resources, energy resources, options, futures, derivatives, securities, access rights, tickets, licenses (including seat licenses, private or government issued licenses or permissions engaged in regulatory activities, medals, badges, etc.), and the like. The financial and transaction entity 3330 may include any of the various assets, systems, devices, machines, facilities, individuals, or other entities mentioned in the present invention or in the documents incorporated by reference herein, such as, but not limited to: financial machine 3352 and its components (e.g., automated teller machines, point of sale terminals, vending machines, self-service terminals, smart card enabled machines, etc.); financial and transaction processes 3350 (e.g., loan processes, software processes (including applications, programs, services, etc.), production processes, banking processes (e.g., loan processes, underwriting processes, investment processes, etc.), financial services processes, diagnostic processes, vouching processes, security processes, etc.); wearable portable devices 3348 (e.g., mobile phones, tablet computers, special purpose portable devices for financial applications, data collectors (including mobile data collectors), sensor-based devices, watches, glasses, ear-worn devices, headsets, clothing integrated devices, armbands, bracelets, neck-worn devices, AR/VR devices, headphones, etc.); worker 3344 (e.g., banking personnel, financial services personnel, manager, engineer, floor manager, vault personnel, inspector, delivery personnel, money handling personnel, process manager, security personnel, etc.); robot system 3342 (e.g., physical robot, collaborative robot (e.g., "cobots"), software robot, etc.); and operating facilities 3340 (e.g., currency production facilities, storage facilities, safes, banking branches, office buildings, banking facilities, financial services facilities, cryptocurrency mining facilities, data centers, transaction floors, high frequency transaction operations, etc.), which may include, but are not limited to, storage and financial services facilities 3338 (e.g., for financial services inventory, components, packaging materials, goods, products, machinery, equipment, and other items); insurance institutions 3334 (e.g., branches, offices, storage facilities, data centers, underwriting services, etc.); and banking facilities 3332 (e.g., for commercial banking, investment, consumer banking, lending, and many other banking activities).
In an embodiment, platform 3300 may include a set of data processing layers 3308, each for providing a set of capabilities, for facilitating the development and deployment of intelligent capabilities for various financial and transactional applications and end uses, e.g., for facilitating automation, machine learning, artificial intelligence applications, intelligent transactions, state management, event management, process management, and the like. In an embodiment, the data processing layer 3308 includes a financial and transaction monitoring system layer 3306, a financial and transaction entity oriented data storage system layer 3310 (in some cases, for convenience, referred to herein simply as data storage layer 3310), an adaptive intelligence system layer 3304, and a financial and transaction management application platform layer 3302. Each data processing layer 3308 can include various services, programs, applications, workflows, systems, components, and modules, as further described herein and in the documents incorporated by reference. In an embodiment, each data processing layer 3308 (and optionally platform 3300 as a whole) is configured to make its one or more elements accessible as a service by other layers 3308 or other systems (e.g., a platform configured to be deployed on a set of cloud infrastructure components in a micro-service architecture, i.e., a service). For example, the data processing layer 3308 may have a set of interfaces 3316, such as Application Programming Interfaces (APIs), agents, services, connectors, wired or wireless communication links, ports, human-accessible interfaces, software interfaces, etc., through which data may be exchanged between the data processing layer 3308 and other layers, systems, or subsystems of the platform 3300, as well as with other systems such as financial entities 3330 or external systems such as cloud-based or internal enterprise systems (e.g., billing systems, resource management systems, CRM systems, supply chain management systems, etc.). Each data processing layer 3308 may include a set of services (e.g., micro-services) for data processing, including facilities for data extraction, conversion, and loading; data cleaning and reset removing construction; a data normalization facility; a data synchronization facility; a data security facility; a computing facility (e.g., for performing predefined computing operations on a data stream and providing an output stream); compression and decompression facilities; analytical facilities (e.g., automated production that provides visualization of data), etc.
In an embodiment, each data processing layer 3308 has a set of application programming interfaces 3316 for data exchange automation with each other data processing layer 3308. These may include data integration capabilities, such as for extracting, converting, loading, normalizing, compressing, decompressing, encoding, decoding, and otherwise processing data packets, signals, and other information exchanged between layers and/or applications 3312, such as converting data from one format or protocol to another as needed so that one layer consumes output from another layer. In an embodiment, data processing layer 3308 is configured to facilitate a topology that shares data collection and distribution across multiple applications and is used by financial monitoring system layer 3306 within platform 3300. The financial monitoring system layer 3306 may include, be integrated with, and/or cooperate with various data collection and management systems 3318, and in some cases, the data collection and management systems 3318 are referred to as data collection systems 3318 for convenience, for collecting and organizing data collected from or about the financial and transaction entities 3330, as well as data collected from or about the various data layers 3308 or services or components thereof. For example, a physiological data stream from a wearable device worn by a worker that is tasked or a consumer engaged in an activity may be distributed via the monitoring system layer 3306 to a plurality of different applications in the management application platform layer 3302, such as one application facilitating monitoring of the worker's physiological, psychological, performance level, attention, or other status, while another application facilitates improving operational efficiency and/or effectiveness. In an embodiment, the monitoring system layer 3306 facilitates alignment (e.g., time synchronization, normalization, etc.) of data collected relative to one or more entities 3330. For example, one or more video streams or other sensor data collected from or about a worker 3344 or other entity in a transaction or financial environment (e.g., one or more video streams or other sensor data collected from a set of camera-enabled IoT devices) may be aligned with a common clock so that the relative timing of a set of videos or other data may be understood by a video-processable system, such as a machine learning system operating on images in the videos, altering images in different frames of the videos, and so forth. In such examples, the monitoring system layer 3306 may also align a set of videos, camera images, sensor data, etc. with other data, such as data streams from wearable devices, data streams generated by financial or transaction systems (e.g., point-of-sale systems, ATMs, self-service terminals, handheld transaction systems, card readers, etc.), data streams collected by mobile data collectors, etc. Configuring the monitoring system layer 3306 as a common platform or set of micro-services accessible across multiple applications can significantly reduce the number of interconnections required by an enterprise in order for a growing set of applications to monitor a growing set of IoT devices and other systems and devices under control thereof.
In an embodiment, the data processing layer 3308 is configured to facilitate shared or common data storage across multiple applications and the topology of the platform 3300 used by the financial and transaction entities and transaction-oriented data storage system layer 3310, which in some cases, for convenience, is referred to herein simply as the data storage layer 3310 or storage layer 3310. For example, the various data collected about the financial entity 3330, as well as the data generated by the other data processing layers 3308, may be stored in the data storage layer 3310 such that any services, applications, programs, etc. of the various data processing layers 3308 may access a common data source (which may include a single logical data source distributed across different physical and/or virtual storage locations). As applications of financial and transactional IoT proliferate, this may facilitate significantly reducing the amount of data storage required to process large amounts of data generated by or associated with the entity 3330. For example, a supply chain or inventory management application in the management application platform layer 3302, such as one for ordering replacement parts of a financial or transactional machine or device, or for re-ordering money or other inventory, may access the same data set as predictive maintenance applications for predicting whether a machine may need replacement parts as to which parts of a group of machines have been replaced. Similarly, predictions may be made for re-supply use of currency or other items. In an embodiment, the data storage system layer 3310 may provide an extremely rich environment for collecting data that may be used to extract features or inputs of intelligent systems such as expert systems, artificial intelligence systems, robotic process automation systems, machine learning systems, deep learning systems, supervised learning systems, or other intelligent systems disclosed in the present invention or in documents incorporated by reference. Thus, each application in the management application platform layer 3302 and each adaptive intelligent system in the adaptive intelligent system layer 3304 may benefit from data collected or generated by each of the other systems. Various data types may be stored in storage layer 3310 using various storage media and data storage types and formats, including, but not limited to: asset and facility data 3320 (e.g., asset identity data, operational data, transaction data, event data, status data, workflow data, maintenance data, pricing data, ownership data, transferability data, and many other types of data related to an asset (which may be a physical asset, digital asset, virtual asset, financial asset, securities asset, or other asset); worker data 3322 (including identity data, role data, task data, workflow data, health data, attention data, mood data, stress data, physiological data, performance data, quality data, and various other types); event data 3324 (including process events, transaction events, exchange events, pricing events, promotional events, discount events, rebate events, rewards events, point of use events, financial events, output events, input events, state change events, operational events, maintenance events, service events, damage events, injury events, replacement events, refund events, recharging events, supply events, etc.), claim data 3354 (e.g., claim data related to insurance claims such as business outage insurance, product liability insurance, insurance related to goods, facilities or equipment, flood insurance, contract related risk insurance, etc.; claim data related to product liability, general liability, worker compensation, injury, and other liabilities; and claim data related to contracts such as supply contract execution claims, product delivery claims, contract claims, damage compensation, points or benefits, access rights claims, warranties, claims, reimbursements, energy production claims, energy claims, etc.), delivery requirements, time requirements, milestones, key performance indicators, etc.); billing data 3358 (e.g., data related to debits, credits, costs, prices, returns, profits, rates of return, valuations, sales, etc.); underwriting data 3360 (e.g., data related to identities of potential and actual parties involved in insurance and other transactions, refinement data, data related to the probability of occurrence and/or the degree of risk associated with an activity, data related to observed activities, and other data for underwriting or estimating risk); access data 3362 (e.g., data related to access rights, tickets, vouchers, licenses, and other access rights described herein, including data structures representing access rights, pricing data 3364 (including spot market pricing, forward market pricing, pricing discount information, promotional pricing, and other information related to the cost or price of items in any platform operated market 3327 and/or external market 3390), and other types of data not shown, such as production data (e.g., data related to the production, service, event, content, etc. of physical or digital goods, and data related to energy production found in databases of utilities or independent service organizations maintaining energy infrastructure, data related to the output of banking services, and data related to the output of mining and energy extraction facilities, the output of drilling and plumbing facilities, etc.), and supply chain data (e.g., data related to supplied items, quantity, pricing, delivery, sources, routes, customs information, etc.).
In an embodiment, the data processing layer 3308 is configured to facilitate a topology of shared adaptation capabilities that may be provided, managed, coordinated, etc. by one or more of a set of services, components, programs, systems, or capabilities of the adaptive intelligent system layer 3304, in some cases, for convenience, the adaptive intelligent system layer 3304 is referred to herein as the adaptive intelligent layer 3304. The adaptive intelligence system layer 3304 may include a set of data processing, artificial intelligence, and computing systems 3314 described in more detail elsewhere in this disclosure. Thus, various resources may be optimally used in a coordinated or shared manner on behalf of operators, enterprises, etc., such as computing resources (e.g., available processing cores, available servers, available edge computing resources, available on-device resources (for a single device or peer-to-peer network), and available cloud infrastructure, etc.), data storage resources (including local storage on a device, storage resources in or on a financial entity or environment (including storage on a device, storage on an asset tag, local area network storage, etc.), network storage resources, cloud-based storage resources, database resources, etc.), networking resources (including cellular network spectrum, wireless network resources, fixed network resources, etc.), energy resources (e.g., available battery power, available renewable energy, fuel, grid-based power, etc.), etc., e.g., to take advantage of multiple applications, programs, workflows, etc. For example, the adaptive intelligence layer 3304 may manage and provide available network resources for financial analysis applications and for remote control applications with finance (as well as many other possibilities) such that low latency resources are used for remote control and longer latency resources are used for analysis applications. As described in greater detail in the present disclosure or in the documents incorporated by reference herein, various modifications may be provided on behalf of various services and capabilities across the various layers 3308, including modifications based on application requirements, quality of service, budget, cost, pricing, risk factors, operational goals, efficiency goals, optimization parameters, return on investment, profitability, uptime/downtime, worker utilization, and the like.
The management application platform layer 3302 (in some cases, abbreviated herein as platform layer 3302 for convenience) may include a set of financial and transactional processes, workflows, activities, events, and applications 3312 (collectively referred to as applications 3312 unless otherwise indicated by context) that enable an operator to manage aspects of the financial or transactional environment or entity 3330 in a common application environment, such as utilizing common data storage in the data storage layer 3310, monitoring or monitoring common data collection in the system layer 3306, and/or one aspect of common adaptive intelligence in the adaptive intelligence layer 3304. Output from the applications 3312 in the platform layer 3302 may be provided to other data processing layers 3308. These may include, but are not limited to: status and state information for various objects, entities, processes, streams, etc.; object information such as identity, attribute, and parameter information of various types of objects of various data types; event and change information, such as for workflows, dynamic systems, processes, programs, protocols, algorithms, and other streams, including time information; result information such as an indication of success and failure, an indication of completion of a process or milestone, an indication of correct or incorrect predictions, an indication of correct or incorrect labeling or classification, and an indication of success (including indicators related to rate of return, engagement, return on investment, profitability, efficiency, timeliness, quality of service, product quality, customer satisfaction, etc.), etc. The output from each application 3312 may be stored in a data store layer 3310, distributed for processing by a data collection layer 3306, and used by an adaptive intelligence layer 3304. Thus, the cross-application nature of platform layer 3302 facilitates convenient organization of all necessary infrastructure elements in order to add intelligence to any given application, such as by providing machine learning on the application about the results, enriching automation of a given application by machine learning based on results from other applications (or other elements of platform 3300), and allowing application developers to focus on application native processes while benefiting from other capabilities of platform 3300.
Referring to FIG. 34, additional details, components, subsystems, and other elements of an alternative embodiment of the platform 3300 of FIG. 33 are shown. In various alternative embodiments, the administration application layer 3302 may include a set of applications, systems, solutions, interfaces, etc., collectively referred to as applications 3312 for convenience, by which an operator or owner or other user of a transaction or financial entity may manage, monitor, control, analyze, or otherwise interact with one or more elements of the entity 3330, such as any of the elements described above in connection with fig. 33. The set of applications 3312 may include, but are not limited to, one or more of various types of applications, such as an investment application 3402 (e.g., without limitation, applications for investment shares, equity, money, merchandise, options, futures, derivatives, real estate, trustworthiness, cryptocurrency, vouchers, and other asset categories); asset management applications 3404 (e.g., without limitation, applications for managing investment assets, real estate, fixed equipment, personal property, real estate, equipment, intellectual property, vehicles, human resources, software, information technology resources, data processing resources, data storage resources, power generation and/or power storage resources, computing resources, and other assets); the lending applications 3410 (e.g., without limitation, applications for personal lending, commercial lending, mortgage lending, small-forehead lending, point-to-point lending, insurance-related lending, asset-warranty lending, warranty-liability lending, corporate liability lending, learning-aid lending, mortgage lending, automotive lending, etc.); risk management applications 3408 (e.g., without limitation, applications for managing information about products, assets, persons, houses, vehicles, devices, components, information technology systems, security events, network security systems, property, health, death, fire, flood, weather, disability, illegal activities, business breaks, infringement, advertisement infringement, defamation, infringement of privacy or image rights, injury, property damage, business damage, default, etc.); payment applications 3433 (e.g., applications for effecting various payments within and between markets, including credit cards, debit cards, electronic transfers, ACH, checks, currency, and other payments); marketing applications 3412 (e.g., without limitation, applications for marketing financial or transactional products or services, advertising applications, marketing platforms or systems for goods, services, or other items, marketing analysis applications, customer relationship management applications, search engine optimization applications, sales management applications, advertising web applications, behavior tracking applications, marketing analysis applications, location-based product or service targeting applications, collaborative filtering applications, recommendation engines for products or services, etc.); the trading applications 3428 (e.g., without limitation, purchasing applications, sales applications, bidding applications, auction applications, reverse auction applications, buy-sell matching applications, securities trading applications, commodity trading applications, option trading applications, futures trading applications, derivative trading applications, cryptocurrency trading applications, voucher trading applications, analysis applications for analyzing financial or trade performance, profitability, return on investment or other metrics, price inquiring applications, etc.); the tax application 3414 (e.g., without limitation, for managing, calculating, reporting, optimizing, or otherwise processing data, events, workflows, or other factors related to tax, collection, tariffs, tax, exemptions, tax or other government collection fees, such as, without limitation, sales tax, income tax, property tax, municipal fees, pollution tax, renewable energy credits, pollution management credits, value added tax, import tax, export tax, etc.); the fraud prevention application 3416 (e.g., without limitation, one or more of an authentication application, a biometric verification application, a transaction pattern-based fraud detection application, a location-based fraud detection application, a user behavior-based fraud detection application, a network address-based fraud detection application, a blacklist application, a whitelist application, a content inspection-based fraud detection application, or other fraud detection applications); financial services, applications, or solutions 3409 (collectively referred to as "financial services," such as but not limited to, financial planning services, tax planning services, portfolio management services, transaction services, lending services, banking services, foreign exchange summary services, currency conversion services, money transfer services, financial management services, property planning services, investment banking services, commercial banking services, foreign exchange services, insurance services, investment management services, hedge fund services, mutual fund services, custody services, credit card services, custody services, check services, debit card services, lending services, ATM services, ETF services, electronic transfer services, overdraft services, statement services, bond check services, notary services, capital market services, brokerage services, broker services, private banking services, insurance brokerage services, underwriting services, annual fee services, life insurance services, health insurance services, retirement insurance services, property insurance services, disaster insurance services, finance and insurance services, reinsurance services, intermediary services, trade, private fund services, investment services, angel investment services, home office investment services, exchange services, payment services, settlement services, financial settlement services, or other financial services); security applications, solutions or services 3418 (abbreviated herein as security applications, such as, but not limited to, any of the fraud prevention applications 3416 described above), as well as physical security systems (e.g., for access control systems (e.g., using biometric access control, fingerprint identification, retinal scanning, passwords and other access controls), safes, cashiers, safes, etc.), monitoring systems (e.g., using cameras, motion sensors, infrared sensors, etc.), network security systems (e.g., for virus detection and remediation, intrusion detection and remediation, spam detection and remediation, phishing detection and remediation, social engineering detection and remediation, network attack detection and remediation, packet detection, traffic detection, DNS attack remediation and detection, etc.), or other security applications; underwriting application 3420 (e.g., without limitation, any application for underwriting any insurance product, any loan, or any other transaction, including any application for detecting, characterizing, or predicting the likelihood and/or extent of risk, including underwriting of any data source, event, or entity mentioned in the document based on the present invention or incorporated by reference herein; the blockchain applications 3422 (e.g., without limitation, distributed ledgers, cryptocurrency applications, or other blockchain-based applications that capture a series of transactions such as debits or credits, purchases or sales, physical price exchanges, smart contract events, etc.), the real estate applications 3424 (e.g., without limitation, real estate brokerage applications, real estate valuation applications, real estate investment trust applications, real estate mortgage or lending applications, real estate assessment applications, real estate marketing applications, etc.), the administration applications 3426 (e.g., without limitation, applications for administering any of the applications, services, transactions, activities, workflows, events, entities, or other items mentioned in documents incorporated by reference herein, such as administration pricing, marketing, securities, insurance offers, human or vendor activity executions, data usage (including data privacy regulations, data storage-related regulations, etc.), banking services, marketing, sales, finance, etc.), the marketing applications, solutions or services 3327 (in the context of some types of applications may also be referred to as market planning applications (in the context of the following, the context of some types of application may also be allowed), for example, but not limited to, an e-commerce marketplace, an auction marketplace, a physical commodity marketplace, a virtual commodity marketplace, an advertising marketplace, a reverse auction marketplace, an advertising network, an attention resource marketplace, an energy trading marketplace, a computing resource marketplace, a networking resource marketplace, a spectrum allocation marketplace, an internet advertising marketplace, a television advertising marketplace, a flat advertising marketplace, a broadcast advertising marketplace, a gaming built-in advertising marketplace, a virtual reality advertising marketplace, an augmented reality marketplace, a real estate marketplace, a hotel marketplace, a travel service marketplace, a financial services marketplace, a blockchain-based marketplace, an encrypted currency marketplace, a voucher-based marketplace, a loyalty program marketplace, a time-sharing marketplace, a car pooling marketplace, a mobile marketplace, a transportation marketplace, a space sharing marketplace, or other marketplace); the warranty application 3417 (e.g., without limitation, an application for warranty or vouching for a product, service, offering, solution, physical product, software, service level, quality of service, financial instrument, liability, mortgage, service performance, or other item); the analyst application 3419 (e.g., without limitation, an analysis application for any data type, application, event, workflow, or entity mentioned in connection with the present invention or in the documents incorporated by reference, such as big data applications, user behavior applications, prediction applications, classification applications, control panels, pattern recognition applications, metering economics applications, financial benefits applications, return on investment applications, scenario planning applications, decision support applications, etc.); pricing applications 3421 (e.g., without limitation, for pricing goods, services (including the invention and any services mentioned in documents incorporated by reference), applications (including the invention and any applications mentioned in documents incorporated by reference), software, data services, insurance, virtual goods, advertising, search engines, keyword placement, and the like; as well as smart contract applications, solutions or services (collectively referred to herein as smart contract applications, such as, but not limited to, any of the smart contract types mentioned herein, such as smart contracts that use certificates or cryptocurrencies for price-making, smart contracts that grant rights, options, futures or equities based on future conditions, smart contracts for securities, commodities, futures, options, derivatives, etc., smart contracts for current or future resources, smart contracts for taking into account or adapting tax, regulatory or compliance parameters, smart contracts for performing arbitrage transactions, etc.), the management application platform 3302 may therefore keep and enable interactions between various different applications 3312 (which term includes the above and other financial or transactional applications, services, solutions, etc.), such that any pairwise or greater combination or permutation of these services may be improved with respect to the same type of stand-alone applications by sharing microservices, sharing data infrastructure, and sharing intelligence.
In an embodiment, the adaptive intelligence system layer 3304 may include a set of systems, components, services, and other capabilities that collectively facilitate the coordinated development and deployment of the intelligence system, such as may enhance the systems, components, services, and other capabilities of one or more applications 3312 at the application platform layer 3302. These adaptive intelligence systems 3304 can include an adaptive edge computing management solution 3430, a robotic process automation system 3442, a set of protocol adapters 3491, a packet acceleration system 3434, an edge intelligence system 3438, an adaptive networking system 3440, a set of status and event managers 3444, a set of opportunity mining programs 3446, a set of artificial intelligence systems 3448, and other systems.
In an embodiment, the financial monitoring system layer 3306 and its data collection system 3318 may include a wide range of systems for collecting data. The layer may include, but is not limited to: real-time monitoring systems 3468 (e.g., on-board monitoring systems such as event and status reporting systems on ATMs, POS systems, self-service terminals, vending machines, etc., OBD and telematics systems on vehicles and equipment, systems providing diagnostic codes and events via event buses, communication ports, or other communication systems, monitoring infrastructure (e.g., cameras, motion sensors, beacons, RFID systems, intelligent lighting systems, asset tracking systems, personnel tracking systems, and environmental sensing systems located in various environments where transactions and other events occur), and removable, replaceable monitoring systems such as portable and mobile data collectors, RFID and other tag readers, smartphones, tablet computers, and other mobile devices capable of collecting data, etc.); software interaction observation system 3450 (e.g., for recording and tracking events related to user interactions with a software user interface, such as mouse movements, touchpad interactions, mouse clicks, cursor movements, keyboard interactions, navigation operations, eye movements, finger movements, gestures, menu selections, etc., as well as software interactions that occur due to other programs (e.g., through an API), etc.); a mobile data collector 3452 (e.g., as broadly described herein and in the documents incorporated by reference); visual monitoring system 3454 (e.g., other systems that use video and static imaging systems, LIDAR, IR, and other systems that monitor the visualization of items, employees, materials, components, machines, equipment, personnel, gestures, expressions, gestures, positions, configurations, and other factors or parameters of entity 3330, as well as inspection systems that monitor processes, worker activities, etc.); point of interaction system 3470 (e.g., point of sale systems, self service terminals, ATMs, vending machines, touch pads, camera-based interaction tracking systems, smart shopping carts, user interfaces for online and in-store vending and commerce systems, tablet computers, and other systems at the point of sale or other interactions of customers or workers involving shopping and/or transactions); physical process viewing system 3458 (e.g., for tracking physical activities of customers, physical activities of traders (e.g., traders, suppliers, merchants, customers, negotiators, brokers, etc.), physical interactions of workers with other workers, interactions of workers with physical entities such as machines and devices, and interactions of physical entities with other physical entities, including but not limited to, using video and still image cameras, motion sensing systems (e.g., including optical sensors, LIDAR, IR and other sensor devices), robotic motion tracking systems (e.g., tracking motion of systems connected to humans or physical entities), machine state monitoring system 3460 (including conditions, states, operating parameters or other metrics of machine states, in-vehicle monitors and external monitors such as clients, servers, cloud resources, ATMs, self-service terminals, automated machines, POS systems, sensors, cameras, smart shopping carts, smart shelves, vehicles, robots or other machines), sensors for use in financial or transaction environments (e.g., but not limited to, offices, backoffice, office, store, virtual store, banking, 62 or other such as well as their web site, etc.), cameras and their own data collection devices (e.g., cameras 64, personal cameras, etc.), mobile cameras, and other data collection devices, such as cameras, etc. for use in-specific cameras, and mobile phone-type systems, and mobile-specific-type cameras (e.g., cameras, etc.),64, and other systems, any of a number of sensor types disclosed in documents including the present invention or incorporated by reference herein); indoor location monitoring systems 3472 (including cameras, IR systems, motion detection systems, beacons, RFID readers, smart lighting systems, triangulation systems, RF and other spectrum detection systems, time-of-flight systems, chemical noses and other chemical sensor devices, and other sensors); user feedback systems 3474 (including survey systems, touch pads, voice-based feedback systems, rating systems, expression monitoring systems, emotion monitoring systems, gesture monitoring systems, etc.); the behavior monitoring system 3478 (e.g., for monitoring sports, shopping behavior, purchasing behavior, clicking behavior, behavior indicative of fraud or fraud, user interface interactions, product returns behavior, behavior indicative of interest, attention, boredom, etc., behavior indicative of emotion (e.g., restlessness, stationary, approaching or changing gestures), etc.); and any of a variety of internet of things (IoT) data collectors 3464, such as the internet of things data collectors described in the present invention and documents incorporated herein by reference.
In an embodiment, the financial entity oriented data storage system layer 3310 may include a series of systems for storing data, such as billing data 3358, access data 3362, pricing data 3364, asset and facility data 3320, worker data 3322, event data 3324, underwriting data 3360, and claims data 3354. These may include, but are not limited to, physical storage systems, virtual storage systems, local storage systems, distributed storage systems, databases, memory, network-based storage, network-attached storage systems (e.g., using NVME, storage attached networks, and other network storage systems), and the like. In an embodiment, the storage layer 3310 may store data in one or more knowledge graphs (e.g., directed acyclic graphs, data hierarchies, data clusters including links and nodes, ad hoc graphs, etc.). In an embodiment, the data store layer 3310 may store data in digital threads, ledgers, etc., for example, to keep a longitudinal record of the entity 3330 over time, including any of the entities described herein. In an embodiment, the data store layer 3310 may use and support a virtual asset tag 3488, which may include data structures associated with and accessible and manageable by the asset as if the tag were physically located on the asset, such as by using access controls, such that data storage and retrieval is optionally linked to a local process, but is also optionally open to remote retrieval and storage options. In an embodiment, the storage layer 3310 may include one or more blockchains 3490, such as blockchains that store identity data, transaction data, entity data of the entity 3330, pricing data, ownership transfer data, data for operation by the smart contract 3431, historical interaction data, etc., such as may be based on roles or may be based on access control of credentials associated with the entity 3330, a service, or one or more applications 3312.
Referring to fig. 35, the adaptive intelligence layer 3304 may include a Robotic Process Automation (RPA) system 3442, which may include a set of components, processes, services, interfaces, and other elements for developing and deploying the automation capabilities of various financial entities 3330, environments, and applications 3312. Without limitation, robotic process automation 3442 may be applied to each process managed, controlled, or coordinated by each of the set of applications 3312 of the platform application layer.
In an embodiment, the robotic process automation 3442 may utilize the plurality of applications 3312 present within the management application platform layer 3302 such that a pair of applications may share data sources (e.g., in the data store layer 3310) and other inputs (e.g., from the monitoring layer 3306) collected with respect to the financial entity 3330, as well as share outputs, events, status information, and outputs, which may collectively provide a richer environment for process automation, including through the use of artificial intelligence 3448 (including various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and any other systems described in the present invention and documents incorporated by reference). For example, the real estate application 3424 may use robotic process automation 3442 to automate real estate inspection processes typically performed or supervised by humans (e.g., by using video or still images from cameras or other devices displaying images of the entity 3330 to automate processes involving visual inspection, e.g., training the robotic process automation 3442 system to enable inspection automation by observing a set of human inspectors or supervisers interactions with interfaces for identifying, diagnosing, measuring, parameterizing or otherwise characterizing possible defects or advantageous properties of houses, buildings or other real estate or items, hi embodiments, interactions of the human inspectors or supervisers may include a data set of labels, where the labels or labels indicate defect types, advantageous properties or other properties, such that the machine learning system can use training dataset learning to identify the same characteristics, which in turn can be used to automate the inspection process such that defects or advantageous characteristics are automatically classified and detected in the video or still image set, which in turn can be used within the real estate solution 3424 to mark items that need further inspection, that should be rejected, that should be revealed to potential buyers, that should be reconciled, etc., in embodiments, the robotic process automation 3442 can involve multiple applications or cross-application sharing of inputs, data structures, data sources, events, states, outputs, or results, for example, the real estate application 3442 can receive information from the marketplace application 3327 that can enrich the robotic process automation 3442 of the real estate application 3442, such as for example, information from a user located in a real estate (e.g., swimming pool, hydrotherapy center, kitchen appliance, etc.), television or other item) that may help populate features regarding real estate to facilitate inspection processes, valuation processes, disclosure processes, etc. This summary covers these and many other examples of multi-application or cross-application sharing for robotic process automation 3442 across applications 3312.
In an embodiment, robotic process automation may be applied to a sharing or aggregation process between pairs of applications 3312 of the application layer 3302, such as, but not limited to, an aggregation process involving the secure application 3418 and the lending application 3410, integrated automation of the blockchain-based application 3422 with the marketplace application 3327, and the like. In an embodiment, the aggregation process may include a shared data structure for multiple applications 3312 (including a data structure that tracks the same transaction on the blockchain but may use a different subset of the available attributes of the data objects maintained in the blockchain, or a data structure that uses a set of nodes and links in a common knowledge graph). For example, transactions indicating changes in ownership of the entity 3330 may be stored in the blockchain and used by multiple applications 3312, e.g., to implement role-based access control, role-based permissions for remote control, identity-based event reporting, etc. In an embodiment, the aggregation process may include a shared process flow across the applications 3312, including a subset of the larger flows involved in one or more of the set of applications 3312. For example, the underwriting or inspection flows regarding the entity 3330 may service the lending solution 3410, the analysis solution 3419, the asset management solution 3404, and so on.
In an embodiment, robotic process automation 3442 may provide for various financial and transaction processes mentioned in the present disclosure and in the documents incorporated by reference herein, including, but not limited to, energy transactions, banking services, transportation, storage, energy storage, maintenance processes, service processes, repair processes, supply chain processes, inspection processes, purchase and sales processes, underwriting processes, compliance processes, regulatory processes, fraud detection processes, fault detection processes, power utilization optimization processes, and the like. An environment for developing robotic process automation may include a set of interfaces for a developer, where the developer may configure the artificial intelligence system 3448 to obtain input from selected data sources of the data store layer 3310 and events or other data from the monitoring system layer 3306 and provide them to, for example, a neural network, as input for classification or prediction, or as a result. The RPA development environment 3442 may be used to obtain output and results 3328 from various applications 3312, as well as to facilitate automatic learning and improvement of classification, prediction, or other activities involved in steps of a process intended to be automated. In an embodiment, the development environment and resulting robotic process automation 3442 may involve a combination of monitoring software program interaction observations 3450 (e.g., through worker interactions with various software interfaces of the application 3312 involving the entity 3330) and physical process interaction observations 3458 (e.g., through worker interactions with or using machines, devices, tools, etc.). In an embodiment, the software interaction observation 3450 may include interactions between software components and other software components, such as how one application 3312 interacts with another application 3312 through an API. In an embodiment, the physical process interaction observation 3458 may include observation (e.g., by a camera, motion detector or other sensor, and detection of the position, movement, etc. of hardware, such as robotic hardware) how a human worker interacts with financial entity 3330 (e.g., worker position (including route through a certain location; the location of a given type of worker during a given set of events, procedures, etc.; how workers use various tools and physical interfaces to operate equipment or other items; the worker's response to various events (e.g., response to alarms and warnings) time; a worker performs a procedure of planning maintenance, update, repair and service processes; the worker adjusts or adjusts programs of items involved in the workflow, etc.) physical process observations 3458 may include tracking the worker's position, angle, effort, speed, acceleration, pressure, torque, etc., as the worker operates on hardware (e.g., using tools), such observations may be made via video data, data detected within the machine (e.g., the position of elements of the machine detected and reported by the position detector), by a wearable device (e.g., a housing containing the position detector, force detector, torque detector, and other detectors, by collecting software interaction observations 3450 and physical process interaction observations 3458, the rpa system 3442 can more fully automate processes involving the financial entity 3330, such as by using software automation in conjunction with physical robots.
In an embodiment, robotic process automation 3442 is used to train a set of physical robots having hardware units that facilitate undertaking tasks that are routinely performed by humans. These may include robots that walk (including up and down stairs), climb (e.g., ladder), move around a facility, attach to an item, grasp an item (e.g., using robotic arms, hands, pliers, etc.), lift an item, carry an item, remove and replace an item, use a tool, etc.
Referring to fig. 35, in an embodiment, a transaction, financial, and marketing support system is provided herein. An example system may include: a robotic process automation circuit configured to interpret information from a plurality of data sources and to interface with a plurality of management applications; wherein each of the plurality of management applications is associated with a separate one of a plurality of financial entities; and wherein the robotic process automation circuit further comprises artificial intelligence circuitry configured to modify a process of at least one of the plurality of management applications in response to the information from the plurality of data sources.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example system may include: wherein the artificial intelligence circuit further comprises at least one of the following: an intelligent contract service circuit; a valuation circuit; an automatic proxy circuit.
An example system may include: wherein the plurality of management applications includes at least two of the following applications: investment applications, asset management applications, lending applications, risk management applications, marketing applications, transaction applications, tax applications, fraud applications, financial services applications, securities applications, underwriting applications, blockchain applications, real estate applications, administration applications, platform marketplace applications, assurance applications, analytics applications, pricing applications, and smart contract applications.
An example system may include: wherein the plurality of data sources comprises at least two of the following applications: access data sources, asset and facility data sources, worker data sources, claim data sources, billing data sources, event data sources, and underwriting data sources.
An example system may include: wherein the plurality of management applications includes a real estate application, and wherein the robotic process automation circuit is further configured to automate a real estate inspection process.
An example system may include: wherein the robotic process automation circuit is further configured to automate the real estate inspection process by performing at least one of the following operations: providing one of a video inspection command or a camera inspection command; scheduling an inspection event with data from the plurality of data sources; and determining an inspection standard in response to the plurality of inspection data and inspection results, and providing an inspection command in response to the plurality of inspection data and inspection results.
An example system may include: wherein the robotic process automation circuit is further configured to automate the real estate inspection process in response to at least one of the plurality of data sources not being accessible to the real estate application.
An example system may include: wherein at least one of the plurality of data sources is inaccessible to each of the at least one of the plurality of management applications having a process that is improved by the robotic automation circuit.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the robotic automation circuit comprises a real estate application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: claims data sources, pricing data sources, asset and facility data sources, worker data sources, and event data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the robotic automation circuit comprises an asset management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: access data sources, pricing data sources, billing data sources, worker data sources, and event data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process modified by the robotic automation circuit comprises a lending management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: asset and facility data sources, claim data sources, worker data sources, and event data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the robotic automation circuit comprises a marketing management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: asset and facility data sources, claim data sources, worker data sources, event data sources, and underwriting data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the robotic automation circuit comprises a transaction management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: asset and facility data sources, claim data sources, worker data sources, and event data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the robotic automation circuit comprises an analysis management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: a data source, a claim data source, a worker data source, and an event data source is accessed.
An example system may include: wherein the robotic process automation circuit is further configured to refine the process of at least one of the plurality of management applications by providing output to at least one of the following entities: external markets, banking facilities, insurance facilities, financial services facilities, operations facilities, collaborative robotic facilities, workers, wearable devices, external processes, and machines.
An example system may include: wherein the robotic process automation circuit is further configured to interpret results from the at least one entity, and wherein the artificial intelligence circuit is further configured to iteratively refine the process in response to the results from the at least one entity.
Referring to fig. 36, a set of opportunity mining programs 3446 may be provided as part of the adaptive intelligence layer 3304 that may be used to find and recommend opportunities to improve one or more elements of the platform 3300, for example, by adding artificial intelligence 3448, automation (including robotic process automation 3446), etc., to or interacting with one or more systems, subsystems, components, applications, etc., of the platform 100. In an embodiment, the opportunity mining program 3446 may be configured or used by a developer of AI or RPA solutions to find opportunities for better solutions and to optimize existing solutions. In an embodiment, the opportunity mining program 3446 may include a set of systems that collect information within the platform 100 and that collect information within a set of environments and entities 3330, information about the set of environments and entities 3330, and information about the set of environments and entities 3330, where the collected information may help identify and prioritize opportunities for increased automation and/or intelligence. For example, the opportunity mining program 3446 may include a system that observes worker clusters by time, by type, and by location (e.g., using cameras, wearable devices, or other sensors) to identify labor-intensive areas and processes in a set of financial environments. For example, these may be presented in a hierarchical or prioritized list, or in the form of a visualization (e.g., displaying a heat map of the customer, worker, or other individual's stay on an environmental map, or displaying a heat map of the route the customer or worker is traveling within the environment) to display places with high labor activity. In an embodiment, analysis 3419 may be used to identify which environments or activities would benefit most from automation to save manpower, optimize profits, optimize profitability, increase uptime, increase throughput, increase transaction traffic, increase security, increase reliability, or other factors.
In an embodiment, the opportunity mining program 3446 may include a system for characterizing the scope of domain-specific or entity-specific knowledge or expertise required to take actions, use programs, use machines, etc., such as observing the identity, credentials, and experience of workers involved in a given process. In situations involving experienced workers (e.g., in complex transactions requiring a lot of experience (e.g., multiparty transactions); in complex logistical office processes involving a lot of expertise or training (e.g., risk management, fine and underwriting processes, asset allocation processes, investment decision processes, etc.); during an update, maintenance, migration, backup, or rebuild on a large or complex machine; or in fine tuning of complex processes that require accumulated experience to work effectively), which may be particularly beneficial in situations where the number of such workers is scarce (e.g., due to retirement or reduced supply of new workers with the same credentials). Thus, a set of opportunity mining programs 3446 can collect data indicating which processes of the entity 3330 or with respect to the entity 3330 most intensively depend on workers having a particular set of experiences or credentials (e.g., workers lacking or rarely lacking experience or credentials) and provide it to the analysis solution 3419 (e.g., for prioritizing the development of the automation 3442). For example, the opportunity mining program 3446 may associate aggregated data (including trend information) about the age, credentials, experiences of the worker (including experiences divided by process type) with data about the process to which the worker relates (e.g., by tracking the location of the worker by type, by tracking time spent on the process by worker type, etc.). A set of high value automation opportunities may be automatically recommended based on the ranking set, e.g., the ranking set of opportunities may be weighted based at least in part on the relative dependencies of a set of processes on the workers that are scarce or are expected to become scarce.
In an embodiment, the set of opportunity mining programs 3446 may use information related to the cost of workers involved in a set of processes, such as by accessing worker data 3322, including human resources database information indicating wages of various workers (as individuals or by type), information about fees charged by service workers or other contractors, and the like. The opportunity mining program 3446 may provide such cost information for association with the process tracking information, for example, to enable the analysis solution 3419 to identify which processes occupy most of the time of the most expensive workers. This may include visualizing such processes, for example, by displaying a hotspot graph of which locations, routes, or processes involve the most expensive time of workers in a financial environment or with respect to the entity 3330. The opportunity mining program 3446 may provide an ordered list, weighted list, or other data set that indicates to the developer which areas are most likely to benefit from further automated or artificial intelligence deployment.
In an embodiment, the context of mining robotic process automation opportunities may include searching an HR database and/or other labor tracking database for areas involving labor intensive processes; searching the system for a field of worker credentials that indicate automation potential; tracking, by the wearable device, the worker cluster to find a labor-intensive machine or process; worker clusters are tracked by worker type by wearable device to find labor intensive processes, etc.
In an embodiment, the opportunity mining may include facilities for requesting appropriate training data sets that may be used to facilitate process automation. For example, certain types of inputs (if available) will provide very high value for automation, such as capturing a video dataset of experienced and/or highly specialized workers performing complex tasks. The opportunity mining program 3446 may search for such video data sets described herein; however, without success (or supplementing the available data), the platform may include a system through which a user, such as a developer, can specify a desired type of data, such as software interaction data (e.g., an expert using a program to perform a particular task), video data (e.g., a video showing a set of experts performing a certain repair, an expert reconstructing a machine, an expert optimizing a certain complex process, etc.), physical process observation data (e.g., video, sensor data, etc.). The specification may be used to request such data, for example, by providing a party providing the requested type of data with some form of price (e.g., monetary consideration, credentials, cryptocurrency, license or rights, revenue share or other price). The principal who provided the pre-existing data and/or took steps such as capturing expert interactions by capturing process video may be rewarded. The resulting library of interactions captured in response to the specification, request, and reward may be captured as a data set in the data store layer 3310, for example, for use by various applications 3312, the adaptive intelligence system 3304, and other processes and systems. In an embodiment, the library may include videos specifically developed as teaching videos, for example, to facilitate development of an automation map that may follow instructions in the videos, such as providing a sequence of steps according to a process or protocol, decomposing a process or protocol into sub-steps of candidate steps for automation, and so forth. In embodiments, such video may be processed through natural language processing, for example, to automatically develop a sequence of tagging instructions that may be used by a developer to facilitate a map, graphic, or other model of a process to assist in developing automation of the process. In an embodiment, a specified set of training data sets may be used to operate as inputs to learning. In this case, the training data may be time-synchronized with other data within the platform 3300 (e.g., output and results from the application 3312, output and results of the financial entity 3330, etc.) such that a given video of a process may be associated with those output and results, enabling learning feedback (e.g., in the video, or by observing software interactions or physical process interactions) that is sensitive to the results of the captured given process.
In an embodiment, the opportunity mining program 3446 may include methods, systems, processes, components, services, and other elements for mining opportunities for smart contract definition, organization, configuration, and execution. Data collected within platform 3300, such as any data processed by data processing layer 3308, stored by data storage layer 3310, collected by monitoring layer 3306 and collection system 3318, collected from entity 3330, or obtained from external sources, may be used to identify advantageous opportunities for an application or configuration of a smart contract. For example, pricing information about the entity 3330 processed or otherwise collected by the pricing application 3421 may be used to identify instances where the same or multiple items are priced differently (in the spot market, futures market, etc.), and the opportunity mining program 3446 may provide an alert indicating opportunities for intelligent contracts to be composed, such as contracts purchased in one environment at a price below a given threshold and sold in another environment at a price above a given threshold, and vice versa. In an embodiment, robotic process automation 3442 may be used to automatically create, configure, and/or execute smart contracts, for example, by training a training set of data related to the experts that make up such contracts or based on feedback on the results of past contracts. Further, smart contract opportunities may also be identified based on patterns, such as where opportunities for indicating options, futures, derivatives, forward market contracts, and other prospective contracts are predicted, such as where smart contracts are created based on predictions of future conditions under which opportunities for favorable exchanges will occur, such as arbitrage transactions, hedging transactions, "in-price" options, tax offers, and the like. In an embodiment, in a first step, the opportunity mining program 3446 looks for price levels for items, services, merchandise, etc. in a set of spot or futures markets. In a second step, the opportunity mining program 3446 determines the advantage conditions (e.g., arbitrage opportunities, tax saving opportunities, favorable options, favorable hedging, etc.) of the smart contract. In the next step, the opportunity mining program 3446 may initiate a smart contract process in which the smart contract is preconfigured with a description of the item, a description of the price or other terms or conditions, a domain for execution (e.g., a set of markets in which the contract is to be composed), and a time. In a next step, the automation process may construct a smart contract and execute the smart contract within the applicable domain. In a final step, the platform may determine the contract, for example, when a condition is met. In an embodiment, the opportunity mining program 3446 may be used to maintain a set of value converters 3447 that may be developed to calculate the exchange value of different items between and across different domains, such as by transacting across domains various resources (e.g., computing, bandwidth, energy, attention, money, credentials, credits (e.g., tax credits, renewable energy credits, pollution credits), cryptocurrency, goods, licenses (e.g., government issued licenses such as rights for spectrum, executing services, etc., and intellectual property licenses, software licenses, etc.), services, and other items) with respect to other such resources, including calculating any cost of transacting across domains in one or a series of contracts (e.g., contracts performed by smart contracts) to convert one resource to another. The value translator 3447 can translate between current (e.g., spot market) value, value in a defined futures market (e.g., energy prices of the previous day), and predicted future value outside of the defined futures market. In an embodiment, the opportunity mining program 3446 may operate across value transformer pairs or other combinations (e.g., across two, three, four, five, or more domains) to define a series of transaction amounts, configurations, domains, and times that will generate value by conducting transactions that are favorable for value transformation. For example, a cryptocurrency token may be exchanged for a pollution credit, which may be used to allow the generation of energy that may be sold at a price that is higher than the cost of creating a smart contract and doing a series of exchanges than the value of a cryptocurrency token.
Referring to fig. 36, in an embodiment, a transaction, financial, and marketing support system is provided herein. An example system may include: a robotic process automation circuit configured to interpret information from a plurality of data sources and to interface with a plurality of management applications; wherein each of the plurality of management applications is associated with a separate one of a plurality of financial entities; wherein the robotic process automation circuit further comprises an opportunity miner component configured to determine a process improvement opportunity for at least one of the plurality of management applications in response to the information from the plurality of data sources; and providing an output to at least one entity associated with the process improvement opportunity in response to the determined process improvement opportunity.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example system may include: wherein the plurality of management applications includes at least two of the following applications: investment applications, asset management applications, lending applications, risk management applications, marketing applications, transaction applications, tax applications, fraud applications, financial services applications, securities applications, underwriting applications, blockchain applications, real estate applications, administration applications, platform marketplace applications, assurance applications, analytics applications, pricing applications, and smart contract applications.
An example system may include: wherein the plurality of data sources comprises at least two of the following applications: access data sources, asset and facility data sources, worker data sources, claim data sources, billing data sources, event data sources, and underwriting data sources.
An example system may include: wherein each of the at least one entity comprises an entity of: external markets, banking facilities, insurance facilities, financial services facilities, operations facilities, collaborative robotic facilities, workers, wearable devices, external processes, and machines.
An example system may include: wherein the opportunity mining component is further configured to determine a plurality of process improvement opportunities for one of the plurality of management applications in response to the information from the plurality of data sources and provide one of a prioritized list or visualization of the plurality of process improvement opportunities to the one of the plurality of management applications.
An example system may include: wherein the opportunity mining program component is further configured to determine the process improvement opportunity in response to at least one of the following parameters: time saving values, cost saving values, and improved result values.
An example system may include: wherein the opportunity miner component is further configured to determine the process improvement opportunity in response to a value conversion from a value conversion application.
An example system may include: wherein the plurality of management applications includes a transaction application, and wherein the robotic process automation circuit is further configured to automate a transaction service process.
An example system may include: wherein the robotic process automation circuit is further configured to automate the transaction service process by performing at least one of: scheduling a transaction event with data from the plurality of data sources; and determining a transaction criteria in response to the plurality of asset data and the transaction outcome and providing a transaction command in response to the plurality of asset data and the transaction management outcome.
An example system may include: wherein the robotic process automation circuit is further configured to automate the transaction service process in response to at least one of the plurality of data sources not being accessible to the transaction application.
An example system may include: wherein the robotic process automation circuit is further configured to refine the process of at least one of the plurality of management applications by providing output to at least one of the following entities: external markets, banking facilities, insurance facilities, financial services facilities, operations facilities, collaborative robotic facilities, workers, wearable devices, external processes, and machines.
An example system may include: wherein the robotic process automation circuit is further configured to interpret results from the at least one entity, and wherein the opportunity mining program component is further configured to iteratively refine the process in response to the results from the at least one entity.
An example system may include: wherein at least one of the plurality of data sources is inaccessible to each of the at least one of the plurality of management applications having a process that is improved by the robotic automation circuit.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the robotic automation circuit comprises a tax application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: claims data sources, pricing data sources, asset and facility data sources, worker data sources, and event data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the robotic automation circuit comprises an asset management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: access data sources, pricing data sources, billing data sources, worker data sources, and event data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process modified by the robotic automation circuit comprises a lending management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: asset and facility data sources, claim data sources, worker data sources, and event data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the robotic automation circuit comprises a marketing management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: asset and facility data sources, claim data sources, worker data sources, event data sources, and underwriting data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the robotic automation circuit comprises an investment management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: asset and facility data sources, claim data sources, worker data sources, and event data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the robotic automation circuit comprises an underwriting management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: a data source, a claim data source, a worker data source, and an event data source is accessed.
Referring to fig. 37, additional details of an embodiment of a platform 3300 are provided, particularly directed to elements of an adaptive intelligence layer 3304 that facilitate improving an edge intelligence system, including an adaptive edge computing management system 3430 and an edge intelligence system 3438. These elements provide a set of systems that adaptively manage "edge" computation, storage, and processing, such as by changing data storage locations as well as storage on devices, processing locations in local systems, networks, and in clouds (e.g., through AI optimization). These elements 3430, 3438 enable a user (e.g., a developer, operator, or host of platform 100) to conveniently define content that constitutes an "edge" for the purpose of a given application dynamically. For example, for environments where data connections are slow or unreliable (e.g., where the facility cannot access the cellular network well (e.g., due to some environmental locations being remote (e.g., in areas where cellular network infrastructure is poor), shielding or interference (e.g., where the presence of density, thick walls, underground locations, or large metallic objects (e.g., safeguards) of the networked system may interfere with network performance), and/or congestion (e.g., where many devices seek access to limited network facilities)), edge computing capabilities may be defined and deployed to operate on computing capabilities of the local area network of the environment, the point-to-point network of the devices, or the local financial entity 3330. Where strong data connections are available (e.g., where good backhaul facilities are present), edge computing capabilities may be set in the network, thus, the adaptive definition and specification of where to enable edge computing operations may be determined under the control of a developer or operator or alternatively automatically (e.g., by an expert system or an automation system, e.g., based on detected environment, entity 3330, or network conditions throughout the network). In embodiments, the edge intelligence system 3438 can enable adaptation of multi-application-aware edge computing (including computing locations occurring within various available network resources, networking manners (e.g., through protocol selection), data storage locations, etc.), e.g., in accordance with requirements, priorities, and values of edge computing capabilities of multiple applications (including ROIs, and the like, rate of return and cost information, such as failure cost), to consider and prioritize QoS, latency requirements, congestion, and cost, including any combination and subset of applications 3312 described herein or in the documents incorporated by reference.
Referring to fig. 37, in an embodiment, a transaction, financial, and marketing support system is provided herein. An example system may include: an adaptive edge computing circuit configured to interpret information from a plurality of data sources and to interface with a plurality of management applications; wherein each of the plurality of management applications is associated with a separate one of a plurality of financial entities; and wherein the adaptive edge computing circuit further comprises an edge intelligence component configured to determine an edge intelligence process improvement of at least one of the plurality of management applications in response to the information from the plurality of data sources.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example system may include: wherein the plurality of management applications includes at least two of the following applications: investment applications, asset management applications, lending applications, risk management applications, marketing applications, transaction applications, tax applications, fraud applications, financial services applications, securities applications, underwriting applications, blockchain applications, real estate applications, administration applications, platform marketplace applications, assurance applications, analytics applications, pricing applications, and smart contract applications.
An example system may include: wherein the plurality of data sources comprises at least two of the following applications: access data sources, asset and facility data sources, worker data sources, claim data sources, billing data sources, event data sources, and underwriting data sources.
An example system may include: wherein each of the at least one entity comprises an entity of: external markets, banking facilities, insurance facilities, financial services facilities, operations facilities, collaborative robotic facilities, workers, wearable devices, external processes, and machines.
An example system may include: wherein the edge intelligence component is further configured to determine a plurality of process improvement opportunities for one of the plurality of management applications in response to the information from the plurality of data sources and provide one of a prioritized list or visualization of the plurality of process improvement opportunities to the one of the plurality of management applications.
An example system may include: wherein the edge intelligence component is further configured to determine a process improvement opportunity in response to at least one of the following parameters: time saving values, cost saving values, and improved result values.
An example system may include: wherein the plurality of management applications includes a security application, and wherein the adaptive edge computing circuit is further configured to automate a security service process.
An example system may include: wherein the adaptive edge computing circuit is further configured to automate the security service process by performing at least one of: scheduling a security event with data from the plurality of data sources; and determining a security standard in response to the plurality of asset data and the security outcome, and providing a security command in response to the plurality of asset data and the security outcome.
An example system may include: wherein the adaptive edge computing circuit is further configured to automate the security services process in response to at least one of the plurality of data sources not being accessible to the security application.
An example system may include: wherein the adaptive edge computing circuit is further configured to refine the process of at least one of the plurality of management applications by providing output to at least one of the following entities: external markets, banking facilities, insurance facilities, financial services facilities, operations facilities, collaborative robotic facilities, workers, wearable devices, external processes, and machines.
An example system may include: wherein the adaptive edge computing circuit is further configured to interpret results from the at least one entity, and wherein the edge intelligence component is further configured to iteratively refine the process in response to the results from the at least one entity.
An example system may include: wherein at least one of the plurality of data sources is inaccessible to each of the at least one of the plurality of management applications having a process improved by the adaptive edge computing circuit.
An example system may include: wherein the at least one of the plurality of management applications having a process improved by the adaptive edge computing circuit comprises a risk application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: claims data sources, pricing data sources, asset and facility data sources, worker data sources, and event data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process improved by the adaptive edge computing circuit comprises an asset management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: access data sources, pricing data sources, billing data sources, worker data sources, and event data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process improved by the adaptive edge computing circuit comprises a security management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: asset and facility data sources, claim data sources, worker data sources, and event data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process improved by the adaptive edge computing circuit comprises a platform marketplace application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: asset and facility data sources, claim data sources, worker data sources, event data sources, and underwriting data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process improved by the adaptive edge computing circuit comprises a platform marketplace application, wherein the adaptive edge computing circuit is further configured to operate an interface to interpret an edge definition, and wherein the edge intelligence component is further configured to determine the edge intelligence process improvement in response to the edge definition.
An example system may include: wherein the edge definition includes an identification of at least one of the following parameters: slow data connections, unreliable data connections, network interference descriptions, network cache descriptions, quality of service requirements, or delay requirements.
Referring to fig. 38, additional details, components, subsystems, and other elements of an alternative embodiment of the storage layer 3310 of the platform 3300 are shown, particularly directed to embodiments that may include a virtual asset tag 3488 provided with a geofence, such as for one or more assets within the asset and facility data 3320 described in the present invention and documents incorporated by reference herein. In an embodiment, a virtual asset tag is a data structure containing data about an entity 3330, such as an asset (which may be a physical asset or a virtual asset), a machine, a device, an inventory, an article, a certificate (e.g., stock certificate), a contract, a component, a tool, a device, or a worker (etc.), where the data is intended to be tagged to the asset, for example, where the data is uniquely associated with a particular asset (e.g., associated with a unique identifier of a single asset) and linked to an asset's proximate location or location (e.g., where a geofence is provided in or near the area or location of the asset, or associated with a geolocated digital storage location or definition domain of the digital asset). Thus, the virtual asset tag is functionally equivalent to a physical asset tag such as an RFID tag in that it provides a local reader or similar device to access the data structure (as if the reader would access the RFID tag), and in an embodiment, access control is managed as if the tag were physically located on the asset; for example, some data may be encrypted with a key that only allows it to be verified as being read, written, modified, etc. by an operator in the vicinity of the tagged financial entity 3330, thereby allowing only local data processing to be separated from remote data processing. In an embodiment, the virtual asset tag may be used to identify the presence of an RF reader or other reader (e.g., by identifying an interrogation signal) and communicate with the reader, such as by means of a protocol adapter, such as through an RF communication link with the reader, despite the absence of a conventional RFID tag. This may be achieved by communications from IoT devices, telematics systems, and other devices residing on the local area network. In embodiments, a set of IoT devices in a marketplace or financial or transaction environment may act as distributed blockchain nodes, e.g., for storing virtual asset tag data, for tracking transactions, and for verifying linked data (e.g., through various consensus protocols), including transaction histories for maintenance, repair, and service. In embodiments, ioT devices in a geofence may collectively verify the location and identity of a fixed asset tagged by a virtual asset tag, e.g., a peer or neighbor verifies the location of other peers or neighbors at a given location, thereby verifying the unique identity and location of the asset. The verification may be performed using a voting protocol, a consensus protocol, or the like. In an embodiment, the identity of the marked financial entity may be maintained in the blockchain. In an embodiment, the asset tag may include information related to the digital thread 3484, such as historical information about the asset and its components, history, etc.
Referring to fig. 38, in an embodiment, a transaction, financial, and marketing support system is provided herein. An example system may include: an adaptive intelligence circuit configured to interpret information from a plurality of data sources and to interface with a plurality of management applications, wherein the adaptive intelligence circuit comprises a protocol adapter component; wherein each of the plurality of management applications is associated with a separate one of a plurality of financial entities; and wherein the adaptive intelligence circuit further comprises an artificial intelligence component configured to determine an artificial intelligence process improvement of at least one of the plurality of management applications in response to the information from the plurality of data sources.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example system may include: wherein at least one of the plurality of data sources is a mobile data collector.
An example system may include: wherein the adaptive intelligence circuit further comprises a protocol adapter component configured to determine a communication protocol that facilitates communication between entities accessing the at least one of the plurality of management applications having improved procedures.
An example system may include: wherein the entity accessing the at least one of the plurality of management applications comprises an operator associated with the at least one of the plurality of management applications, and wherein the protocol adapter component is further configured to determine that the communication protocol is a protocol that enables encrypted communication in response to the mobile data collector determining that the operator is in proximity to the tagged financial entity.
An example system may include: wherein the mobile data collector collects data from at least one virtual asset tag that is geofenced.
An example system may include: wherein the adaptive intelligence circuit further comprises a protocol adapter component configured to determine a communication protocol that facilitates communication between entities accessing the at least one of the plurality of management applications having improved procedures.
An example system may include: wherein the entity accessing the at least one of the plurality of management applications includes an operator associated with the at least one of the plurality of management applications, and wherein the protocol adapter component is further configured to determine that the communication protocol is a protocol that enables encrypted communication in response to the at least one geofenced virtual asset tag determining that the operator is in proximity to a tagged financial entity.
An example system may include: wherein at least one of the plurality of data sources is an internet of things data collector.
An example system may include: wherein the adaptive intelligence circuit further comprises a protocol adapter component configured to determine a communication protocol that facilitates communication between entities accessing the at least one of the plurality of management applications having improved procedures.
An example system may include: wherein the entity accessing the at least one of the plurality of management applications includes an operator associated with the at least one of the plurality of management applications, and wherein the protocol adapter component is further configured to determine that the communication protocol is a protocol that enables encrypted communication in response to the internet of things data collector determining that the operator is in proximity to the tagged financial entity.
An example system may include: wherein at least one of the plurality of data sources is a blockchain circuit, and wherein the adaptive intelligence circuit utilizes the adaptive intelligence circuit to interpret the information from the blockchain circuit.
An example system may include: wherein the plurality of management applications includes at least two of the following applications: investment applications, asset management applications, lending applications, risk management applications, marketing applications, transaction applications, tax applications, fraud applications, financial services applications, securities applications, underwriting applications, blockchain applications, real estate applications, administration applications, platform marketplace applications, assurance applications, analytics applications, pricing applications, and smart contract applications.
An example system may include: wherein the plurality of data sources comprises at least two of the following applications: access data sources, asset and facility data sources, worker data sources, claim data sources, billing data sources, event data sources, and underwriting data sources.
An example system may include: wherein each of the at least one entity comprises an entity of: external markets, banking facilities, insurance facilities, financial services facilities, operations facilities, collaborative robotic facilities, workers, wearable devices, external processes, and machines.
An example system may include: wherein the artificial intelligence component is further configured to determine a plurality of process improvement opportunities for one of the plurality of management applications in response to the information from the plurality of data sources and provide one of a prioritized list or visualization of the plurality of process improvement opportunities to the one of the plurality of management applications.
An example system may include: wherein the artificial intelligence component is further configured to determine a process improvement opportunity in response to at least one of the following parameters: time saving values, cost saving values, and improved result values.
An example system may include: wherein the plurality of management applications includes a risk management application, and wherein the adaptive intelligence circuit is further configured to automate a risk management process.
An example system may include: wherein the adaptive intelligence circuit is further configured to automate the risk management process by performing at least one of: scheduling a risk event with data from the plurality of data sources; determining a risk criterion in response to a plurality of asset data and risk results, and providing a risk command in response to the plurality of asset data and risk management results; and adjusting the geofence location to provide at least one of improved access to an operator associated with at least one of the plurality of management applications or to improve communication security of at least one of the plurality of management applications.
An example system may include: wherein the adaptive intelligence circuit is further configured to automate the risk management process in response to at least one of the plurality of data sources not being accessible to the risk management application.
An example system may include: wherein the adaptive intelligence circuit is further configured to improve the process of at least one of the plurality of management applications by providing output to at least one of the following entities: external markets, banking facilities, insurance facilities, financial services facilities, operations facilities, collaborative robotic facilities, workers, wearable devices, external processes, and machines.
An example system may include: wherein the adaptive intelligence circuit is further configured to interpret results from the at least one entity, and wherein the artificial intelligence component is further configured to iteratively refine the process in response to the results from the at least one entity.
An example system may include: wherein at least one of the plurality of data sources is inaccessible to each of the at least one of the plurality of management applications having a process modified by the adaptive intelligence circuit.
An example system may include: wherein the at least one of the plurality of management applications having a process improved by the adaptive smart circuit comprises a smart contract application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: claims data sources, pricing data sources, asset and facility data sources, worker data sources, and event data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process improved by the adaptive intelligence circuit comprises an asset management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: access data sources, pricing data sources, billing data sources, worker data sources, and event data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the adaptive intelligence circuit comprises a security management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: asset and facility data sources, claim data sources, worker data sources, and event data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process improved by the adaptive intelligence circuit comprises a marketing management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: asset and facility data sources, claim data sources, worker data sources, event data sources, and underwriting data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the adaptive intelligence circuit comprises a pricing management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: asset and facility data sources, claim data sources, worker data sources, and event data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process improved by the adaptive intelligence circuit comprises a warranty management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: a data source, a claim data source, a worker data source, and an event data source is accessed.
Referring to fig. 39, in an embodiment, a unified RPA system 3442, such as for developing and deploying one or more automation capabilities, may include or enable the capabilities of a robotic operational analysis 3902, for example, for analyzing a set of robotic operational behaviors including location, mobility, and routing with respect to mobile robots, and movements with respect to robotic components, such as use of robotic components in various protocols or procedures, such as banking procedures, underwriting procedures, insurance procedures, risk assessment procedures, risk mitigation procedures, inspection procedures, exchange procedures, sales procedures, purchasing procedures, delivery procedures, warehousing procedures, assembly procedures, transportation procedures, maintenance and repair procedures, data collection procedures, and the like.
In an embodiment, RPA system 3442 may include or enable the ability to machine learn about unstructured data 3908, such as learning about human markers, tags, or training sets that allow characterization of unstructured data, extraction of content from unstructured data, generation of diagnostic codes or other activities like abstractions from the content of unstructured data, or the like. For example, RPA system 3442 may include subsystems or capabilities for processing PDFs (e.g., technical data sheets, functional specifications, maintenance instructions, user manuals, and other documents about financial entities 3330 such as machines and systems), for processing manually entered notes (e.g., notes related to problem diagnosis, notes related to prescribed or recommended actions, notes related to characterization of operational activities, notes related to maintenance and repair operations, etc.), for processing information such as unstructured content contained on websites, social media feeds, etc. (e.g., information about products or systems in a financial environment that may be obtained from a vendor website).
In an embodiment, RPA system 3442 may include a unified platform with a set of RPA capabilities, as well as a system for monitoring (e.g., system of monitoring layer 3306 and data collection system 3318); a system for raw data processing 3904 (e.g., by Optical Character Recognition (OCR), natural language processing (NPL), computer vision processing, sound processing, sensor processing, etc.); system 3908 for workflow characterization and management; analytical capabilities 3910; artificial intelligence capability 3448; and a management system 3914, e.g., for policies, governance, provisioning (e.g., related to services, roles, access control, etc.), etc. The RPA system 3442 may include the capabilities of a set of micro services, such as in a micro service architecture. The RPA system 3442 may have a set of interfaces to other platform layers 3308 and to external systems for data exchange so that the RPA system 3442 may be accessed as an RPA platform, i.e., service, by external systems that may benefit from one or more automation capabilities.
In an embodiment, RPA system 3442 may include a quality of work characterization capability 3912, such as a capability to identify high quality work as compared to other work. This may include: recognizing that manual work is different from work performed by machines, recognizing which manual work may be of highest quality (e.g., work involving most experienced or most expensive personnel), recognizing which machines perform work that may be of highest quality (e.g., work performed by machines that learn feedback from many results extensively compared to newly deployed machines), and recognizing which work historically provides favorable results (e.g., based on analysis or correlation with past results). A set of thresholds may be applied that may be changed under the control of a developer or other user of the RPA system 3442, for example, by type, quality level, etc. to indicate which datasets indicative of past work are to be used for training within the machine learning system that facilitates automation.
Referring to fig. 39, in an embodiment, a transaction, financial, and marketing support system is provided herein. An example system may include: a robotic process automation circuit configured to interpret information from a plurality of data sources and to interface with a plurality of management applications; wherein each of the plurality of management applications is associated with a separate one of a plurality of financial entities; and wherein the robotic process automation circuit further comprises a robotic operation analysis component configured to determine a robotic operation process improvement of at least one of the plurality of management applications in response to the information from the plurality of data sources.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example system may further include: a management system circuit configured to accommodate the robotic manipulation process improvement by managing at least one of robotic manipulation, provisioning robotic manipulation, or a robotic manipulation strategy.
An example system may include: wherein the robotic operational process improvements include robotic workflow characterization and improvements.
An example system may further include: an opportunity mining circuit configured to adapt the operational process improvements to one of the plurality of management applications.
An example system may include: wherein the robot handling process improvements include robot quality of work characterization and improvements.
An example system may include: wherein the robotic operation analysis component includes a robotic machine learning component for processing information from a plurality of data sources to determine the robotic operation process improvement.
An example system may include: wherein the robotic manipulation analysis component includes a raw data processing component for processing information from a plurality of data sources to determine the robotic manipulation process improvement.
An example system may include: wherein the plurality of management applications includes at least two of the following applications: investment applications, asset management applications, lending applications, risk management applications, marketing applications, transaction applications, tax applications, fraud applications, financial services applications, securities applications, underwriting applications, blockchain applications, real estate applications, administration applications, platform marketplace applications, assurance applications, analytics applications, pricing applications, and smart contract applications.
An example system may include: wherein the plurality of data sources comprises at least two of the following applications: access data sources, asset and facility data sources, worker data sources, claim data sources, billing data sources, event data sources, and underwriting data sources.
An example system may include: wherein each of the at least one entity comprises an entity of: external markets, banking facilities, insurance facilities, financial services facilities, operations facilities, collaborative robotic facilities, workers, wearable devices, external processes, and machines.
An example system may include: wherein the robotic operation analysis component is further configured to determine a plurality of process improvement opportunities for one of the plurality of management applications in response to the information from the plurality of data sources and provide one of a prioritized list or visualization of the plurality of process improvement opportunities to the one of the plurality of management applications.
An example system may include: wherein the robotic operation analysis component is further configured to determine a process improvement opportunity in response to at least one of the following parameters: time saving values, cost saving values, and improved result values.
An example system may include: wherein the plurality of management applications includes a supervisory management application, and wherein the robotic process automation circuit is further configured to automate a supervisory management process.
An example system may include: wherein the robotic process automation circuit is further configured to automate the supervisory management process by performing at least one of: scheduling a supervisory event with data from the plurality of data sources; and determining a regulatory standard in response to the plurality of asset data and the regulatory result, and providing a regulatory command in response to the plurality of asset data and the regulatory management result.
An example system may include: wherein the robotic process automation circuit is further configured to automate the supervisory management process in response to at least one of the plurality of data sources not being accessible to the supervisory management application.
An example system may include: wherein the robotic process automation circuit is further configured to refine the process of at least one of the plurality of management applications by providing output to at least one of the following entities: external markets, banking facilities, insurance facilities, financial services facilities, operations facilities, collaborative robotic facilities, workers, wearable devices, external processes, and machines.
An example system may include: wherein the robotic process automation circuit is further configured to interpret results from the at least one entity, and wherein the robotic operation analysis component is further configured to iteratively refine the process in response to the results from the at least one entity.
An example system may include: wherein at least one of the plurality of data sources is inaccessible to each of the at least one of the plurality of management applications having a process that is improved by the robotic process automation circuit.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the robotic process automation circuit comprises an investment application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: claims data sources, pricing data sources, asset and facility data sources, worker data sources, and event data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the robotic process automation circuit comprises an asset management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: access data sources, pricing data sources, billing data sources, worker data sources, and event data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the robotic process automation circuit comprises a security management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: asset and facility data sources, claim data sources, worker data sources, and event data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the robotic process automation circuit comprises a marketing management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: asset and facility data sources, claim data sources, worker data sources, event data sources, and underwriting data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the robotic process automation circuit comprises a pricing management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: asset and facility data sources, claim data sources, worker data sources, and event data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the robotic process automation circuit comprises a assurance management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: a data source, a claim data source, a worker data source, and an event data source is accessed.
Referring to FIG. 40, various systems, methods, processes, services, components, and other elements are shown in an embodiment for supporting blockchains and smart contract platforms for a long-term marketplace 4000 for event access rights. Within a transaction support system such as described in connection with various embodiments of platform 3300, the blockchain application 3422 and associated smart contracts 3431 may be used to enable a forward market 4002 for event access rights, such as one or more event tickets, seat licenses, access rights, passes (e.g., background passes) or representations, other items including or embodying rights to attend, enter, view, consume or otherwise participate in an event (which may be a live event, recorded event, event of a physical location, digital content event or other event of which access is controlled), such as having ownership indications (including identity information, event information, information about terms and conditions, etc.) and ownership transfer (including terms and conditions, etc.), to be securely stored on a blockchain configured by blockchain application 3422, unless otherwise indicated by context, for example, where blockchain 3422 includes transaction classification accounts (including tickets and other evidence of event access rights) in blockchain 4008. In an embodiment, such blockchain-based access tokens may be traded in a marketplace application 3327, such as an application for operating with or for the spot or forward marketplace 4002. In an embodiment, the forward market 4002 within or operated by the platform may be or have a forward market, such as a market in which future rights are granted, triggered, or appear based on the occurrence of an event, satisfaction of a condition, etc., such as a market enabled by a smart contract 3431 that operates on one or more data structures in or associated with the platform operated market 3327 or external market 3390 to execute or apply rules, terms, conditions, etc., optionally facilitating transactions recorded in a blockchain (e.g., in a distributed ledger on the blockchain), which in turn may initiate other processes and facilitate other smart contract operations. In such embodiments, the conditions that trigger the event may include an event promoter or other party that schedules an event with a defined set of parameters, a generated event with such parameters, etc., and the blockchain-based access token 4008 may be used (optionally in conjunction with the smart contract 3431 and the one or more monitoring systems 3306) to identify the presence or existence of an access token that satisfies the defined set of parameters or events, such as in the external market 3390, and initiate operations with respect to the access token, such as reporting the existence of the availability of the access token, transferring access rights to the access token, transferring ownership, setting a price, etc. In an embodiment, the monitoring system 3306 may monitor the external market 3390 for related events, tokens, etc., as well as information indicating the occurrence of one or more conditions that result in triggering, granting, or occurrence of conditions that affect access to the tokens or events. As an illustrative example, a sporting event access token 4008 for a post-season game may be used to grant when a particular team appears in a particular game (e.g., super bowl), at which time the rights to a particular seat's ticket may be automatically assigned to the individual listed in the distributed taxonomy who owns that team's ticket right in the distributed ledger enabled by the blockchain. Thus, the distributed ledger or other blockchain 3422 can securely maintain multiple prospective owners of the event token 4008 for the same event, so long as access rights can be partitioned such that they are mutually exclusive, but can be assigned to a particular owner when conditions (e.g., a particular seat of a game, concert, etc.) occur, and assign ownership to a particular owner based on the occurrence of conditions that determine which prospective owner has the right to be the actual owner (e.g., the team of that owner successfully participated in the game). In the example of a sports league, the blockchain may thus maintain as many owners as there are mutually exclusive conditions (e.g., by allocating seats among all teams in a super bowl league or among all teams in a college elema league resolution area). The defined set of parameters may include location (where an event has not been scheduled occurred), participants (team, person, etc.), price (e.g., price of access token is below a defined threshold), time (e.g., hour, day, month, year, or other period), event type (sporting event, concert, comedy show, dramatic show, political event, etc.), and the like. In an embodiment, one or more monitoring systems 3306 or other data collection systems may be used to monitor one or more external markets 3390 or marketplaces operated by the platform (e.g., in e-commerce websites and applications, auction websites and applications, social media websites and applications, exchange websites and applications, ticketing websites and applications, travel websites and applications, hotel websites and applications, concert publicity websites and applications, or other websites or applications) or other entities to obtain an indication of available events, to be used to define potentially partitionable or mutually exclusive access rights conditions (e.g., to identify events configurable in a multiparty distributed ledger, where conditional access is distributed among different intended owners, optionally via one or more opportunity mining programs 3446), and to assign the actual conditions of rights to particular owners based on a conditional trigger. Thus, the blockchain may be used to form or market in any form of event or access token by securely storing access rights in a distributed ledger, and may be automated or marketable by configuring data collection and operating on the collected data to determine a set of business rules when ownership should be granted, transferred, etc. After granting one or more (or a group or group of) transactions, the transaction access token may continue, with the blockchain providing a secure way of verifying access. Security may be provided by encrypting the chain as with cryptocurrency tokens (and cryptocurrency tokens themselves may include long-market cryptocurrency tokens for event access), proof of work, proof of equity, or other methods for verification in the event of a dispute.
In an embodiment, platform 400 may include or interact with various applications, services, solutions, etc., such as those described in connection with platform 3300, e.g., pricing application 3421 (e.g., pricing for setting and monitoring or having access rights, underlying access rights, tokens, fees, etc.), analysis application 3419 (e.g., for monitoring, reporting, predicting, or otherwise analyzing all aspects of platform 4000, e.g., optimizing offerings, time, pricing, etc., identifying and predicting patterns, establishing rules and or matters, modeling or understanding for use by a person or machine learning system, as well as for many other purposes), transaction application 3428 (e.g., for trading or exchanging or having access rights or underlying access rights or tokens), security application 3418, etc.
Referring to fig. 40, in an embodiment, a transaction, financial, and marketing support system is provided herein. An example system may include: a robotic process automation circuit configured to interpret information from a plurality of data sources and to interface with a plurality of management applications; wherein each of the plurality of management applications is associated with a separate one of a plurality of financial entities; and wherein the robotic process automation circuit further comprises an opportunity mining component configured to determine a robotic operational process improvement of at least one of the plurality of management applications in response to the information from the plurality of data sources.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example system may further include: a data collection circuit configured to collect and record physical process observation data, wherein the physical process observation data is one of the plurality of data sources.
An example system may further include: a data collection circuit configured to collect and record software cross-observation data, wherein the software cross-observation data is one of the plurality of data sources.
An example system may include: wherein the plurality of management applications includes at least two of the following applications: a long-term marketplace application, an event access token application, a security application, a blockchain application, a platform marketplace application, an analytics application, a pricing application, and an intelligent contract application.
An example system may include: wherein the plurality of data sources comprises at least two of the following applications: access data sources, asset and facility data sources, worker data sources, claim data sources, billing data sources, event data sources, and underwriting data sources.
An example system may include: wherein each of the at least one entity comprises an entity of: external markets, banking facilities, insurance facilities, financial services facilities, operations facilities, collaborative robotic facilities, workers, wearable devices, external processes, and machines.
An example system may include: wherein the opportunity mining component is further configured to determine a plurality of process improvement opportunities for one of the plurality of management applications in response to the information from the plurality of data sources and provide one of a prioritized list or visualization of the plurality of process improvement opportunities to the one of the plurality of management applications.
An example system may include: wherein the opportunity mining component is further configured to determine a process improvement opportunity in response to at least one of the following parameters: time saving values, cost saving values, and improved result values.
An example system may include: wherein the plurality of management applications includes a transaction management application, and wherein the robotic process automation circuit is further configured to automate a transaction management process.
An example system may include: wherein the robotic process automation circuit is further configured to automate the transaction management process by performing at least one of: scheduling a transaction event with data from the plurality of data sources; and determining a transaction criteria in response to the plurality of asset data and the transaction outcome and providing a transaction command in response to the plurality of asset data and the transaction management outcome.
An example system may include: wherein the robotic process automation circuit is further configured to automate the transaction management process in response to at least one of the plurality of data sources not being accessible to the transaction management application.
An example system may include: wherein the robotic process automation circuit is further configured to refine the process of at least one of the plurality of management applications by providing output to at least one of the following entities: external markets, banking facilities, insurance facilities, financial services facilities, operations facilities, collaborative robotic facilities, workers, wearable devices, external processes, and machines.
An example system may include: wherein the robotic process automation circuit is further configured to interpret results from the at least one entity, and wherein the opportunity mining component is further configured to iteratively refine the process in response to the results from the at least one entity.
An example system may include: wherein at least one of the plurality of data sources is inaccessible to each of the at least one of the plurality of management applications having a process that is improved by the robotic process automation circuit.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the robotic process automation circuit comprises a long-term marketplace application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: claims data sources, pricing data sources, asset and facility data sources, worker data sources, and event data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the robotic process automation circuit comprises an event access token management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: access data sources, pricing data sources, billing data sources, worker data sources, and event data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the robotic process automation circuit comprises a security management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: asset and facility data sources, claim data sources, worker data sources, and event data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the robotic process automation circuit comprises a blockchain management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: asset and facility data sources, claim data sources, worker data sources, event data sources, and underwriting data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the robotic process automation circuit comprises a pricing management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: asset and facility data sources, claim data sources, worker data sources, and event data sources.
An example system may include: wherein the at least one of the plurality of management applications having a process that is improved by the robotic process automation circuit comprises an analysis management application, and wherein the at least one of the plurality of data sources comprises at least one of the following data sources: a data source, a claim data source, a worker data source, and an event data source is accessed.
Referring to FIG. 41, using the various enabling capabilities of the data processing platform 3300 described herein, a platform operated marketplace 3327 for a long-term marketplace of access rights for one or more events may be configured in a control panel 4118 or other user interface of the operator of the platform operated marketplace 3327. The operator may use the user interface or control panel 4118 to take a series of steps to execute or perform an algorithm to create or have a long-term market event access rights token as described in connection with fig. 40. In an embodiment, one or more steps of an algorithm for creating or having a long-term market event access rights token within the control panel 4118 may include identifying one or more access rights for one or more events at component 4102 to identify access rights, for example by monitoring one or more platform operated markets 3327 or external markets 3390 for messages, announcements, or other data indicating events or access rights. The control panel 4118 can be configured with interface elements (including application programming elements) that allow events to be imported into the platform marketplace 3327, such as by linking to an environment that provides or maintains access rights, which can include using APIs for backend ticketing systems, and the like. In the control panel 4118, at component 4104, one or more conditions (of the type described herein) of access rights can be configured (e.g., by interfacing with a user), such as by defining a set of mutually exclusive conditions that, when triggered, assign access rights to different individuals or entities. The user interface of the control panel 4118 may include a set of drop-down menus, tables, forms, etc. with default, templated, recommended, or preconfigured conditions (e.g., conditions appropriate for various types of access rights). For example, access to post-season games for sporting events may be preconfigured to set access conditions to a particular team engaged in post-season games, where the team is a member of a group of teams that may be engaged in the game, and to assign access to a given seat among mutually exclusive potential teams that may be engaged in the game (e.g., teams engaged in a super bowl union). As another example, access rights to an as yet unplanned entertainment event may be preconfigured to set conditions such as location, date range, and selected artist or group. Once the conditions and other parameters of the access rights are configured, at component 4108, the blockchain can be used to maintain data required to provision, allocate and exchange or have access rights ownership (and optionally, an underlying access token associated with or having access rights), e.g., via a ledger. For example, event tickets may be stored as cryptographically secure tokens on a ledger, and another token may be created and stored on a blockchain for each or access rights that may result in ticket ownership. Blockchains may be used to store tokens, identity information, transaction information (e.g., for exchanging or otherwise having rights and/or underlying tokens), and other data. At component 4110, the smart contract 3431 can be used to embody the conditions configured at component 4104, operate on the blockchain created at component 4108, and operate on other data (e.g., data indicative of facts, conditions, events, etc.) in the platform-operated marketplace 3327 and/or the external marketplace 3390. The smart contract may be configured at component 4110 to apply one or more rules, perform one or more conditional operations, etc., to data that may include event data 3324, access data 3362, pricing data 3364, or other data regarding access rights or related thereto. Upon completion of the configuration of the one or more blockchains and the one or more smartcontracts, at component 4112, the blockchains and smartcontracts may be deployed in a marketplace operated by the platform, e.g., for interaction by one or more consumers or other users who may, e.g., contract smartcontracts in a marketplace interface such as a website, application, etc., e.g., by purchasing or otherwise having rights to future events, at which point the platform may store relevant data, e.g., pricing data and identity data, of one or more parties to the smartcontracts on the blockchain or on the platform 3300, e.g., using the adaptive smartsystem 3304 or other capabilities. At component 4114, once the smart contract is executed, component 4114 can monitor event data 3324, access data 3362, pricing data 3364, or other data (e.g., events) that can satisfy one or more conditions or trigger application of one or more rules of the smart contract, such as by monitoring system layer 3306, platform operated marketplace 3327, and/or one or more external marketplaces 3390. For example, game results or future entertainment event announcements may be monitored and intelligent contract conditions may be met. At component 4116, when the condition is met, a smart contract can be determined, executed, etc., to update or otherwise operate the blockchain, such as by transferring the underlying access token and/or ownership of the access token. Thus, by operating the above components, an operator of platform operated marketplace 3327 may discover, configure, deploy, and have executed a set of smart contracts that provide and deliver access to or have access to future events that are transferred to consumers or others via password protection and on blockchains. In an embodiment, the adaptive intelligence system layer 3304 may be used to monitor the steps of the algorithms described above, and one or more artificial intelligence systems may be used to automatically perform the entire process, one or more sub-steps, or sub-algorithms, such as by robotic process automation. This may occur as described above, for example, by having the artificial intelligence system learn a training set of data obtained by observation, for example, by monitoring human users for their software interactions as they perform the steps described above. Once trained, the adaptive intelligence layer 3304 may enable the platform 3300 to provide a fully automated platform for discovering and delivering access rights to future events.
Referring to fig. 42, in an embodiment, provided herein is a platform having a system, method, process, service, component, and other elements for enabling a blockchain and smart contract platform 4200 for long-term market demand aggregation. In this case, as described above, the demand aggregate blockchain and smart contract platform 4200, having various features and enabled by capabilities similar to those described in connection with platform 3300 and platform 4000, may be based on a set or event 4204 that affects or represents future demands for the offering 4202, which set or event 4204 may include a set of products, services, etc. (which may include physical merchandise, virtual merchandise, software, physical services, software, access rights, entertainment content, or many other items). Blockchain 3422, such as a distributed ledger enabled, may record interest indicators from a set of principals regarding products, services, etc., such as indicators defining parameters that the principal is willing to commit to purchasing their underlying products or services. Interest may be expressed or submitted in a demand aggregation interface 4322, which may be included in or associated with one or more websites, applications, communication systems, etc., which may operate independently, or may include aspects of a platform operated marketplace 3327 or external marketplace 3390. The commitment may be made and managed through the smart contract 3431 or other transaction mechanism. These commitments may include various parameters 4208, such as price parameters of one or more desired offerings 4202, specifications (e.g., shoe size, garment size, etc. other apparel aspects, or performance characteristics of information technology, such as bandwidth, storage capacity, pixel density, etc.), time, etc. Thus, the blockchain 3422 can be used to aggregate future demands in the forward market 4002 for various products and services, and can be processed by manufacturers, distributors, retailers, etc. to help plan the demands, for example, to obtain help (optionally in the analytics system 3419 with pricing, inventory management, supply chain management, smart manufacturing, instant manufacturing, product design, and many other activities). In configuring a set of parameters 4208, offerings 4202 need not exist, whether products, services, or other items; for example, an individual may indicate a willingness to pay up to $1000 to purchase a 65 inch, 32K quantum dot television display at or before month 1 of 2022. In an embodiment, a provider may offer a range of potential configurations and conditions that consumers may be interested in and optionally promise to purchase within defined conditions. In embodiments, the consumer may present the desired items and configurations. In an embodiment, an artificial intelligence system (which may be a rule-based system, e.g., enabled by adaptive intelligence system 3304) may handle a set of potential configurations (e.g., all having 4K or greater capabilities and all pricing below $500) with different parameters 4208 for a subset of configurations that are consistent with each other, and the subset of configurations may be used to aggregate future promise demands for offerings that meet a sufficiently large subset at a profitable price. In an embodiment, the adaptive intelligence system 3204 may use a fuzzy logic system, an ad hoc mapping, or the like to group potential configurations so that a human expert may determine a configuration that is sufficiently close to the identified configuration so that it may be presented as a new alternative. In an embodiment, the artificial intelligence system 3448 may be trained to learn to determine and present new configurations of the offering 4202 based on training data sets created by human experts.
In an embodiment, provided herein is a platform 4200 having systems, methods, processes, services, components, and other elements for enabling blockchains and smart contract platforms for long-term market accommodation rights. The accommodation offering 4210 may include a combination of products, services, and access rights that may be handled as other offerings, including aggregate demand for the offering 4210 in the long-term marketplace 4002. In an embodiment, the long-term market capabilities mentioned above may include those for housingThe access tokens 4008 for the sink and future accommodations, e.g., hotel rooms, shared spaces provided by individuals (e.g., airbn TM Space), accommodation and breakfast, workspace, conference room, conference space, fitness accommodation, health and wellness accommodation, dining accommodation, and the like. The accommodation offering 4210 may be linked to other access tokens 4008, e.g., in a package; for example, hotel rooms in a city within a walking distance of a sporting event may be linked by or on the same blockchain or linked blockchain (e.g., by linking ownership or access rights to the same ledger) such that when a condition is met (e.g., a team of fans participates in a super bowl), the grant of ownership of an access token to the event is also automatically established (and optionally, e.g., initiated automatically via an application programming interface of the platform) accommodation rights (e.g., by booking hotel rooms and dining). Thus, the event forward market can implement a convenient, secure forward market by automatically processing packages of event access tokens, accommodations, and other elements on the blockchain. In an embodiment, in addition to the access token 4008 to the event, configured long-term market parameters 4208 (including condition parameters) may also be provided for accommodation, such as where hotel rooms or other accommodation are reserved in advance after certain conditions (e.g., conditions related to prices within a given time window) are met. For example, during a musical section, the accommodation offerings 4210 of a four-star hotel may be preconfigured to be booked if and when accommodation (e.g., a room with an extra-large bed and city landscape) becomes available within a given time window. Thus, by automatically identifying (e.g., by the monitoring system 3306) that conditions for pre-configured commitments represented on a blockchain (e.g., a distributed ledger) are met and automatically initiating (optionally including by executing a smart contract) that the needs are fulfilled or met (e.g., by automatically booking a room or other accommodation), the needs for accommodation can be pre-aggregated and conveniently met.
In embodiments, provided herein is a platform having systems, methods, processes, services, components, and other elements for enabling blockchains and smart contract platforms for long-term market transport rights. As with accommodation, transport offerings 4212, as well as various predefined or ordered items, may be aggregated and implemented using the platform 4200. As with the accommodation offerings 4210, the travel offerings 4212 may be linked to other access tokens 4008 (e.g., event tickets, accommodations, services, etc.), for example, in many other examples, to automatically reserve flights at or below a predefined price threshold if and when a team of fans participates in the super bowl. The travel offerings 4212 may also be provided separately (e.g., where the travel is automatically booked for ticketing based on commitments in a distributed ledger, provided that tickets are provided at a given price within a given time window, as with other goods and services, an aggregation on the blockchain 3422, such as a distributed ledger, may be used for demand planning, for determining which resources to deploy to which routes or travel types, etc. the transportation offerings 4212 may be configured with predefined or event 4204 and parameters 4208, such as with respect to price, mode of transportation (air, bus, rail, private car, co-ride, etc.), level of service (e.g., first class, business class, etc.), payment style (e.g., using loyalty programs, reward points or specific currencies, including cryptocurrencies), time (e.g., defined time periods or associated with event locations (e.g., designated as occurrence locations or specific locations for a given type of event (e.g., annual super bowls)), route (e.g., direct stops from consumer destination to specific location or event occurrence locations or multiple times), etc.
In an embodiment, the platform 4200 may include or interact with various applications, services, solutions, etc., such as those described in connection with the platform 3300, e.g., pricing applications 3421 (e.g., for setting and monitoring pricing for goods, services, access rights, tokens, fees, etc.), analysis applications 3419 (e.g., for monitoring, reporting, predicting, or otherwise analyzing all aspects of the platform 4000, e.g., optimizing offerings, time, pricing, etc., identifying and predicting patterns, establishing rules and or matters, establishing models or understanding for use by a person or machine learning system, as well as for many other purposes), transaction applications 3428 (e.g., for trading or exchanging goods, services, or other offerings 4202, tokens, etc., or having access rights, futures, or options), security applications 3418, etc.
Referring to fig. 43, using the various enabling capabilities of the data processing platform 3300 described herein, a platform operated marketplace 3327 for the forward market of future offerings 4202 may be configured in a control panel 4318 or other user interface of the operator of the platform operated marketplace 3327. The operator may use the user interface or control panel 4318 to take a series of steps to execute or perform an algorithm to create the offering 4210 as described in connection with fig. 42. In an embodiment, one or more steps of an algorithm for creating or having future offerings 4210 within the control panel 4318 may comprise: at component 4302, offering data 4320 is identified, which offering data 4320 may come from a platform-operated marketplace 3327 or external marketplace 3390, such as via a demand aggregation interface 4322 presented to one or more consumers within one of them, or may be entered via a website or application created for demand aggregation of offerings 4210 or a user interface of the website or application, such as by specifying various possible parameters 4208 and or issues 4204 of such offerings 4210 to request consumer interest or consumer commitments (e.g., commitments made through smart contracts).
The control panel 4318 may be configured with interface elements (including application programming elements) that allow offerings to be managed in the platform marketplace 3327, such as by linking to a set of environments in which various components of the offerings 4202 (e.g., descriptions of goods and services, prices, access rights, etc.) are specified, provided, or maintained, which may include using APIs for backend ticketing systems, e-commerce systems, ordering systems, fulfillment systems, and the like. In the control panel 4318, the component 4304 may configure one or more parameters 4208 or issues 4204 (e.g., via interaction with a user), such as conditions including or describing offerings (of the type described herein), such as by defining a set of conditions that induce consumers to commit to sharing offerings 4202, induce offerings allocation rights, and the like. The user interface of the control panel 4318 can include a set of drop-down menus, tables, forms, etc. with default, templated, recommended, or preconfigured conditions, parameters 4208, or matters 4204, etc. (e.g., conditions, parameters, or matters, etc. appropriate for various types of offerings 4202). For example, access to a new series of shoes may be preconfigured to set the provisioning conditions to provide shoes of a particular style and color by a particular designer, and may be preconfigured to accept a commitment to purchase shoes if access is provided below a particular price for a particular period of time. As another example, the demand for an as yet unplanned entertainment event may be preconfigured to set conditions such as location, date range, and selected artist or group. Once the conditions and other parameters of the offering 4202 are configured, the component 4308 may configure the blockchain to maintain data (and optionally underlying access tokens, virtual merchandise, digital content items, etc. included in or associated with the offering) required to provision, allocate, and exchange ownership of the item including the offering, e.g., via a ledger. For example, a virtual good of video may be stored as a cryptographic security token on a ledger, and if and when each intelligent contract for or purchasing the virtual good becomes available under defined conditions, another token may be created and stored on the blockchain for each or having access to each of the ownership rights that may result in or purchase the virtual good. Blockchains may be used to store tokens, identity information, transaction information (e.g., for exchanging or otherwise having rights and/or underlying tokens), virtual goods, license keys, digital content, entertainment content, and other data. The component 4310 can configure the smart contract 3431 to embody the conditions configured at the component 4304, operate on the blockchain created at the component 4308, and operate on other data (e.g., data indicative of facts, conditions, events, etc.) in the marketplace 3327 and/or the external marketplace 3390 of platform operations. The smart contract may be configured at step 4310 to apply one or more rules, perform one or more conditional operations, etc., to data that may include offering data 4320, event data 3324, access data 3362, pricing data 3364, or other data regarding a set of offerings 4202 or related thereto. Upon completion of the configuration of the one or more blockchains and the one or more smartcontracts, at component 4312, the blockchains and smartcontracts can be deployed in a marketplace 3327 operated by the platform, e.g., for interaction by one or more consumers or other users who can make smartcontracts, e.g., in a marketplace interface such as a website, application, etc., or demand aggregation interface 4322, e.g., by executing an indication of commitments to purchase, participate in, or otherwise consume future offerings 4202, at which point the platform can store relevant data, e.g., pricing data and identity data, of one or more parties of the smartcontracts, e.g., on the blockchain or on platform 3300, using, e.g., adaptive smartsystem 3304 or other capabilities. At component 4314, once the smart contract is executed, the platform can monitor offerings data 4320, event data 3324, access data 3362, pricing data 3364, or other data (e.g., events) that can satisfy one or more conditions or trigger application of one or more rules of the smart contract, such as through monitoring system layer 3306, platform-operated marketplace 3327, and/or one or more external marketplaces 3390. For example, the bulletins of the offerings may be monitored, such as on an e-commerce website, auction website, etc., and the smart contract conditions may be satisfied by one or more offerings 4202.
At component 4316, when the condition is met, a smart contract can be determined, executed, etc., to update or otherwise operate the blockchain, such as by transferring goods, services, underlying access tokens, and/or ownership of the access tokens, and transferring the desired price (e.g., obtained by a payment system). Thus, through the steps described above, the operator of platform-operated marketplace 3327 can discover, configure, deploy, and have executed a set of smart contracts that aggregate the needs for offerings 4202, and provide and deliver or have access to those offerings that are transferred to consumers or others via password protection and on blockchain. In an embodiment, the adaptive intelligence system layer 3304 may be used to monitor the steps of the algorithms described above, and one or more artificial intelligence systems may be used to automatically perform the entire process, one or more sub-steps, or sub-algorithms, such as by robotic process automation. This may occur as described above, for example, by having the artificial intelligence system learn a training set of data obtained by observation, for example, by monitoring human users for their software interactions as they perform the steps described above. Once trained, the adaptive intelligence layer 3304 may enable the platform 3300 to provide a fully automated platform for discovering and delivering offerings, as well as for demand aggregation of such offerings 4202 and automated handling of access rights and ownership of such offerings 4202.
Referring to fig. 44, in an embodiment, provided herein is a platform having systems, methods, processes, services, components, and other elements for enabling a blockchain and smartcontract platform 4400 for crowd sourcing innovation. In such embodiments, a principal seeking a set of innovations 4402, such as an invention, creative work, innovation, technical solution to a set of problems, satisfaction of technical specifications, or other advancement, may configure, for example, on blockchain 3422 (optionally including a distributed ledger) a set of conditions 4410 that are capable of being expressed in smart contract 3431, which are required to meet the requirements. The reward 4412 may be used to generate innovations 4402 given a set of capabilities, or to satisfy a given set of parameters 4408 on a given date (e.g., specifications for 5G folded cell phones may be produced at a price of less than $100 per day before the end of 2019). Satisfaction of conditions 4410 may be measured by monitoring system 3306, by one or more experts, or by trained artificial intelligence system 3448 (e.g., a system trained to evaluate responses based on a training set created by the experts). In an embodiment, the platform 4400 may include a control panel 4414 for configuring specifications, requirements or other conditions 4410, rewards 4412, time and other parameters 4408 (e.g., submitted data or any required qualifications, formats, regional requirements, certificates, credentials, etc. that may be required by a presenter), and the platform 4400 may automatically configure the blockchain 3422 to store parameters 4408 and smart contracts 3431 to operate, for example, in conjunction with a website, application, or other market environment, to provide rewards 4412, receive and record submitted data 4418 (e.g., on the blockchain 3422), allocate rewards 4412, etc., with events, transactions, and activities optionally recorded in the blockchain using distributed ledgers. In an embodiment, consideration 4412 may be used to distribute across multiple submissions, such as where the innovation needs to solve multiple problems, so that it may be assessed whether the submissions 4418 meet certain conditions, and if and when a complete solution (including aggregating multiple submissions 4418) may be implemented, the consideration is distributed among the contribution submissions 4418, unlocking the consideration, at which time the appropriate portion of the consideration may be distributed for the contribution submissions 4418 recorded on the distributed ledger. The submission materials may include software, technical data, proprietary technology, algorithms, firmware, hardware, mechanical drawings, prototypes, proof of concept devices, systems, and many other forms, which may be identified, described, or otherwise recorded on the blockchain 3422 (e.g., a distributed ledger), such as through one or more links to one or more resources (which may be secured by passwords or other techniques). Thus, the submitted material may be described and evaluated for purposes of assigning consideration 4412 (e.g., by one or more independent experts, by an artificial intelligence system (which may be trained by the expert), etc.), and then locked, such as by encryption, secure storage, etc., unless and until consideration is distributed by a distributed ledger. Thus, the platform provides a secure system for exchanging information related to innovations that provides compensation for rewards, such as in crowd sourcing or other innovation planning. The artificial intelligence system 3448 can be trained, for example, by using a training set of data of expert interactions with the submittal material 4418 to automatically evaluate the submittal material 4418 for automatic allocation of rewards or pre-filled evaluation for human expert validation. In an embodiment, the artificial intelligence system 3448 may train, for example, through a training set of data reflecting expert interactions with the control panel 4414, optionally in combination with, for example, outcome information from the analysis system 3419, to create rewards 4412, set conditions 4410, specify innovations 4402, and set other parameters 4408, thereby providing fully or semi-automatic capabilities for one or more of these capabilities.
Referring to fig. 45, using the various enabling capabilities of the data processing platform 3300 described herein, a platform operated marketplace 3327 for crowd-sourced innovation 4400 may be configured in the crowd-sourced control panel 4414 or other user interface of the operator of the platform operated marketplace 3327. The operator may use the user interface or crowdsourcing control panel 4414 to take a series of steps to execute or perform an algorithm to create a crowdsourcing offer as described in connection with fig. 44. In an embodiment, one or more of the described components are used to create rewards 4412 within control panel 4414, at component 4502, can include identifying potential offers, such as innovations 4402 of interest (e.g., as indicated by demand indications in the platform operated market 3327 or external market 3390, or by indications of stakeholders of the enterprise over various communication channels).
The control panel 4414 may be configured with a crowdsourcing interface 4512, such as with elements (including application programming elements) that allow for managing crowdsourcing offerings in the platform marketplace 3327 and/or one or more external markets 3390. In control panel 4414, at component 4504, a user may configure one or more parameters 4408 or conditions 4410, such as conditions (of the type described herein) that include or describe a crowd-sourced offer, such as by defining a set of conditions 4410 that trigger consideration 4412 and determine that consideration 4412 is assigned to a set of submitters. The user interface of the control panel 4414 may include a set of drop-down menus, tables, forms, etc. with default, templated, recommended, or preconfigured conditions, parameters 4408, conditions 4410, etc. (e.g., conditions, parameters, etc. appropriate for various types of crowd-sourced offers). Once the conditions and other parameters of the offer are configured, at component 4508, the smart contract 3431 and blockchain 3422 can be used to maintain data needed to provision, distribute, and exchange data related to the offer, e.g., via a ledger. Blockchains may be used to store tokens, identity information, transaction information (e.g., for information exchange), technical descriptions, virtual goods, license keys, digital content, entertainment content, and other data, content, or information that may be relevant to the submission of the material 4418 or the reward 4412. At component 4510, the smart contract 3431 may be used to embody the conditions configured at step 4504, operate on the blockchain created at component 4508, and operate on other data (e.g., data indicative of facts, conditions, events, etc., such as facts, conditions, events, etc., related to the submitted profile data 4418) in the platform operated marketplace 3327 and/or the external marketplace 3390. The smart contract 3431 can apply one or more rules, perform one or more conditional operations, etc., to data such as the submission profile data 4418, data indicating that parameters or conditions are satisfied, as well as identity data, transaction data, time data, and other data in response to the component 4510. Upon completion of the configuration of the one or more blockchains and the one or more smartcontracts, at component 4512, the blockchains and smartcontracts may be deployed in a marketplace 3327, external marketplace 3390, or other environment operated by the platform, such as for interaction by one or more submitters or other users who may, for example, make smartcontracts in crowd-sourced interfaces 4512 such as websites, applications, etc., for example, by submitting submission materials 4418 and requesting rewards 4412, at which point the platform may, for example, use adaptive smartsystem 3304 or other capabilities to store relevant data, such as submission materials data 4418 and identity data of one or more parties who make smartcontracts on the blockchain or on platform 3300. At component 4514, once the smart contract is executed, the platform may monitor the platform operating market 3327 and/or one or more external markets 3390 through the monitoring system layer 3306 or the like to obtain submission profile data 4418, event data 3324, or other data that may satisfy or indicate that one or more conditions 4410 are met or that one or more rules of the smart contract 3431 are applied, for example, to trigger rewards 4412.
At component 4516, a smart contract may be determined, executed, etc. when a condition is met, to update or otherwise operate the blockchain 3422, such as by transferring a price (e.g., via a payment system) and transferring access rights to the submission material 4418. Thus, through the steps described above, the operator of platform operated marketplace 3327 can discover, configure, deploy, and have executed a set of smart contracts crowd-sourced innovations via cryptographic protection and transferred from innovations to parties seeking innovations on the blockchain. In an embodiment, the adaptive intelligence system layer 3304 may be used to monitor the steps of the algorithms described above, and one or more artificial intelligence systems may be used to automatically perform the entire process, one or more sub-steps, or sub-algorithms, such as by robotic process automation. This may occur as described above, for example, by having the artificial intelligence system learn a training set of data obtained by observation, for example, by monitoring human users for their software interactions as they perform the steps described above. Once trained, the adaptive intelligence layer 3304 may enable the platform 3300 to provide a fully automated platform for crowd-sourced innovations.
Referring to fig. 46, in an embodiment, a platform is provided herein having systems, methods, processes, services, components, and other elements for enabling blockchain and smart contract platform 4600 for crowd-sourced evidence. As with other embodiments described above in connection with purchasing innovations, product requirements, etc., the blockchain 3422, for example, may optionally embody a distributed ledger, may be configured with a set of intelligent contracts 3431 to manage rewards 4612 for submitting evidence 4618, such as infringement evidence, prior art evidence, publishing evidence, usage evidence, business sales evidence, fraud evidence, false statement evidence, illegal intrusion evidence, delinquent evidence, false statement evidence, default or defaulting evidence, evidence engaged in illicit, evidence engaged in adventure, evidence not as evidence, offensive evidence, infringement evidence, crime evidence, offensive evidence, policy or program, evidence of where individuals are located (optionally including known or preferred locations), evidence of other relationships of social networks or individuals, evidence of business relationships of individuals or enterprises, evidence of individuals or enterprises, defect evidence, injury evidence, counterfeit evidence, identity evidence (e.g., DNA, fingerprint recognition, video, photography, etc.), damage, confusing evidence (e.g., under trademark infringement or underrun evidence) or in the case of a person's law or law, or with a rule, or other program. In an embodiment, blockchains 3422, which may optionally be distributed in a distributed ledger, for example, may be used to configure requests for evidence 4618 (which may be formal legal requests, e.g., coupons or alternative forms of requests such as in the case of fact collection), as well as terms and conditions 4610 related to evidence, e.g., rewards 4612 for submitting evidence 4618, a set of terms and conditions 4610 related to the use of evidence 4618 (e.g., whether only issued under coupons, whether the submitting party has anonymous rights, the nature of the program in which evidence may be used, allowed conditions for using evidence 4618, etc.), and various parameters 4608, e.g., time parameters, the nature of the desired evidence (e.g., scientifically verified evidence such as DNA or fingerprints, video clips, photographs, witness words, etc.), and other parameters 4608.
Platform 4600 can include crowd-sourced interfaces 4620, which can be included in or provided in conjunction with a website, application, control panel, communication system (e.g., for sending email, text, voice message, advertisement, broadcast message, or other message) by which messages can be presented in interface 4620 or sent to relevant individuals (whether targeted or not, such as in the case of a summons or broadcast message sent to an individual, company, organization, etc. such as at a given location) via an appropriate link to smart contract 3431 and associated blockchain 3422, such that a reply message to commit evidence 4618 and associated attachments, links, or other information can be automatically associated with blockchain 3422 (e.g., via an API or data integration system) such that blockchain 3422 and any optionally associated distributed ledgers maintain a secure, explicit record of evidence 4618 of commit in response to the request. Where consideration 4612 is provided, blockchain 3422 and/or smart contract 3431 may be used to record the time of submission, nature of the submission, and party of the submission such that when the submission meets the conditions of consideration 4612 (e.g., in many other examples where the subject in the criminal case is arrested or where the patent is invalid when using the submitted prior art, etc.), blockchain 3422 and any distributed ledgers stored thereby may be used to identify the submitter and communicate consideration 4612 (which may take any form of the consideration mentioned in the present invention) by executing smart contract 3431. In an embodiment, the blockchain 3422 and any associated ledgers may include identification information for submitting evidence 4618 without containing actual evidence 4618 so that the information may be kept secret (e.g., encrypted or stored separately from the pure identification information) but access conditions (e.g., legal citations, authorized orders, or identification or verification of persons with legal access rights through the identity or security application 3418) are met or verified. The reward 4612 may be provided based on the result of the situation or situation associated with the evidence 4618, based on a set of rules (which may be automatically applied in some cases, e.g., in connection with using the smart contract 3431, an automation system, a rule processing system, an artificial intelligence system 3448, or other expert systems), which in embodiments may include rules trained based on training data sets created by human experts. For example, the machine vision system may be used to evaluate the evidence of counterfeiting based on the image of the item, and the principal submitting the evidence of counterfeiting with a benefit such as a voucher or other price-bearing reward may be distributed by the intelligent contract 3431, blockchain 3422, and any distributed ledger. Thus, platform 4600 may be used for a variety of fact collection and evidence collection purposes to facilitate compliance, deterrent misbehavior, reduce uncertainty, reduce information asymmetry, and the like.
Referring to FIG. 47, using the various enabling capabilities of the data processing platform 3300 described herein, crowd-sourced evidence of a platform operated marketplace 4600 may be configured in the crowd-sourced interface 4620 or other user interface of the operator of the platform operated marketplace 4600. The operator may use the user interface 4620 or the crowd-sourced control panel 4614 to take a series of steps to execute or perform an algorithm to create a crowd-sourced request to obtain evidence 4618 as described in connection with fig. 46. In an embodiment, interacting with one or more of the components to create a reward 4612 within the control panel 4614 may include: at component 4702, potential rewards 4612 are identified, such as which evidence 4618 may have value in a given instance (e.g., may be indicated by stakeholders or representatives of the entity (e.g., individuals or businesses such as lawyers, agents, investigators, principals, auditors, snoops, underwriters, inspectors, etc.) through various communication channels).
The control panel 4614 may be configured with a crowdsourcing interface 4620, for example, with elements (including application programming elements, data integration elements, messaging elements, etc.) that allow for managing crowdsourcing requests in the platform marketplace 4600 and/or the one or more external markets 3390. In control panel 4614, at component 4704, a user may configure one or more parameters 4608 or conditions 4610, such as conditions (types described herein) including or describing a crowd-sourced request, such as by defining a set of conditions 4610 that trigger consideration 4612 and determine a set of submitters that assign consideration 4612 to evidence 4618. The user interface of the control panel 4614 (which may include or be associated with the crowd-sourced interface 4620) may include a set of drop-down menus, tables, forms, etc. with default, templated, recommended, or preconfigured conditions, parameters 4608, conditions 4610, etc. (e.g., conditions appropriate for various types of crowd-sourced requests). Once the conditions and other parameters of the request are configured, at component 4708, the smart contract 3431 and blockchain 3422 can be used to maintain, for example, via ledgers, the data needed to supply, distribute, and exchange data related to the request and submission of evidence 4618. The smart contract 3431 and blockchain 3422 may be configured with identity information, transaction information (e.g., for information exchange), technical information, and other evidence data 4618 of the type described in connection with fig. 46, including any data, testimonials, photo or video content, or other information that may be relevant to the conditions 4610 of the evidence 4618 submission or return 4612. At component 4710, the smart contract 3431 can be used to embody the conditions 4610 configured at component 4704, operate on the blockchain 3422 created at component 4708, and operate on other data (e.g., data indicative of facts, conditions, events, etc., such as facts, conditions, events, etc., related to submitting profile data 4618, such as websites indicative of the outcome of legal cases or partial cases, websites reporting surveys, etc.) in the platform operating market 4600 and/or external market 3390 or other information websites or resources. The smart contract 3431 may apply one or more rules configured at the component 4710, perform one or more conditional operations, etc., in response to data such as evidence data 4618, data indicating that parameters 4608 or conditions 4610 are satisfied, as well as identity data, transaction data, time data, and other data. Upon completion of the configuration of the one or more blockchains 3422 and the one or more smart contracts 3431, at component 4712, the blockchains 3422 and the smart contracts 3431 may be deployed in a marketplace 4600, an external marketplace 3390, or other website or environment operated by the platform, such as for interaction by one or more submitters or other users who may, for example, contract the smart contracts 3431 in a crowdsourcing interface 4620 such as a website, application, etc., such as by submitting evidence 4618 and requesting rewards 4612, at which time the platform 4600 may, for example, use the adaptive smart system 3304 or other capabilities to store relevant data, such as submission data 4618 and identity data of one or more parties to contract the smart contract 3431 on the blockchain 3422 or on the platform 4600. At component 4714, once the smart contract 3431 is executed, the platform 4600 may monitor the platform operating market 4600 and/or one or more external markets 3390 or other sites via the monitoring system layer 3306 or the like to obtain submission profile data 4618, event data 3324, or other data that may satisfy or indicate that one or more conditions 4610 are met or that trigger the application of one or more rules of the smart contract 3431, for example, to trigger rewards 4612.
At component 4716, upon satisfaction of condition 4610, a smart contract 3431 or the like can be determined, executed, etc., to update or otherwise operate the blockchain 3422, such as by transferring the price (e.g., via a payment system) and transferring access rights to evidence 4618. Thus, through the steps described above, the operator of platform-operated marketplace 4600 can discover, configure, deploy, and have executed a set of smart contracts 3431 that crowd-source evidence that is secured via passwords and transferred from evidence collectors to parties seeking evidence over blockchain 3422. In an embodiment, the adaptive intelligence system layer 3304 may be used to monitor the steps of the algorithms described above, and one or more artificial intelligence systems may be used to automatically perform the entire process, one or more sub-steps, or sub-algorithms, such as by robotic process automation 3442. This may occur, for example, by having the artificial intelligence system 3448 learn a training set of data obtained by observation, such as monitoring the human user for their software interactions as they perform the steps described above. Once trained, the adaptive intelligence layer 3304 may enable the platform 3300 to provide a fully automated platform for crowd-sourced evidence.
In an embodiment, evidence may relate to the collection of facts or data that may be supported by various applications and solutions by the marketplace platform 3300, including the evidence crowd-sourcing platform 4600, such as for underwriting applications 3420 (e.g., underwriting insurance policies, loans, guarantees, vouchers, etc.), including the fine-computation process; risk management solutions 3408 (e.g., manage the various risks mentioned by the present invention); tax solutions (e.g., evidence related to supporting deductions and tax amounts, etc.); the lending solution 3410 (e.g., evidence of ownership and or value of mortgage, evidence of authenticity of statement, etc.); regulatory solutions 3426 (e.g., regarding compliance with various regulations that may manage or be performed by the entity 3330 and the processes, behaviors, or activities of the entity 3330); and fraud prevention solutions 3416 (e.g., for detecting fraud, false statements, misbehavior, defamation, etc.).
Evidence collection may include evidence collection about the entity 3330 and its identity, assertion, claiming, action, or behavior, as well as many other factors, and may be implemented by crowd-sourcing in the crowd-sourcing platform 4600 or by the data collection system 3318 and the monitoring system 3306, optionally by automation via the process automation 3442 and adaptive intelligence such as using the artificial intelligence system 3448.
In an embodiment, provided herein is an evidence collection platform, whether crowd-sourced platform 4600 or a more general data collection platform 3300 that may or may not include crowd-sourcing, having systems, methods, processes, services, components, and other elements for enabling blockchains and smart contract platforms for aggregating identity and behavioral information of insurance underwriting 3420. In an embodiment, blockchains with optional distributed ledgers may be used to record a set of events, transactions, activities, identities, facts, and other information associated with the underwriting process 3420, such as the identity of the applicant, the identity of the principal who may be willing to provide the insurance, information about the risk that may be underwritten (any type of insurance, such as property insurance, life insurance, travel insurance, infringement insurance, health insurance, housing insurance, business liability insurance, product liability insurance, car insurance, fire insurance, flood insurance, disaster insurance, retirement insurance, loss insurance, and many other insurance traditionally underwritten by insurance policies, in addition to many other types of risk that are not traditionally underwritten), information about the scope of underwriting, liability outside, etc., information about terms and conditions such as pricing, benefits, interest (e.g., for life insurance), etc. The blockchain 3422 and associated smart contracts 3431 can be coordinated with or used via websites, applications, communication systems, messaging systems, markets, etc. for providing insurance and recording information submitted by the applicant such that the insurance application has a record of security, specifications for submitting the information, with access control capabilities that allow only authorized parties, roles, and services to access the submitted information (e.g., subject to policy, regulation, and access terms and conditions). Blockchain 3422 may be used to underwire 3420, such as by recording information related to pricing, underwriting scope, etc. (including evidence mentioned above regarding evidence collection), such as information collected by the underwriter, submitted by the applicant, collected by artificial intelligence system 3448, or submitted by others (e.g., in the case of crowd-sourced platform 4600). In an embodiment, blockchain 3422, smart contracts 3431, and any distributed ledgers may be used to facilitate the provision and underwriting of small insurance, e.g., for defined risks associated with defined activities (which define time periods that are narrower than typical insurance policies). For example, insurance related to adverse weather events may be obtained on the day of the wedding. Blockchain 3422 may facilitate risk allocation and underwriting campaign coordination for a group of principals, such as where a group of principals agree to bear part of the risk recorded in the ledger. For example, a ledger may allow a principal to assume any portion of the risk, accumulating part of the insurance, until the risk is fully covered as the remaining accumulation and aggregation of multiple principals agreeing to be covered for activities, risks, etc. as recorded on the ledger. Ledgers may be used to allocate payments when covered risk events occur. In an embodiment, the artificial intelligence system 3448 may be used to collect and analyze underwriting data, such as underwriting data trained by human expert underwriters. In an embodiment, the automation system 3442, e.g., a system using artificial intelligence 3448 such as artificial intelligence trained to identify and verify events, may be used to determine that an event has occurred (e.g., roof has collapsed, car has damaged, etc.) from, e.g., video, images, sensors, ioT devices, witness submissions (e.g., on a social network), etc., such that operations on a distributed ledger may be initiated to pay an insurance amount, including initiating appropriate debits and credits reflecting transfer of funds from an underwriting/insuring party to an insuring party. Thus, blockchain-based ledgers can simplify and automate most insurance processes by reliably verifying identity, maintaining confidentiality of information as needed, automatically accumulating evidence needed for pricing and underwriting, automatically processing information indicative of the occurrence of insurance events, and automatically determining and fulfilling contracts when verified events occur.
Lending platform
Referring to fig. 48, an embodiment of a financial, transactional, and market support system 3300 is shown in which a lending support system 4800 is enabled, and in which a platform-oriented market 3327 may include a lending platform 3410. The loan support system 4800 may include a set of systems, applications, processes, modules, services, layers, devices, components, machines, products, subsystems, interfaces, connections, and other elements (collectively referred to as "platforms," "loan platforms," "systems," etc., in the alternative, unless the context indicates otherwise) that cooperate (e.g., through data integration and organization in a service-oriented architecture) to enable intelligent management of a set of entities 3330 that may exist, run, transact, etc., within, or own, run, support, or otherwise implement one or more applications, services, etc., of the loan platform 3410 or the external market 3390,Solutions, programs, etc., that involve the loan transaction or the loan related entity, or may be part of, integrated with, linked to, or run by the platform 3300 and the system 4800. Unless the context indicates otherwise, references herein to a set of services should be construed as these and other various systems, applications, processes, modules, services, layers, devices, components, machines, products, subsystems, interfaces, connections, and other types of elements. A group may include multiple members or a single member. As with other embodiments of system 3300, system 4800 can have various data processing layers, with various components, modules, systems, services, components, functions, and other elements described in connection with other embodiments of the present invention and documents incorporated by reference herein. This may include various adaptive intelligence systems 3304, monitoring systems 3306, data collection systems 3318, and data storage systems 3310, as well as a set of interfaces 3316 for each of these systems and/or platforms 3300 and various other elements of system 4800, connecting these systems and/or elements, and/or between these systems and/or elements. In an embodiment, the interface 3316 may include: an application programming interface 4812; data integration techniques (collectively referred to as ETL systems 4814) for extracting, converting, cleaning, normalizing, deduplicating, loading, etc., as data is moved between various services using various protocols and formats; and various ports, portals, connectors, gateways, wired connections, sockets, virtual private networks, containers, secure channels, and other connections (collectively referred to as ports 4818) configured in a one-to-one, one-to-many, or many-to-many manner between elements in unicast, broadcast, and multicast transmissions, etc. The interface 3316 may include a real-time operating system (RTOS) 4810 (e.g., freeRTOS TM An operating system), enabled by, integrated with, or connected to the real-time operating system, the real-time operating system having a deterministic execution mode, wherein a user can define the execution mode, e.g., based on a priority allocation for each thread of execution. An example of the RTOS 4810 may be embedded on a microcontroller or the like of the internet of things device, such as a microcontroller for monitoring the various entities 3330. RTOS 4810 can provide real-time scheduling (e.g., scheduling to monitoring system 3306 and dataData transfer of the collection system 3318, scheduling inter-task communication between the various service elements, and other timing and synchronization elements). In an embodiment, interface 3316 may use or include a set of libraries (which enable secure connections between small, low power consumption edge devices, such as internet of things devices for monitoring entity 3330), various cloud deployment services of platform 3300 and system 4800, and a set of edge devices and systems enabling these devices, such as run AWS IoTGreengrass TM And/or AWS Lambda TM Functions, etc., to allow for local computing, data communication configuration, execution of machine learning models (e.g., for prediction or classification), synchronization of device or device data, and communication between devices and services. This may include using local device resources such as serial ports, GPUs, sensors, and cameras. In an embodiment, the data may be encrypted for secure end-to-end communication.
In the context of the loan support system 4800 and the set of loan solutions 3410, the entity 3330 may include any one of the various assets, systems, devices, machines, facilities, individuals, or other entities mentioned in the present disclosure or in the documents incorporated by reference herein, such as, but not limited to: machine 3352 and its components (e.g., machines that are loan subjects or mortgages, such as various vehicles and devices; and machines for conducting loan transactions, such as automated teller machines, point of sale terminals, vending machines, self-service terminals, smart card-enabled machines; and many other machines, including machines for supporting small loans, payday loans, etc.); financial and transaction process 3350 (e.g., lending process, inspection process, mortgage tracking process, valuation process, credit investigation process, credibility process, banking process, interest rate setting process, software process (including applications, programs, services, etc.), production process, collection process, banking service process (e.g., lending process, underwriting process, investment process, etc.), financial service process, diagnosis process, guarantee process, security process, evaluation process, payment process, valuation process, issuing process, warranty process, merger process, banking process, collection process, redemption prevention process, ownership transfer process, ownership verification process, mortgage monitoring process, etc.); wearable and portable devices 3348 (e.g., mobile phones, tablet computers, financial application-specific portable devices, data collectors (including mobile data collectors), sensor-based devices, watches, glasses, ear-worn devices, head-worn devices, clothing-integrated devices, armbands, bracelets, neck-hung devices, AR/YR devices, headphones, etc.); worker 3344 (e.g., banking staff, credit officer, financial services personnel, manager, inspector, broker (e.g., mortgage broker), lawyer, underwriter, supervisor, evaluator, valuator, process supervisor, security personnel, etc.); robot system 3342 (e.g., physical robot, collaborative robot (e.g., "cobots"), software robot, etc.); and facilities 3338 (e.g., banking facilities, inventory warehousing facilities, factories, homes, buildings, storage facilities (e.g., for loan-related mortgages, property as a loan subject, inventory (e.g., related to inventory loans), personal property, components, packaging materials, goods, products, machinery, equipment, etc.), banking facilities (e.g., for commercial banking, investment, consumer banking, lending, and many other banking activities), etc., in embodiments, the entity 3330 may include external markets 3390 of finance, goods, e-commerce, advertising, etc., as well as other markets 3390 (including current and future markets), such as external markets in which various goods and services transactions are conducted, such that monitoring of the market 3390 and its internal entities 3330 may provide lending-related information, for example, for various entities that may include mortgage 4802 or assets for asset vouching lending, the monitoring system 3306 may monitor the mortgage 4802 or assets through not only cameras, sensors, or other monitoring systems 3306, etc., but may also collect data regarding the value, price, or other status of the mortgage 4802 or assets through various types of data collection systems 3318, etc., such as by determining the market status of the mortgage 4802 or asset in a similar status, with similar age, similar description, similar location, etc., in embodiments, the adaptive intelligence system 3304 may include a clustering system 4804, such as by similarity of attributes to a system including mortgage 4802, principal, etc, the entities 3330, including assets, etc., are grouped or clustered, such as k-means clustering systems, self-organizing map systems, or other systems described herein and in the documents incorporated by reference. For example, the clustering system may manage a set of mortgages, assets, principals, and loans so that they may be monitored and analyzed based on common attributes so that the performance of a subset of transactions may be used to predict the performance of other transactions, which in turn may be used to underwire 3420, pricing 3421, fraud prevention applications 3416, or other applications, including any of the services, solutions, or applications described in connection with fig. 48 and 49 or elsewhere in the present invention or in documents incorporated by reference. In an embodiment, status information about a mortgage 4802 or asset is continuously monitored by a monitoring system 3306 (e.g., a set of sensors on the mortgage 4802 or asset, a set of sensors or cameras in the mortgage 4802 or asset environment, etc.), and market information is collected by a data collection system 3318 in real-time so that the status and market information can be arranged in time and serve as a basis for a real-time estimate of the value of the mortgage or asset and a prospective prediction of the future value of the mortgage or asset. The present and predicted values of a mortgage 4802 or property may be based on a model that may be accessed and used (e.g., in a smart contract 3431) to enable automatic or machine-assisted lending of the mortgage or property, such as a small loan underwriting or release of the mortgage 4802 or property. Data aggregation of a set of mortgages 4802 or a set of assets (e.g., a collection or group of mortgages 4802, or a group of assets owned by an entity 3330) may enable real-time portfolio valuation and larger scale lending, including valuations and lending by smart contracts 3431 that automatically adjust interest rates and other terms and conditions based on real-time condition monitoring and real-time market data collection and integration based on individual or aggregate values of the mortgage 4802 or assets. Transactions, principal information, ownership transfers, terms and condition changes, and other information may be stored in the blockchain 3422, including loan transactions and information about mortgage 4802 or assets (e.g., market data and status information of mortgage 4802 or assets). The smart contract 3431 may be used to require principal confirmation of status information and/or market value information, such as statement and assurance supported or verified by the monitoring system 3306 (which may mark fraud in the fraud detection system 3416). The loan model 4808 may be used to rate mortgage 4802 or assets, determine loan qualifications based on the status and/or value of the mortgage 4802 or asset, set pricing (e.g., interest rates), adjust terms and conditions, and the like. The lending model 4808 may be created by a group of specialists, for example using analysis 3419 of past lending transactions. The lending model 4808 may be populated with data from the monitoring system 3306 and the data collection system 3318, may extract data from the storage system 3310, and the like. The lending model 4808 may be used to configure parameters of the smart contract 3431 such that the terms and conditions of the smart contract can be automatically adjusted based on adjustments in the lending model 4808. The lending model 4808 may be used to improve upon by artificial intelligence 3448, such as by training it about a set of results, such as results of a lending transaction (e.g., payment results, default results, fulfillment results, etc.), results about a mortgage 4802 or asset (e.g., price or value patterns of a mortgage or asset over time), results about an entity (e.g., default, redemption, fulfillment results, pay-in-time, overdue payments, bankrupts, etc.), and so forth. Training may be used to adjust and improve model parameters and performance, including classification for mortgage or asset (e.g., automatic classification of type and/or condition, such as vision-based classification using camera-based monitoring system 3306), value prediction, default prediction, performance prediction, etc. of mortgage 4802 or asset. In an embodiment, the configuration or processing of the intelligent contracts 3431 for mortgage 4802 or asset lending may be learned and automatically performed in a Robotic Process Automation (RPA) system 3442, such as by training the RPA system 3442 to create the intelligent contracts 3431, configure parameters of the intelligent contracts 3431, confirm ownership of the mortgage 4802 or asset, set terms and conditions of the intelligent contracts 3431, initiate a guaranty benefit for the intelligent contracts, monitor status or performance of the intelligent contracts 3431, terminate or initiate termination of a breach of the intelligent contracts 3431, stop the intelligent contracts 3431, redeem the mortgage 4802 or asset, transfer ownership, etc., as they employ similar task and action training sets in the creation, configuration, ownership confirmation, initiation of a guaranty benefit, monitoring, termination, redemption, etc. of the intelligent contracts 3431 training set, such as by using the monitoring system 3306 to monitor expert entities 3330 (e.g., human manager). Once the RPA system 3442 is trained, it may efficiently create the ability to provide large-scale loans between various entities and assets (available as mortgage 4802), which may provide guarantees or guarantees, etc., thereby making the loan easier to use in a wider variety of situations, entities 3330, and mortgages 4802. The RPA system 3442 itself may be modified by artificial intelligence 3448, such as by continually adjusting model parameters, weights, configurations, etc. based on results of loan performance results, mortgage valuation results, default results, equity results, interest rate results, profitability results, return on investment results, etc. The smart contracts 3431 may include or be used for direct borrowing, silver group borrowing, and secondary borrowing contracts, individual loans, or aggregate batch loans, among others.
In an embodiment, the lending solution 3410 of the administration application platform layer 3302 may, in alternative embodiments, include, integrate with, or interact with a set of applications 3312 (e.g., in other embodiments of the platform 3300), such as an application by which a borrower, carrier or owner of a insurer, transaction or financial entity, or other user may manage, monitor, control, analyze, or otherwise interact with one or more elements associated with a loan (e.g., as a lender party, loan body, mortgage of a loan, or otherwise with a loan-related entity 3330). This may include any of the elements described above in connection with fig. 33. The set of applications 3312 may include a lending application 3410 (e.g., without limitation, for personal lending, commercial lending, mortgage lending, small forehead lending, point-to-point lending, insurance related lending, property guarantee lending, guaranteed debt lending, corporate debt lending, learning-aid lending, subsidy lending, mortgage lending, municipal lending, host debt, automotive lending, payday lending, mortgage with payable funds, guaranty transactions, mortgage with guaranteed or guaranteed payments (e.g., refunds, annuities, etc.). The loan solution 3410 may include, integrate, or link one or more of a variety of other types of applications that may be related to the loan, such as an investment application 3402 (e.g., without limitation, for investment batch loans, corporate debts, bonds, silver group loans, municipal debts, main debts, or other types of debt-related securities); the property management application 3404 (e.g., without limitation, for managing properties that may be the loan entity, loan mortgages, properties that provide guarantees to loans, loan guaranty mortgages or certification of credit, bond-related properties, investment properties, real properties, fixtures, personal properties, real properties, equipment, intellectual property, vehicles, and other properties); the risk management application 3408 (e.g., without limitation, for managing risks or responsibilities with respect to a loan body, a loan party, or an activity related to loan performance, such as a product, property, person, home, vehicle, device, component, information technology system, security event, network security system, property, health, mortality, fire, flood, weather, disability, business disruption, injury, property loss, business damage, default, etc.); marketing applications 3412 (e.g., without limitation, applications for marketing loans or batch loans, customer relationship management applications for loans, search engine optimization applications for attracting interested parties, sales management applications, advertising web applications, behavior tracking applications, marketing analysis applications, location-based product or service targeting applications, collaborative filtering applications, recommendation engines for loan related products or services, etc.); transaction applications 3428 (e.g., without limitation, applications for transactions of loans or batch loans, portions of loans, loan-related interest, etc., such as purchasing applications, sales applications, bidding applications, auction applications, reverse auction applications, bid-and-ask matching applications, etc.); the tax application 3414 (e.g., without limitation, for managing, calculating, reporting, optimizing, or otherwise processing data, events, workflows, or other factors related to tax-related effects of a loan); the fraud prevention applications 3416 (e.g., without limitation, one or more of an authentication application, a biometric authentication application, a transaction pattern based fraud detection application, a location based fraud detection application, a user behavior based fraud detection application, a network address based fraud detection application, a blacklist application, a whitelist application, a content inspection based fraud detection application, or other fraud detection applications); security applications, solutions or services 3418 (referred to herein as security applications, such as, but not limited to, any of the above-described fraud prevention applications 3416, as well as physical security systems (e.g., for access control systems (e.g., using biometric access control, fingerprint, retinal scan, password, and other access controls), safes, cages, safes, etc.), monitoring systems (e.g., using cameras, motion sensors, infrared sensors, and other sensors), network security systems (e.g., for virus detection and remediation, intrusion detection and remediation, spam detection and remediation, phishing detection and remediation, social engineering detection and remediation, network attack detection and remediation, packet detection, traffic detection, DNS attack remediation and detection, etc.), or other security applications; underwriting application 3420 (e.g., without limitation, any application for underwriting any loan, guarantee, or other loan-related transaction or obligation, including any application for detecting, characterizing, or predicting the likelihood and/or extent of risk, including underwriting of any data source, event, or entity described in documents based on the present invention or incorporated by reference herein); the blockchain application 3422 (e.g., without limitation, a distributed ledger that captures a series of transactions, such as debit or credit, purchase or sale, physical exchange of value, smart contract event, etc., a cryptocurrency application or other blockchain-based application); a real estate application 3424 (e.g., without limitation, a real estate brokerage application, a real estate valuation application, a real estate mortgage or loan application, a real estate valuation application, etc.); the administration application 3426 (e.g., without limitation, an application for administering loan terms and conditions, such as a licensing principal, a licensing mortgage, a licensing repayment period, a licensing interest rate, a desired disclosure, a desired underwriting process, joint conditions, etc.); market applications, solutions, or services 3327 (referred to as market applications such as, but not limited to, loan joint markets, blockchain-based markets, cryptocurrency markets, token-based markets, markets for items used as mortgages, or other markets); the guarantee or guarantee application 3417 (e.g., without limitation, for use in connection with guaranteeing or guaranteeing an item as a loan body, a loan mortgage, etc., such as a product, service, offering, solution, physical product, software, service level, quality of service, financial instrument, debt, mortgage, service performance, or other item); the analyst application 3419 (e.g., without limitation, analysis applications such as big data applications, user behavior applications, prediction applications, classification applications, control panels, pattern recognition applications, metering economics applications, financial benefits applications, return on investment applications, scenario planning applications, decision support applications, etc.) for any of the data types, applications, events, workflows, or entities described in the documents of the present invention and incorporated by reference herein; the pricing application 3421 (e.g., without limitation, for pricing interest rates and other terms and conditions of loans). Thus, the administration application platform 3302 may keep and enable interactions between various different applications 3312 (which term includes the above and other financial or transactional applications, services, solutions, etc.), such that any pairwise or larger combination or permutation of these services may be improved over the same type of stand-alone applications by sharing micro-services, sharing data infrastructure, and sharing intelligence.
In an embodiment, the data collection system 3318 and monitoring system 3306 may monitor one or more events related to loans, debts, bonds, warranty agreements, or other lending transactions, such as events related to: applying for loans; providing a loan; accepting a loan; providing loan underwriting information; providing a credit report; deferring the payment required; setting the interest rate of loans; postponing payment requirements; determining mortgages or assets of the loan; verifying ownership of a loan mortgage or guarantee; recording the change of ownership of property; evaluating the value of a loan mortgage or guarantee; checking property involved in a loan, status changes of entities related to the loan, value changes of entities related to the loan, operating state changes of borrowers, financial rating changes of borrowers, economic value changes of items provided as a guarantee; providing a loan insurance; providing a property insurance proof associated with the loan; providing loan qualification; determining a loan guarantee; carrying out loan; repayment of the loan; delineating a loan; collect loan; clearing loans; setting the terms and conditions of loans; redemption-stopping property limited by the loan; and modifying the terms and conditions of the loan.
Micro-service lending platform for data collection services, blockchains and intelligent contracts
In an embodiment, a platform is provided herein that includes various services, components, modules, programs, systems, devices, algorithms, and other elements for lending. An example platform or system for lending, comprising: a set of micro-services having a set of application programming interfaces enabling connection between and with the micro-services through a program external to the platform, wherein the micro-services comprise: (a) A multimodal data collection service collection that collects information about the debit transaction and monitors entities associated with the debit transaction; (b) A set of blockchain services for maintaining a secure historical ledger for loan-related events, the blockchain services having access control features that manage access rights for a set of parties involved in the loan; (c) A set of application programming interfaces, data integration services, data processing workflows and user interfaces for processing loan-related events and loan-related activities; and (d) a set of smart contract services for specifying the terms and conditions of smart contracts that govern at least one of loan terms and conditions, loan-related events, and loan-related activities.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example system, comprising: wherein the lending-related entity includes a group of entities among borrowers, guarantors, equipment, merchandise, systems, fixtures, buildings, storage facilities, and mortgages.
An example system, comprising: wherein the mortgage is monitored and the mortgage is selected from the group consisting of: vehicles, ships, airplanes, buildings, houses, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, commodities, securities, currencies, value certificates, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.
An example system, comprising: wherein the multimodal data collection service set includes services selected from: a set of internet of things systems that monitor an entity; a set of cameras monitoring an entity; a set of software services that extract information related to an entity from a public information site; a set of mobile services reporting information related to an entity; a set of wearable devices worn by a human entity; a set of user interfaces through which an entity provides information about the entity; and a set of crowdsourcing services for requesting and reporting information related to the entity.
An example system, comprising: wherein the loan-related event is selected from the group consisting of: applying for loans; providing a loan; accepting a loan; providing loan underwriting information; providing a credit report; deferring the payment required; setting the interest rate of loans; postponing payment requirements; determining mortgages of the loan; verifying ownership of a loan mortgage or guarantee; recording the change of ownership of property; evaluating the value of a loan mortgage or guarantee; checking property involved in a loan, status changes of entities related to the loan, value changes of entities related to the loan, operating state changes of borrowers, financial rating changes of borrowers, economic value changes of items provided as a guarantee; providing a loan insurance; providing a property insurance proof associated with the loan; providing loan qualification; determining a loan guarantee; carrying out loan; repayment of the loan; delineating a loan; collect loan; clearing loans; setting the terms and conditions of loans; redemption-stopping property limited by the loan; and modifying the terms and conditions of the loan.
An example system, comprising: wherein the set of terms and conditions of the loan specified and managed by the set of smart contract services are selected from the group consisting of: liability principal amount, liability balance, fixed interest rate, variable interest rate, payment amount, payment plan, end-of-line clearing plan, mortgage description, mortgage substitutability description, principal, insured, guarantor, personal guarantor, retention, deadline, contract, redemption status, violation status, and outcome of the violation.
An example system, comprising: wherein a set of parties to the loan are selected from: primary borrowers, secondary borrowers, borrowing groups, corporate borrowers, government borrowers, banking borrowers, warranty borrowers, bond purchasers, non-warranty borrowers, warranty providers, borrowers, debtors, underwriters, inspectors, valuators, auditors, valuation professionals, government officers, and accountants.
An example system, comprising: wherein the loan-related activities comprise activities selected from the group consisting of: searching for a party interested in participating in a loan transaction; applying for loans; carrying out loan; legal contracts forming a loan; monitoring loan performance; repayment of the loan; reorganizing or modifying loans; settlement of loans; monitoring a loan mortgage; constructing a loan silver group; redemption-stopping loan; and completing the loan transaction.
An example system, comprising: wherein the loan is of at least one of the following types: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance arrangements, payday loans, refund expectations, learning-aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, and subsidy loans.
An example system, comprising: wherein the set of smart contract services configures at least one smart contract to automatically perform loan-related actions based on information collected by the multimodal data collection service set.
An example system, comprising: wherein the loan-related action is selected from the group consisting of: providing a loan; accepting a loan; carrying out loan; setting the interest rate of loans; postponing payment requirements; modifying the interest rate of the loan; verifying ownership of the mortgage; recording the change of ownership; evaluating the value of the mortgage; initiating a check of the mortgage; collect loan; clearing loans; setting the terms and conditions of loans; providing a notification to be provided to the borrower; redemption-stopping property limited by the loan; and modifying the terms and conditions of the loan.
An example system, comprising: wherein the platform or system may further include an automated agent that processes events related to at least one of the value, status, and ownership of the mortgage and takes actions related to loans to which the mortgage is limited.
Referring to fig. 49, additional applications, solutions, programs, systems, services, etc. that may be present in the lending solution 3410 are shown and may be interchangeably contained in the platform 3302 with other elements described in connection with fig. 48, elsewhere in the present invention, and in the documents incorporated by reference herein. Additional entities 3330 are also shown in the figures, which should be understood to be interchangeable with other entities 3330 described in connection with the various embodiments described herein. In addition to the elements already mentioned above, the lending solution 3410 may further include: a set of applications, solutions, programs, systems, services, etc. that include one or more social network analysis solutions 4904 that may find and analyze information about various entities 3330 described in one or more social networks (e.g., without limitation, information about a principal, principal behavior, asset status, events related to a principal or asset, facility status, mortgage 4802, or location of an asset, etc.), such as by allowing a user to configure queries that may be initiated and managed between a set of social networking sites using data collection system 3318 and monitoring system 3306; the loan management solution 4948 (e.g., for managing or responding to one or more events related to a loan (such events include: applying for a loan; providing a loan; accepting a loan; providing loan underwriting information, providing credit reports, deferring required payments, setting up the rate of interest of the loan, deferring payment requirements, determining mortgage of the loan, verifying ownership of the mortgage or guarantee, recording changes in ownership of property, evaluating the value of the mortgage or guarantee of the loan, checking property involved in the loan, status changes of entities related to the loan, value changes of entities related to the loan, change in the borrower's working status, change in the borrower's financial rating, change in the economic value of the items provided as guarantee, providing loan insurance, providing property insurance evidence related to the loan, providing loan qualification evidence, determining the loan guarantee, underwriting of the loan, repayment of the loan, earning the loan, setting up terms and conditions of the loan, retaining property limited by the loan, modifying terms and conditions of the loan, terms and conditions for setting up the loan (e.g., principal amount of money, liability balance, fixed interest, variable interest, payment amount, payment plan, ultimate principal, clearing plan, default illustration, equitable, default, equitable, guarantee, default, etc.), searching for a party interested in participating in a loan transaction; processing a loan application; carrying out loan; legal contracts forming a loan; monitoring loan performance; repayment of the loan; reorganizing or modifying loans; clearing loans; monitoring a loan mortgage; constructing a loan silver group; redemption-stopping loan; retracting the loan; combining a set of loans; analyzing loan performance; processing loan violations; transferring ownership of the asset or mortgage; completing a loan transaction); rating solution 6801 (e.g., for rating entity 3330 (e.g., principal 4910, mortgage 4802, asset 4918, etc.), such as a rating involving reputation, financial status, physical status, value, whether there is a defect, quality, or other attribute); regulatory and/or compliance solutions 3426 (e.g., to implement specifications, applications, and/or monitoring of one or more policies, rules, regulations, procedures, agreements, procedures, etc., such as policies, rules, regulations, procedures, agreements, procedures, etc., relating to loan transaction terms and conditions, steps required to form a loan transaction, steps required to perform a loan transaction, steps required to guarantee or mortgage, steps required to underwriting, steps required to set prices, interest rates, etc., steps required to provide necessary legal disclosure and notification (e.g., to present annual percentage interest rates), etc.); a custody solution or set of custody services 6502 (e.g., for custody of a set of assets 4918, mortgage 4802, etc. (including cryptocurrency, currency, securities, stocks, bonds, agreements to prove ownership rights, etc.), such as principal 4910, customer or other entity 3330, which represents a need for assistance in maintaining item security, or a bond intended to provide assurance, support, or assurance for a bond, such as a bond involving a loan transaction); marketing solution 6702 (e.g., for enabling a borrower to market a loan to a group of potential borrowers, targeting a group of borrowers that are eligible for a transaction type, configuring marketing or promotional messages (including placement location and duration of messages), configuring advertising and promotional channels for loan transactions, configuring promotional or loyalty program parameters, etc.); proxy solution 4944 (e.g., for proxy of a set of loan transactions, such as mortgage loans, among a set of parties) that may allow a user to configure a set of preferences, profiles, parameters, etc., to find a set of prospective transaction opponents for the loan transaction; bond management solutions 4934, for example, for managing, reporting, joining, merging, or otherwise processing a set of bonds (e.g., municipal bonds, corporate bonds, performance bonds, etc.); a vouching monitoring solution 4930, for example, for monitoring, categorizing, predicting, or otherwise processing reliability, quality, status, health, financial, physical, or other information about a vouched-for, a vouchers, a set of mortgages supporting vouching, a set of assets supporting vouching, and the like; negotiating a solution 4932, such as a set of terms and conditions for assisting, monitoring, reporting, facilitating, and/or automatically negotiating a loan transaction (e.g., without limitation, a bond principal amount, a bond balance, a fixed interest rate, a variable interest rate, a payment amount, a payment plan, a maximum end point repayment plan, a mortgage description, a mortgage substitutability description, a principal, a insured, a guarantor, a guaranty, a personal guaranty, a retention, a time limit, a contract, a redemption condition, a violation condition, and a violation outcome), which may include a set of user interfaces for configuring negotiation parameters, profiles, preferences, and the like, such as a user interface using or providing information by the loan model 4808, and a user interface using or automated by the set of artificial intelligence services and systems 3448, automated by or assisted by the robotic process automation 3442 or other adaptive intelligence system 3304; the collection solution 4938 for reclaiming loans, which may optionally be automated using, providing information from, or automating by, or assisting with a set of artificial intelligence services and systems 3448, by robotic process automation 3442 or other adaptive intelligence system 3304, for example, based on monitoring the status or condition of various entities 3330 with monitoring system 3306 and data collection system 3318 to trigger collection, for example, when one or more contracts are not satisfied, mortgage conditions are poor, principal's financial condition is below a threshold, etc.; a merge solution 4940 for merging a set of loans, for example, using a loan model 4808 configured to model a set of combined loans and automated using or by one or more adaptive intelligence systems 3304; the warranty solution 4942, for example, for monitoring, managing, automatically executing, or otherwise processing a set of warranty transactions, for example, using the lending model 4808 configured to model warranty transactions and automated using or by one or more of the adaptive intelligence systems 3304; liability reorganization solution 4928, e.g., for reorganizing a set of loans or liabilities, e.g., using a lending model 4808 configured to model an alternate scenario for reorganizing a set of loans or liabilities and automated using or by one or more adaptive intelligence systems 3304; and/or an interest rate setting solution 4924, such as a set of rules or models for setting or configuring a set of interest rates for a loan transaction, or for automatically setting an interest rate based on information collected by the data collection system 3318 or monitoring system 3306 (e.g., about a condition, state, health, location, geographic location, storage conditions, or other relevant information about any entity 3330), which may set an interest rate or facilitate a set of interest rate settings for a loan, such as using a loan model 4808 configured to model a set of interest rate scenarios for a loan and using one or more adaptive intelligence systems 3304 or automations therewith. As with the solutions referenced in connection with fig. 48, various solutions may share the adaptive intelligence system 3304, monitoring system 3306, data collection system 3318, and storage system 3310, for example by integration into platform 4800 in a micro-service architecture with various suitable data integration services, APIs, and interfaces.
As with the entity 3330 described in connection with fig. 49, the entity 3330 may further include: a range of entities involved in loans, liability transactions, bonds, warranty agreements, and other lending transactions, such as: mortgage 4802 and asset 4918 (e.g., vehicle, vessel, aircraft, building, residence, real estate, undeveloped property, farm, crop, facility 3338 (e.g., municipal facility, factory, warehouse, storage facility, processing facility, factory building, etc.), system, set of inventory, merchandise, securities, currency, value vouchers, tickets, cryptocurrency, consumables, edible items, beverages, precious metals, jewelry, precious stones, intellectual property, contractual rights, legal rights, antiques, fixtures, equipment, furniture, tools, machinery, and personal property) for guaranteeing, securing, guaranteeing, or supporting payment obligations; a set of principals 4910 (e.g., one or more primary borrowers, secondary borrowers, lending groups, corporate borrowers, government borrowers, banking borrowers, guaranteed borrowers, bond buyers, unsecured borrowers, guarantying people, guarantying providers, borrowers, debtors, underwriters, inspectors, evaluators, auditors, valuation professionals, government officers, and accountants); a set of lending agreements 4920 (e.g., loans, bonds 4912, lending agreements, corporate debt agreements, subsidy loan agreements, warranty agreements, merger agreements, silver-base agreements, vouchering agreements, underwriting agreements, etc.), which may include a set of terms and conditions that platform 4800 may search for, collect, monitor, modify, or otherwise handle, such as rates, payment plans, payment amounts, principal amounts, statements and guarantees, reimbursements, contracts, and other terms and conditions; a set of vouchers 4914 (e.g., provided by individual vouchers, corporate vouchers, government vouchers, municipal vouchers, etc. to vouch for or support payment obligations or other obligations of the lending agreement 4920), a set of performance activities 4922 (e.g., paying principal and/or interest, maintaining required insurance, maintaining ownership, satisfying contracts, maintaining the status of mortgage 4802 or asset 4918, conducting business as required by the agreement, etc.), and devices 4952 (e.g., internet of things devices that may be placed on or in merchandise, devices or other items, e.g., as mortgage 4802 or asset 4918 for supporting payment obligations or satisfying contracts or other requirements, or may be placed on or in the package of merchandise, and in other environments in which facility 3338 or entity 3330 may be located), hi embodiments, agreement 4920 may be used for bonds, warranty agreements, banking agreements, merger agreements, settlement agreements or loans, such as one or more of automotive loans, inventory loans, capital equipment loans, performance guarantees, fixed property improvement loans, architectural loans, accounts receivable type warranty loans, invoice financing arrangements, warranty arrangements, payday loans, refund prospective loans, learning aid loans, banking loans, ownership loans, housing loans, risk liabilities loans, intellectual property loans, contract liabilities loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, and subsidy loans.
As mentioned elsewhere herein and in documents incorporated by reference herein, artificial intelligence (e.g., any of the techniques or systems described in this disclosure) may be used in conjunction with various transaction and marketplace entities 3330 and related processes and applications to facilitate: (a) Optimization, automation, and/or control of various functions, workflows, applications, features, resource utilization, and other factors; (b) Identification or diagnosis of various states, entities, patterns, events, contexts, behaviors, or other elements; and/or (c) predict various states, events, contexts, or other factors. As artificial intelligence has increased, a number of domain-specific and/or general artificial intelligence systems have become available and may continue to proliferate. As developers seek solutions to domain-specific problems, such as those related to the entity 3330 and the application of the platform 100 described herein, they face challenges in selecting artificial intelligence models (e.g., which neural networks, machine learning systems, expert systems, etc. to select) and in discovering and selecting which inputs can use artificial intelligence effectively and efficiently for a given problem. As described above, the opportunity mining program 153 may help discover opportunities to improve automation and intelligence; however, once opportunities are discovered, the selection and configuration of artificial intelligence solutions remains a significant challenge that may continue to grow with the proliferation of artificial intelligence solutions.
One set of solutions to these challenges is an artificial intelligence memory 157 for enabling collection, organization, recommendation, and presentation of relevant groups of artificial intelligence systems based on one or more attributes of domain and/or domain-related problems. In an embodiment, the artificial intelligence memory 157 may include a set of interfaces to the artificial intelligence system, such as enabling the downloading of related artificial intelligence applications, establishing links or other connections to the artificial intelligence system (e.g., links to cloud-deployed artificial intelligence systems through APIs, ports, connectors, or other interfaces), and so forth. The artificial intelligence memory 157 may include descriptive content, such as metadata or other descriptive material, about each of the various artificial intelligence systems that indicates the suitability of the system for addressing a particular type of problem (e.g., prediction, NLP, image recognition, pattern recognition, motion detection, route optimization, or many other problems) and/or for operating with a particular domain of input, data, or other entities. In an embodiment, artificial intelligence memory 157 may be organized by category, such as domain, input type, processing type, output type, computing requirements and capabilities, cost, energy usage, and other factors. In an embodiment, the interface to the application memory 157 may obtain input from a developer and/or from a platform (e.g., from the opportunity miner 153) that indicates one or more attributes of a problem that may be solved by artificial intelligence, and may provide a set of recommendations, e.g., via an artificial intelligence attribute search engine, for a subset of artificial intelligence solutions that may represent favorable candidates based on the developer's domain-specific problem.
In an embodiment, the criteria for determining the recommendation may include an expected human supervision level. This may include: knowing the level and type of decisions delegated to human workers (e.g., decisions to purchase securities, making market decisions, obtaining intellectual property permissions, financial limits on actions and orders (e.g., whether RPA can order or promise for transactions below a certain amount, above which transactions are human participation)), the level and type of expected manual supervision of robotic process automation operations, the expected level of manual supervision and/or management of model training and training dataset selection. Other considerations may include the level and type of expected human participation in model version management (e.g., determining historical break points at which input data should be discarded), and the like.
In an embodiment, criteria for determining recommendations may include security considerations such as resistance training and complex environments such as cyber attacks, viruses, and the like. Other security considerations may include security and management of historical training data sets, including audit trails. The security considerations may include traceability and accuracy of the model, the manner in which the model or control parameters are updated, the person who owns the rights to update the model, the manner in which the updates are recorded, the manner in which the results are associated with the model updates, the implementation and manner in which version control is implemented, and the like. Another safety consideration is the recording of AI results for audit trails, including financial results and performance results.
In an embodiment, the criteria for determining recommendations may include availability of different AI types, models, algorithms or systems (including heuristic/model-based AI, neural networks, etc.). Availability may be limited by the computing environment in which the user intends to use (e.g., a given cloud platform, local IT system or network (edge network or other network)), etc., and whether a given type, model, or algorithm is running in the client environment. In an embodiment, the computing factors and configuration may be used as criteria. For example, the type of processor available for running an AI solution in a client environment may be a factor, including: a chipset, a module, a device, a cloud component, a number and architecture of processor types (e.g., multi-core processor availability, GPU availability, CPU availability, FPGA availability, custom ASIC availability, etc.), etc. Further, the computing factors that may be represented as minimum capability criteria may include available processing power for solution training (e.g., utilizing cloud computing resources) and solution operation deployment environments/capabilities (e.g., ioT, in-vehicle networks, edge networks, mesh networks, in-house deployment IT solutions, stand-alone or other deployment environments). Additional criteria may include software and interface criteria such as software environment like operating system (Linux, mac, PC, etc.), languages and protocols to access input data sources for solution training, and to access APIs for runtime data, data integration, and output.
In an embodiment, the criteria may include various network factors such as available network type, available network bandwidth (input and output) for AI solutions and AI operations, network uptime, network redundancy, variability in lead time (ordering of data may vary), and any other network and network criteria described herein.
In an embodiment, the criteria may include absolute performance or quality of service factors, or performance or quality of service factors relative to other AI and/or non-AI solutions (e.g., conventional models or rule-based solutions). Criteria may include speed/delay, training/configuration time and AI solutions, time at which AI solutions provide operating condition results, accuracy, reliability (e.g., ability to resolve results), consistency, no deviation, quality metrics based on results (such as Return On Investment (ROI), rate of return (e.g., operational output of AI management), profitability, revenue and other economic metrics), safety measure performance, security measure performance, energy consumption (e.g., overall consumption, time-based consumption (e.g., ability to shift processing from rush hour to off-peak hour)), ability to acquire renewable or low carbon energy sources for model training and/or operation, cost management of new model training programs (power cost, delay and verification of new models), and the like.
In an embodiment, the criteria may include the ability of the client to access a given type or model due to license requirements and restrictions, client policies (as described elsewhere herein), regulations (including within the jurisdiction of the client), jurisdictions of data sources (e.g., european data privacy laws and safe harbors), jurisdictions managing specific models, algorithms, etc. (e.g., export control of related technologies), rights (e.g., training data or operational data), and the like. In addition, recommendations may be affected by the type of problem to be solved and whether there are specialized algorithms or methods (e.g., quantum annealing-based travel promoter solvers, even classical heuristic methods that provide reasonable baseline results) that are optimized for the type of problem.
In an embodiment, the criteria may include compliance or adherence to regulatory guidelines and policies. There may be policies regarding which input data sources are available to train the AI solution. There may be policies regarding which input sources may be used during operation. For example, the input data source may be reviewed for potential bias, appropriate representation (demographic or problem space representation), scope, and so forth. There may be standards regarding certification or approval of solutions by regulatory authorities, certification organizations, internal IT reviews, and the like. Policies and procedures may exist that must be deployed or enforced in terms of security (e.g., physical security of the system, network security, etc.), security requirements (e.g., user security, security of output products, etc.), and the like.
In an embodiment, the criteria for recommending AI solutions may include criteria regarding availability of data, such as availability of data sources of sufficient size, granularity, quality, reliability, location, time zone, accuracy, etc. for efficient model training. Additional criteria regarding data availability may include data costs for: input of model training and input of model operation. Additional criteria may include data availability for operation of the AI solution, etc. Criteria for AI selection may also include upstream data processing requirements, primary data management considerations such as dimension clean-up and data validation, and the like.
In an embodiment, the criteria for solution selection may include the applicability of the model or solution to a given task or workflow of "problem". Criteria may include the baseline performance of a given model relative to other models performing known task types (e.g., convolutional neural networks for 2D object classification, gated recurrent neural networks for tasks prone to explosive errors, etc.). In an embodiment, the selection of the solution may be based on a solution having a configuration similar to how a biological brain solves a similar task (e.g., where a sequence of neural network models is arranged as a simulated sequence or stream, which may include series elements, parallel elements, feedback loops, conditional logic connections, graphic driving elements, and other stream features), e.g., a stream of a modular or quasi-modular process, such as a stream involving the brain of a human or other species, such as for visual or auditory processing, speech recognition, speech, motion tracking, image recognition, facial recognition, motion coordination, haptic recognition, spatial orientation, and the like. Criteria may include applying an AI-like heuristic as a guardrail or operation affecting a smaller area.
In an embodiment, the criteria may include model deployment considerations such as model update requirements (e.g., frequency and requirements of model deactivation), management of historical models and maintenance of historical decision engines, likelihood of distributed decision capability, model management rules (e.g., duration of time that model or input data is considered valid for training), and the like.
In an embodiment, the search results or recommendations may be based at least in part on collaborative filtering, such as by requiring a developer to indicate or select elements of the favorable model, and by clustering, such as by using a similarity matrix, k-means clustering, or other clustering techniques that associate similar developers, similar domain-specific questions, and/or similar artificial intelligence solutions. Artificial intelligence memory 157 may include e-commerce features such as ratings, reviews, links to related content, and mechanisms for provisioning, licensing, delivery, and payment (including assigning payments to branches and/or contributors), including mechanisms that operate using intelligent contracts and/or blockchain features to automatically perform purchasing, licensing, payment tracking, transaction settlement, or other functions.
In an embodiment, once a solution is selected or recommended, the solution must be configured for the particular client and problem to be solved. The configuration may include, but is not limited to, any of the factors mentioned above in connection with solution model selection. It is important to configure a set of neural network types (e.g., modules) in a stream (with the options of series elements, parallel elements, feedback loops, conditional logic connections, graphics driven streams, etc.) to identify the relative advantages and disadvantages (based on any or selection factors described above) of each AI solution for a particular task involved in the stream. In the illustrative and non-limiting example of a flow, a) identifies something by visual classification (e.g., with CNN), b) predicts its future state (e.g., with gating RNN), c) optimizes the future state (using feed-forward neural network). The configuration options include: selecting one or more neural network types (including a mix of different neural networks and/or other model types in the various streams described above); selecting an input model type; setting initial model weights; setting a model size (e.g., the number of layers in the deep neural network); selecting a computing deployment environment; selecting an input data source for training; selecting an input data source for operation; selecting a feedback function/outcome measure; selecting one or more data integration languages for input and output; configuring an API for model training; configuring an API for model input; configuring an API for output; configuring access control (role-based, user-based, policy-based, etc.); configuring safety parameters; configuring a network protocol; configuration storage parameters (type, location, deadline); configuration economics (e.g., admission pricing, cost allocation, etc.), etc. Additional configuration options may include: configuration data streams (e.g., streams from multiple stock exchanges into a centralized decision engine); configuring a high availability, fault tolerant environment (e.g., requiring transaction system failure to reach an operational state that meets service level requirements), price-based data acquisition policies (e.g., detailed financial data may require additional expense); in combination with heuristic methods; coordinating a massively parallel decision environment (e.g., a distributed vision system), and the like. If there are areas that need to be considered further (e.g., pushing decisions to edges to monitor particular events), additional configuration may include building a decision model.
In an embodiment, another set of solutions that may be deployed alone or with other elements of the platform (including artificial intelligence memory) may include a set of functional imaging capabilities that may include a monitoring system 3306 and a data collection system 3318, and in some cases may include a physical process viewing system 3458 and/or a software interaction viewing system 3450, for example, for monitoring various transaction and market entities 3330. In embodiments, a functional imaging system may provide considerable insight into the type of artificial intelligence that may be most effective in most effectively solving a particular type of problem. As noted elsewhere in the present invention and in the documents incorporated by reference herein, as the scale, complexity and interconnection of computing and networking systems grow, they exhibit information overload, noise, network congestion, energy waste and many other problems. As the internet of things evolves to hundreds of billions of devices, and indeed countless potential interconnections, optimization becomes very difficult. One source of insight is the human brain, which faces similar challenges, has evolved reasonable solutions over thousands of years, solving a series of very difficult optimization problems. The human brain operates with a large number of neural networks organized into interconnected modular systems, each of which has a degree of flexibility to address specific problems, ranging from the regulation of biological systems and maintenance of homeostasis, to the detection of extensive static and dynamic patterns, to the identification of threats and opportunities, and the like.
Establishing a Robotic Process Automation (RPA) system includes selecting an optimal AI solution and configuration. There may be goals for training an RPA system, typically with respect to human interaction with software and/or hardware (e.g., tools) and use of the system in operation, both of which may be enhanced by knowing how the human brain is working in solving the problem. In a single neural network solution (using one network to solve the problem in a single step, such as a single step transition), the process may involve setting the initial weight of the input, selecting the input data source, selecting the network type (e.g., convolutional or non-convolutional, gated or non-gated, deep or non-deep network, etc.), the number of layers, and the type of input provided to it (and if there is a complex output, providing it with an output). The idea is to select inputs and weights that the human brain tends to use to solve the same problem. For a mixture of multiple AI modules/systems and/or a combination of AI with a more traditional software system (e.g., control system, analytical model, rule-based system, conditional logic system, etc.), the value may be the above value plus a perceptual arrangement of the time series of the process, e.g., reflecting a brain activity pattern in the following cases: visual, auditory, tactile, and other sensory information is processed to identify conditions, contexts, motions, objects, etc., and then other areas (behaving differently) perform a number of things such as solving logical problems, computing, following algorithms, expanding possibilities, etc. For these, "Legao building blocks" (each composed of different neural networks or other AI types) may be ordered, arranged in parallel, linked by conditional logic, etc., to implement a solution for automating the process.
In an embodiment, identification information of the inference type and/or the processing type may be provided by performing brain imaging, such as functional MRI or other magnetic imaging, electroencephalogram (EEG) or other imaging, for example by identifying a broad range of brain activities (e.g. activity bands such as delta wave, theta wave, alpha wave and gamma wave), by identifying a set of brain regions activated and/or inactive during user-set interactions for training the intelligent agent (e.g. new cortical regions such as Fp1 (participation judgment and decision), F7 (participation imagination and imitation), F3 (participation analysis reasoning), T3 (participation speech), C3 (participation fact storage), TS (participation mediation and co-occurrence), P3 (participation tactical navigation), O1 (participation visual engineering), fp2 (participation process management), F8 (participation belief system), F4 (participation classification), T4 (participation hearing and intuition), C4 (participation artistic creations), T6 (participation strategy), O2 (participation abstract games), and/or combinations of the above), or by other neural science, how these intelligent agents may be involved in specific task flows, may be trained by means of certain human-related techniques of the intelligent agent's. In an embodiment, the intelligent agent may be configured with a neural network type or combination of types selected to replicate or simulate a processing activity similar to the brain region activity of a human expert performing a set of activities for which the intelligent agent is to be trained. As one of many possible examples, a trader may be shown to use the visual processing area O1 and the strategic game area P4 of the neocortex in conducting a successful trade, and the neural network may be configured with a convolutional neural network for providing efficient replication of visual pattern recognition and a gated recurrent neural network for replicating strategic games. In an embodiment, a neural network repository representing combinations of neural network types that mimic or simulate neocortical activity may be configured to allow selection and implementation of modules that replicate the combinations of human experts for performing various activities that are the subject of intelligent agent development, for example, involving robotic process automation. In embodiments, various neural network types from the library may be configured in series and/or parallel to represent processing flows that may be arranged to mimic or replicate processing flows in the brain, e.g., based on spatiotemporal imaging of the brain while involved in activity as an automated object. In an embodiment, an intelligent software agent for agent development may be trained, for example, using any of the training techniques described herein, to select a set of neural network resource types, to arrange the neural network resource types according to a process flow, to configure input data sources for the set of neural network resources, and/or to automatically deploy the set of neural network types on available computing resources to initiate training of the configured set of neural network resources to perform a desired intelligent agent/automation workflow. In an embodiment, an intelligent software agent for agent development operates on an input dataset of spatiotemporal imaging data of a human brain (e.g., an expert who is performing a workflow that is the subject of further development), and uses the spatiotemporal imaging data to automatically select and configure the selection and placement of the set of neural network types to begin learning. Thus, a system for developing intelligent agents may be configured to (optionally automatically) select neural network types and/or arrangements based on spatiotemporal neocortical activity patterns of human users involved in the workflow for which the agents are trained. Once developed, the intelligent agent/process automation system formed may be trained as described herein.
In an embodiment, a system for developing a smart agent (including the agents described above for developing a smart agent) may use information from human user brain imaging to infer (optionally automatically) which data sources should be selected as input to the smart agent. For example, for a process in which the neocortex region O1 is in a highly active state (involving visual processing), visual input (e.g., visual representation of information from a camera, or price pattern, etc.) may be selected as an advantageous data source. Similarly, for processes involving region C3 (involving storage and retrieval of facts), a data source (e.g., a blockchain-based distributed ledger) may be selected that provides reliable fact information. Thus, a system for developing intelligent agents may be configured to (optionally automatically) select an input data type and input data source based on the spatiotemporal neocortical activity pattern of a human user involved in the workflow for which the agent is trained.
Functional magnetic resonance imaging (fMRI) and the like, electroencephalography (EEG), computed Tomography (CT), and other brain imaging systems have improved to identify brain activity patterns in real-time and correlate with behavior, stimulation information, environmental condition data, gestures, eye movements and the like in time so that by functional imaging, the platform can determine and categorize brain modules, operations, systems, and/or functions used during performance of a set of tasks or activities, such as tasks or activities involving the software interactive viewing system 3345, physical process viewing 3340, or a combination thereof, alone or in combination with other information collected by the monitoring system 3306. Such classification may facilitate selection and/or configuration of a set of artificial intelligence solutions, such as a set of artificial intelligence solutions from an artificial intelligence memory, that includes a set of capabilities and/or functions similar to the set of modules and functions of a human brain in performing an activity, such as a Robotic Process Automation (RPA) system 3442 for initial configuration of tasks automatically performed by expert humans.
In embodiments, the system may receive and/or monitor a set of inputs related to the user, including image/video feeds, audio feeds, motion sensors, heartbeat monitors, other related biosensors, and the like. In embodiments, the system may also receive input related to actions taken by the monitored user, such as input to a computing device or actions taken with respect to the physical environment in which the user is working. In an embodiment, all of the collected data is time stamped so that, for example, the video feed can capture a series of images of the user as the user performs a task, and can simultaneously capture the user's eye movements (e.g., gaze tracking) to determine what the user is interested in (e.g., what the user sees on the screen). During this time, the system may also track the user's heart rate or other biosensor measurements to determine whether the user is engaged in a task that requires attention or is less likely to require attention. The system may also track the actions taken and may further determine the time spent between these actions. The RPA solution may then distribute processing (e.g., heavier, computationally intensive activities) to AI solutions on cloud platforms (e.g., deep neural networks with multiple layers) and use a more compact model (e.g., tinylml TM Models) place less computationally intensive tasks (e.g., tasks where humans quickly make decisions on minimum input data) on an edge or IoT device platform.
In an embodiment, the system may determine the relative time spent between these actions such that a long period of inactivity may indicate that the user is engaged in a job requiring a lot of thought, while a short period of inactivity may indicate that the user is engaged in a job requiring less thought, more activity. The system may also monitor audio feeds and/or status of the computing device that the user is using when the inactivity period occurs, which may indicate that the user is distracted rather than focused. Assuming the user is working actively and does not exhibit distraction, the system may generate feature vectors related to the work being performed by the user, the feature vectors indicating time-stamped data entries, which may then be fed into the machine learning model. In an embodiment, the machine learning model may determine a brain region (or brain regions) from a set of brain regions that may be involved during operation. In an embodiment, the machine learning model may be trained using a training data set (including labeled training vectors), wherein the labels of each training vector indicate the brain region (or brain regions) the subject participated in generating the training vector. For example, each training vector may be labeled using one or more of the following: fp1 (participation judgment and decision), F7 (participation imagination and imitation), F3 (participation analysis reasoning), T3 (participation speech), C3 (participation fact storage), TS (participation mediation and co-emotion), P3 (participation tactical navigation), O1 (participation visual engineering), fp2 (participation process management), F8 (participation belief system), F4 (participation expert classification), T4 (participation listening and intuition), C4 (participation artistic creation), T6 (participation prediction), P4 (participation strategic game), O2 (participation abstraction). In some embodiments, the training vector may indicate additional data, such as the type of task being performed, whether the subject successfully completed the task, or other suitable information.
In embodiments, these machine learning models may be trained for different types of work tasks, such as negotiating, drafting, data entry, replying to emails, analyzing data, auditing documents, and so forth. Further, in some embodiments, such a machine learning model may be trained by one principal, but used by other principals. In these embodiments, the machine learning model (and/or training data vector) may be purchased and sold through the marketplace. Such machine learning models may be used in a wider range of RPA systems, such that the output of the model may be used as a specific signal in the RPA learning process.
Typically, data from an organization is used to predict the location of the organization in the marketplace and adjust the flow within the organization accordingly. In an exemplary embodiment, robotic imaging may be used to capture users (e.g., employees or workers) within an organization as they complete various tasks and processes, while also associating this information with the completion of these tasks/processes. Various analyses (e.g., efficiencies) are obtained regarding the successful completion of the task. The data obtained from the tracking/monitoring user is then used to determine which factors indicate that some users are more successful in completing the task than others (e.g., based on physical activity of the user at the time the task was properly performed, the brain area activated, physical strength of the user, etc.). This may be based on scanning/monitoring the user as he completes the task. In some exemplary embodiments, the following two types of data are separated using a system: data related to users who successfully completed the task, and data related to users who completed less successful. The system may analyze the biological data of workers to determine why one worker is more successful than the other workers. In some exemplary embodiments, the analysis may also be combined with data from the machine to determine whether the worker is using the machine accurately/efficiently. These biological data from workers can also be used to determine if more people are needed to improve efficiency. The results of historical data and process competition are used to see if improvements should be made by training, selecting employees that perform better than others in performing certain tasks, etc. For example, the analysis of the results obtained and the contribution of the results may be used as a feedback function for weighting the values of specific capabilities for designing AI solutions intended to perform the same or similar tasks. In some exemplary embodiments, various data and analyses as described above may be used to determine whether improvements made based on the analysis also improve the organization's market positioning.
An operator skilled in performing a task may establish a firm memory connection with the muscle function, i.e. muscle memory, which translates into easy-to-complete actions that would be difficult to complete or at least require repeated attempts, slower operating speeds, etc. without such a connection. A system that can distinguish between actions accomplished using muscle memory and other actions can better identify which actions are worth following/repeating/learning.
Knowing the mechanisms of muscle memory, such as knowing the path of travel of muscle memory from cognitive (visual, auditory, etc.) inputs, may be the basis for understanding how human action automation is achieved. This may involve repetitive types of actions, correlation of one type of action with another type of action based on similarity, e.g., body posture, expected outcome (throwing a hammer into a holster, etc.).
Another value may be to know how two people form a muscle memory that enables them to "enter a rhythm", for example when exchanging actual items. What the cues they exchange are, visually identifiable actions (placement/direction of hand) and how to interpret these actions.
In an embodiment, the imaging system may analyze brain images of multiple members of a team for a set of tasks or workflows involving different types of expertise. Team performance may be tracked and AI solutions may be configured to replicate the types of neural processing performed by different team members, such as movement tracking and coordination performed by one team member and execution decisions made by another team member.
In an embodiment, the imaging system may analyze brain images of multiple members of a simulation test or negotiation exercise session for a set of verbal communications, etc., regarding a negotiation point, point-by-point count, etc. In addition to brain images, audio capture and biological indicators of response to communication can be acquired to increase the range of multidimensional data that is useful for automating how to learn human behavior related to successful negotiations and the like.
Considering the degree of abstraction that humans use to trigger actions, such as identifying alert tones or identifying actions of colleagues, we may be less abstract in machine-to-machine communications, e.g. the input triggering an alert tone may trigger direct machine-to-machine communications, or if the colleagues are machines, they may indicate their own location in daily work to indicate that they are ready to hand over work. This is similar to the way robots are automated, where a less intelligent degree of automation is achieved, even with simple macro commands, "intelligence" is extracted from the process to make it more robust, and there are strategies and methods that can be applied to these biological types of inputs, which are abstract beyond what is needed. This reduction in complexity may itself be trained in the system as they recognize that innumerable "soft" triggers (e.g., image recognition) may translate into "hard" triggers.
By using Fp1 (participation judgment and decision), P3 (participation in tactical navigation), O1 (participation in visual engineering), fp2 (participation in process management), F8 (participation in belief systems), and T4 (participation in listening and intuition), etc., in some embodiments, the training vector may indicate a system that mixes audio and visual concepts. The system may use an expert system to monitor a set of inputs and reconfigure the inputs to monitor assets including image feeds at various electromagnetic frequencies (e.g., visible light, heat, UV, etc.) as well as audio feeds from these frequencies to determine usage, sounds used, and sounds that may be of interest. When examples include fixed assets (assets that cannot be moved), environmental measurements of the environment and signatures of whether the product is used or not, such as lack of movement, hot stamping, or lack of product, may be measured. The changing environment within the room, contact of the user or other fixtures with the asset may cause the sensor to reconfigure in order to seek space. When fixed in a room, such systems may determine that environmental conditions may be detrimental to the asset, such as high intensity external lighting (UV content too high) relative to more suitable lighting. Further, perceived use motion is included. In a more mobile asset, detection and resolution of benign movements (rather than movements that may have a greater tendency to age or damage the asset) may be recorded and may be described as an aggregate feed.
The combination of risk management-F3 (analytical reasoning) and Fp1 (judgment and decision) -analysis and decision in the human brain is informative by experience and knowledge, which may be incomplete, limited, passive, active, factual, emotional, etc. AI can identify situations (sensors, image recognition, proximity, text and dialogue analysis, etc.) and use fact-based stored results of similar situations to apply better risk management in decisions. This can be used to make better purchasing and financial decisions for the consumer. In other applications, it may be applied to emergency responses, police actions, etc.
In embodiments, the AI solution may be configured to operate primarily with a companion risk manager of the AI solution, e.g., sharing common inputs and resources, but focusing on identifying risk, externality, and other factors that are not needed for core process automation but may improve governance, safety, emergency response, and other aspects.
In embodiments, the AI solution may be configured to operate primarily with a companion risk manager of the AI solution, e.g., sharing common inputs and resources, but focusing on identifying risk, externality, and other factors that are not needed for core process automation but may improve governance, safety, emergency response, and other aspects.
Thus, the platform may include a system that obtains input from a functional imaging system to optionally configure a set of artificial intelligence capabilities for the robotic process automation system based on attribute matching between one or more biological systems (e.g., brain systems) and one or more artificial intelligence systems. Selection and configuration may also include selection of inputs for robotic process automation and/or artificial intelligence configured based at least in part on functional imaging of the brain while the worker performs tasks such as selection of visual inputs for brain visual system high activation (e.g., images from cameras), selection of acoustic inputs for brain auditory system high activation, selection of chemical inputs for brain olfactory system high activation (e.g., chemical sensors), and the like. Thus, an improved way of biologically-aware robotic process automation systems is to have the initial configuration or iterative improvement automated or guided under developer control by imaging derived information collected while workers perform expert tasks that may benefit from automation.
With functional imaging, tasks involving serial processing and parallel processing can be appreciated, as well as the type of AI solutions that may be best suited for similar tasks (e.g., whether it is best to receive both language and visual data/input simultaneously (in parallel) or sequentially). Is there the order in which the user receives data that may suggest the best performance ranking? Analysis of the functional image can identify which computational tasks are processed through visual input at the fastest speed relative to text (language processing), and can improve the matching of tasks to optimal inputs/stimuli.
With functional imaging, the efficiency resulting from pairing or multiple combinations of stimuli (e.g., whether tasks/commands are most efficiently delivered by providing multiple different inputs at a time and/or whether it is preferable to omit certain stimuli from the inputs/commands) can be determined.
With functional imaging, tasks or events to be performed/resolved may be ordered based on probabilistic improvements in performance of subsequent tasks (where the tasks may be computing or actual actions performed by the device based on data/stimulus input).
With functional imaging, the negative impact on performance/computation may be measured based on "noise", where noise may be unwanted data, uncorrelated data, or overwhelming data size, similar to determining "negative stimulus" (in the human environment, this may be ambient noise distinguishing human sounds in a cascade of auditory inputs, or ambient lighting in image recognition, or motion when objects in the computation area, etc.).
As one of many possible examples, a market host may be shown to use the prediction area T6 and the decision and decision area Fp1 when configuring a new market, for example to predict favorable market configuration parameters (e.g., to optimize market efficiency, profitability, and/or fairness) and generate decisions related to the market parameters, and a neural network may be configured with a neural network for providing a predicted efficient replication and a neural network for replicating the decisions. Market configuration parameters may include, but are not limited to, assets, asset types, asset descriptions, ownership verification methods, delivery of transactional merchandise, market pre-estimated scale, market advertising methods, market control methods, regulatory limits, data sources, in-house transaction detection techniques, liquidity requirements, admission requirements (e.g., whether to conduct dealer-to-dealer transactions, dealer-to-customer transactions, or customer-to-customer transactions), anonymity (e.g., to determine whether to disclose a trade opponent identity), continuity of order processing (e.g., continuous or periodic order processing), interactions (e.g., bilateral or multilateral), price discovery, pricing driven factors (e.g., order-driven pricing or quote-driven pricing), price formation (e.g., centralized price formation or decentralized price formation), custody requirements, allowed order types (e.g., limit, market and non-market), supported market types (e.g., dealer market, auction market, absolute auction market, minimum bid auction market, reverse auction market, closed auction market, netherlands auction market, multi-step auction market (e.g., two-step, three-step, n-step, etc.), long term market, futures market, secondary market, derivative market, or market, total market (e.g., mutual funds), etc.), trade rules (e.g., minimum bid unit, trade suspension, open/close time, custody requirements, liquidity requirements, geographic location rules, jurisdictional rules, public rules, inner screen trade restrictions, benefit conflict rules, etc. the price formation or decentralized price formation may be a combination of the price types, time rules (e.g., related to spot market transactions, futures transactions, etc.), asset marketing requirements (e.g., financial reporting requirements, audit requirements, minimum capital requirements), minimum deposit amount, minimum transaction amount, validation rules, commission rules, charging rules, market lifetime rules (e.g., long term market versus short term market with time constraints), and transparency (e.g., amount and scope of information propagated).
The RPA system may perform tasks related to visual algorithms using AI systems related to biological brain functions F3 (participation in analytical reasoning) and O1 (participation in visual engineering) in combination. For example, tasks related to visual algorithms may include processing image sensor data by an O1 vision engineering system to determine what the RPA system "observes" and how to interpret, classify, identify, etc. the "observed" content. The F3 analysis inference system may then perform: 1) Reasoning to determine factors that lead to the current state of the "observed" content, and 2) prediction to determine the future state of the "observed" content based on the current state of the visual data. The RPA system may use T6 prediction functions to help perform such predictions. These inferences can be useful in determining the cause of problems, inefficiency, or problems in the system to be analyzed. Prediction may be helpful in determining a solution to the problem and/or potential efficiency improvement. AI systems using F3, O1, and/or T6 may then also be used to select machine learning models suitable for performing problem resolution and/or efficiency promotion. For example, in a manufacturing environment, the RPA system and AI system may obtain data from a plurality of visual IoT sensors from one or more sites in a manufacturing plant. The O1 vision engineering system may determine and/or categorize what the vision data observes, such as one or more machines, products, assembly lines, etc. The F3 analytic inference system may determine whether one or more machines, products, assembly lines, etc., indicate a problem or inefficiency. The T6 system may then make predictions and forward the predictions to the appropriate machine learning model to determine a solution and/or efficiency improvement for the problem.
Referring to fig. 50, in an embodiment, device 4952 may be a connection device that connects (e.g., through any of a variety of interfaces 3316) to an internet of things (IoT) data collection service 4908, which may be part of or integrated with data collection system 3318 and monitoring system 3306 of platform 4800. The interfaces 3316 may include network interfaces, APIs, SDKs, ports, agents, connectors, gateways, cellular network facilities, data integration interfaces, data migration systems, cloud computing interfaces (including interfaces with computing capabilities, e.g., AWS IoT Greengrass) TM 、Amazon TM Lambda TM And similar systems), etc. For example, ioT data collection service 4908 may be used to obtain data from a set of edge data collection devices in the internet of things, such as low power sensor devices (e.g., to sense movement of an entity, to sense temperature, pressure, or other attributes of entity 3330 or its environment, etc.), capture entities3330, edge devices with more comprehensive functional support (e.g., raspberryPi TM Or other computing device, unix TM Devices, including for example microcontrollers, FPGAs, ASICs, etc.), and devices running embedded systems. In an embodiment, ioT data collection service 4908 may collect data about mortgage 4802 or asset 4918, such as data about location, status (health, physical, etc.), quality, security, possession, etc. For example, personal properties such as precious stones, vehicles, artwork, etc. may be monitored by motion sensors and/or cameras with known locations (or locations confirmed by GPS or other positioning systems). The camera may provide evidence that the item remains undamaged and is owned by the party 4910, e.g., a mortgage 4802 indicating that it is still an appropriate and sufficient loan. In embodiments, this may include mortgages for small loans, such as clothing, collectibles, and other items.
In an embodiment, lending platform 4800 has a set of data integration micro-services including data collection service 3318, monitoring service 3306, blockchain service 3422, and smart contract service 3431 for processing lending entities and transactions. The smart contract service 3431 may obtain data from the data collection service 3318 and the monitoring service 3306 (e.g., from IoT devices) and automatically execute a set of rules or conditions embodying the smart contract based on the collected data. For example, upon identifying that the mortgage 4802 of the loan has been damaged (e.g., as evidenced by a camera or sensor), the smart contract service 3431 may automatically initiate a loan payment request, automatically initiate a redemption-stopping process, automatically initiate an action requiring replacement or backup of the mortgage, automatically initiate a check process, automatically alter a mortgage-based payment or interest rate term (e.g., setting the interest rate to a level of unsecured loan instead of secured loan), etc. The smart contract events may be recorded by the blockchain service 3422 on a blockchain, such as in a distributed ledger. Automatic monitoring of mortgage 4802 and property 4918 and processing of loans through smart contract service 3431 may facilitate lending to a wider range of parties 4910, as well as based on a wider range of mortgages than traditional loans 4802 and property 4918, as the borrower can more certainly master the mortgage status. Monitoring system 3306 and data collection system 3318 may also monitor and collect data from external markets 3390 or related markets operating through platform 4800 to learn the value of mortgage 4802 and property 4918 at any time, thereby ensuring that the items remain of sufficient value and fluidity to ensure that loans are repaid. For example, eBay can be monitored TM And public e-commerce auction sites to confirm that the type and status of personal property is likely to be easily handled by the borrower in the liquidity public market, so that if the borrower breaks the offer, the borrower must receive payment. In this way, loans may be issued and managed for various personal properties that are typically difficult to use as mortgages. In an embodiment, the automated redemption-stopping process may be initiated by a smart contract that may include automatically initiating placement of a mortgage on a public auction website (e.g., eBay when a redemption-allowed default condition occurs (e.g., no payment has been processed) TM Or an auction site adapted for a particular type of property), a mortgage is automatically secured (e.g., by locking a connected device, such as a smart lock, smart container, etc. containing or securing the mortgage), a set of instructions for transporting the mortgage is automatically configured for a carrier, shipping agent, etc., a set of instructions for transporting the mortgage is automatically configured for a drone, robot, etc. In an embodiment, a system is provided that facilitates redemption of a mortgage. An example system for facilitating redemption of a mortgage may include: a set of data collection and monitoring services for monitoring at least one condition of the lending agreement; and a set of smart contract services for establishing terms and conditions of the lending agreement, the terms and conditions including redemption-stopping terms and conditions of at least one item providing a mortgage ensuring performance of a repayment obligation of the lending agreement, wherein the set of smart contract services automatically initiates a redemption-stopping process for the mortgage upon detection of a breach based on data collected by the data collection and monitoring service. Certain additional aspects of the example systems will be described below, any one or more of which may be In certain embodiments. An example system, comprising: wherein the set of smart contract services initiates a signal to at least one of a smart lock and a smart container to lock the mortgage. An example system, comprising: wherein the set of smart contract services configures and initiates a listing of the mortgage on the open auction website. An example system, comprising: wherein the set of smart contract services configures and provides a set of transmission instructions for the mortgage. An example system, comprising: wherein the set of smart contract services configures a set of instructions for the drone to transmit the mortgage. An example system, comprising: wherein the set of smart contract services configures a set of instructions for the robot to transmit the mortgage. An example system, comprising: wherein the set of smart contract services initiates a process for automatically replacing a set of replacement mortgages. An example system, comprising: wherein the set of smart contract services initiates a negotiation regarding the redemption of the offer to the borrower. An example system, comprising: wherein the negotiations are managed by a robotic process automation system that trains based on a training set of redemption-stopping negotiations. An example system, comprising: wherein the negotiating involves modifying at least one of the interest rate, the payment terms, and the mortgage of the debit transaction.
Referring to fig. 51, in an embodiment, a lending platform 4800 is provided having an internet of things (IoT) data collection platform 4908 (with various IoT devices and edge devices described in the present disclosure) for monitoring at least one of a set of assets 4918 and a set of mortgages 4802 of a loan, bond or debt transaction. Platform 4800 can include a vouchers and/or vouchers monitoring solution 4930 for monitoring assets 4918 and/or mortgage 4802 based on data collected by IoT data collection platform 4908, e.g., vouchers and/or vouchers monitoring solution 4930 use various adaptive intelligence systems 3304, e.g., systems that can use models (which can be adjusted, enhanced, trained, etc., e.g., using artificial intelligence 3448) that determine conditions or value of an item based on images, sensor data, location data, or other types of data collected by IoT data collection platform 4908. Monitoring may include monitoring the location of mortgage 4802 or asset 4918, the behavior of principal 4910, the financial condition of principal 4910, etc. The vouchers and/or vouchers monitoring solutions 4930 may include a set of interfaces through which a user may configure parameters for monitoring, such as rules or thresholds regarding conditions, behaviors, attributes, financial value, location, etc., in order to obtain an alert regarding mortgage 4802 or asset 4918. For example, the user may set rules that the mortgage must remain in a given jurisdiction, a threshold percentage of mortgage to loan balance, a minimum status condition (e.g., no damage or defect), and so forth. The configured parameters may be used to provide an alert to personnel responsible for monitoring loan compliance and/or used or embodied in one or more intelligent contracts that may take input from the interface of the guarantee and/or guarantee monitoring solution 4930 to configure redemption stopping conditions, rate modifying conditions, rate accelerating conditions, and the like. Platform 4800 can have a loan management solution 4948 that allows a loan manager to access information from IoT data collection system 4908 and/or a guarantee monitoring solution 4930 so that a user can manage various actions (various types described herein, e.g., setting interest rates, redemption stopping, sending notifications, etc.) with respect to a loan based on the status of mortgage 4802 or property 4918, based on events involving entity 3330, based on behavior, based on loan related actions (e.g., payments), and other factors. The loan management solution 4948 may include a set of interfaces, workflows, models (including the adaptive intelligence system 3304) that configure for particular types of loans (of the types described herein), and allow users to configure parameters, set rules, set thresholds, design workflows, configure smart contract services, configure blockchain services, etc. to facilitate automated or assisted management of loans, for example, enabling automated processing of loan actions through smart contracts in response to data collected from the IoT data collection system 4908, or enabling generation of a set of recommended actions for human users based on the data.
In an embodiment, a loan platform is provided having an intelligent contract and a distributed ledger platform for managing at least one of ownership of a set of mortgage and a set of events related to the set of mortgage. For example, a set of smart contract services 3431 may transfer ownership of a mortgage 4802 or other asset 4918 upon identifying an event of unpaid or other breach, occurrence of a redemption-suppressing condition (e.g., failing to satisfy a contract or failing to satisfy an obligation), etc., wherein the ownership transfer and related events are recorded in a distributed ledger by a set of blockchain services 3422, such as a distributed ledger that provides a secure record of ownership of the asset 4918 or mortgage 4802. For example, a loan contract included in a smart contract may require a minimum proportion (or multiple) of the mortgage 4802 value beyond the loan balance. Based on the collected data regarding mortgage value (e.g., by monitoring one or more external markets 3390 or the markets of platform 4800), the smart contract may calculate whether the contract is satisfied and record the results on the blockchain. If the contract is not satisfied, e.g., the marketing factor indicates that the mortgage type has been reduced and the loan balance is still high, the smart contract may initiate redemption, including recording ownership transfers on the distributed ledger via blockchain service 3422. The smart contract may also handle events related to the entity 3330 (e.g., principal 4910). For example, a loan contract may require the principal to maintain the liability level below a threshold or ratio, maintain the income level, maintain the profit level, etc. The monitoring system 3306 or data collection system 3318 may provide data used by the smart contract service 3431 to determine compliance with contracts and to implement automated actions, including logging redemption and ownership transfer events on distributed ledgers. In another example, the contract may relate to the behavior of the principal 4910 or the legal status of the principal 4910, e.g., requiring the principal to take no particular action on the property. For example, a contract may require the principal to adhere to a partitioning specification that prohibits certain real estate uses. IoT data collection system 4908 may be used to monitor principals 4910, property, or other items to determine compliance with a contract, or to trigger an alarm or automatic action if not in compliance.
Referring to fig. 52, in an embodiment, a lending platform is provided having a crowd-sourced system for obtaining relevant information regarding at least one of a status of a set of mortgages on a loan and a status of an entity associated with a guarantee of the loan. Accordingly, in an embodiment, provided herein is a platform having systems, methods, processes, services, components, and other elements for enabling a system, method, process, service, component, and other elements for blockchain and smart contract platform 5200 for crowd sourcing loan related information. As with other embodiments described above in connection with purchasing innovations, product requirements, etc., the blockchain 3422 (e.g., optionally embodying a distributed ledger) may be configured with a set of smart contracts 3431 to manage rewards 5212 for submitting loan information 5218, such as evidence of property ownership, evidence of ownership, information about mortgage status, information about mortgage location, information about principal identity, information about principal reputation, information about principal activity or behavior, information about principal business practices, information about contract fulfillment status, information about receivables, information about accounts payable, information about mortgage value, and many other types of information. In an embodiment, the blockchain 3422 (e.g., optionally distributed in a distributed ledger) may be used to configure requests for the information 5218 and terms and conditions 5210 related to the information, such as a reward 5212 for submitting the information 5218, a set of terms and conditions 5210 related to the use of the information 5218, and various parameters 5208, such as timing parameters, properties of the required information (e.g., independently verified information such as ownership records, video clips, photos, witness words, etc.), and other parameters 5208.
The platform 5200 can include a crowdsourcing interface 5220, which can be included in or provided in conjunction with a website, application, control panel, communication system (e.g., for sending email, text, voice message, advertisement, broadcast message, or other message), through which messages can be presented in the interface 5220 or sent to relevant individuals (whether targeted, such as in the case of a particular individual making a request, or in the case of a broadcast message to an individual, company, organization, etc. such as at a given location) via an appropriate link to the smart contract 3431 and associated blockchain 3422, such that a reply message submitting the information 5218 and relevant attachments, links, or other information can be automatically associated with the blockchain 3422 (e.g., via an API or data integration system) such that the blockchain 3422 and any optionally associated distributed ledgers maintain a secure, explicit record of the information 5218 submitted in response to the request. Where consideration 5212 is provided, blockchain 3422 and/or smart contracts 3431 may be used to record the time of submission, nature of the submission, and party of the submission such that when the submission meets the conditions of consideration 5212 (e.g., when a loan transaction for which information 5218 is useful is completed), blockchain 3422 and any distributed ledgers stored thereby may be used to identify the submitter, and communicate consideration 5212 (which may take any of the forms of the subtotals mentioned in this disclosure) by executing smart contract 3431. In an embodiment, the blockchain 3422 and any associated ledgers may include identification information for submitting the information 5218 without containing the actual information 5218 so that the information may be kept secret (e.g., encrypted or stored separately from the pure identification information) but access conditions need to be met or verified (e.g., identifying or verifying a person with legitimate access by the identity or security application 3418). The reward 5212 may be provided based on the circumstances or the outcome of the circumstances related to the information 5218, based on a set of rules (which may be automatically applied in some circumstances, e.g., in connection with using the smart contract 3431, an automation system, a rule processing system, an artificial intelligence system 3448, or other expert system), which in embodiments may include rules trained based on training data sets created by human experts. For example, the machine vision system may be used to evaluate evidence of the presence and/or condition of a mortgage based on an image of the item, and the party providing mortgage-related information, such as a voucher or other price-making reward, may be distributed with the reward 5212 through the smart contract 3431, blockchain 3422, and any distributed ledgers. Thus, the platform 5200 may be used for a variety of fact collection and information collection purposes to facilitate verification of mortgages, verification of statements about behavior, verification of occurrence of compliance conditions, verification of occurrence of default conditions, prevention of improper behavior or false statements, reduction of uncertainty, reduction of information asymmetry, and the like.
In an embodiment, the information may relate to fact collection or data collection for various applications and solutions that may be supported by the marketplace platform 3300, including the crowdsourcing platform 5200, including for underwriting applications 3420 (e.g., various types of loans, vouchers, and other items); risk management solutions 3408 (e.g., manage various risks described in the present invention, such as risks associated with personal loans, complete loans, batch loans, etc.); the loan solution 3410 (e.g., evidence of the ownership and or value of the mortgage, evidence of the authenticity of the statement, evidence of the fulfillment or adherence to the loan contract, etc.); regulatory solutions 3426 (for compliance with various regulations that may manage or be performed by the entity 3330 and the processes, behaviors, or activities of the entity 3330); and fraud prevention solutions 3416 (e.g., for detecting fraud, false statements, misbehavior, defamation, etc.). For example, a capital loan for a building may include contracts for property usage, such as allowing certain usage and prohibiting other usage, allowing prescribed occupancy, etc., and the crowdsourcing platform 5200 may solicit and provide consideration comments about compliance information for the building (e.g., requesting the masses to confirm that the building is actually being used for the intended usage allowed by regional regulations). Crowd sourced information may be combined with information from monitoring system 3306. In an embodiment, for example, the adaptive intelligence system 3304 may continuously monitor the property, mortgage 4802, or other entity 3330, and upon identifying (e.g., through an AI system such as a neural network classifier) a suspicious event (e.g., an event that may indicate a violation of a loan contract), the adaptive intelligence system 3304 may provide a signal to the crowdsourcing system 5220 indicating that a crowdsourcing process should be initiated to verify whether or not a violation exists. In an embodiment, this may include classifying contract-related conditions using a machine classifier, providing data classifying and identifying the relevant entity, and automatically configuring a crowd-sourced request based on a model or set of rules, etc., that identifies the requested information content related to the entity 3330 and the provided rewards 5212. In an embodiment, the reward 5212 may be configured by an expert, the reward 5212 may be based on a set of rules (e.g., for loan parameters, terms and conditions of the contract in the smart contract 3431 (e.g., loan value, remaining deadline, etc.), value of the mortgage 4802, etc.), and/or the reward 5212 may be set by the robotic process automation 3442, e.g., the RPA system 3442 may train in various situations based on a set of expert activity training sets that set the reward, which together show which rewards are appropriate in a given situation. The robotic process automation 3442 of the reward configuration may be continually refined by the artificial intelligence 3448, e.g., based on continuous feedback on crowd-sourced results, e.g., successful results (e.g., verification of violations, yield results, etc.).
Information collection may include information collection about the entity 3330 and its identity, assertion, statement, action, or behavior, among other factors, and may be achieved through crowdsourcing in the platform 5200 or through the data collection system 3318 and the monitoring system 3306, optionally through automation and adaptive intelligence via process automation 3442 (e.g., using artificial intelligence system 3448).
Referring to fig. 53, using the various enabling capabilities of the data processing platform 3300 described herein, crowd-sourced evidence of the platform operated marketplace 5200 may be configured in the crowd-sourced interface 5220 or other user interface of the operator of the platform operated marketplace 5200. The operator may use the user interface or crowdsourcing control panel 5414 to take a series of steps to execute or perform an algorithm to create a crowdsourcing request to obtain information 5218 as described in connection with fig. 52. In an embodiment, one or more steps of the algorithm for creating the reward 5212 within the control panel 5414 may comprise: at component 5302, potential rewards 5312 are identified, such as which information 5318 may be valuable in a given situation (e.g., may be indicated by a stakeholder or representative of the entity (e.g., a person or business, such as a lawyer, agent, investigator, principal, auditor, spy, underwriter, inspector, etc.) through various communication channels).
The control panel 5414 can be configured with a crowdsourcing interface 5220, for example with elements (including application programming elements, data integration elements, messaging elements, etc.) that allow for managing crowdsourcing requests in the platform marketplace 5200 and/or one or more external marketplaces 5204. In the control panel 5414, at component 5304, a user can configure one or more parameters 5208 or conditions 5210, such as conditions (types described herein) that include or describe a crowd-sourced request, such as by defining a set of conditions 5210 that trigger a reward 5212 and determine a set of submitters that assign the reward 5212 to the information 5218. The user interface of the control panel 5414 (which may include or be associated with the crowdsourcing interface 5220) may include a set of drop-down menus, tables, forms, etc. with default, templated, recommended, or preconfigured conditions, parameters 5208, conditions 5210, etc. (e.g., conditions appropriate for various types of crowdsourcing requests). Once the conditions and other parameters of the request are configured, at component 5308, the smart contract 3431 and blockchain 3422 can be used to maintain, for example, via ledgers, the data needed to provision, distribute, and exchange data related to the request and submission of information 5218. The smart contracts 3431 and blockchains 3422 can be configured as identity information, transaction information (e.g., for information exchange), technical information, and other evidence data 518 of the type described in connection with fig. 52, including any data, testimonials, photo or video content, or other information that may be relevant to the information 5218 submitting or rewarding 5212 conditions 5210. At component 5310, the smart contract 3431 can be used to embody the conditions 5210 configured at component 5304, operate on the blockchain 3422 created at component 5308, and operate on other data (e.g., data indicative of facts, conditions, events, etc., such as facts, conditions, events, etc., related to submitting the profile data 5218, such as websites indicative of the outcome of legal cases or partial cases, websites reporting surveys, etc.) in the platform-operated marketplace 5200 and/or external marketplace 5204 or other information websites or resources. The smart contract 3431 can apply one or more rules, perform one or more conditional operations, etc., to data such as evidence data 5218, data indicating that parameters 5208 or conditions 5210 are satisfied, as well as identity data, transaction data, time data, and other data in response to the configuration of the component 5310. Upon completion of the configuration of the one or more blockchains 3422 and the one or more smartcontracts 3431, at component 5312, the blockchains 3422 and smartcontracts 3431 can be deployed in a platform operated marketplace 5200, external marketplace 5204, or other website or environment, e.g., for interaction by one or more submitters or other users who can make smartcontracts 3431, e.g., in a crowdsourcing interface 5220 such as a website, application, etc., e.g., by submitting information 5218 and requesting rewards 5212, at which time the platform 5200 can store relevant data, e.g., submission data 5218 and identity data, of one or more parties of smartcontracts 3431, e.g., using adaptive smartsystem 3304 or other capabilities, on blockchain 3422 or on platform 5200. At component 5314, upon execution of the smart contract 3431, the platform 5200 can monitor the platform operating market 5200 and/or one or more external markets 5204 or other sites through a monitoring system layer 3306 or the like to obtain submission profile data 5218, event data 3324, or other data that can satisfy or indicate that one or more conditions 5210 are met or that trigger application of one or more rules of the smart contract 3431, for example to trigger rewards 5212.
At component 5316, upon satisfaction of condition 5210, a smart contract 3431 or the like can be determined, executed, etc., to update or otherwise operate the blockchain 3422, such as by transferring a price (e.g., via a payment system) and transferring access rights to information 5218. Thus, through the steps described above, the operator of the platform operated marketplace 5200 can discover, configure, deploy, and have executed a set of intelligent contracts 3431 that crowd-source loan-related information (e.g., information about the value or status of mortgage 4802, contract compliance, fraud or false statements, etc.), as well as information that is transferred from the information collector to the party seeking the information via password protection and on blockchain 3422. In an embodiment, the adaptive intelligence system layer 3304 may be used to monitor the steps of the algorithms described above, and one or more artificial intelligence systems may be used to automatically perform the entire process, one or more sub-steps, or sub-algorithms, such as by robotic process automation 3442. This may occur, for example, by having the artificial intelligence system 3448 learn a training set of data obtained by observation, such as monitoring the human user for their software interactions as they perform the steps described above. Once trained, the adaptive intelligence layer 3304 may enable the platform 3300 to provide a fully automated platform for crowd-sourced loan information.
Referring to fig. 54, in an embodiment, a loan platform is provided having an intelligent contract system 3431 that automatically adjusts the interest rate of a loan based on information collected through at least one of an internet of things system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services. Platform 4800 can include a rate automation solution 4924, which can include a set of interfaces, workflows, and models (which can include, use, or be enabled by various adaptive intelligence systems 3304), and other components for automating rate settings based on a set of conditions that can include terms and conditions of intelligence contracts 3431, market conditions (of platform market and/or external market 3390), conditions monitored by monitoring system 3306 and data collection system 3318, and the like (e.g., conditions of entity 3330, including but not limited to conditions of principal 4910, mortgage 4802, asset 4918, and the like). For example, a user of interest rate automation solution 4924 may set (e.g., in a user interface) rules, thresholds, model parameters, etc., that determine or recommend interest rates for loans based on the foregoing, such as based on interest rates that a borrower may obtain from a secondary borrower, borrower's risk factors (including risks predicted based on one or more predictive models using artificial intelligence 3448), or the system may automatically recommend or set such rules, thresholds, parameters, etc. (optionally recommended or set by learning a training set based on results over a period of time). Interest rates may be determined based on market factors (e.g., competitive interest rates offered by other borrowers). The interest rate may be calculated for new loans, modifications of existing loans, re-financing, redemption cases (e.g., changing from a guaranteed loan interest rate to an unsecured loan interest rate), etc.
Intelligent contracts for automatically reorganizing liabilities based on monitored conditions
Referring to fig. 55, in an embodiment, a lending platform is provided having an intelligent contract that automatically reorganizes liabilities based on monitored conditions. Platform 4800 can include debt reorganization solution 4928, which can include a set of interfaces, workflows, and models (which can include, use, or be enabled by various adaptive intelligence systems 3304), and other components for automating debt reorganization based on a set of conditions, which can include terms and conditions of intelligent contracts 3431, market conditions (of platform market and/or external market 3390), conditions monitored by monitoring system 3306 and data collection system 3318, and the like (e.g., conditions of entity 3330, including, but not limited to, conditions of principal 4910, mortgage 4802, asset 4918, and the like). For example, a user of debt reorganization solution 4928 may create, configure (e.g., using one or more templates or libraries), modify, set, or otherwise process (e.g., in a user interface of debt reorganization solution 4928) various rules, thresholds, processes, workflows, model parameters, etc., which determine or recommend debt reorganization actions for a loan based on one or more events, conditions, states, actions, etc., where reorganization may be based on various factors, such as current market interest rate, rate of interest available to a borrower from a secondary borrower, risk factors for a borrower (including risk predicted using artificial intelligence 3448 based on one or more predictive models), conditions of other debts (e.g., new debts of borrowers, debt clearance of borrowers, etc.), conditions of mortgage 4802 or asset for providing guarantee or support for a loan, conditions of business or business operations (e.g., accounts receivable, payable, etc.). Reorganization may include changes in interest rates, priority changes for the insured principal, mortgage 4802 or asset 4918 to provide support or guaranty for liabilities, changes in principal, changes in guaranty, changes in payment plans, changes in principal amounts (e.g., including forgiving or accelerated payment), etc. In an embodiment, liability reorganization solution 4928 may automatically recommend or set such rules, thresholds, actions, parameters, etc. (optionally by learning a training set based on results over a period of time) to generate a recommended reorganization plan that may specify a series of actions required to complete the recommended reorganization, which actions may be performed automatically and involve conditional execution of steps based on monitoring conditions and/or smart contract terms that may be created, configured, and/or accounted for by the liability reorganization plan.
The reorganization plan may be determined and executed based at least in part on marketing factors (e.g., competitive interest rates offered by other borrowers, mortgage value, etc.) as well as regulatory and/or compliance factors. The reorganization plan may be generated and/or executed for existing loan modification, re-financing, redemption situations (e.g., changing from a guaranteed loan interest rate to an unsecured loan interest rate), bankruptcy or non-repudiation situations, situations involving market changes (e.g., changes in current interest rate), etc. In an embodiment, the adaptive intelligence system 3304, including the artificial intelligence 3448, may be trained based on the results of the reorganization actions and/or by an expert based on a training set of reorganization actions to generate a set of predictions, classifications, control instructions, plans, models, etc. to automatically create, manage, and/or execute one or more aspects of the reorganization plan. In an embodiment, provided herein is an intelligent contract system for modifying loans, the system having a set of computing services. An example platform or system, comprising: (a) A set of data collection and monitoring services for monitoring a set of entities involved in a loan; and (b) a set of smart contract services for managing smart debit contracts, wherein the set of smart contract services processes information from the set of data collection and monitoring services and automatically reorganizes liabilities based on the monitored conditions. Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments.
Referring to FIG. 56, in an embodiment, a lending platform 4800 is provided having a social network monitoring system 4904 for verifying the reliability of loan assurance. Platform 4800 can include a vouching and/or vouching monitoring solution 4930, which can include a set of interfaces, workflows, and models (which can include, use, or be enabled by various adaptive intelligence systems 3304), and other components for enabling the monitoring of vouching and/or vouching for loan transactions based on a set of conditions, which can include terms and conditions of intelligent contracts 3431, market conditions (of platform market and/or external market 3390), conditions monitored by monitoring system 3306 and data collection system 3318, and the like (e.g., conditions of entity 3330, including, but not limited to, principal 4910, mortgage 4802, asset 4918, and the like). For example, a user of the vouchers and/or vouchers monitoring solution 4930 may set (e.g., in a user interface) rules, thresholds, model parameters, etc., that are determined or recommended for the lending transaction based on borrower risk factors, market risk factors, and/or mortgage 4802 or risk factors of the asset 4918 (including risks predicted based on one or more predictive models using artificial intelligence 3448), or platform 4800 may automatically recommend or set such rules, thresholds, parameters, etc. (optionally by learning a training set based on results over a period of time). The vouchers and/or vouchers monitoring solutions 4930 may configure a set of social network analysis services 4904 and/or other monitoring systems 3306 and/or data collection systems 4818 to search, parse, extract, and process data from one or more social networks, websites, etc., for example, data that may contain information about mortgage 4802 or property 4918 (e.g., a photograph of a vehicle, ship or other personal property, a photograph of a home or other real property showing principal 4910, a photograph or text describing the activity of principal 4910 (including photographs or text indicating financial risk, physical risk, health risk, or other risk that may be related to the quality of the vouchers and/or the ability of the borrower to pay back loan), for example, a photograph showing that a borrower is driving a common passenger car in an off-road environment may be marked as indicating that the vehicle cannot be relied upon entirely as a higher automobile loan.
Social network monitoring system for verifying quality of personal vouchers of loans
Accordingly, in an embodiment, provided herein is a social network monitoring system for verifying conditions of loan guarantee. An example platform or system, comprising: (a) A set of social networking data collection and monitoring services by which data is collected by a set of algorithms for monitoring social networking information about entities involved in loans; and (b) an interface connected to the set of social networking services that enables configuration of parameters of social networking data collection and monitoring services to obtain information related to the vouching conditions. Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments.
An example system, comprising: wherein the set of social networking data collection and monitoring services obtains information about the financial status of the entity that is the sponsor of the loan.
An example system, comprising: wherein the financial condition is determined based at least in part on the following information about the entity contained in the social network: a public valuation of the entity; a set of properties owned by the entity as indicated by the public record; a valuation of a set of properties owned by the entity; the bankruptcy condition of the entity; the redemption-stopping status of the entity; contract violation status of the entity; an offending state of the entity; crime status of the entity; the export regulation status of the entity; a forbidden state of the entity; the tariff status of the entity; tax status of the entity; credit reporting of the entity; credit rating of an entity; the website rating of the entity; a set of customer reviews of the physical product; social network rating of the entity; a set of credentials of the entity; a set of intermediaries for an entity; a set of proofs of an entity; a set of behaviors of an entity; the location of the entity; and the geographic location of the entity.
An example system, comprising: wherein the loan is of at least one of the following types: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance arrangements, payday loans, refund expectations, learning-aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, and subsidy loans.
An example system, comprising: an example system wherein the platform or system may further include an interface to the social networking data collection and monitoring service, comprising: wherein the data collection and monitoring service is configured to obtain information regarding the status of a set of mortgages on the loan, wherein the set of mortgages is selected from the group consisting of: vehicles, ships, airplanes, buildings, houses, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, commodities, securities, currencies, value certificates, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.
An example system, comprising: wherein the condition of the mortgage includes a condition attribute from the following set of condition attributes: the quality of the mortgage, the condition of the mortgage, the ownership status of the mortgage, the occupancy status of the mortgage, the lien status of the mortgage, the brand new or use status of the item, the type of the item, the category of the item, the description of the item, the product feature set of the item, the model of the item, the brand of the item, the manufacturer of the item, the condition of the item, the environment of the item, the condition of the item, the value of the item, the storage location of the item, the geographic location of the item, the age of the item, the maintenance history of the item, the use history of the item, the accident history of the item, the fault history of the item, the ownership history of the item, the price of the item type, the value of the item type, the assessment of the item, and the assessment of the item.
An example system, comprising: wherein the interface is a graphical user interface for enabling a workflow through which a human user inputs parameters to establish social network data collection and monitoring requests.
An example system, comprising: wherein the platform or system may further comprise a set of intelligent contract services that manage intelligent lending contracts, wherein the intelligent contract services process information from the set of social network data collection and monitoring services and automatically take actions related to the loan.
An example system, comprising: wherein the action is at least one of the following actions: a redemption-stopping action, a lien management action, a interest rate adjustment action, a default initiation action, a mortgage replacement, and a refund of the loan.
An example system, comprising: wherein the platform or system may further comprise a robotic process automation system that trains based on a training set of human user interactions with the interface connected to the set of social networking data collection and monitoring services to configure data collection and monitoring actions based on a set of attributes of the loan.
An example system, comprising: wherein the attribute of the loan is obtained from a set of intelligent contract services that manage the loan.
An example system, comprising: wherein the robotic process automation system is configured to iteratively train and refine based on a set of results from a set of social network data collection and monitoring requests.
An example system, comprising: wherein the training includes training the robotic process automation system to determine a set of domains to which to apply the social networking data collection and monitoring service.
An example system, comprising: wherein the training includes training the robotic process automation system to configure social network data collection and monitor the content of the search.
Internet of things (IoT) data collection and monitoring system for verifying quality of personal assurance of loans
Still referring to fig. 56, in an embodiment, a lending platform having an internet of things data collection and monitoring system for verifying the reliability of a loan guarantee is provided. The vouchers and/or vouchers monitoring solutions 4930 may include data from and collected by configuration of a set of internet of things services 4908, which may include various IoT devices, edge computing and processing capabilities, etc., as described in connection with embodiments, such as services that monitor the environments in which various entities 3330 and their lending transactions are involved.
In an embodiment, a monitoring system for verifying conditions of a loan guarantee is provided herein. In embodiments, a set of algorithms may be used to initiate data collection, manage data collection, etc. by the IoT device, e.g., based on the above conditions, including conditions related to borrower or borrower risk factors, market risk factors, physical risk factors, etc. For example, ioT systems may be used to capture videos or images of a house during severe weather, such as to determine if the house is at risk of flooding, wind, etc., thereby confirming whether the house is predictable as a sufficient mortgage for a housing loan, credit line, or other loan transaction.
An example platform or system, comprising: (a) A set of internet of things data collection and monitoring services by which data is collected by a set of algorithms for monitoring information collected from entities involved in loans and information about entities involved in loans; and (b) an interface connected to the set of internet of things data collection and monitoring services that enables configuration of parameters of social network data collection and monitoring services to obtain information related to the vouching conditions. Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments.
An example system, comprising: wherein the set of internet of things data collection and monitoring services obtains information about the financial condition of an entity that is a guarantor of the loan.
An example system, comprising: wherein the financial condition is determined based at least in part on the following information about the entity collected by the internet of things device: a public valuation of the entity; a set of properties owned by the entity as indicated by the public record; a valuation of a set of properties owned by the entity; the bankruptcy condition of the entity; the redemption-stopping status of the entity; contract violation status of the entity; an offending state of the entity; crime status of the entity; the export regulation status of the entity; a forbidden state of the entity; the tariff status of the entity; tax status of the entity; credit reporting of the entity; credit rating of an entity; the website rating of the entity; a set of customer reviews of the physical product; social network rating of the entity; a set of credentials of the entity; a set of intermediaries for an entity; a set of proofs of an entity; a set of behaviors of an entity; the location of the entity; and the geographic location of the entity.
An example system, comprising: wherein the loan is of at least one of the following types: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance arrangements, payday loans, refund expectations, learning-aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, and subsidy loans.
An example system, comprising: wherein the platform or system may further comprise an interface to the set of internet of things data collection and monitoring services. An example system, comprising: wherein the set of data collection and monitoring services is for obtaining information about the condition of a set of mortgages on the loan, wherein the set of mortgages is selected from the group consisting of: vehicles, ships, airplanes, buildings, houses, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, commodities, securities, currencies, value certificates, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.
An example system, comprising: wherein the condition of the mortgage includes a condition attribute from the following set of condition attributes: the quality of the mortgage, the condition of the mortgage, the ownership status of the mortgage, the occupancy status of the mortgage, the lien status of the mortgage, the brand new or use status of the item, the type of the item, the category of the item, the description of the item, the product feature set of the item, the model of the item, the brand of the item, the manufacturer of the item, the condition of the item, the environment of the item, the condition of the item, the value of the item, the storage location of the item, the geographic location of the item, the age of the item, the maintenance history of the item, the use history of the item, the accident history of the item, the fault history of the item, the ownership history of the item, the price of the item type, the value of the item type, the assessment of the item, and the assessment of the item.
An example system, comprising: wherein the interface is a graphical user interface for enabling a workflow through which a human user inputs parameters to determine monitoring actions of the internet of things data collection and monitoring service.
An example system, comprising: wherein the platform or system may further comprise a set of intelligent contract services that manage intelligent lending contracts, wherein the set of intelligent contract services process information from the set of internet of things data collection and monitoring services and automatically take actions related to the loan.
An example system, comprising: wherein the action is at least one of the following actions: a redemption-stopping action, a lien management action, a interest rate adjustment action, a default initiation action, a mortgage replacement, and a refund of the loan.
An example system, comprising: wherein the platform or system may further comprise a robotic process automation system that trains based on a training set of human user interactions with the interface connected to the set of internet of things data collection and monitoring services to configure data collection and monitoring actions based on a set of attributes of the loan.
An example system, comprising: wherein the attribute of the loan is obtained from a set of intelligent contract services that manage the loan.
An example system, comprising: wherein the robotic process automation system is configured to iteratively train and refine based on a set of results from a set of internet of things data collection and monitoring service activities.
An example system, comprising: wherein the training comprises training the robotic process automation system to determine a set of domains to which the internet of things data collection and monitoring service is to be applied.
An example system, comprising: wherein the training includes training the robotic process automation system to configure the internet of things data collection and monitor content of service activities.
RPA bank loan negotiating person trained based on training set of expert borrower and borrower interactions
Referring to fig. 57, in an embodiment, a loan platform is provided having a Robotic Process Automation (RPA) system 3442 for negotiating a set of terms and conditions for a loan. The RPA system 3442 may provide automation for negotiating one or more aspects of a solution 4932 that may enable automated negotiations and/or provide advice or planning for negotiations related to loan transactions. The negotiation solution 4932 and/or RPA system 3442 for negotiating may include a set of interfaces, workflows, and models (which may include, use or be enabled by the various adaptive intelligence systems 3304), and other components for automating one or more aspects of negotiations of one or more terms and conditions of a loan transaction based on a set of conditions, etc., which may include terms and conditions of the intelligent contract 3431, market conditions (of the platform marketplace and/or external marketplace 3390), conditions monitored by the monitoring system 3306 and the data collection system 3318, etc. (e.g., conditions of the entity 3330, including, but not limited to, conditions of the principal 4910, mortgage 4802, asset 4918, etc.). For example, a user of the negotiation solution 4932 may create, configure (e.g., using one or more templates or libraries), modify, set, or otherwise process (e.g., in a user interface of the negotiation solution 4932 and/or RPA system 3442) various rules, thresholds, conditional flows, workflows, model parameters, etc., that determine or recommend negotiation actions or plans for the lending transaction based on one or more events, conditions, states, actions, etc., where the negotiation plan may be based on various factors, such as current market rates, rates that a borrower may obtain from a secondary borrower, risk factors for the borrower, risk factors for one or more insurers, market risk factors, etc. (including risks predicted based on one or more predictive models using artificial intelligence 3448), liabilities conditions, conditions of mortgage 4802 or property 4918 for providing a guarantee or support for a loan, conditions of an enterprise or enterprise operation (e.g., receivables, accounts payable, etc.), conditions of principal 4910 (e.g., equity, wealth, debt, location, and other conditions), behaviors of principal (e.g., behavior indicating preference, behavior indicating a negotiation style), etc. The negotiations may include negotiations of: loan transaction terms and conditions, debt reorganization, redemption activities, setting interest rates, changes in interest rates, priority changes for the insured party, changes in mortgage 4802 or asset 4918 for providing support or guaranty for the debt, changes in party, changes in the guaranty, changes in payment plans, changes in principal amounts (e.g., including forgiving or accelerating payments), and many other transactions or terms and conditions. In an embodiment, the negotiation solution 4932 may automatically recommend or set rules, thresholds, actions, parameters, etc. (optionally by learning a training set based on results over a period of time) to generate a recommended negotiation plan that may specify a series of actions required to complete the recommended or expected negotiation results (e.g., within a range of acceptable results), which actions may be performed automatically and involve conditional execution of steps based on monitoring conditions and/or smart contract terms that may be created, configured, and/or accounted for by the negotiation plan. The negotiation plan may be determined and executed based at least in part on marketing factors (e.g., competitive interest rates offered by other borrowers, mortgage value, etc.) as well as regulatory and/or compliance factors. The negotiation plan may be generated and/or executed for the setting up of new loans, the setting up of guarantees and guarantees, secondary loans, modification of existing loans, re-financing, redemption situations (e.g., changing from guaranteed to unsecured loan interest), bankruptcy or non-repudiation situations, situations involving market changes (e.g., changes in current interest rates), etc. In an embodiment, the adaptive intelligence system 3304, including the artificial intelligence 3448, may be trained based on the results of the negotiation actions and/or by an expert based on a training set of negotiation actions to generate a set of predictions, classifications, control instructions, plans, models, etc. to automatically create, manage, and/or execute one or more aspects of the negotiation plan.
In an embodiment, a robotic process automation system for negotiating loans is provided herein. An example platform or system, comprising: (a) A set of data collection and monitoring services for collecting a training set of interactions between entities of a set of loan transactions; (b) An artificial intelligence system that trains based on the interactive training set to classify a set of loan negotiation actions; and (c) a robotic process automation system that trains to negotiate terms and conditions of a loan on behalf of a loan party based on a set of loan transaction interactions and a set of loan transaction results. Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments.
An example system, comprising: wherein the set of data collection and monitoring services includes the following: the system comprises a group of internet of things systems, a group of network management systems and a group of network management systems, wherein the group of internet of things systems is used for monitoring the entity; the group of cameras are used for monitoring the entity; a set of software services for retrieving information related to the entity from a public information site; a set of mobile devices for reporting information related to the entity; a set of wearable devices for wearing by a human entity; a set of user interfaces through which the entity provides information about the entity; and a set of crowdsourcing services for requesting and reporting information related to the entity.
An example system, comprising: wherein the entity is a set of parties to a loan transaction.
An example system, comprising: wherein the set of principals is selected from the group consisting of: primary borrowers, secondary borrowers, borrowing groups, corporate borrowers, government borrowers, banking borrowers, warranty borrowers, bond purchasers, non-warranty borrowers, warranty providers, borrowers, debtors, underwriters, inspectors, valuators, auditors, valuation professionals, government officers, and accountants.
An example system, comprising: wherein the artificial intelligence system comprises at least one of the following systems: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, and simulation systems.
An example system, comprising: wherein the robotic process automation is trained based on a set of interactions of a principal with a set of user interfaces involved in a set of lending processes.
An example system, comprising: wherein after the negotiation is completed, the smart contracts of the loan are automatically configured by a set of smart contract services based on the results of the negotiation.
An example system, comprising: wherein at least one of a result of the negotiating and a negotiation event is recorded in a distributed ledger associated with the loan.
An example system, comprising: wherein the loan is one of the following types: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance arrangements, payday loans, refund expectations, learning-aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, and subsidy loans.
An example system, comprising: wherein the artificial intelligence system comprises at least one of the following systems: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, and simulation systems.
RPA bank loan resurfacing negotiating person trained based on training set of resurfacing interaction of expert borrowers and borrowers
In an embodiment, a robotic process automation system for negotiating loan re-financing is provided herein. An example platform or system, comprising: (a) A set of data collection and monitoring services for collecting a set of training sets of interactions between entities of loan re-financing activities; (b) An artificial intelligence system that trains based on the interactive training set to classify a set of loan re-financing actions; and (c) a robotic process automation system that trains to conduct loan resurf activities on behalf of the loan party based on the set of loan resurf interactions and the set of loan resurf results. Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments.
An example system, comprising: wherein the loan repaying campaign comprises: initiating a re-financing offer; initiating a re-financing request; configuring a re-financing rate; configuring a re-financing payment plan; configuring a re-financing balance; configuring a financing mortgage; managing the use of re-financing returns; removing or setting the lien associated with resurfacing; verifying the repayment ownership; managing the inspection process; filling an application program; negotiating resuash terms and conditions; and ending the re-financing.
An example system, comprising: wherein the set of data collection and monitoring services includes the following: the system comprises a group of internet of things systems, a group of network management systems and a group of network management systems, wherein the group of internet of things systems is used for monitoring the entity; the group of cameras are used for monitoring the entity; a set of software services for retrieving information related to the entity from a public information site; a set of mobile devices for reporting information related to the entity; a set of wearable devices for wearing by a human entity; a set of user interfaces through which the entity provides information about the entity; and a set of crowdsourcing services for requesting and reporting information related to the entity.
An example system, comprising: wherein the entity is a set of parties to a loan transaction.
An example system, comprising: wherein the set of principals is selected from the group consisting of: primary borrowers, secondary borrowers, borrowing groups, corporate borrowers, government borrowers, banking borrowers, warranty borrowers, bond purchasers, non-warranty borrowers, warranty providers, borrowers, debtors, underwriters, inspectors, valuators, auditors, valuation professionals, government officers, and accountants.
An example system, comprising: wherein the artificial intelligence system comprises at least one of the following systems: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, and simulation systems.
An example system, comprising: wherein the robotic process automation is trained based on a set of interactions of a principal with a set of user interfaces involved in a set of lending processes.
An example system, comprising: wherein after completion of the re-financing process, the smart contracts of the re-financing loan are automatically configured by a set of smart contract services based on the results of the re-financing activity.
An example system, comprising: wherein at least one of the resurfacing result and event is recorded in a distributed ledger associated with the resurfacing loan.
An example system, comprising: wherein the loan is one of the following types: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance arrangements, payday loans, refund expectations, learning-aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, and subsidy loans.
An example system, comprising: wherein the artificial intelligence system comprises at least one of the following systems: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, and simulation systems.
RPA bank loan payee trained based on training set of expert and borrower collection interactions
Referring to fig. 58, in an embodiment, a lending platform is provided having a robotic process automation system for retrieving loans. The RPA system 3442 may provide automation for one or more aspects of the payment solution 4938 that may enable automated payment and/or provide advice or planning for payment collection activities associated with the debit transaction. The checkout solution 4938 and/or RPA system 3442 for checkout may include a set of interfaces, workflows, and models (which may include, use or be enabled by the various adaptive intelligence systems 3304), and other components for automating one or more aspects of the checkout actions of one or more terms and conditions of the debit transaction checkout process based on a set of conditions, etc., which may include the terms and conditions of the intelligent contract 3431, market conditions (of the platform market and/or external market 3390), conditions monitored by the monitoring system 3306 and data collection system 3318, etc. (e.g., conditions of the entity 3330, including, but not limited to, conditions of principal 4910, mortgage 4802, asset 4918, etc.). For example, a user of the collection solution 4938 may create, configure (e.g., using one or more templates or libraries), modify, set, or otherwise process (e.g., in a user interface of the collection solution 4938 and/or RPA system 3442) various rules, thresholds, conditional flows, workflows, model parameters, etc., that determine or recommend actions or plans for a loan transaction or loan monitoring solution based on one or more events, conditions, states, actions, etc., where the collection plan may be based on various factors, such as payment, borrower status, mortgage 4802 or asset 4918 status, borrower risk factor, one or more guarantor risk factors, market risk factor, etc. (including those predicted based on one or more predictive models using artificial intelligence 3448), debt 4802 or equity, etc., an account, etc., a status indicating a response (e.g., an account, an equity, etc.), a state (e.g., an account, an agreement, an equity, etc.), a state (e.g., indicating a state, an equity, etc.), a state, an equity, etc. The collection may include collection of funds about the loan, communication encouraging payment, and the like. In an embodiment, the checkout solution 4938 may automatically recommend or set rules, thresholds, actions, parameters, etc. (optionally by learning a training set based on results over a period of time) to generate a recommended checkout plan that may specify a series of actions required to complete the recommended or expected checkout result (e.g., within a range of acceptable results), which actions may be automatically performed and involve conditional execution of steps based on monitoring conditions and/or smart contract terms that may be created, configured, and/or accounted for by the checkout plan. The collection plan may be determined and executed based at least in part on marketing factors (e.g., competitive interest rates offered by other borrowers, mortgage value, etc.) as well as regulatory and/or compliance factors. The collection plan may be generated and/or executed for new loan setup, secondary loans, existing loan modification, re-financing, redemption-stopping situations (e.g., changing from a guaranteed loan interest rate to an unsecured loan interest rate), bankruptcy or non-repudiation situations, situations involving market changes (e.g., changes in current interest rate), etc. In an embodiment, the adaptive intelligence system 3304, including the artificial intelligence 3448, may be trained based on the results of the checkout actions and/or by an expert based on a training set of checkout actions to generate a set of predictions, classifications, control instructions, plans, models, etc. to automatically create, manage, and/or execute one or more aspects of the checkout plan.
In an embodiment, a robotic process automation system for processing loan receipts is provided herein. An example platform or system, comprising: (a) A set of data collection and monitoring services for collecting a training set of interactions between entities of a set of loan transactions involving collection of a set of payments for a set of loans; (b) An artificial intelligence system that trains based on the interactive training set to classify a set of loan collection actions; and (c) a robotic process automation system that trains to take a loan receipt action on behalf of a loan party based on a set of loan transaction interactions and a set of loan receipt results. Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments.
An example system, comprising: wherein the loan payment action taken by the robotic process automation system is selected from the group consisting of: initiating a collection process; transferring the loan to the agency for collection; configuring a receipt communication; scheduling a receipt communication; configuring the content of the checkout communication; configuring a settlement loan offer; terminating the collection action; delay the collection of money; configuring an offer to replace a payment plan; initiating litigation; redemption of the product is initiated; initiating a bankruptcy process; a process of re-possession; and setting mortgage retention rights.
RPA bank loan merger trained based on training set of merger interactions of expert with other borrowers
Referring to fig. 59, in an embodiment, a lending platform is provided having a robotic process automation system for merging a set of loans. The RPA system 3442 may provide automation for one or more aspects of the merge solution 4940 that may enable automatic merging and/or provide advice or planning for merge activities related to lending transactions. The merge solution 4940 and/or RPA system 3442 for merging may include a set of interfaces, workflows, and models (which may include, use or be enabled by the various adaptive intelligence systems 3304), and other components for automating one or more aspects of the merge action or merge process of a lending transaction based on a set of conditions, etc., which may include terms and conditions of the intelligent contracts 3431, market conditions (of the platform market and/or the external market 3390), conditions monitored by the monitoring system 3306 and the data collection system 3318, etc. (e.g., conditions of the entity 3330, including, but not limited to, conditions of the principal 4910, the mortgage 4802, the asset 4918, etc.). For example, a user of the merge solution 4940 may create, configure (e.g., using one or more templates or libraries), modify, set, or otherwise process (e.g., in a user interface of the merge solution 4940 and/or RPA system 3442) various rules, thresholds, conditional flows, workflows, model parameters, etc., that determine or recommend a merge action or plan for a loan transaction or set of loans based on one or more events, conditions, states, actions, etc., where the merge plan may be based on various factors, such as payment, interest rates for the set of loans, current interest rates in the flat market or external market, borrower status for a set of loans, mortgage 4802 or property 4918 status, borrower risk factors, one or more guarantor risk factors, market risk factors, etc. (including risks predicted based on one or more predictive models using artificial intelligence 3448), liability status, status of mortgage 4802 or property 4918 used to provide security or support for the loans, status of business or business operations (e.g., receivables, payables, etc.), status of principal 0 (e.g., equity, wealth, debt, location, and other status), principal's behavior (e.g., behavior indicating preferences, behavior indicating preferential repayment of liabilities), etc. The merge may include a merge of terms and conditions for a set of loans, a selection of appropriate loans, a payment term setting for a merge loan, a repayment plan setting for an existing loan, communication encouraging a merge, and the like. In an embodiment, the merge solution 4940 may automatically recommend or set rules, thresholds, actions, parameters, etc. (optionally by learning a training set based on results over a period of time) to generate a recommended merge plan that may specify a series of actions required to complete the recommendation or to expect the merge result (e.g., within a range of acceptable results), which actions may be performed automatically and involve conditional execution of steps based on monitoring conditions and/or smart contract terms that may be created, configured, and/or accounted for by the merge plan. The merge plan may be determined and executed based at least in part on marketing factors (e.g., competitive interest rates offered by other borrowers, mortgage value, etc.) as well as regulatory and/or compliance factors. The merge plan may be generated and/or executed for the establishment of a new merge loan, a secondary loan associated with the merge, a modification of an existing loan associated with the merge, a resumption term of the merge, a redemption-stopping condition (e.g., a change from a guaranteed loan interest rate to a non-guaranteed loan interest rate), a bankruptcy or non-repudiation condition, a condition involving a market change (e.g., a change in current interest rate), and the like. In an embodiment, the adaptive intelligence system 3304, including the artificial intelligence 3448, may be trained based on the results of the merge action and/or by an expert based on a training set of merge actions to generate a set of predictions, classifications, control instructions, plans, models, etc. to automatically create, manage, and/or execute one or more aspects of the merge plan.
In an embodiment, a robotic process automation system for merging a set of loans is provided herein. An example platform or system, comprising: (a) A set of data collection and monitoring services for collecting information about a set of loans, and for collecting a training set of interactions between entities of a set of loan-merge transactions; (b) An artificial intelligence system that trains based on the interactive training set to classify a set of loans as candidate loans to be consolidated; and (c) a robotic process automation system that trains based on a set of loan merge interactions to manage the merger of at least a subset of the set of loans on behalf of the merged principal.
An example system, comprising: wherein the set of data collection and monitoring services includes the following: the system comprises a group of internet of things systems, a group of network management systems and a group of network management systems, wherein the group of internet of things systems is used for monitoring the entity; the group of cameras are used for monitoring the entity; a set of software services for retrieving information related to the entity from a public information site; a set of mobile devices for reporting information related to the entity; a set of wearable devices for wearing by a human entity; a set of user interfaces through which the entity provides information about the entity; and a set of crowdsourcing services for requesting and reporting information related to the entity.
RPA (remote procedure for advanced) warranty loan negotiating person trained based on expert and borrower warranty interaction training set
Referring to fig. 60, in an embodiment, a lending platform having a robotic process automation system for managing warranty transactions is provided. The RPA system 3442 may provide automation to one or more aspects of the warranty solution 4942 that may enable automated warranty and/or provide advice or planning for warranty activities associated with lending transactions (e.g., transactions involving receivables warranties). The warranty solution 4942 and/or RPA system 3442 for warranty may include a set of interfaces, workflows, and models (which may include, use or be enabled by the various adaptive intelligence systems 3304), and other components for automating one or more aspects of the warranty actions of one or more terms and conditions of the warranty transaction based on a set of conditions, etc., which may include terms and conditions of the intelligent contracts 3431, market conditions (of the platform market and/or external market 3390), conditions monitored by the monitoring system 3306 and data collection system 3318, etc. (e.g., conditions of the entity 3330, including, but not limited to, conditions of the principal 4910, mortgage 4802 and asset 4918, accounts receivable and inventory, etc.). For example, a user of the warranty solution 4942 may create, configure (e.g., using one or more templates or libraries), modify, set, or otherwise process (e.g., in a user interface of the warranty solution 4942 and/or RPA system 3442) various rules, thresholds, conditional flows, workflows, model parameters, etc., which determine or recommend warranty actions or plans for warranty transactions or monitoring solutions based on one or more events, conditions, states, actions, etc., wherein the warranty plans may be based on various factors such as receivables status, ongoing work conditions, inventory conditions, delivery and/or shipment conditions, payment conditions, borrower conditions, mortgage 4802 or property 4918 conditions, borrower risk factors, one or more warranty risk factors, market risk factors, etc. (including those predicted using one or more predictive models), financial instruments 3448, financial for support, e.g., financial institution (e.g., financial institution) and/or carrier's performance, etc.), financial institution (e.g., status, equity status, equity, etc.), financial institution (e.g., status, equity, etc.). The policy may include policy on loans, communication encouraging payment, etc. In an embodiment, the warranty solution 4942 may automatically recommend or set rules, thresholds, actions, parameters, etc. (optionally by learning a training set based on results over a period of time) to generate a recommended warranty plan that may specify a series of actions required to complete the recommended or expected warranty result (e.g., within a range of acceptable results), which actions may be automatically performed and involve conditional execution of steps based on monitoring conditions and/or smart contract terms that may be created, configured, and/or accounted for by the warranty plan. The warranty plan may be determined and executed based at least in part on market factors (e.g., competitive interest rates or other terms and conditions offered by other borrowers, value of mortgage, value of receivables, interest rates, etc.) and regulatory and/or compliance factors. The policy may be generated and/or executed for the establishment of new policy arrangements, modification of existing policy arrangements, etc. In an embodiment, the adaptive intelligence system 3304, including the artificial intelligence 3448, may be trained based on the results of the warranty actions and/or by an expert based on a training set of warranty actions to generate a set of predictions, classifications, control instructions, plans, models, etc. to automatically create, manage, and/or execute one or more aspects of the warranty plans.
In an embodiment, a robotic process automation system for merging a set of loans is provided herein. An example platform or system, comprising: (a) A set of data collection and monitoring services for collecting information about entities involved in a set of warranty loans and for collecting a training set of interactions between entities of a set of warranty loan transactions; (b) An artificial intelligence system that trains based on the interactive training set to classify entities involved in the set of warranty loans; and (c) a robotic process automation system that trains based on the set of warranty loan interactions to manage warranty loans. Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments.
An example system, comprising: wherein the set of data collection and monitoring services includes the following: the system comprises a group of internet of things systems, a group of network management systems and a group of network management systems, wherein the group of internet of things systems is used for monitoring the entity; the group of cameras are used for monitoring the entity; a set of software services for retrieving information related to the entity from a public information site; a set of mobile devices for reporting information related to the entity; a set of wearable devices for wearing by a human entity; a set of user interfaces through which the entity provides information about the entity; and a set of crowdsourcing services for requesting and reporting information related to the entity.
RPA mortgage agent trained based on training set of expert agent interactions with borrowers
Referring to fig. 61, in an embodiment, a lending platform is provided having a robotic process automation system for brokering loans. For example, the loan may be a mortgage loan.
The RPA system 3442 may provide automation for one or more aspects of the proxy solution 4944 that may enable automated proxy and/or provide advice or plans for proxy activities related to loan transactions, such as for proxy of a group mortgage, housing, credit line, automotive, architectural, or any type of other loan described herein. The proxy solution 4944 and/or RPA system 3442 for the proxy may include a set of interfaces, workflows, and models (which may include, use or be enabled by the various adaptive intelligence systems 3304), and other components for automating one or more aspects of proxy actions or proxy processes for the loan transaction based on a set of conditions, etc., which may include terms and conditions of the intelligent contract 3431, market conditions (of the platform market and/or the external market 3390), conditions monitored by the monitoring system 3306 and the data collection system 3318, etc. (e.g., conditions of the entity 3330, including, but not limited to, conditions of the principal 4910, mortgage 4802, asset 4918, etc., and conditions of interest, available borrowers, available terms, etc.). For example, a user of the proxy solution 4944 may create, configure (e.g., using one or more templates or libraries), modify, set, or otherwise process (e.g., in a user interface of the proxy solution 4944 and/or RPA system 3442) various rules, thresholds, conditional flows, workflows, model parameters, etc., that determine or recommend proxy actions or plans for proxy of a set of loans of one or more types based on one or more events, conditions, states, actions, etc., where the proxy plans may be based on various factors, such as the interest rate of the set of loans available from various primary and secondary borrowers, the borrower's permissible attributes (e.g., based on revenue, wealth, location, etc.), current interest rates in the platform market or external market, borrower status of a set of loans, status or other attributes of mortgage 4802 or property 4918, borrower risk factors, one or more guarantor risk factors, market risk factors, etc. (including risks predicted based on one or more predictive models using artificial intelligence 3448), liability status, status of mortgage 4802 or property 4918 that may be used to provide security or support for a set of loans, status of an enterprise or enterprise operation (e.g., accounts receivable, accounts payable, etc.), status of principal 4910 (e.g., equity, wealth, debt, location, and other status), principal's behavior (e.g., behavior indicating preference, behavior indicating preferential repayment), etc. Agents may include agents regarding terms and conditions of a set of loans, selection of appropriate loans, payment term settings for consolidated loans, repayment plan settings for existing loans, communication of incentive lends, and the like. In an embodiment, the agent solution 4944 may automatically recommend or set rules, thresholds, actions, parameters, etc. (optionally by learning a training set based on results over a period of time) to generate a recommended agent plan that may specify a series of actions required to complete the recommended or expected agent results (e.g., within a range of acceptable results), which actions may be automatically performed and involve conditional execution of steps based on monitoring conditions and/or smart contract terms that may be created, configured, and/or accounted for by the agent plan. The agent plan may be determined and executed based at least in part on market factors (e.g., competitive interest rates offered by other borrowers, property values, borrower attributes, mortgage values, etc.) as well as regulatory and/or compliance factors. The proxy plan may be generated and/or executed for the establishment of new loans, secondary loans, modification of existing loans, resurfacing terms, situations involving market changes (e.g., changes in current interest rate or property value), and the like. In an embodiment, the adaptive intelligence system 3304, including the artificial intelligence 3448, may be trained based on the results of agent actions and/or by experts based on a training set of agent activities to generate a set of predictions, classifications, control instructions, plans, models, etc. to automatically create, manage, and/or execute one or more aspects of the agent plan.
In an embodiment, provided herein is a robotic process automation system for automatically brokering mortgage loans. An example platform or system, comprising: (a) A set of data collection and monitoring services for collecting information about entities involved in a set of mortgage activities, and for collecting a training set of interactions between entities of a set of mortgage transactions; (b) An artificial intelligence system that trains based on the interactive training set to categorize entities involved in the set of mortgages; and (c) a robotic process automation system that trains to proxy mortgage based on at least one of the set of mortgage activities and the set of mortgage interactions. Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments.
An example system, comprising: wherein at least one of the set of mortgage activities and the set of mortgage interactions includes an activity of: a marketing campaign; a set of potential borrower identifications; identifying property; mortgage identification; borrower qualification; searching ownership; ownership verification; evaluating property; checking property; property valuation; revenue verification; demographic analysis of borrowers; identifying a sponsor; determining available interest rate; determining available payment terms and conditions; existing mortgage analysis; comparing and analyzing the existing mortgage clause with the new mortgage clause; the application workflow is completed; number of application fields; mortgage protocol programming; arranging a mortgage protocol; negotiating mortgage terms and conditions with the sponsor; negotiating mortgage terms and conditions with the borrower; transferring ownership; setting a retention right; mortgage protocol achievement.
An example system, comprising: wherein the set of data collection and monitoring services includes the following: the system comprises a group of internet of things systems, a group of network management systems and a group of network management systems, wherein the group of internet of things systems is used for monitoring the entity; the group of cameras are used for monitoring the entity; a set of software services for retrieving information related to the entity from a public information site; a set of mobile devices for reporting information related to the entity; a set of wearable devices for wearing by a human entity; a set of user interfaces through which the entity provides information about the entity; and a set of crowdsourcing services for requesting and reporting information related to the entity.
An example system, comprising: wherein the artificial intelligence system uses a model that processes attributes of entities involved in the set of mortgages, wherein the attributes are selected from the group consisting of: mortgage property; assets for mortgages; an identity of the principal; interest rate; payment balance; payment terms; payment planning; mortgage type; property types; the financial condition of the principal; a payment status; status of property; and the value of the property.
An example system, comprising: wherein managing the mortgage includes managing at least one of: mortgage property; candidate mortgage identification according to the current situation of a group of borrowers; mortgage offer programming; content programming conveying mortgage offers; mortgage offer arrangement; mortgage offer communication; mortgage offer modification negotiations; mortgage protocol programming; executing a mortgage protocol; mortgage modification of a set of mortgage loans; the retention weight transfer is carried out; applying for workflow processing; checking and managing; a set of assets to be mortgage assessment management; setting interest rate; delay of payment requirement; setting a payment plan; mortgage protocol achievement. An example system, comprising: wherein the entity is a set of parties to a loan transaction. An example system, comprising: wherein the set of principals is selected from the group consisting of: primary borrowers, secondary borrowers, borrowing groups, corporate borrowers, government borrowers, banking borrowers, warranty borrowers, bond purchasers, non-warranty borrowers, warranty providers, borrowers, debtors, underwriters, inspectors, valuators, auditors, valuation professionals, government officers, and accountants.
An example system, comprising: wherein the artificial intelligence system comprises at least one of the following systems: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, and simulation systems.
An example system, comprising: wherein the robotic process automation trains based on a set of interactions of the principal with a set of user interfaces involved in a set of mortgage-related activities. An example system, comprising: wherein after the negotiation is completed, the smart contracts of the mortgage are automatically configured by a set of smart contract services based on the results of the negotiation. An example system, comprising: wherein at least one of a result of the negotiating and a negotiation event is recorded in a distributed ledger associated with the loan. An example system, comprising: wherein the artificial intelligence system comprises at least one of the following systems: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, and simulation systems.
Crowd sourcing and automated classification system for verifying the condition of bond issuers
Referring to fig. 62, in an embodiment, a lending platform is provided having a crowdsourcing and automated classification system for verifying the condition of bond issuers. The RPA system 3442 may provide automation for one or more aspects of a bond management solution 4934 that may enable automated bond management and/or provide advice or plans for bond management activities related to bond transactions, such as for municipal bonds, corporate bonds, government bonds, or other bonds that may be supported by bond issuer assets, mortgages, or commitments. The bond management solution 4934 and/or RPA system 3442 for bond management may include a set of interfaces, workflows and models (which may include, use or be enabled by the various adaptive intelligence systems 3304) and other components for automating one or more aspects of bond management actions or management processes for bond transactions based on a set of conditions, etc., which may include terms and conditions of the intelligent contracts 3431, market conditions (of the platform market and/or the external market 3390), conditions monitored by the monitoring system 3306 and the data collection system 3318, etc. (e.g., conditions of the entity 49130, including, but not limited to, conditions of the principal 0, mortgage 4802, asset 4918, etc., and conditions of interest, available borrowers, available terms, etc.). For example, a user of the bond management solution 4934 may create, configure (e.g., using one or more templates or libraries), modify, set, or otherwise process (e.g., in a user interface of the bond management solution 4934 and/or RPA system 3442) various rules, thresholds, conditional flows, workflows, model parameters, etc., which determine or recommend bond management actions or plans for managing a given set of bonds of one or more types based on one or more events, conditions, states, actions, etc., wherein the bond management plans may be based on various factors, such as rates available from various primary and secondary borrowers or issuers, allowable attributes of the issuers and buyers (e.g., based on revenue, financial, location, etc.), current rates in the platform market or external market, the conditions of the issuers of a set of bonds, the conditions or other attributes of the collateral 4802 or the asset 4918, risk factors of the issuers, one or more collateral, a performance factor (e.g., based on one or more conditions, status, actions, etc.), a predicted performance (e.g., a predicted performance, such as a value, etc.), a performance (e.g., a predicted performance, a value, etc.), a performance (e.g., a predicted performance, a performance, etc.) of the performance factor (e.g., a financial instrument, etc.), a performance factor (e.g., a financial instrument, etc.). Bond management may include management of terms and conditions regarding various groups of bonds, selection of appropriate bonds, communication encouraging transactions, and the like. In an embodiment, the bond management solution 4934 may automatically recommend or set rules, thresholds, actions, parameters, etc. (optionally by learning a training set based on results over a period of time) to generate a recommended bond management plan that may specify a series of actions required to complete the recommended or expected bond management result (e.g., within a range of acceptable results), which actions may be automatically performed and involve conditional execution of steps based on monitoring conditions and/or smart contract terms that may be created, configured, and/or accounted for by the bond management plan. The bond management plan may be determined and executed based at least in part on market factors (e.g., competitive interest rates offered by other publishers, property value, attributes of publishers, value of mortgages or assets, etc.) and regulatory and/or compliance factors. The bond management plan may be generated and/or executed for the establishment of new bonds, secondary loans or transactions that provide support for bonds, modification of existing bonds, circumstances involving market changes (e.g., changes in current interest rates or property values), etc. In an embodiment, the adaptive intelligence system 3304, including the artificial intelligence 3448, may be trained based on the results of bond management actions and/or by an expert based on a training set of bond management activities to generate a set of predictions, classifications, control instructions, plans, models, etc. to automatically create, manage, and/or execute one or more aspects of the bond management plan.
System for changing subsidy loan interest rates or other terms based on parameters monitored by internet of things (IoT)
Referring to fig. 63, in an embodiment, a lending platform is provided having a system for changing loan terms and conditions based on parameters monitored by IoT. The loan may be a subsidized loan. The RPA system 3442 may provide automation for one or more aspects of a loan management solution 4948 that may enable automated loan management and/or provide advice or planning for loan management activities related to loan transactions, such as for personal, corporate, subsidized, learning-aid, or other loans, including loans that may be warranted by borrowers' properties, mortgages, or promises. The loan management solution 4948 and/or RPA system 3442 for loan management may include a set of interfaces, workflows, and models (which may include, use or be enabled by various adaptive intelligence systems 3304), and other components for automating one or more aspects of the loan management actions or management processes of a loan transaction based on a set of conditions, etc., which may include terms and conditions of the intelligent contract 3431, market conditions (of the platform market and/or external market 3390), conditions monitored by the monitoring system 3306 and data collection system 3318, etc. (e.g., conditions of the entity 3330, including, but not limited to, conditions of the principal 4910, mortgage 4802, property 4918, etc., as well as conditions of interest, available borrowers, available terms, etc.). For example, a user of the loan management solution 4948 may create, configure (e.g., using one or more templates or libraries), modify, set, or otherwise process (e.g., in a user interface of the loan management solution 4948 and/or the RPA system 3442) various rules, thresholds, conditional flows, workflows, model parameters, etc., that determine or recommend a loan management action or plan for managing a given set of one or more types of loans based on one or more events, conditions, states, actions, etc., where the loan management plan may be based on various factors, such as interest rates available from various primary and secondary borrowers or issuers, borrower permission attributes (e.g., based on revenue, wealth, location, etc.), the current interest rate of the platform market or external market, the status of the principal of a set of loans, the status or other attributes of mortgage 4802 or property 4918, the risk factors of the borrower, the risk factors of one or more sponsors, market risk factors, etc. (including risks predicted based on one or more predictive models using artificial intelligence 3448), liability status, the status of mortgage 4802 or property 4918 that may be used to provide a set of loans with a guarantee or support, the status of business or business operations (e.g., accounts receivable, accounts payable, etc.), the status of principal 4910 (e.g., equity, wealth, liability, location, and other status), the behavior of principal (e.g., behavior indicating preferences, behavior indicating preferential liability repayment, behavior of payment preferences or communication preferences), and the like. Loan management may include management of terms and conditions regarding groups of loans, selection of appropriate loans, communication to encourage transactions, and so forth. In an embodiment, the loan management solution 4948 may automatically recommend or set rules, thresholds, actions, parameters, etc. (optionally by learning a training set based on results over a period of time) to generate a recommended loan management plan that may specify a series of actions required to complete the recommended or expected loan management result (e.g., within a range of acceptable results), which actions may be performed automatically and involve conditional execution of steps based on monitoring conditions and/or smart contract terms that may be created, configured, and/or accounted for by the loan management plan. The loan management program may be determined and executed based at least in part on market factors (e.g., competitive interest rates offered by other publishers, property values, attributes of publishers, value of mortgages or assets, etc.) and regulatory and/or compliance factors. The loan management program may be generated and/or executed for the establishment of new loans, secondary loans or transactions that provide support for the loans, collection, mergers, redemption-stopping, bankruptcy or non-liability cases, modification of existing loans, cases involving market changes (e.g., changes in current interest rate or property value), and the like. In an embodiment, the adaptive intelligence system 3304, including the artificial intelligence 3448, may be trained based on the results of the loan management actions and/or by an expert based on a training set of the loan management actions to generate a set of predictions, classifications, control instructions, plans, models, etc. to automatically create, manage, and/or execute one or more aspects of the loan management plan.
Automated blockchain custody service
Referring to FIG. 64, in an embodiment, a lending platform is provided having automated blockchain custody services and solutions for managing a set of custody assets. The RPA system 3442 may provide automation for one or more aspects of a custody solution 6502 that may enable automated custody management and/or provide advice or planning for custody activities related to a group of assets, such as assets involved in or vouching for loan transactions or assets that a customer seeks to custody for security or management purposes, such as any of the types of assets described herein, including cryptocurrency and other currencies, stock certificates and other ownership certificates, securities, and the like. The custody solution 6502 and/or RPA system 3442 for handling custody activities may include a set of interfaces, workflows, and models (which may include, use or be enabled by the various adaptive intelligence systems 3304), and other components for automating one or more aspects of custody actions or management processes of the trust or custody of a set of assets 4918 based on a set of conditions, etc., which may include the terms and conditions of the intelligent contracts 3431, market conditions (of the platform market and/or external market 3390), conditions monitored by the monitoring system 3306 and data collection system 3318, etc. (e.g., the conditions of the entity 3330, including, but not limited to, the conditions of the principal 4910, mortgage 4802, asset 4918, etc.). For example, a user of the custody solution 6502 may create, configure (e.g., using one or more templates or libraries), modify, set, or otherwise process (e.g., in a user interface of the custody solution 6502 and/or RPA system 3442) various rules, thresholds, conditional flows, workflows, model parameters, etc., that determine or recommend custody actions or plans for managing a given set of one or more types of assets based on one or more events, conditions, states, actions, conditions, etc., where the custody plans may be based on various factors, such as available storage options, asset retrieval bases, asset ownership transfer bases, etc., the status of assets 4918 requiring custody services, behaviors of principals (e.g., behavior indicative of preferences), etc. The custody services may include management of terms and conditions for groups of assets, selection of appropriate terms and conditions for trust and custody, selection of parameters for ownership transfer, selection and provision of storage devices, selection and provision of secure infrastructure for data storage, and the like. In an embodiment, custody solution 48802 may automatically recommend or set rules, thresholds, actions, parameters, etc. (optionally by learning a training set based on results over a period of time) to generate a recommended custody plan that may specify a series of actions required to complete a recommended or expected custody service result (e.g., within a range of acceptable results), which actions may be performed automatically and involve conditional execution of steps based on monitoring conditions and/or smart contract terms that may be created, configured, and/or accounted for by the custody plan. The custody plan may be determined and executed based at least in part on market factors (e.g., competitive terms and conditions provided by other custodians, property value, customer attributes, mortgage or asset value, cost of physical storage, cost of data storage, etc.) and regulatory and/or compliance factors. In an embodiment, the adaptive intelligence system 3304, including the artificial intelligence 3448, may be trained based on the results of the custody actions and/or by an expert based on a training set of custody actions to generate a set of predictions, classifications, control instructions, plans, models, etc. to automatically create, manage, and/or execute one or more aspects of the custody plan. In an embodiment, actions regarding the custody of a set of assets may be stored in blockchain 3422, such as in a distributed ledger.
In an embodiment, a system for processing trust and custody of a set of assets is provided herein. An example platform or system for processing trust and custody of a set of assets, comprising: (a) A set of asset identification services for identifying a set of assets responsible for custody by the financial institution; and (b) a set of identity management services by which the financial institution verifies the identities and credentials of a set of entities that are entitled to act on the asset and a set of blockchain services. Wherein at least one of the identification information of the one asset and the set of assets is stored in a blockchain, and wherein events related to the set of assets are recorded in a distributed ledger. Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments.
An example system, comprising: wherein the credentials include an owner credential, an agent credential, a beneficiary credential, a delegate credential, and a custodian credential.
In an embodiment, the events related to the set of assets include transferring ownership, owner death, owner disability, owner bankruptcy, redemption prevention, setting up a lien, using an asset as a mortgage, designating a beneficiary, loaning on an asset as a mortgage, providing notification about an asset, checking an asset, evaluating an asset, reporting an asset for tax purposes, assigning an ownership of an asset, disposing of an asset, selling an asset, purchasing an asset, and designating an ownership status.
In an embodiment, the platform or system further comprises a set of data collection and monitoring services for monitoring at least one of the set of assets, the set of entities, and the set of events related to the assets.
In an embodiment, the set of entities includes at least one of an owner, a beneficiary, an agent, a trustee, and a custodian.
In an embodiment, the platform or system further comprises a set of smart contract services for managing custody of the set of assets, wherein at least one event related to the set of assets is automatically managed by the smart contract based on a set of terms and conditions contained in the smart contract and based on information collected by the set of data collection and monitoring services.
In an embodiment, the events related to the set of assets include transferring ownership, owner death, owner disability, owner bankruptcy, redemption prevention, setting up a lien, using an asset as a mortgage, designating a beneficiary, loaning on an asset as a mortgage, providing notification about an asset, checking an asset, evaluating an asset, reporting an asset for tax purposes, assigning an ownership of an asset, disposing of an asset, selling an asset, purchasing an asset, and designating an ownership status.
Referring to fig. 65, in an embodiment, a loan platform is provided having a underwriting system for loans with a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting loan entities and transactions. RPA system 3442 may provide automation for one or more aspects of an underwriting solution 3420 that may enable automated underwriting and/or provide advice or plans for underwriting activities related to loan transactions, such as for personal, corporate, subsidized, learning-aid, or other loans, including loans that may be warranted by borrowers' properties, mortgages, or commitments. The underwriting solution 3420 and/or RPA system 3442 for underwriting may include a set of interfaces, workflows, and models (which may include, use or be enabled by the various adaptive intelligence systems 3304), and other components for automating one or more aspects of the underwriting actions or management processes of loan transactions based on a set of conditions, etc., which may include terms and conditions of the intelligent contracts 3431, market conditions (of the platform market and/or external market 3390), conditions monitored by the monitoring system 3306 and data collection system 3318, etc. (e.g., conditions of the entity 3330, including, but not limited to, conditions of the principal 4910, mortgage 4802, asset 4918, etc., as well as conditions of interest, available borrowers, available terms, etc.). For example, a user of the underwriting solution 3420 may create, configure (e.g., using one or more templates or libraries), modify, set, or otherwise process (e.g., in a user interface of the underwriting solution 3420 and/or RPA system 3442) various rules, thresholds, conditional flows, workflows, model parameters, etc., that determine or recommend underwriting actions or plans for managing a set of loans of a given type or types based on one or more events, conditions, states, actions, etc., where the underwriting plans may be based on various factors, such as interest rates available from various primary and secondary borrowers or issuers, borrower's allowable attributes (e.g., based on revenue, wealth, location, etc.), the current interest rate of the platform market or external market, the status of the principal of a set of loans, the status or other attributes of mortgage 4802 or property 4918, the risk factors of borrowers, the risk factors of one or more sponsors, market risk factors, etc. (including risks predicted based on one or more predictive models using artificial intelligence 3448), liability status, the status of mortgage 4802 or property 4918 that may be used to provide a set of loans with a guarantee or support, the status of business or business operations (e.g., accounts receivable, accounts payable, etc.), the status of principal 4910 (e.g., equity, wealth, debt, location, and other status), the behavior of principal (e.g., behavior indicating preferences, behavior indicating preferential liabilities, payment preferences, or communication preferences), etc. The underwriting may include aspects regarding the management of terms and conditions of the loans of the groups, the selection of appropriate loans, communication related to the underwriting process, and the like. In an embodiment, the underwriting solution 3420 may automatically recommend or set rules, thresholds, actions, parameters, etc. (optionally by learning a training set based on results over a period of time) to generate a recommended underwriting plan that may specify a series of actions required to complete the recommended or expected underwriting results (e.g., within a range of acceptable results), which actions may be performed automatically and involve conditional execution of steps based on monitoring conditions and/or smart contract terms that may be created, configured, and/or accounted for by the underwriting plan. The underwriting plan may be determined and executed based at least in part on market factors (e.g., competitive interest rates, property values, borrower behavior, demographic trends, payment trends, attributes of the issuer, value of mortgages or assets, etc.) and regulatory and/or compliance factors. The marketing plan may be generated and/or executed for new loans, secondary loans or transactions that provide support for the loans, collections, mergers, redemption-stopping, bankruptcy or non-balance cases, modification of existing loans, cases involving market changes (e.g., changes in current interest rate or property value), redemption-stopping activities, and the like. In an embodiment, the adaptive intelligence system 3304, including the artificial intelligence 3448, may be trained based on the results of the underwriting actions and/or by an expert based on a training set of underwriting actions to generate a set of predictions, classifications, control instructions, plans, models, etc. to automatically create, manage, and/or execute one or more aspects of the underwriting plan. In an embodiment, the underwriting events and results may be recorded in blockchain 3422, such as stored in a distributed ledger, for secure access and retrieval by authorized users. The adaptive intelligence system 3304 can, for example, use various artificial intelligence 3448 or expert systems disclosed herein and in the documents incorporated by reference herein to improve or automate one or more aspects of the underwriting, such as training a model, neural network, deep learning system, etc., by training a set of training based on expert interactions and/or training set of results of the underwriting activities.
Referring to fig. 66, in an embodiment, a loan platform is provided having a loan marketing system with a set of data integration micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing loans to a set of potential parties. The system 4800 can implement one or more aspects of a loan marketing solution 6702 that can enable automated loan marketing and/or provide advice or plans for a loan marketing campaign related to a loan transaction, such as for a personal loan, a corporate loan, a subsidized loan, an assisted loan, or other loan, including a loan that can be warranted by a borrower's property, mortgage, or promise. The loan marketing solution 6702 (which in embodiments may include or use the RPA system 3442 for loan marketing) may include a set of interfaces, workflows, and models (which may include, use or be enabled by the various adaptive intelligence systems 3304) and other components for automating one or more aspects of the loan management actions or management processes for loan transactions based on a set of conditions, etc., which may include terms and conditions of the intelligent contracts 3431, market conditions (of the platform market and/or the external market 3390), conditions monitored by the monitoring system 3306 and the data collection system 3318, etc. (e.g., conditions of the entity 3330, including, but not limited to, conditions of the principal 4910, mortgage 4802, property 4918, etc., as well as conditions of interest, available borrowers, available terms, etc.). For example, a user of the loan marketing solution 6702 may create, configure (e.g., using one or more templates or libraries), modify, set, or otherwise process (e.g., in a user interface of the loan marketing solution 6702 and/or the RPA system 3442) various rules, thresholds, conditional flows, workflows, model parameters, etc., that determine or recommend marketing actions or plans for managing a loan for a set of loans of a given type or types based on one or more events, conditions, states, actions, etc., where the loan marketing plans may be based on various factors, such as interest rates available from various primary and secondary borrowers or issuers, capital returns available for the loan, borrower permissions or desired attributes (e.g., based on revenue, financial, location, etc.), the current interest rate of the platform market or external market, the status of the principal of a set of loans, the status or other attributes of mortgage 4802 or property 4918, the risk factors of the borrower, the risk factors of one or more sponsors, market risk factors, etc. (including those predicted based on one or more predictive models using artificial intelligence 3448), liability status, the status of mortgage 4802 or property 4918 for providing a set of loans with a guarantee or support, the status of business or business operations (e.g., accounts receivable, accounts payable, etc.), the status of principal 4910 (e.g., equity, financial, debt, location, and other status), the behavior of the principal (e.g., behavior indicating preferences, behavior indicating preferential liability repayment, behavior of payment preferences or communication preferences), and the like. Loan marketing may include management of terms and conditions regarding groups of loans, selection of appropriate loans, communication related to the loan marketing process, and the like. In an embodiment, the loan marketing solution 6702 may automatically recommend or set rules, thresholds, actions, parameters, etc. (optionally by learning a training set based on results over a period of time) to produce a recommended loan marketing plan that may specify a series of actions required to complete a recommended or expected loan marketing result (e.g., within a range of acceptable results), which actions may be performed automatically and involve conditional execution of steps based on monitoring conditions and/or smart contract terms that may be created, configured, and/or accounted for by the loan marketing plan. The loan marketing program may be determined and executed based at least in part on marketing factors (e.g., competitive interest rates, property values, borrower behavior, demographic trends, payment trends, properties of the borrower, value of mortgage or property, etc.) and regulatory and/or compliance factors. The loan marketing program may be generated and/or executed for new loans, secondary loans or transactions that provide support for the loans, collections, mergers, redemption-stopping situations (e.g., redemption-stopping alternatives), bankruptcy or non-repudiation situations, modifications to existing loans, situations involving market changes (e.g., changes in current interest rates, available capital or property values), and the like. In an embodiment, the adaptive intelligence system 3304, including the artificial intelligence 3448, may be trained based on the results of the loan marketing campaign and/or by an expert based on a training set of the loan marketing campaign to generate a set of predictions, classifications, control instructions, plans, models, etc. to automatically create, manage, and/or execute one or more aspects of the loan marketing plan. In an embodiment, loan marketing events and results may be recorded in blockchain 3422, such as stored in a distributed ledger, for secure access and retrieval by authorized users. The adaptive intelligence system 3304 may, for example, use various artificial intelligence 3448 or expert systems disclosed herein and in the documents incorporated by reference to improve or automate one or more aspects of entity ratings, such as training models, neural networks, deep learning systems, etc. by training sets based on expert interaction and/or training sets of results of loan underwriting activities.
Referring to FIG. 67, in an embodiment, a loan platform is provided having a rating system with a set of data integration micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating a set of loan related entities. The system 4800 can implement one or more aspects of an entity rating solution 6801 that can enable automatic entity rating and/or provide advice or plans for entity rating activities related to loan transactions, such as for personal loans, corporate loans, subsidized loans, learning-aid loans, or other loans, including loans that can be guaranteed by borrowers' properties, mortgages, or promises. The entity rating solution 6801 (which in embodiments may include or use the RPA system 3442 for entity rating) may include a set of interfaces, workflows, and models (which may include, use or be enabled by various adaptive intelligence systems 3304) and other components for automating one or more aspects of the entity rating actions or ratings processes of loan transactions based on a set of conditions, attributes, events, etc., which may include attributes of the entity 3330 (e.g., value, quality, location, equity, price, physical condition, health, collateral, security, ownership, etc.), terms and conditions of the intelligent contract 3431 (e.g., which may be configured or populated based on a set of rated loan ratings, etc.), market conditions of the regulatory factors, (platform market and/or external market 3390), conditions monitored by the monitoring system 3306 and data collection system 3318, etc. (e.g., conditions of the entity 3330, including, but not limited to, conditions of the equity, and equity 49102, 4918, etc.), availability of the principal, etc. For example, a user of the entity rating solution 6801 may create, configure (e.g., using one or more templates or libraries), modify, set, or otherwise process (e.g., in a user interface of the entity rating solution 6801 and/or RPA system 3442) various rules, thresholds, conditional flows, workflows, model parameters, etc., that determine or recommend an entity rating action or plan for rating a set of loans of a given one or more types based on one or more events, attributes, parameters, features, conditions, states, actions, etc., based on various factors (e.g., based on revenue, wealth, 491 location, etc., or principal 0, relative to others, or based on the condition of mortgage 4802 or property 4918, etc.), the prevailing conditions of the platform market or external market, the condition of the principal of a set of loans, the condition or other attributes of mortgage 4802 or property 4918, the risk factors of borrowers, the risk factors of one or more insurers, the market risk factors, etc. (including the risk predicted based on one or more predictive models using artificial intelligence 3448), the liability condition, the condition of mortgage 4802 or property 4918 for providing a set of loans with a guarantee or support, the condition of the business or business's business operations (e.g., accounts receivable, accounts payable, etc.), the condition of principal 4910 (e.g., equity, wealth, liabilities, location and other conditions), the behavior of principal (e.g., indicating preferred behavior, indicating preferential liability repayment rights, etc.), behavior of payment preferences or communication preferences), and the like. The entity ratings may include management of terms and conditions regarding the loans of the groups, selection of appropriate loans, communication related to the entity rating process, and so forth. In an embodiment, the entity rating solution 6801 may automatically recommend or set rules, thresholds, actions, parameters, etc. (optionally by learning a training set based on results over a period of time) to generate a recommended entity rating plan that may specify a series of actions required to complete a recommended or expected entity rating result (e.g., within a range of acceptable results), which actions may be performed automatically and involve conditional execution of steps based on monitoring conditions and/or smart contract terms that may be created, configured, and/or accounted for by the entity rating plan. The entity rating plan may be determined and executed based at least in part on market factors (e.g., competitive interest rates, property values, borrower behavior, demographic trends, payment trends, attributes of the issuer, value of mortgages or assets, etc.) and regulatory and/or compliance factors. Entity rating plans may be generated and/or executed for new loans, secondary loans or transactions that provide support for the loans, collections, mergers, redemption-stopping situations (e.g., redemption-stopping alternatives), bankruptcy or non-repudiation situations, modifications to existing loans, situations involving market changes (e.g., changes in current interest rates, available capital or property values), and the like. In an embodiment, the adaptive intelligence system 3304, including artificial intelligence 3448, may be trained based on the results of the entity rating actions and/or by experts based on a training set of entity rating activities to generate a set of predictions, classifications, control instructions, plans, models, etc. to automatically create, manage, and/or execute one or more aspects of the entity rating plan. In an embodiment, entity rating events and results may be recorded in blockchain 3422, such as stored in a distributed ledger, for secure access and retrieval by authorized users. The adaptive intelligence system 3304 may, for example, use various artificial intelligence 3448 or expert systems disclosed herein and in the documents incorporated by reference, to improve or automate one or more aspects of entity ratings, such as training models, neural networks, deep learning systems, etc. by training sets based on expert interactions and/or training sets of results of entity ratings activities.
Referring to fig. 68, in an embodiment, a lending platform is provided having a regulatory and/or compliance system 3426 with a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for automatically facilitating compliance with at least one of laws, regulations, and policies applicable to lending transactions. The system 4800 can implement one or more aspects of a regulatory and compliance solution 3426 that can implement automated crowd-sourcing and automated classification system regulatory and compliance and/or provide advice or planning for regulatory and compliance activities related to loan transactions, such as for personal, corporate, subsidized, learning-aid, or other loans, including loans that can be warranted by borrowers' properties, mortgages, or commitments. The regulatory and compliance solution 3426 (which in embodiments may include or use an RPA system 3442 for automating regulatory and compliance activities based on training sets of expert interactions in regulatory and/or compliance activities) may include a set of interfaces, workflows and models (which may include, use or be implemented by various adaptive intelligence systems 3304) and other components for implementing the regulatory and compliance activities of loan transactions or automating one or more aspects of the regulatory and/or compliance processes based on a set of policies, regulations, laws, requirements, specifications, conditions, attributes, events, etc., which may include the attributes of the entity 3330 involved in the loan transactions or the terms and conditions applicable to the entity 3330 (including the loans and conditions of the intelligent contracts 3431 (which may be configured or filled based on a set of allowed terms and conditions, etc., for example), and the market platform and/or external various systems 3390, such as the rate of interest and conditions, 49102, the rate of interest and conditions, the conditions of the monitoring system, 4918, the equity and the like, and the conditions of the like, such as those of the equity, can be collected by the monitoring system 49102, and the equity, and the conditions of the equity, and the like. For example, a user of the regulatory and compliance solution 3426 may create, configure (e.g., using one or more templates or libraries), modify, set, or otherwise process (e.g., in a user interface of the regulatory and/or compliance solution 3426 and/or RPA system 3442) various rules, thresholds, conditional flows, workflows, model parameters, etc., which are determined or recommended based on one or more events, attributes, parameters, features, conditions, states, actions, etc., for managing a set of regulatory and compliance actions or plans for a given one or more types of loans, where the regulatory and compliance plans may be based on various factors (e.g., based on allowing interest rates, requiring notification (e.g., for annual percentage reporting), borrowers allowed (e.g., students applying federal subsidized loans), borrowers allowed, issuers allowed, incomes (e.g., low-income loans), wealths (e.g., policies allow loans offered only to capital-rich parties), locations (e.g., geographically limited loan plans, such as for municipal development), conditions of the flat market or external market (e.g., conditions requiring loan interest rates not to exceed a threshold calculated at current interest rates), conditions of a group of loan parties, conditions or other attributes of mortgage 4802 or property 4918, risk factors of borrowers, risk factors of one or more guarantors, market risk factors, etc. (including risks predicted based on one or more predictive models using artificial intelligence 3448), and the like, debt conditions, conditions of mortgage 4802 or property 4918 for providing a guarantee or support for a set of loans, conditions of business or business operations (e.g., accounts receivable, accounts payable, etc.), conditions of principal 4910 (e.g., equity, wealth, debt, location, and other conditions), behaviors of principal (e.g., behaviors indicating preferences, behaviors indicating debt priority repayment, payment preferences, or communication preferences), etc. Supervision and compliance may include management of terms and conditions regarding each group loan, proper loan selection, provision of required notification, underwriting policies, communication related to supervision and compliance processes, and the like. In an embodiment, the regulatory and compliance solution 49101 may automatically recommend or set rules, thresholds, actions, parameters, etc. (optionally by learning a training set based on results over a period of time) to generate a recommended regulatory and compliance plan that may specify a series of actions required to complete the recommended or expected regulatory and compliance results (e.g., within a range of acceptable results), which actions may be automatically performed and involve conditional execution of steps based on monitoring conditions and/or smart contract terms that may be created, configured, and/or accounted for by the regulatory and compliance plan. The regulatory and compliance programs may be determined and executed based at least in part on market factors (e.g., competitive interest rates, property values, borrower behaviors, demographic trends, payment trends, attributes of the distributor, value of mortgages or assets, etc.) and regulatory and/or compliance factors provided by other distributors. The policing and compliance program may be generated and/or executed for new loans, secondary loans or transactions that provide support for the loans, collections, mergers, redemption-stopping situations (e.g., redemption-stopping alternatives), bankruptcy or non-repudiation situations, modifications to existing loans, situations involving market changes (e.g., changes in current interest rates, available capital or property values), and the like. In an embodiment, the adaptive intelligence system 3304, including the artificial intelligence 3448, may be trained based on the results of the regulatory and compliance actions and/or by an expert based on a training set of regulatory and compliance activities to generate a set of predictions, classifications, control instructions, plans, models, etc. to automatically create, manage, and/or perform one or more aspects of the regulatory and compliance plans. In an embodiment, regulatory and compliance events and results may be recorded in blockchain 3422, for example, stored in a distributed ledger, for secure access and retrieval by authorized users. The adaptive intelligence system 3304 can, for example, use various artificial intelligence 3448 or expert systems disclosed herein and in the documents incorporated by reference, to improve or automate one or more aspects of supervision and compliance, such as training a model, neural network, deep learning system, etc., by training a set of training based on expert interactions and/or training a set of results of supervision and compliance activities.
An example loan platform is provided herein having a set of data-integrated micro services including data collection and monitoring services, blockchain services, and smart contract services for processing loan entities and transactions. An example system, comprising: the system comprises an internet of things and a sensor platform for monitoring at least one of a set of assets and a set of loans, bonds or liabilities trade mortgages. An example system, comprising: an intelligent contract and distributed ledger platform for managing at least one of ownership of a set of mortgage and a set of events related to the set of mortgage. An example system, comprising: an intelligent contract system for automatically adjusting the interest rate of a loan based on information collected through at least one of an internet of things system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services. An example system, comprising: a crowd-sourcing system for obtaining information about at least one of a state of a set of mortgages on a loan and a state of an entity associated with a loan guarantee. An example system, comprising: an intelligent contract for automatically adjusting the interest rate of a loan based on at least one of regulatory factors and marketing factors of a particular jurisdiction. An example system, comprising: an intelligent contract for automatically reorganizing liabilities based on monitored conditions. An example system, comprising: and the social network monitoring system is used for verifying the reliability of the loan guarantee. An example system, comprising: and the data collection and monitoring system of the Internet of things is used for verifying the reliability of loan guarantee. An example system, comprising: a robotic process automation system for negotiating a set of terms and conditions for a loan. An example system, comprising: a robotic process automation system for retrieving a loan. An example system, comprising: a robotic process automation system for merging a set of loans. An example system, comprising: and the robot process automation system is used for managing the insurance loan. An example system, comprising: and the robot process automation system is used for proxy mortgage loan. An example system, comprising: crowd sourcing and automated classification systems for verifying the condition of bond issuers. An example system, comprising: social network monitoring systems employing artificial intelligence are used to categorize conditions concerning bonds. An example system, comprising: an artificial intelligence internet of things data collection and monitoring system is used for classifying conditions related to bonds. An example system, comprising: a system for changing terms and conditions of subsidy loans based on parameters monitored by internet of things (IoT). An example system, comprising: a system for changing terms and conditions of subsidized loans based on parameters monitored in a social network. An example system, comprising: a system for changing terms and conditions of subsidized loans based on parameters monitored by crowd sourcing. An example system, comprising: an automated blockchain custody service for managing a set of custody assets. An example system, comprising: a underwriting system for loans having a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for underwriting lending entities and transactions. An example system, comprising: a loan marketing system having a set of data-integrating micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for marketing loans to a set of potential parties. An example system, comprising: a rating system having a set of data-integrated micro-services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for rating a set of loan-related entities. An example system, comprising: a compliance system having a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for automatically facilitating compliance with at least one of laws, regulations, and policies associated with lending transactions.
An example loan platform is provided herein having an internet of things and a sensor platform for monitoring at least one of a set of assets and a set of loans, bonds, or liabilities trade mortgages. An example system, comprising: an intelligent contract and distributed ledger platform for managing at least one of ownership of a set of mortgage and a set of events related to the set of mortgage. An example system, comprising: an intelligent contract system for automatically adjusting the interest rate of a loan based on information collected through at least one of an internet of things system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services. An example system, comprising: a crowd-sourcing system for obtaining information about at least one of a state of a set of mortgages on a loan and a state of an entity associated with a loan guarantee. An example system, comprising: an intelligent contract for automatically adjusting the interest rate of a loan based on at least one of regulatory factors and marketing factors of a particular jurisdiction. An example system, comprising: an intelligent contract for automatically reorganizing liabilities based on monitored conditions. An example system, comprising: and the social network monitoring system is used for verifying the reliability of the loan guarantee. An example system, comprising: and the data collection and monitoring system of the Internet of things is used for verifying the reliability of loan guarantee. An example system, comprising: a robotic process automation system for one or more of negotiating a set of terms and conditions of a loan, retrieving a loan, merging a set of loans, managing a warranty loan, or a proxy mortgage. An example system, comprising: crowd sourcing and automated classification systems for verifying the condition of bond issuers. An example system, comprising: social network monitoring systems employing artificial intelligence are used to categorize conditions concerning bonds. An example system, comprising: an artificial intelligence internet of things data collection and monitoring system is used for classifying conditions related to bonds.
An example system, comprising: a system for changing terms and conditions of subsidized loans based on parameters monitored by at least one of internet of things (IoT), social network, or crowd sourcing.
An example system, comprising: an automated blockchain custody service for managing a set of custody assets. An example system, comprising: a underwriting system for loans having a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for underwriting lending entities and transactions. An example system, comprising: a loan marketing system having a set of data-integrating micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for marketing loans to a set of potential parties. An example system, comprising: a rating system having a set of data-integrated micro-services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for rating a set of loan-related entities. An example system, comprising: a compliance system having a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for automatically facilitating compliance with at least one of laws, regulations, and policies associated with lending transactions.
An example loan platform is provided herein having an intelligent contract and a distributed ledger platform for managing at least one of ownership of a set of mortgage and a set of events related to the set of mortgage. An example system, comprising: an intelligent contract system for automatically adjusting the interest rate of a loan based on information collected through at least one of an internet of things system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services. An example system, comprising: a crowd-sourcing system for obtaining information about at least one of a state of a set of mortgages on a loan and a state of an entity associated with a loan guarantee. An example system, comprising: an intelligent contract for automatically adjusting the interest rate of a loan based on at least one of regulatory factors and marketing factors of a particular jurisdiction. An example system, comprising: an intelligent contract for automatically reorganizing liabilities based on monitored conditions. An example system, comprising: and the social network monitoring system is used for verifying the reliability of the loan guarantee.
An example system, comprising: and the data collection and monitoring system of the Internet of things is used for verifying the reliability of loan guarantee. An example system, comprising: a robotic process automation system for negotiating a set of terms and conditions for a loan. An example system, comprising: a robotic process automation system for retrieving a loan. An example system, comprising: a robotic process automation system for at least one of merging a set of loans, managing an warranty loan, or a proxy mortgage. An example system, comprising: crowd sourcing and automated classification systems for verifying the condition of bond issuers. An example system, comprising: social network monitoring systems employing artificial intelligence are used to categorize conditions concerning bonds. An example system, comprising: an artificial intelligence internet of things data collection and monitoring system is used for classifying conditions related to bonds. An example system, comprising: a system for changing terms and conditions of subsidized loans based on parameters monitored by at least one of internet of things (IoT), social network, or crowd sourcing. An example system, comprising: an automated blockchain custody service for managing a set of custody assets. An example system, comprising: a underwriting system for loans having a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for underwriting lending entities and transactions. An example system, comprising: a loan marketing system having a set of data-integrating micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for marketing loans to a set of potential parties. An example system, comprising: a rating system having a set of data-integrated micro-services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for rating a set of loan-related entities. An example system, comprising: a compliance system having a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for automatically facilitating compliance with at least one of laws, regulations, and policies associated with lending transactions.
An example loan platform is provided herein having an intelligent contract system that automatically adjusts the interest rate of a loan based on information collected through at least one of an internet of things system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services. An example system, comprising: a crowd-sourcing system for obtaining information about at least one of a state of a set of mortgages on a loan and a state of an entity associated with a loan guarantee. An example system, comprising: an intelligent contract for automatically adjusting the interest rate of a loan based on at least one of regulatory factors and marketing factors of a particular jurisdiction. An example system, comprising: an intelligent contract for automatically reorganizing liabilities based on monitored conditions. An example system, comprising: and the social network monitoring system is used for verifying the reliability of the loan guarantee. An example system, comprising: and the data collection and monitoring system of the Internet of things is used for verifying the reliability of loan guarantee. An example system, comprising: a robotic process automation system for negotiating a set of terms and conditions for a loan. An example system, comprising: a robotic process automation system for at least one of withdrawing a loan, merging a set of loans, managing an warranty loan, or a proxy mortgage loan. An example system, comprising: crowd sourcing and automated classification systems for verifying the condition of bond issuers. An example system, comprising: social network monitoring systems employing artificial intelligence are used to categorize conditions concerning bonds. An example system, comprising: an artificial intelligence internet of things data collection and monitoring system is used for classifying conditions related to bonds. An example system, comprising: a system for changing terms and conditions of subsidized loans based on parameters monitored by at least one of internet of things (IoT), social network, or crowd sourcing. An example system, comprising: an automated blockchain custody service for managing a set of custody assets. An example system, comprising: a underwriting system for loans having a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for underwriting lending entities and transactions. An example system, comprising: a loan marketing system having a set of data-integrating micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for marketing loans to a set of potential parties. An example system, comprising: a rating system having a set of data-integrated micro-services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for rating a set of loan-related entities. An example system, comprising: a compliance system having a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for automatically facilitating compliance with at least one of laws, regulations, and policies associated with lending transactions.
An example loan platform is provided herein having a crowd-sourced system for obtaining information about at least one of a status of a set of mortgages on a loan and a status of an entity associated with a loan guarantee. An example system, comprising: an intelligent contract for automatically adjusting the interest rate of a loan based on at least one of regulatory factors and marketing factors of a particular jurisdiction. An example system, comprising: an intelligent contract for automatically reorganizing liabilities based on monitored conditions. An example system, comprising: and the social network monitoring system is used for verifying the reliability of the loan guarantee. An example system, comprising: and the data collection and monitoring system of the Internet of things is used for verifying the reliability of loan guarantee. An example system, comprising: a robotic process automation system for negotiating at least one of a set of terms and conditions of a loan, retrieving a loan, merging a set of loans, managing a warranty loan, or a proxy mortgage. An example system, comprising: crowd sourcing and automated classification systems for verifying the condition of bond issuers. An example system, comprising: social network monitoring systems employing artificial intelligence are used to categorize conditions concerning bonds. An example system, comprising: an artificial intelligence internet of things data collection and monitoring system is used for classifying conditions related to bonds. An example system, comprising: a system for changing terms and conditions of subsidized loans based on parameters monitored by at least one of internet of things (IoT), social network, or crowd sourcing. An example system, comprising: an automated blockchain custody service for managing a set of custody assets. An example system, comprising: a underwriting system for loans having a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for underwriting lending entities and transactions. An example system, comprising: a loan marketing system having a set of data-integrating micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for marketing loans to a set of potential parties. An example system, comprising: a rating system having a set of data-integrated micro-services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for rating a set of loan-related entities. An example system, comprising: a compliance system having a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for automatically facilitating compliance with at least one of laws, regulations, and policies associated with lending transactions.
An example loan platform is provided herein having an intelligent contract that automatically adjusts the interest rate of a loan based on at least one of regulatory factors and market factors for a particular jurisdiction. An example system, comprising: an intelligent contract for automatically reorganizing liabilities based on monitored conditions. An example system, comprising: and the social network monitoring system is used for verifying the reliability of the loan guarantee. An example system, comprising: and the data collection and monitoring system of the Internet of things is used for verifying the reliability of loan guarantee. An example system, comprising: a robotic process automation system for negotiating at least one of a set of terms and conditions of a loan, retrieving a loan, merging a set of loans, managing a warranty loan, or a proxy mortgage. An example system, comprising: crowd sourcing and automated classification systems for verifying the condition of bond issuers. An example system, comprising: social network monitoring systems employing artificial intelligence are used to categorize conditions concerning bonds.
An example system, comprising: an artificial intelligence internet of things data collection and monitoring system is used for classifying conditions related to bonds.
An example system, comprising: a system for changing terms and conditions of subsidized loans based on parameters monitored by at least one of internet of things (IoT), social network, or crowd sourcing.
An example system, comprising: an automated blockchain custody service for managing a set of custody assets.
An example system, comprising: a underwriting system for loans having a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for underwriting lending entities and transactions.
An example system, comprising: a loan marketing system having a set of data-integrating micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for marketing loans to a set of potential parties.
An example system, comprising: a rating system having a set of data-integrated micro-services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for rating a set of loan-related entities.
An example system, comprising: a compliance system having a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for automatically facilitating compliance with at least one of laws, regulations, and policies associated with lending transactions.
An example lending platform is provided herein having an intelligent contract that automatically reorganizes liabilities based on monitored conditions. An example system, comprising: and the social network monitoring system is used for verifying the reliability of the loan guarantee. An example system, comprising: and the data collection and monitoring system of the Internet of things is used for verifying the reliability of loan guarantee. An example system, comprising: a robotic process automation system for negotiating at least one of a set of terms and conditions of a loan, retrieving a loan, combining a set of loans, managing a warranty loan, and brokering a mortgage loan. An example system, comprising: crowd sourcing and automated classification systems for verifying the condition of bond issuers. An example system, comprising: social network monitoring systems employing artificial intelligence are used to categorize conditions concerning bonds. An example system, comprising: an artificial intelligence internet of things data collection and monitoring system is used for classifying conditions related to bonds. An example system, comprising: a system for changing terms and conditions of subsidized loans based on parameters monitored by at least one of internet of things (IoT), social network, crowd sourcing. An example system, comprising: an automated blockchain custody service for managing a set of custody assets. An example system, comprising: a underwriting system for loans having a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for underwriting lending entities and transactions. An example system, comprising: a loan marketing system having a set of data-integrating micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for marketing loans to a set of potential parties. An example system, comprising: a rating system having a set of data-integrated micro-services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for rating a set of loan-related entities. An example system, comprising: a compliance system having a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for automatically facilitating compliance with at least one of laws, regulations, and policies associated with lending transactions.
An example loan platform is provided herein having a social network monitoring system for verifying the reliability of loan assurance. An example system, comprising: and the data collection and monitoring system of the Internet of things is used for verifying the reliability of loan guarantee. An example system, comprising: a robotic process automation system for negotiating at least one of a set of terms and conditions of a loan, retrieving a loan, merging a set of loans, managing a warranty loan, or a proxy mortgage. An example system, comprising: crowd sourcing and automated classification systems for verifying the condition of bond issuers. An example system, comprising: social network monitoring systems employing artificial intelligence are used to categorize conditions concerning bonds. An example system, comprising: an artificial intelligence internet of things data collection and monitoring system is used for classifying conditions related to bonds. An example system, comprising: a system for changing terms and conditions of subsidized loans based on parameters monitored by at least one of internet of things (IoT), social network, or crowd sourcing. An example system, comprising: an automated blockchain custody service for managing a set of custody assets. An example system, comprising: a underwriting system for loans having a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for underwriting lending entities and transactions. An example system, comprising: a loan marketing system having a set of data-integrating micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for marketing loans to a set of potential parties. An example system, comprising: a rating system having a set of data-integrated micro-services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for rating a set of loan-related entities. An example system, comprising: a compliance system having a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for automatically facilitating compliance with at least one of laws, regulations, and policies associated with lending transactions.
An example loan platform is provided herein having an internet of things data collection and monitoring system for verifying the reliability of loan assurance. An example system, comprising: a robotic process automation system for negotiating at least one of a set of terms and conditions of a loan, retrieving a loan, merging a set of loans, managing a warranty loan, or a proxy mortgage. An example system, comprising: crowd sourcing and automated classification systems for verifying the condition of bond issuers. An example system, comprising: social network monitoring systems employing artificial intelligence are used to categorize conditions concerning bonds. An example system, comprising: an artificial intelligence internet of things data collection and monitoring system is used for classifying conditions related to bonds. An example system, comprising: a system for changing terms and conditions of subsidized loans based on parameters monitored by at least one of internet of things (IoT), social network, or crowd sourcing. An example system, comprising: an automated blockchain custody service for managing a set of custody assets. An example system, comprising: a underwriting system for loans having a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for underwriting lending entities and transactions. An example system, comprising: a loan marketing system having a set of data-integrating micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for marketing loans to a set of potential parties. An example system, comprising: a rating system having a set of data-integrated micro-services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for rating a set of loan-related entities. An example system, comprising: a compliance system having a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for automatically facilitating compliance with at least one of laws, regulations, and policies associated with lending transactions.
An example loan platform is provided herein having a robotic process automation system for negotiating a set of terms and conditions for a loan. An example system, comprising: a robotic process automation system for at least one of withdrawing a loan, merging a set of loans, managing an warranty loan, or a proxy mortgage loan. An example system, comprising: crowd sourcing and automated classification systems for verifying the condition of bond issuers. An example system, comprising: social network monitoring systems employing artificial intelligence are used to categorize conditions concerning bonds. An example system, comprising: an artificial intelligence internet of things data collection and monitoring system is used for classifying conditions related to bonds. An example system, comprising: a system for changing terms and conditions of subsidized loans based on parameters monitored by at least one of internet of things (IoT), social network, or crowd sourcing. An example system, comprising: an automated blockchain custody service for managing a set of custody assets. An example system, comprising: a underwriting system for loans having a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for underwriting lending entities and transactions. An example system, comprising: a loan marketing system having a set of data-integrating micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for marketing loans to a set of potential parties. An example system, comprising: a rating system having a set of data-integrated micro-services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for rating a set of loan-related entities. An example system, comprising: loans, and has a compliance system with a set of data-integrated micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for automatically facilitating compliance with at least one of laws, regulations, and policies associated with loan transactions.
An example loan platform is provided herein having a robotic process automation system for retrieving a loan. An example system, comprising: a robotic process automation system for at least one of merging a set of loans, managing an warranty loan, or a proxy mortgage. An example system, comprising: crowd sourcing and automated classification systems for verifying the condition of bond issuers. An example system, comprising: social network monitoring systems employing artificial intelligence are used to categorize conditions concerning bonds. An example system, comprising: an artificial intelligence internet of things data collection and monitoring system is used for classifying conditions related to bonds. An example system, comprising: a system for changing terms and conditions of subsidized loans based on parameters monitored by at least one of internet of things (IoT), social network, or crowd sourcing. An example system, comprising: an automated blockchain custody service for managing a set of custody assets. An example system, comprising: a underwriting system for loans having a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for underwriting lending entities and transactions. An example system, comprising: a loan marketing system having a set of data-integrating micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for marketing loans to a set of potential parties. An example system, comprising: a rating system having a set of data-integrated micro-services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for rating a set of loan-related entities. An example system, comprising: a compliance system having a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for automatically facilitating compliance with at least one of laws, regulations, and policies associated with lending transactions.
An example loan platform is provided herein having a robotic process automation system for merging a set of loans. An example system, comprising: a robotic process automation system for managing at least one of an insurance loan or a proxy mortgage. An example system, comprising: crowd sourcing and automated classification systems for verifying the condition of bond issuers. An example system, comprising: social network monitoring systems employing artificial intelligence are used to categorize conditions concerning bonds. An example system, comprising: an artificial intelligence internet of things data collection and monitoring system is used for classifying conditions related to bonds. An example system, comprising: a system for changing terms and conditions of subsidized loans based on parameters monitored by at least one of internet of things (IoT), social network, or crowd sourcing. An example system, comprising: an automated blockchain custody service for managing a set of custody assets. An example system, comprising: a underwriting system for loans having a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for underwriting lending entities and transactions. An example system, comprising: a loan marketing system having a set of data-integrating micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for marketing loans to a set of potential parties. An example system, comprising: a rating system having a set of data-integrated micro-services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for rating a set of loan-related entities. An example system, comprising: a compliance system having a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for automatically facilitating compliance with at least one of laws, regulations, and policies associated with lending transactions.
An example loan platform is provided herein having a robotic process automation system for managing warranty loans. An example system, comprising: and the robot process automation system is used for proxy mortgage loan. An example system, comprising: crowd sourcing and automated classification systems for verifying the condition of bond issuers. An example system, comprising: social network monitoring systems employing artificial intelligence are used to categorize conditions concerning bonds. An example system, comprising: an artificial intelligence internet of things data collection and monitoring system is used for classifying conditions related to bonds. An example system, comprising: a system for changing terms and conditions of subsidized loans based on parameters monitored by at least one of internet of things (IoT), social network, or crowd sourcing. An example system, comprising: an automated blockchain custody service for managing a set of custody assets. An example system, comprising: a underwriting system for loans having a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for underwriting lending entities and transactions. An example system, comprising: a loan marketing system having a set of data-integrating micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for marketing loans to a set of potential parties. An example system, comprising: a rating system having a set of data-integrated micro-services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for rating a set of loan-related entities. An example system, comprising: a compliance system having a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for automatically facilitating compliance with at least one of laws, regulations, and policies associated with lending transactions.
An example loan platform is provided herein having a robotic process automation system for brokering mortgage loans. An example system, comprising: crowd sourcing and automated classification systems for verifying the condition of bond issuers. An example system, comprising: social network monitoring systems employing artificial intelligence are used to categorize conditions concerning bonds. An example system, comprising: an artificial intelligence internet of things data collection and monitoring system is used for classifying conditions related to bonds. An example system, comprising: a system for changing terms and conditions of subsidized loans based on parameters monitored by at least one of internet of things (IoT), a social network. An example system, comprising: a system for changing terms and conditions of subsidized loans based on parameters monitored by crowd sourcing. An example system, comprising: an automated blockchain custody service for managing a set of custody assets. An example system, comprising: a underwriting system for loans having a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for underwriting lending entities and transactions. An example system, comprising: a loan marketing system having a set of data-integrating micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for marketing loans to a set of potential parties. An example system, comprising: a rating system having a set of data-integrated micro-services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for rating a set of loan-related entities. An example system, comprising: a compliance system having a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for automatically facilitating compliance with at least one of laws, regulations, and policies associated with lending transactions.
An example lending platform is provided herein having a crowdsourcing and automated classification system for verifying the condition of a bond issuer. An example system, comprising: social network monitoring systems employing artificial intelligence are used to categorize conditions concerning bonds. An example system, comprising: an artificial intelligence internet of things data collection and monitoring system is used for classifying conditions related to bonds. An example system, comprising: a system for changing terms and conditions of subsidized loans based on parameters monitored by at least one of internet of things (IoT), social network, or crowd sourcing. An example system, comprising: an automated blockchain custody service for managing a set of custody assets. An example system, comprising: a underwriting system for loans having a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for underwriting lending entities and transactions. An example system, comprising: a loan marketing system having a set of data-integrating micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for marketing loans to a set of potential parties. An example system, comprising: a rating system having a set of data-integrated micro-services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for rating a set of loan-related entities. An example system, comprising: a compliance system having a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for automatically facilitating compliance with at least one of laws, regulations, and policies associated with lending transactions.
An example lending platform having a social network monitoring system employing artificial intelligence for categorizing conditions regarding bonds is provided herein. An example system, comprising: an artificial intelligence internet of things data collection and monitoring system is used for classifying conditions related to bonds. An example system, comprising: a system for changing terms and conditions of subsidized loans based on parameters monitored by at least one of internet of things (IoT), social network, or crowd sourcing. An example system, comprising: an automated blockchain custody service for managing a set of custody assets. An example system, comprising: a underwriting system for loans having a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for underwriting lending entities and transactions. An example system, comprising: a loan marketing system having a set of data-integrating micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for marketing loans to a set of potential parties. An example system, comprising: a rating system having a set of data-integrated micro-services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for rating a set of loan-related entities. An example system, comprising: a compliance system having a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for automatically facilitating compliance with at least one of laws, regulations, and policies associated with lending transactions.
An example lending platform having an internet of things data collection and monitoring system employing artificial intelligence for classifying conditions regarding bonds is provided herein. An example system, comprising: a system for changing terms and conditions of subsidized loans based on parameters monitored by at least one of internet of things (IoT), social network, or crowd sourcing. An example system, comprising: an automated blockchain custody service for managing a set of custody assets. An example system, comprising: a underwriting system for loans having a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for underwriting lending entities and transactions. An example system, comprising: a loan marketing system having a set of data-integrating micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for marketing loans to a set of potential parties. An example system, comprising: a rating system having a set of data-integrated micro-services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for rating a set of loan-related entities. An example system, comprising: a compliance system having a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for automatically facilitating compliance with at least one of laws, regulations, and policies associated with lending transactions.
An example loan platform is provided herein having a system to change terms and conditions of subsidized loans based on parameters monitored by the internet of things (IoT). An example system, comprising: a system for changing terms and conditions of subsidized loans based on parameters monitored in a social network or by at least one of crowd sourcing. An example system, comprising: an automated blockchain custody service for managing a set of custody assets. An example system, comprising: a underwriting system for loans having a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for underwriting lending entities and transactions. An example system, comprising: a loan marketing system having a set of data-integrating micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for marketing loans to a set of potential parties. An example system, comprising: a rating system having a set of data-integrated micro-services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for rating a set of loan-related entities. An example system, comprising: a compliance system having a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for automatically facilitating compliance with at least one of laws, regulations, and policies associated with lending transactions.
An example loan platform is provided herein having a system to change terms and conditions of subsidized loans based on parameters monitored in a social network. An example system, comprising: a system for changing terms and conditions of subsidized loans based on parameters monitored by crowd sourcing. An example system, comprising: an automated blockchain custody service for managing a set of custody assets. An example system, comprising: a underwriting system for loans having a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for underwriting lending entities and transactions. An example system, comprising: a loan marketing system having a set of data-integrating micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for marketing loans to a set of potential parties. An example system, comprising: a rating system having a set of data-integrated micro-services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for rating a set of loan-related entities. An example system, comprising: a compliance system having a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for automatically facilitating compliance with at least one of laws, regulations, and policies associated with lending transactions.
An example loan platform is provided herein having a system that changes terms and conditions of subsidized loans based on parameters monitored by crowd sourcing. An example system, comprising: an automated blockchain custody service for managing a set of custody assets. An example system, comprising: a underwriting system for loans having a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for underwriting lending entities and transactions. An example system, comprising: a loan marketing system having a set of data-integrating micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for marketing loans to a set of potential parties. An example system, comprising: a rating system having a set of data-integrated micro-services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for rating a set of loan-related entities. An example system, comprising: a compliance system having a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for automatically facilitating compliance with at least one of laws, regulations, and policies associated with lending transactions.
An example loan platform is provided herein having an automated blockchain custody service for managing a set of custody assets. An example system, comprising: a underwriting system for loans having a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for underwriting lending entities and transactions. An example system, comprising: a loan marketing system having a set of data-integrating micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for marketing loans to a set of potential parties. An example system, comprising: a rating system having a set of data-integrated micro-services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for rating a set of loan-related entities. An example system, comprising: a compliance system having a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for automatically facilitating compliance with at least one of laws, regulations, and policies associated with lending transactions.
An example loan platform is provided herein having a underwriting system for loans with a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for underwriting loan entities and transactions. An example system, comprising: a loan marketing system having a set of data-integrating micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for marketing loans to a set of potential parties. An example system, comprising: a rating system having a set of data-integrated micro-services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for rating a set of loan-related entities. An example system, comprising: a compliance system having a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for automatically facilitating compliance with at least one of laws, regulations, and policies associated with lending transactions.
An example loan platform is provided herein having a loan marketing system with a set of data-integrated micro-services, including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services, for marketing loans to a set of potential parties. An example system, comprising: a rating system having a set of data-integrated micro-services including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and an intelligent contract service for rating a set of loan-related entities. An example system, comprising: a compliance system having a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for automatically facilitating compliance with at least one of laws, regulations, and policies associated with lending transactions. In an embodiment, provided herein is a lending platform having a rating system with a set of data-integrated micro services including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating a set of loan-related entities; and a compliance system having a set of data-integrated micro-services including data collection and monitoring services, blockchain services, artificial intelligence services, and intelligent contract services for automatically facilitating compliance with at least one of laws, regulations, and policies associated with a lending transaction.
In an embodiment, a database service may be provided that embodies, enables, or is associated with a blockchain, ledger (e.g., distributed ledger, etc.), such as any of the embodiments described herein or any of the embodiments incorporated by reference into a document. In an embodiment, the database service may include a ledger database service that is transparent, immutable, and cryptographically verifiable, such as Amazon TM QLDB TM And (5) database services. The database services may be included in one or more layers or micro-services of the system 3300 or in connection with one or more layers or micro-services of the system 3300, such as the adaptive smart services layer 3304 or the data store layer 3308. For example, the service may be used in conjunction with a centralized ledger that records all changes or transactions and maintains an immutable record of those changes, such as by various environments or processes tracking entities,tracking lending history through a series of transactions, or verifying facts associated with underwriting processes, claims, or laws or regulations. Ledgers may be owned by a single trusted entity or a group of trusted entities and may be shared with any other entity (e.g., entities that cooperate in coordination of transactions, production processes, federated services, etc.). In contrast to relational databases, the database service may provide a non-variable, cryptographically verifiable ledger entry without the need for custom audit or tracking records. In contrast to blockchain frameworks, such database services may include the ability to perform queries, create tables, index data, and the like. The database service may selectively ignore the requirements for many blockchain frameworks that degrade performance, such as the consistency requirements prior to submitting a transaction, or the database service may employ optional consistency features. In an embodiment, the database service may include a transparent, immutable, cryptographically verifiable ledger that a user may use to build an application that acts as a logging system in which multiple parties conduct transaction processing within a centralized trusted entity or group of entities. The database service may supplement or replace the building of audit functions into relational databases or use conventional distributed ledger capability in a blockchain framework. The database service may use an immutable transaction log that can track all application data changes and maintain a comprehensive and verifiable change history. In an embodiment, transactions may be configured to meet the requirements of atomicity, consistency, isolation, and durability (ACID) to be recorded in a log configured to prevent deletion or modification. The changes may be linked in an encrypted manner such that the changes are auditable and verifiable, such as in a history that a user may query or analyze, such as using a conventional query type, such as an SQL query. In an embodiment, the database service may be provided in a serverless form, thus eliminating the need to provide specific server capacity or configuration read/write limitations. To launch the database service, the user may create a ledger, definition table, etc., which will automatically expand to support the application requirements. And blockchain-based classification In contrast, the database service may ignore the requirement for distributed consistency, so the database service may perform more transactions simultaneously.
In embodiments of the invention involving blockchain or distributed ledgers, custody blockchain services, such as Amazon, may be used TM Safekeeping Blockchain TM It may include facilities for conveniently creating and managing an extended blockchain network. The custody block chain service may be provided as part of a hierarchical data service architecture described herein. In the event that a user requires immutable, verifiable capabilities provided by a blockchain or ledger, they may also seek the ability to allow multiple parties to conduct transaction processing, execute contracts (e.g., in the smart contract embodiments described herein), share data, etc., without a trusted central authority. Because of the significant amount of time and technical expertise required to build a traditional blockchain framework, each participant in the licensed network must provide hardware, install software, create and manage access control credentials, and configure network settings. As a given blockchain application grows, some activity is also required to extend the network, monitor resources on blockchain nodes, add or delete hardware, and manage network availability. In an embodiment, the custody blockchain service may provide management of each of these requirements and enablement capabilities. This may include supporting an open source blockchain framework and enabling selection, setting, and deployment of selected frameworks in a control panel, console, or other user interface, where a user may select the framework they prefer, add network members, and configure member nodes that will process transaction requests. The custody blockchain service may automatically create a blockchain network (e.g., a blockchain network that may have multiple accounts of multiple nodes across each member) and configure software, security, and network settings. The custody block chain service may protect and manage network credentials, such as through a key management service, so that clients may manage keys. In an embodiment, the custody blockchain service may include one or more APIs, such as a voting API, for example, a voting API that allows network members to vote (e.g., vote add or delete members). With a given application program An increase in application usage of an order (e.g., any of the applications described in connection with platform 3300), a user may add more capacity to the blockchain network, such as through simple API calls. In an embodiment, the custody blockchain service may have a range of combinations of computation and memory capacity, for example, to enable a user to select the correct combination of resources for a given blockchain-based application.
Referring to FIG. 69, a system for automated loan management is shown. Various entities/parties 6938 may have connections with loans 6924, with the loans 6924 including borrower 6940, borrower 6942, neutral third party (e.g., an evaluator), or interested third party (e.g., a regulatory agency, a corporate employee, etc.) third party 6944. The loan 6924 may be governed by an intelligent lending contract 6990 that includes information on loan terms and conditions 6929, loan actions 6930, loan events 6932, borrower priorities 6928, and the like. The intelligent lending contract 6990 may be recorded in a loan entry 6941 in the distributed ledger 6963. The smart lending contract 6990 may be stored as blockchain data 6934.
In an illustrative example, controller 6922 may receive mortgage data 6974, such as mortgage related events 6908, mortgage attributes 6910, environmental data 6912 regarding the environment in which mortgage 6902 is located, sensor data 6914, where sensor 6904 may be attached to the mortgage and contain the mortgage or a situation in proximity to the mortgage. In an embodiment, mortgage data may be obtained by: the internet of things circuit 6920, camera systems, networking monitoring systems, internet monitoring systems, mobile device systems, wearable device systems, user interface systems, and interactive crowdsourcing systems.
The controller 6922 may also monitor and/or receive data from the social networking information 6958, and the financial status 6992 may infer from the social networking information 6958, such as a rating of the principal, a tax status of the principal, a credit report of the principal, a credit rating of the principal, a website rating of the principal, a set of customer reviews of the principal's product, a social networking rating of the principal, a set of credentials of the principal, a set of referrals of the principal, a set of credentials of the principal, a set of behaviors of the principal, and so forth. The controller 6922 may also receive market information 6948, such as pricing 6950; financial data 6954 such as a public valuation of the principal, a set of properties owned by the entity as indicated by the public record, a valuation of a set of properties owned by the principal, a bankruptcy condition of the principal, a redemption status of the entity, a contract default status of the entity, a violation status of the entity, a crime status of the entity, an export regulation status of the entity, a banned status of the entity, a tariff status of the entity, a tax status of the entity, a credit report of the entity, a credit rating of the entity, and the like.
In an embodiment, the artificial intelligence system 6962 may be part of the controller 6922 or located on a remote system. AI system 6962 may include a valuation circuit 6964 and a value model improvement circuit 6966, valuation circuit 6964 configured to determine a value of a mortgage based on mortgage data 6974 and a valuation model; the value model improvement circuit 6966 improves the valuation model based on the first set of received mortgage data 6974 and the result of the mortgage associated with the first set of received mortgage data being used as a loan for the guarantee. The AI system 6962 may include an automatic proxy circuit 6970 to take action based on mortgage events, loan events, and the like. The actions may include loan-related actions, such as: providing a loan; accepting a loan; carrying out loan; setting the interest rate of loans; postponing payment requirements; modifying the interest rate of the loan; verifying ownership of the mortgage; recording the change of ownership; evaluating the value of the mortgage; initiating a check of the mortgage; collect loan; clearing loans; setting the terms and conditions of loans; providing a notification to be provided to the borrower; redemption-stopping property limited by the loan; modifying the terms and conditions of the loan, etc. The action may include a mortgage-related action, such as: verifying ownership of one of the assigned set of mortgages; recording a change in ownership of one of the assigned set of mortgages; evaluating the value of one of the assigned set of mortgages; initiating a check of one of the assigned set of mortgages; initiating maintenance of one of the assigned set of mortgages; initiating a guarantee of one of the assigned set of mortgages; modifying the terms and conditions of one of the assigned set of mortgages, etc. AI system 6962 may include clustering circuitry 6972 to create groups of mortgages based on common attributes. Clustering circuitry 6972 may also determine a set of cancelled mortgages, where the cancelled mortgage has a common attribute with one or more mortgages. Data about the countermortgage may be collected and used as a proxy for the mortgage. The smart contract circuitry 6968 may create smart debit contracts 6990, as elsewhere herein.
Referring to fig. 70, the controller may include a blockchain service circuit 7044 configured to interpret a plurality of access control features 7048, e.g., corresponding to a principal associated with the loan 7030 and a principal associated with the blockchain data 7040. The system may include a data collection circuit 7012 configured to interpret entity information 7002, mortgage data 7004, and the like, for example, corresponding to an entity associated with a loan transaction corresponding to a loan, mortgage condition, and the like. The system may include an intelligent contract circuit 7022 configured to specify loan terms and conditions 7024, contracts 7028, and the like, related to the loan. The system may include a loan management circuit 7032 configured to interpret a loan-related action 7034 and/or an event 7038 in response to the entity information, the plurality of access control features, and the loan terms and conditions, wherein the loan-related event is associated with the loan; implementing a loan-related activity in response to the entity information, the plurality of access control features, and the loan terms and conditions, wherein the loan-related activity is associated with the loan; and wherein each of the blockchain service circuitry, the data collection circuitry, the smart contract circuitry, and the loan management circuitry further includes a respective Application Programming Interface (API) component configured to facilitate communication between the circuitry of the system. For example, the borrower 7008 may be coupled to the controller through a secure access control interface 7052 (e.g., through access control instructions 7054) configured to be coupled to the controller through secure access control circuitry 7050. The data collection circuit 7012 may be configured to receive mortgage data 7004 and entity information 7002, such as information about a lender, borrower or third party, mortgage, machine or property associated with the lender, product of the lender, and the like. Mortgage data 7004 may include: the type of mortgage, the value of the mortgage, the price of the type of mortgage, the value of the mortgage, the description of the mortgage, the product feature set of the mortgage, the model of the mortgage, the brand of the mortgage, the manufacturer of the mortgage, the age of the mortgage, the fluidity of the mortgage, the shelf life of the mortgage, the condition of the mortgage, the valuation of the mortgage, the state of the mortgage, the environment of the mortgage, the state of the mortgage, the storage location of the mortgage, the history of the mortgage, the ownership of the mortgage, the administrator of the mortgage, the security of the mortgage, the condition of the mortgage, the retention of the mortgage, the storage condition of the mortgage, the maintenance history of the mortgage, the history of the use of the mortgage, the accident history of the mortgage, the fault history of the mortgage, the ownership of the mortgage, the condition of the mortgage, the location of the jurisdiction, and the like. The data collection circuit 7012 may determine mortgage conditions based on the received data. The received data 7002, 7004 and mortgage conditions 7010 may be provided to an AI circuit 7042, which may include an automated agent circuit 7014 (e.g., processing events 7018, 7020), an intelligent contract service circuit 7022, and a loan management circuit 7032.
Referring to FIG. 71, an illustrative and non-limiting example method for processing a loan 7100 is shown. The example method may include interpreting a plurality of access control features (step 7102); interpreting the entity information (step 7104); specifying loan terms and conditions (step 7108); executing a contract-related event in response to the entity information (step 7110); interpreting the loan-related event (step 7112); performing a loan action in response to the event (step 7114); providing a user interface (step 7118); creating an intelligent lending contract (step 7120); and recording the smart loan contract as blockchain data (step 7122).
Referring to fig. 72, a system 7200 for adaptive intelligent and robotic process automation functions for trading, finance, and marketing support is shown. The system 7200 can include a controller 7223, which can include a data collection circuit 7202, the data collection circuit 7202 receiving mortgage data 7201 and determining a mortgage condition 7204. The controller 7223 may also include a plurality of AI circuits 7254. The plurality of AI circuits 7254 may include a valuation circuit 7208, which may include a valuation model improvement circuit 7210 and a clustering circuit 7212. The plurality of AI circuits 7254 may include an intelligent contract service circuit 7214 which includes an intelligent loan contract 7216 for a loan 7225. The plurality of AI circuits 7254 may include an automatic proxy circuit 7218 which takes loan-related actions 7220. The controller 7223 may also include reporting circuitry 7222 and market value monitoring circuitry 7224, which also determines a mortgage status 7204. The controller 7223 may also include a secure access user interface 7228 which receives access control instructions 7230 from the borrower 7242. The access control instructions 7230 are provided to a secure access control circuit 7232 which provides instructions to the blockchain service circuit 7234, which blockchain service circuit 7234 interprets the access control features 7238 and provides access rights to the borrower 7242 or other party. The blockchain service circuit 7234 stores mortgage data and unique mortgage ID as blockchain data 7235.
Referring to fig. 73, a method 7300 for automated smart contract creation and mortgage distribution is shown. Method 7300 may include receiving first and second mortgage data regarding a mortgage (7302); creating an intelligent lending contract (7304); associating (7308) the mortgage data with a unique identifier of the mortgage; and storing the unique identifier and mortgage in the blockchain structure (7310). The method may also include interpreting a condition of the mortgage based on the mortgage data (7312); identifying a mortgage event (7314); reporting a mortgage event (7318); and performing an action in response to the mortgage (7320). Method 7300 may also include identifying a set of counteracting mortgages (7322); accessing market information related to the countermortgage or mortgage (7314); and modifying the terms or conditions of the loan based on the market information (7328). The method 7300 may further include receiving an access control instruction (7330); interpreting a plurality of access control features (7332); and providing access to the mortgage date (7334).
Referring to FIG. 74, an illustrative and non-limiting example system for processing a loan 7400 is shown. The example system may include a controller 7401. The controller 7411 may include data collection circuitry 7412, valuation circuitry 7444, a user interface 7454 (e.g., for interfacing with a user 7406), blockchain services circuitry 7458, and a number of artificial intelligence circuitry 7442, including intelligent contract services circuitry 7422, loan management circuitry 7492, clustering circuitry 7432, automated agent circuitry 7414 (e.g., for processing loan related events 7439 and loan actions 7438).
Blockchain service circuitry 7458 may be configured to interface with distributed ledger 7440. Data collection circuitry 7412 may be configured to receive data related to a plurality of mortgages 7404 or data related to the environment of a plurality of mortgages 7402. The valuation circuit 7444 may be configured to determine a value for each of the plurality of mortgages based on the valuation model 7452 and the received data. The smart contract service circuit 7422 may be configured to interpret a smart loan contract 7431 for a loan and modify the smart loan contract 7431 by assigning at least a portion of the plurality of mortgages as a guarantee of the loan based on the value of each of the determined plurality of mortgages such that the value of each of the determined plurality of mortgages 7428 is sufficient to provide the guarantee of the loan. Blockchain service circuitry 7458 may also be configured to record at least a portion of the assigned mortgage 7428 to an entry in the distributed ledger 7440, wherein the entry is used to record an event related to the loan. Each of the blockchain service circuitry, the data collection circuitry, the valuation circuitry, and the smart contract circuitry may further include a corresponding Application Programming Interface (API) component configured to facilitate communication between the system circuitry.
Modifying the smart lending contract 7431 may also include specifying terms and conditions 7424 that govern one of: loan terms, loan conditions, loan-related events, and loan-related activities. Each of the terms and conditions 7424 may include at least one member of the following: loan principal amount, loan balance, fixed interest rate, variable interest rate description, payment amount, payment plan, end-of-line clearing plan, mortgage description, mortgage replacement description, description of at least one of the parties, insured description, insurer description, guaranty description, personal guaranty, lien, redemption prevention status, default outcome, contract relating to any of the foregoing, and deadline of any of the foregoing.
The loan 7430 may include at least one of the following loan types: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance arrangements, payday loans, refund expectations, learning-aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, and subsidy loans.
The mortgage may include at least one of: vehicles, ships, airplanes, buildings, houses, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, commodities, securities, currencies, value certificates, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.
The data collection circuit 7412 may also be configured to receive result data 7410 related to the loan 7430 and the corresponding mortgage, and wherein the valuation circuit 7444 includes an artificial intelligence circuit configured to iteratively refine (7450) the valuation model 7452 based on the result data 7410.
The valuation circuit 7444 may also include a market value data collection circuit 7448 configured to monitor and report market information related to the value of at least one of the plurality of mortgages. The market value data collection circuit 7448 is also configured to monitor pricing or financial data for items similar to mortgages in at least one public market.
Clustering circuitry 7432 may be configured to identify a set of countering items 7434 for evaluating a mortgage based on similarity to an attribute of the mortgage.
The mortgage attribute may be selected from the following attributes: the type of mortgage, the age of the mortgage, the condition of the mortgage, the history of the mortgage, the storage conditions of the mortgage, and the geographic location of the mortgage.
The data collection circuit 7412 may also be configured to interpret the condition 7411 of the mortgage.
The data collection circuit may further comprise at least one of the following: the system comprises an internet of things system, a camera system, a networking monitoring system, an internet monitoring system, a mobile device system, a wearable device system, a user interface system and an interactive crowdsourcing system.
The loan includes at least one of the following loan types: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance arrangements, payday loans, refund expectations, learning-aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, and subsidy loans.
The loan management circuit 7492 may be configured to interpret an event related to the loan 7439 and perform a loan-related action 7438 in response to the loan-related event.
The loan-related events may include events related to at least one of: the value of the loan, the condition of the loan mortgage, or the ownership of the loan mortgage.
The loan-related actions may include at least one of: modifying loan terms and conditions, providing notice to one of the parties, providing notice to the borrower of the loan as needed, and redeeming the loan subject property.
The respective API components of the circuit may also include a user interface configured to interact with a plurality of users of the system.
Each of the plurality of users may include: a party of the plurality of parties, an entity of the plurality of entities, or a representation of any of the foregoing. At least one of the plurality of users may include: a potential principal, a potential entity, or a representation of any of the foregoing.
Referring to FIG. 75, an illustrative and non-limiting example method for processing a loan 7500 is shown. The example method may include receiving data related to a plurality of mortgages (step 7502); setting a value for each of a plurality of mortgages (step 7504); allocating at least a portion of the plurality of mortgages as a guarantee of the loan (step 7508); and recording at least a portion of the plurality of mortgages in a distributed ledger into an entry, wherein the entry is used to record an event related to the loan (step 7510). The smart loan contract for the loan may be modified (step 7512).
The terms and conditions of the loan may be specified (step 7514). Each of the terms and conditions are selected from the following: liability principal amount, liability balance, fixed interest rate, variable interest rate, payment amount, payment plan, end-of-line clearing plan, principal, insured, insurer, vouchers, personal vouchers, liens, duration, contract, redemption stopping condition, default condition, and outcome of the violation.
Result data relating to the loan may be received (step 7518). The valuation model may be iteratively refined based on the outcome data and the corresponding mortgage (step 7520). Market information related to the value of at least one of the plurality of mortgages may be monitored (step 7522).
A set of items similar to one of the plurality of mortgages may be identified based on the similarity to the attribute of the one of the plurality of mortgages (step 7524).
A condition of one of the plurality of mortgages may be interpreted (step 7528).
An event may be reported that relates to a value of one of the plurality of mortgages, a condition of one of the plurality of mortgages, or ownership of one of the plurality of mortgages (step 7530).
An event related to a value of one of the plurality of mortgages, a condition of one of the plurality of mortgages, or ownership of one of the plurality of mortgages may be interpreted (step 7532); and may perform an action related to the guaranty loan in response to an event related to one of the plurality of mortgages of the guaranty loan (step 7534).
The loan-related actions may be selected from the following actions: providing a loan; accepting a loan; carrying out loan; setting the interest rate of loans; postponing payment requirements; modifying the interest rate of the loan; verifying ownership of the mortgage; recording the change of ownership; evaluating the value of the mortgage; initiating a check of the mortgage; collect loan; clearing loans; setting the terms and conditions of loans; providing a notification to be provided to the borrower; redemption-stopping property limited by the loan; and modifying the terms and conditions of the loan.
Referring to fig. 76, an illustrative and non-limiting example system 7600 for adaptive intelligence and robotic process automation capability is shown. The example system may include a controller 7601. The controller may include data collection circuitry 7628 that may collect data from a variety of sources and systems, such as mortgage data 7632, environmental data regarding the mortgage 7634, and the like, including: the system comprises an internet of things system, a camera system, a networking monitoring system, an internet monitoring system, a mobile device system, a wearable device system, a user interface system and an interactive crowdsourcing system. The data collection circuit 7628 may identify a mortgage event 7630 based on the received data 7632, 7634.
The controller 7601 may also include various AI circuits 7644, including a valuation circuit 7602, which may determine the value of a mortgage based in part on the received data 7632, 7634. The valuation circuit 7602 may include a market value monitoring circuit 7606 configured to determine market data regarding or counteracting a mortgage, wherein the market data may facilitate valuation of the mortgage. The AI circuitry may also include smart contract service circuitry 7610 to facilitate services related to loans 7629, such as creating smart contracts 7622, identifying terms and conditions 7624 of smart contracts 7622, identifying borrower priorities, and tracking value allocations 7626 between borrowers. The smart contract service circuit 7610 may provide data to the blockchain service circuit 7636, where the blockchain service circuit 7636 is capable of creating and modifying loan entries 7627 on the distributed ledger 7625, where the loan entries 7627 may include terms and conditions, data regarding mortgages used to vouch for a loan, borrower priority and value allocation, and the like. The AI circuit 7644 may also include a mortgage sorting circuit 7640 that creates a countermortgage 7604, the countermortgage 7604 sharing at least one attribute with one of the mortgages, wherein the common attribute may be a category of the item, a age of the item, a condition of the item, a history of the item, ownership of the item, caretaker of the item, guarantee of the item, owner condition of the item, lien of the item, storage condition of the item, geographic location of the item, and jurisdiction of the item. The use of the countermortgage 7642 may assist the market value monitoring circuit 7606 in obtaining relevant market data and an overall determination of the value of the mortgage.
The data collection circuit 7628 may utilize the received data and the determination of the value of the mortgage to identify a mortgage event 7630. The automatic proxy circuit 7646 may take action 7648 based on the mortgage event 7630. Act 7648 may be a loan-related act, such as: providing a loan; accepting a loan; carrying out loan; setting the interest rate of loans; postponing payment requirements; modifying the interest rate of the loan; collect loan; clearing loans; setting the terms and conditions of loans; providing a notification to be provided to the borrower; redemption-stopping property limited by the loan; modifying the terms and conditions of the loan, etc. Act 7648 may be a mortgage-related act, such as: verifying ownership of one of a set of mortgages; recording a change in ownership of one of a set of mortgages; evaluating the value of one of a set of mortgages; initiating a check of one of a set of mortgages; initiating maintenance of one of a set of mortgages; initiating a guaranty for one of a set of mortgage; and modifying the terms and conditions of one of a set of mortgages.
Referring to FIG. 77, an illustrative and non-limiting example method 7700 for loan creation and management is shown. An example method 7700 may include receiving data related to a set of mortgages that provide a guarantee for a loan (step 7702), and receiving data related to an environment of one of the set of mortgages (step 7704). An intelligent loan contract for the loan may be created (step 7706), and the set of mortgages may be recorded in the intelligent loan contract (step 7708). A loan entry may be recorded in the distributed ledger (step 7770), wherein the loan entry includes an intelligent lending contract or a reference to an intelligent contract.
The value of each of a set of mortgages may be determined (step 7772), and the value of the mortgage may be apportioned among the borrowers based on the priority of the different borrowers (step 7776). The valuation model may be modified based on a learning set (step 7774) that includes a set of valuation determinations for a set of mortgages, the results of loans with the mortgages as guarantees, and the valuations for the mortgages.
A mortgage event may be determined based on the received data or a valuation of one of the mortgages (step 7778). A loan-related action may be performed in response to the determined mortgage event (step 7780), wherein the loan-related action includes: providing a loan; accepting a loan; carrying out loan; setting the interest rate of loans; postponing payment requirements; modifying the interest rate of the loan; collect loan; clearing loans; setting the terms and conditions of loans; providing a notification to be provided to the borrower; redemption-stopping property limited by the loan; modifying the terms and conditions of the loan, etc.
A mortgage-related action may be performed in response to the determined mortgage event (step 7782), wherein the loan-related action includes: verifying ownership of one of a set of mortgages; recording a change in ownership of one of a set of mortgages; evaluating the value of one of a set of mortgages; initiating a check of one of a set of mortgages; initiating maintenance of one of a set of mortgages; initiating a guaranty for one of a set of mortgage; modifying the terms and conditions of one of a set of mortgages, etc.
One or more sets of countermortgages may be identified (step 7784), wherein each item in the set of countermortgages shares a common attribute with at least one of the mortgages. The market information may then be monitored to obtain data related to counteracting mortgages (step 7786). The value of the mortgage may be updated with monitored market information regarding one or more counteracting mortgages (step 7788). The loan entries in the distributed ledger may be updated with the updated value of the mortgage (7730).
Referring to fig. 78, an example system 7800 for adaptive intelligence and robotic process automation capabilities for trading, finance, and marketing support is illustrated. System 7800 can include a controller 7801, and controller 7801 can include a plurality of AI circuits 7820. Multiple AI circuits 7820 may include smart contract service circuit 7810 to create and modify smart loan contracts 7812 for loans 7818. The smart lending contract 7812 may include terms and conditions 7814 of the loan 7818, contracts specifying desired mortgage values, information about the loan 7818 and mortgages, information about the borrower including borrower priority including apportionment 7816 of mortgage values among the borrowers.
Multiple AI circuits 7820 may include a rating circuit 7802 configured to determine one or more values 7808 of a mortgage based on a rating model 7809 and mortgage data 7840. The valuation circuit 7802 may include a mortgage sorting circuit 7803 to identify countermortgages 7807 based on common attributes with mortgages used to provide a guarantee for the loan 7818. Market value monitoring circuitry 7806 may receive market information 7842 regarding mortgages and countering mortgage 7807. Market information 7842 may be used by valuation model 7809 to determine mortgage value 7808. Valuation circuitry 7802 may also include valuation model improvement circuitry 7804 to improve valuation model 7809 for determining value 7808. The valuation model improvement circuit 7804 may utilize a training set that includes previously determined values 7808 of mortgages and data regarding the loan results of those mortgages as a guarantee.
Multiple AI circuits 7820 may include a loan management circuit 7822, which may include a value comparison circuit 7828 to compare the mortgage value 7808 to the desired value of the mortgage specified in the loan contract to determine a mortgage compensation value 7830. The smart contract service circuit 7810 may determine terms of the loan 7818 or conditions 7814 in response to the mortgage compensation value 7830, wherein the terms of the conditions 7814 are related to a loan component, such as a lender of the smart loan contract 7812, a mortgage of the loan, a loan-related event, a loan-related activity, and so forth. The terms of the condition may be a loan principal amount, a loan balance, a fixed interest rate, a variable interest rate description, a payment amount, a payment plan, a final maximum clearing plan, a mortgage description, a mortgage replacement description, a description of the principal, a insured description, a guaranty description, a personal guaranty, a retention, a redemption prevention condition, a default outcome, a contract related to any of the foregoing, a deadline for any of the foregoing, and the like. The terms of the condition may be a liability principal amount, a liability balance, a fixed interest rate, a variable interest rate, a payment amount, a payment plan, a final clearing plan, a principal, a insured, a guaranty, a personal guaranty, a retention, a deadline, a contract, a redemption condition, a violation condition, and a violation outcome, among others. Smart contract service circuitry 7810 may modify smart lending contract 7812 to include new terms or conditions 7814, such as terms and conditions 7814 determined in response to mortgage compensation value 7830.
Loan management circuit 7822 may also include an automatic proxy circuit 7824 to take action 7826 based on mortgage compensation value 7830. Action 7826 may be a mortgage-related action, such as: verifying ownership of the mortgage; recording a change in ownership of the mortgage; evaluating the value of the mortgage; initiating a check of the mortgage; initiating maintenance of the mortgage; initiating a guaranty for the mortgage; modifying mortgage terms and conditions, etc.
Action 7826 may be a loan-related action, such as: providing a loan; accepting a loan; carrying out loan; setting the interest rate of loans; postponing payment requirements; modifying the interest rate of the loan; collect loan; clearing loans; setting the terms and conditions of loans; providing a notification to be provided to the borrower; redemption-stopping property limited by the loan; modifying the terms and conditions of the loan, etc.
The controller 7801 may also include data collection circuitry 7832 to receive mortgage data 7840 and determine a mortgage event 7834. Reporting circuitry 7836 may then report mortgage event 7834 and mortgage data 7840. The blockchain service circuitry 7838 may create and update blockchain data 7825 that stores a copy of the smart lend contract 7812.
Referring to FIG. 79, an illustrative and non-limiting method for robotic process automation for trading, finance, and marketing activities is shown. An example method may include: data relating to one or a group of mortgages is received (step 7902), wherein the mortgage serves as a guarantee of the loan. The value of the mortgage is determined based on the received data and the valuation model (step 7904). An intelligent lending contract is created (step 7906) that specifies information about the loan, including a contract specifying a desired value for a mortgage required to secure the loan.
The value of the mortgage may be compared to the value of the mortgage specified in the contract (step 7908) and a mortgage compensation value may be determined (step 7910), wherein the mortgage compensation value may be positive if the value of the mortgage exceeds the desired value of the mortgage and negative if the mortgage value is less than the desired value of the mortgage. A loan-related action may be performed in response to the mortgage compensation value (step 7912). Terms or conditions may be determined in response to the mortgage compensation value (step 7914), and the smart loan contract may be modified in response to the mortgage compensation value (step 7916).
The assessment model may be modified based on the first set of assessment determinations for the first set of mortgages and a set of corresponding loan results with the first set of mortgages as a guarantee using the following system (step 7918): machine learning systems, model-based systems, rule-based systems, deep learning systems, neural networks, convolutional neural networks, feed-forward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, simulation systems, hybrid systems of at least two of any of the foregoing, and the like.
A set of counteracting mortgages may be identified based on the common properties of the mortgages (step 7920), such as the category of the mortgage, the age of the mortgage, the condition of the mortgage, the history of the mortgage, the ownership of the mortgage, the manager of the mortgage, the guaranty of the mortgage, the condition of the owner of the mortgage, the lien of the mortgage, the storage condition of the mortgage, the geographic location of the mortgage, and the jurisdiction of the mortgage. Market information may be monitored to obtain data related to counteracting mortgages (step 7922), such as pricing or financial data; and may modify the smart lending contract in response to the market information (step 7924). An action may be automatically initiated based on the market information (step 7926). The action may include modifying a term of the loan; issuing a notice of the breach of the contract; initiating a redemption-stopping action to modify the condition of the loan; providing notification to the loan party; providing necessary notification to the borrower of the loan; redemption-stopping property limited by the loan; verifying ownership of the mortgage; recording a change in ownership of the mortgage; evaluating the value of the mortgage; initiating a check of the mortgage; initiating maintenance of the mortgage; initiating a guaranty for the mortgage; modifying mortgage terms and conditions, etc.
Referring to FIG. 80, an illustrative and non-limiting example system 8000 for adaptive intelligence and robotic process automation capability is shown. The example system may include a controller 8001, the controller 8001 including a data collection circuit 8028 configured to receive mortgage data 8032 regarding a plurality of mortgages used to vouch for a set of loans 8018. The data collection circuit 8028 may further comprise: the system comprises an internet of things system, a camera system, a networking monitoring system, an internet monitoring system, a mobile device system, a wearable device system, a user interface system and an interactive crowdsourcing system. Mortgages may include vehicles, ships, aircraft, buildings, houses, real estate, undeveloped property, farms, crops, municipalities, warehouses, a group of inventory, commodities, securities, currencies, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contract rights, antiques, fixtures, furniture, tools, machinery, personal property, and the like. The set of loans may include: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance arrangements, payday loans, refund expectations, learning aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, subsidy loans, and the like. The group loan 8018 may be distributed among multiple borrowers as a means of dispersing the risk of loans.
The controller 8001 may also include a plurality of AI circuits 8044, including a mortgage classification circuit 8020, to identify a set of mortgages 8022 from among the mortgages that are related by sharing a common attribute, wherein the common attribute is in the received mortgage data 8032, such as a type of mortgage, a category of the mortgage, a value of the mortgage, a price of the type of the mortgage, a value of the mortgage, a description of the mortgage, a product feature set of the mortgage, a model of the mortgage, a brand of the mortgage, a manufacturer of the mortgage, a service life of the mortgage, a mobility of the mortgage, a shelf life of the mortgage, a condition of the mortgage, an estimate of the mortgage, a state of the mortgage, an environment of the mortgage, a state of the mortgage, a storage location of the mortgage, a history of the mortgage, an ownership of the mortgage, a manager of the mortgage, a guarantee of the mortgage, an ownership of the mortgage, a product feature set of the mortgage, a quality of the mortgage, a condition of the mortgage, a maintenance of the mortgage, a location of the mortgage, a fault of the mortgage, a history of the mortgage, etc. The mortgage sorting circuit 8020 may also identify a countermortgage 8023, where the countermortgage 8023 and the mortgage have a common attribute.
The reporting circuitry 8034 may also report mortgage events 8030 based on the mortgage data 8032. The automatic proxy circuit 8008 may automatically perform action 8009 based on the mortgage event 8030. Action 8009 may be a mortgage-related action, such as: verifying ownership of one of the plurality of mortgages; recording a change in ownership of one of the plurality of mortgages; evaluating a value of one of the plurality of mortgages; initiating an inspection of one of the plurality of mortgages; initiating maintenance of one of the plurality of mortgages; initiating a guaranty for one of the plurality of mortgages; modifying terms and conditions of one of the plurality of mortgages, etc. Act 8009 can be a loan-related act, such as: providing a loan; accepting a loan; carrying out loan; setting the interest rate of loans; postponing payment requirements; modifying the interest rate of the loan; collect loan; clearing loans; setting the terms and conditions of loans; providing a notification to be provided to the borrower; redemption-stopping property limited by the loan; modifying the terms and conditions of the loan, etc.
The controller 8001 may also include an intelligent contract service circuit 8010 to create an intelligent loan contract 8012 for a single loan or group of loans 8018, wherein the intelligent loan contract 8012 identifies a mortgage subset 8016 selected from a group of related mortgages 8022 that share a common attribute to serve as a guarantee for the group of loans 8018. The smart contract service circuit 8010 may also redefine the mortgage subset 8016 based on the updated value of the mortgages, thereby rebalancing the mortgages for a set of loans based on the value of the mortgage. The identity of the mortgage subset 8016 may be identified in real time as the common attributes change in real time (e.g., the status of the mortgage or whether the mortgage is in transit for a defined period of time). Further, the smart contract service circuit 8010 may determine terms or conditions 8014 of the loan based on the value of one of the mortgages, wherein the terms or conditions 8014 are related to the loan component (e.g., the loan party, the loan mortgage, the loan related event, and the loan related activity). The terms or conditions 8014 may be a loan principal amount, a loan balance, a fixed interest rate, a variable interest rate description, a payment amount, a payment plan, a final maximum payback plan, a mortgage description, a mortgage replacement description, a description of the party, a insured description, a insurer description, a guaranty description, a personal guaranty, a lien, a redemption status, a default outcome, a contract related to any of the foregoing, a deadline for any of the foregoing, and the like.
The controller may also include a valuation circuit 8002 to determine the value 8040 of each mortgage in the mortgage subset based on the received data and the valuation model 8042. Valuation model improvement circuit 8004 may modify valuation model 8042 based on the first set of valuation determinations for the first set of mortgages and the corresponding set of loan results with the first set of mortgages as a guarantee. The valuation model improvement circuit 8004 may include: machine learning systems, model-based systems, rule-based systems, deep learning systems, neural networks, convolutional neural networks, feed-forward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, simulation systems, hybrid systems comprising at least two of any of the foregoing, and the like. The valuation circuit 8002 may also include a market value data collection circuit 8006 for monitoring and reporting market information 8038, such as pricing or financial data related to counteracting a mortgage 8023 or a group of mortgages 8022.
Referring to fig. 81, a method 8100 for automated trading, financial and marketing activities is illustrated. A method may include: receiving data related to a mortgage (step 8102); identifying a group of mortgages, wherein items in the group share a common attribute or feature (step 8104); identifying a subset of the group as a guarantee for a group of loans (8108); and creating an intelligent lending contract for the group loan, wherein the intelligent lending contract identifies a subset of the group serving as a guarantee (step 8110). The common attributes of the set of mortgage shares may be in the received data.
The value of each mortgage may be determined using the received data and the valuation model (8112). The subset of mortgages used as vouches may then be redefined based on the value of the different mortgages (8114). The terms or conditions of at least one of the smart loan contracts may be determined based on the value of at least one of the mortgages in the subset of the set (8118), and the smart loan contract is modified to include the determined terms or conditions (8120). Furthermore, in some embodiments, the valuation model (8122) may be modified based on the first set of valuation determinations for the first set of mortgages and a corresponding set of loan results with the first set of mortgages as a guarantee.
A set of countermortgages may be identified (step 8124), wherein each member of the set of countermortgages shares a common attribute with a set of multiple items. The information market may be monitored and a set of mortgage-counteracting market information may be reported (step 8126).
Fig. 82 illustrates a system 8200 that includes a data collection circuit 8224 that is configured to receive data 8202 related to a set of parties to a loan 8212. The data collection circuit may also be configured to receive mortgage-related data 8208 related to a set of mortgages 8214 that serve as a guarantee of a loan, and determine a condition of the set of mortgages, wherein the change in interest rate may be based on the condition of the set of mortgages. Mortgages may be vehicles, ships, aircraft, buildings, houses, real estate, undeveloped property, farms, crops, municipalities, warehouses, a group of inventory, commodities, securities, currencies, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contract rights, antiques, fixtures, furniture, tools, machinery, personal property, and the like. The received data may include an attribute of the set of parties to the loan, wherein the change in interest rate may be based in part on the attribute. The data collection circuitry may include systems such as internet of things circuitry, image capture devices, networking monitoring circuitry, internet monitoring circuitry, mobile devices, wearable devices, user interface circuitry, interactive crowdsourcing circuitry, and the like. For example, the data collection circuitry may include internet of things circuitry 8254 configured to monitor attributes of the set of parties to the loan. The data collection circuit may include a wearable device 8206 associated with at least one of the set of principals, the wearable device configured to obtain human-related data 8204, and wherein the received data comprises at least a portion of the human-related data. The data collection circuitry may include user interface circuitry 8226 configured to receive data from the lending parties and to provide data from at least one of the lending parties as part of the received data. The data collection circuit may include an interactive crowdsourcing circuit 8238 configured to request data related to at least one of the set of parties to the loan, receive the requested data, and provide at least a subset of the requested data as part of the received data. The data collection circuitry may include internet monitoring circuitry 8240 configured to retrieve data related to the lending party from at least one public information site 8222. The system may include an intelligent contract circuit 8232 configured to create an intelligent loan contract 8234 for a loan 82316. The loan may be of a type selected from the following loan types: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance arrangements, payday loans, refund expectations, learning aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, subsidy loans, and the like. The smart contract circuitry may be configured to determine the terms or conditions 8218 of the smart debit contract based on the attributes and modify the smart debit contract to include the terms or conditions. The terms or conditions may be related to a loan component, such as a loan party, a loan mortgage, a loan related event, a loan related activity, and the like. The terms or conditions may be a loan principal amount, a loan balance, a fixed interest rate, a variable interest rate description, a payment amount, a payment plan, a final maximum clearing plan, a mortgage description, a mortgage replacement description, a description of the principal, a insured description, a guaranty description, a personal guaranty, a retention, a redemption prevention condition, a default outcome, a contract relating to any of the foregoing, a deadline for any of the foregoing, and the like. The system may include an automated agency circuit 8236 configured to automatically perform a loan-related action 8220 in response to the received data, wherein the loan-related action is a change in the interest rate of the loan, and wherein the smart contract circuit may be further configured to update the smart loan contract with the changed interest rate. The system may include a valuation circuit 8238 configured to determine a value of at least one of the set of mortgages, for example, based on the received data and the valuation model 8230. The smart contract circuitry may be configured to determine terms or conditions of the smart loan contract based on the value of at least one of the set of mortgages, and modify the smart loan contract to include the terms or conditions. The terms or conditions may be related to a loan component, such as a loan party, a loan mortgage, a loan related event, a loan related activity, and the like. The terms or conditions may be a loan principal amount, a loan balance, a fixed interest rate, a variable interest rate description, a payment amount, a payment plan, a final maximum clearing plan, a mortgage description, a mortgage replacement description, a description of the principal, a insured description, a guaranty description, a personal guaranty, a retention, a redemption prevention condition, a default outcome, a contract relating to any of the foregoing, a deadline for any of the foregoing, and the like. The valuation circuit may include a valuation model improvement circuit 8242 that may modify the valuation model, for example, based on a first set of valuation determinations 8244 for a first set of mortgages and a corresponding set of loan results with the first set of mortgages as a guarantee. The valuation model refinement circuit may include a system, such as a machine learning system, a model-based system, a rule-based system, a deep learning system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a bayesian system, a simulation system, a hybrid system comprising at least two of any of the foregoing, or the like. The change in interest rate may also be based on the value of at least one of the set of mortgages. The valuation circuit may include a market value data collection circuit 8246 configured to monitor and report mortgage-counteracting market information 8248 related to the value of the mortgage. The market value data collection circuit may be configured to monitor one of pricing or financial data for counteracting mortgages in at least one public market and report the monitored one of pricing or financial data. The system may include a mortgage sorting circuit 8250 configured to identify a set of counteracting mortgages 8252, wherein each member of the set of counteracting mortgages shares a common attribute with at least one of the set of mortgages. The common attribute may be a category of the item, a age of the item, a condition of the item, a history of the item, ownership of the item, caretaker of the item, guarantee of the item, owner condition of the item, lien of the item, storage condition of the item, geographic location of the item, jurisdiction of the item, and the like.
FIG. 83 illustrates a method 8300 that includes receiving data related to at least one of a set of parties to a loan (8302); creating an intelligent loan contract for the loan (8304); executing a loan-related action in response to the received data, wherein the loan-related action is a change in interest rate of the loan (8308); and updating the smart loan contract using the changed interest rate (8310). The method may also include receiving data related to a set of mortgages that serve as a guarantee of the loan (8314); determining a condition of the set of mortgages (8318); and performing a loan-related action in response to the condition of the set of mortgages, wherein the loan-related action may be a change in interest rate of the loan (8320). The method may also include receiving data related to a set of mortgages serving as loan guarantees (8322); determining a condition of at least one of the set of mortgages (8324); determining terms or conditions of the intelligent lending contract based on a condition of at least one of the set of mortgages (8328); and modifying the intelligent lending contract to include the terms or conditions (8330). The method may include identifying a set of countermortgages, wherein each member of the set of countermortgages shares a common attribute with at least one of the set of mortgages; and monitoring the set of countermortgages in the public marketplace, and may also report the monitored data. The method may include, for example, changing the interest rate of a loan secured by at least one mortgage in a set of mortgages based on the monitored set of counteracting mortgages.
Fig. 84 illustrates a system 8400 that includes a data collection circuit 8418 configured to obtain data 8402 relating to at least one party (e.g., primary borrower, secondary borrower, lender, corporate borrower, government borrower, bank borrower, guaranteed borrower, bond issuer, bond buyer, non-guaranteed borrower, guaranteed provider, borrower, debt, underwriter, evaluator, auditor, valuator, government officer, accountant, etc.) of a set of parties 8406 of a loan 8408 from a public information source 8404 (e.g., web site, news article, social network, crowd-sourced information, etc.). The data collection circuit may also be configured to receive mortgage related data 8410 related to a set of mortgages 8412 that serve as a guarantee of a loan, and determine a condition of at least one of the set of mortgages, wherein the change in interest rate is further based on the condition of the at least one of the set of mortgages. The acquired data may include a financial condition of at least one of the set of parties to the loan. The financial condition may be determined based on at least one attribute of at least one of the set of parties to the loan, the attribute selected from the following attributes: a public valuation of a principal, a valuation of a set of properties owned by the principal as indicated by a public record, a valuation of a set of properties owned by the principal, a bankruptcy condition of the principal, a redemption status of the principal, a contract violation status of the principal, a crime status of the principal, an export regulation status of the principal, a bankruptcy status of the principal, a tariff status of the principal, a tax status of the principal, a credit report of the principal, a credit rating of the principal, a website rating of the principal, a set of customer reviews of the principal's product, a social network rating of the principal, a set of credentials of the principal, a set of transfer of the principal, a set of certificates of the principal, a set of activities of the principal, a location of the principal, a geographic location of the principal, a jurisdiction of the principal, and the like. The system may include a smart contract circuit 8424 configured to create a smart loan contract 8426 for the loan 8408. The smart contract circuitry may be configured to specify terms and conditions in the smart loan contract, wherein one of the terms or conditions in the smart loan contract governs one of the loan related event or the loan related activity. The system may include an automatic proxy circuit 8428 configured to automatically perform a loan-related action 8416 in response to the acquired data, wherein the loan-related action is a change in the interest rate of the loan, and wherein the smart contract circuit is further configured to update the smart loan contract with the changed interest rate. The automated agent circuit may be configured to identify an event related to the loan (e.g., a value of the loan, a condition of the loan mortgage, or ownership of the loan mortgage) based at least in part on the received data. The automated agent circuit may be configured to perform an action of the following in response to a loan-related event: providing a loan; accepting a loan; carrying out loan; setting the interest rate of loans; postponing payment requirements; modifying the interest rate of the loan; verifying ownership of at least one of the set of mortgages; evaluating a value of at least one of the set of mortgages; initiating a check of at least one of the set of mortgages; setting or modifying the terms and conditions 8414 of the loan (liability principal amount, liability balance, fixed interest rate, variable interest rate, payment amount, payment plan, end-of-the-day maximum clearing plan, principal, insured, insurer, guarantor, personal guaranty, retention, deadline, contract, redemption status, default status, and outcome of the default); providing a notification to one of the parties; providing necessary notification to the borrower of the loan; and redemption-limited property. The loan may include, for example, the following loan types: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance arrangements, payday loans, refund expectations, learning aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, subsidy loans, and the like. The acquired data may be related to the set of mortgages, for example: vehicles, ships, airplanes, buildings, houses, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, commodities, securities, money, value certificates, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, tools, machinery, personal property, and the like. The system may include a valuation circuit 8420 configured to determine a value of at least one of the set of mortgages based on the acquired data and the valuation model 8422. The valuation circuit may include a valuation model improvement circuit 8430 that modifies the valuation model based on the first set of valuation determinations 8432 for the first set of mortgages and the corresponding set of loan results with the first set of mortgages as a guarantee. The valuation model refinement circuit may include a machine learning system, a model-based system, a rule-based system, a deep learning system, a neural network, a convolutional neural network, a feed forward neural network, a feedback neural network, an ad hoc map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a bayesian system, a simulation system, a hybrid system including at least two of any of the foregoing, and the like. The smart contract circuitry may be further configured to determine terms or conditions of the smart loan contract based on the value of at least one of the set of mortgages, and modify the smart loan contract to include the terms or conditions, modify the terms or conditions of the loan based on the mortgage-counteracting market information related to the value of the mortgage, and the like. The system may include a mortgage sorting circuit 8438 configured to identify a set of countermortgages, wherein each member of the set of countermortgages 8440 shares a common attribute with at least one of the set of mortgages (e.g., category of the item, age of the item, condition of the item, history of the item, ownership of the item, administrator of the item, guarantee of the item, owner condition of the item, lien of the item, storage condition of the item, geographic location of the item, jurisdictional of the item, etc.). The valuation circuit may also include a market value data collection circuit 8434 configured to monitor and report mortgage-counteracting market information 8436 related to the value of the mortgage, to monitor pricing or financial data of the mortgage in the public market, etc., and to report the monitored pricing or financial data.
FIG. 85 illustrates a method 8500 that includes obtaining data related to at least one of a set of parties to a loan from a common source, wherein the common source may be selected from the following sources: websites, news articles, social networks, and crowd sourcing information (8502). The method may include creating an intelligent lending contract (8504). The method may include performing a loan-related action in response to the acquired data, wherein the loan-related action is a change in interest rate of the loan (8506). The method may include updating the smart lending contract with the changed interest rate (8508). The method may include receiving mortgage-related data relating to a set of mortgages that serve as a guarantee of a loan (8510), and determining a condition of at least one of the set of mortgages, wherein the change in interest rate is further based on the condition of the at least one of the set of mortgages (8512). The method may include identifying a loan-related event (8514) based at least in part on the loan-related data, and performing an action in response to the loan-related event (8518), such as: providing a loan; accepting a loan; carrying out loan; setting the interest rate of loans; postponing payment requirements; modifying the interest rate of the loan; verifying ownership of at least one of the set of mortgages; evaluating a value of at least one of the set of mortgages; initiating a check of at least one of the set of mortgages; setting or modifying the terms and conditions of the loan; providing a notification to one of the parties; providing necessary notification to the borrower of the loan; redemption-stopping property limited by loan, etc. The method may include determining a value of at least one of the set of mortgages based on the valuation model and at least one of the mortgage-related data or the acquired data. The method may include determining at least one of a term or condition of the intelligent lending contract based on a value of at least one of the set of mortgages. The method may include modifying the intelligent lending contract to include at least one of a term or a condition. The method may include modifying a valuation model based on a first set of valuation determinations for a first set of mortgages and a corresponding set of loan results with the first set of mortgages as a guarantee. The method may include identifying a set of countermortgages, wherein each member of the set of countermortgages shares a common attribute with at least one of the set of mortgages (8520); monitoring one of pricing data or financial data of at least one of the set of counteracting mortgages in the at least one public marketplace (8522); reporting the monitored data of at least one of the set of counteracting mortgages (8524); and modifying the terms or conditions of the loan based on the reported monitoring data (8528).
Fig. 86 shows a system 8600 that includes a data collection circuit 8620 configured to receive data 8602 related to a status 8604 of a loan 8612 and data related to a set of mortgages 8606 that serve as a guarantee of the loan. The data collection circuitry may utilize systems such as an internet of things system, a camera system, a networked monitoring system, an internet monitoring system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system 8632 to monitor one or more of the loan entities. For example, the interactive crowdsourcing system may include a user interface 8634 for requesting information related to one or more of the lending entities from the crowdsourcing site 8618, and wherein the user interface is configured to allow one or more of the lending entities to enter information of one or more of the lending entities. In another example, the networked monitoring system may include a web search circuit 8621 configured to search public information sites to obtain information related to one or more of the loan entities. The system may include a blockchain service circuit 86144 configured to maintain a security history ledger 8646 for loan-related events, such as interpreting a plurality of access control features 8608 corresponding to a plurality of principals 8610 associated with the loan. The system may include a loan assessment circuit 8648 configured to determine a loan status based on the received data. The data collection circuitry may receive data related to one or more lending entities 8614, wherein the loan valuation circuitry may determine whether the contract is met based on the data related to one or more of the lending entities. The loan assessment circuit may be configured to determine an execution state of the status of the loan based on the received data and the status of one or more of the loan entities, and wherein the determination of the loan state is determined based in part on the status of at least one or more of the loan entities and the execution state of the status of the loan. For example, the condition of the loan may relate to at least one of payment fulfillment and contract satisfaction. The data collection circuitry may include market data collection circuitry 8636 configured to receive financial data 8638 regarding at least one of a plurality of parties associated with the loan. The loan assessment circuit may be configured to determine a financial condition of at least one of a plurality of parties associated with the loan based on the received financial data, wherein the at least one of the plurality of parties may be a primary borrower, a secondary borrower, a lender, a corporate borrower, a government borrower, a banking borrower, a guaranteed borrower, a bond issuer, a bond buyer, an unsecured borrower, a guaranty provider, a borrower, a debtor, a underwriter, an inspector, an evaluator, an auditor, a valuation professional, a government officer, an accountant, and the like. The received financial data may relate to attributes of an entity of one of the plurality of principals, such as: a public valuation of a principal, a valuation of a set of properties owned by the principal as indicated by a public record, a valuation of a set of properties owned by the principal, a bankruptcy condition of the principal, a redemption status of an entity, a contract breach status of an entity, a violation status of an entity, a crime status of an entity, an export regulation status of an entity, a banned status of an entity, a tariff status of an entity, a tax status of an entity, a credit report of an entity, a credit rating of an entity, a website rating of an entity, a set of customer reviews of a product of an entity, a social network rating of an entity, a set of credentials of an entity, a set of referrals of an entity, a set of proofs of an entity, a set of behaviors of an entity, a location of an entity, a geographic location of an entity, and the like. The system may include a smart contract circuit 8626 configured to create a smart loan contract 8628 for a loan. The smart contract circuitry may be configured to determine terms or conditions of the smart loan contract based on the value of at least one of the set of mortgages, and modify the smart loan contract to include terms or conditions, wherein the terms and conditions may be a bond principal amount, a bond balance, a fixed interest rate, a variable interest rate, a payment amount, a payment plan, a terminal-most clearing plan, a principal, a insured person, a guaranty, a personal guaranty, a retention, a deadline, a contract, a redemption condition, a default outcome, and the like. The system may include an automated agency circuit 8630 configured to perform loan actions 8616 based on the loan status, wherein the blockchain service circuit may be configured to update the historical ledgers of the events using the loan actions. The system may include a valuation circuit 8622 configured to determine a value of at least one of the set of mortgages based on the received data and the valuation model 8624. The valuation circuit may include a valuation model improvement circuit 8640 that modifies the valuation model based on the first set of valuation determinations for the first set of mortgages and the corresponding set of loan results with the first set of mortgages as a guarantee. The valuation model refinement circuit may include a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feed forward neural network, a feedback neural network, an ad hoc mapping, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a bayesian system, and a simulation system. The valuation circuit may include a market value data collection circuit 8642 configured to monitor and report market information for the countermortgage related to the value of the mortgage. The market value data collection circuit may also be configured to monitor pricing or financial data for counteracting mortgages in the public market, e.g., report the monitored pricing or financial data. The smart contract circuitry may also be configured to modify terms or conditions of the loan based on the mortgage-counteracting market information related to the value of the mortgage. The system may include a mortgage sorting circuit 8650 configured to identify a set of countermortgages 8652, wherein each member of the set of countermortgages shares a common attribute with at least one of the set of mortgages. The common attribute may be the type of mortgage, the age of the mortgage, the condition of the mortgage, the history of the mortgage, the ownership of the mortgage, the manager of the mortgage, the guaranty of the mortgage, the owner of the mortgage, the lien of the mortgage, the storage condition of the mortgage, the geographic location of the mortgage, the jurisdiction of the mortgage, etc.
FIG. 87 illustrates a method 8700 that includes maintaining a security history ledger for loan-related events (8702); receiving data relating to a status of the loan (8704); receiving a set of mortgage-related data, the set of mortgages acting as a guarantee of a loan (8708); determining a status of the loan (8710); performing a loan action based on the loan status (8712); and updating a historical ledger for loan-related events (8714). The method may also include receiving data related to one or more lending entities (8718), and determining whether a contract for the loan is met based on the received data (8720). The method may further include determining an execution status of the loan condition, wherein the determination of the loan status is based in part on the execution status of the loan condition. The method may also include receiving financial data relating to at least one party to the loan. The method may also include determining a financial condition of at least one party to the loan based on the financial data. The method may also include determining a value of at least one set of mortgages based on the received data and the valuation model. The method may also include determining at least one of a term or condition of the loan based on the value of at least one of the mortgages (8722), and modifying the smart loan contract to include the at least one of the term or condition (8724). The method may include identifying 270 a set of countermortgages, wherein each member of the set of countermortgages shares a common attribute with at least one of the set of mortgages (8728); data relating to the set of countermortgages is received, wherein determining the value of at least one set of mortgages is based in part on the received data relating to the set of countermortgages (8730).
Referring to fig. 88, an illustrative and non-limiting example smart contract system for managing mortgages of a loan 8800 is shown. The example system may include a controller 8801. The controller 8801 may include: a data collection circuit 8812 configured to monitor the status of the loan 8830 and the status of the loan mortgage 8828; a number of artificial intelligence circuits including intelligent contract circuit 8822, the intelligent contract circuit configured to process information from the data collection circuit 8812 and, responsive to at least one of a status of the loan or a status of the loan mortgage, automatically initiate at least one of a replacement, removal, or addition of one or more items of the mortgage of the loan based on the information and the intelligent loan contract 8831; and blockchain service circuitry 8858 configured to interpret the plurality of access control features 8880 corresponding to the at least one principal associated with the loan and record at least one substitution, removal, or addition in the distributed ledger 8840 for the loan. The data collection circuit may also include at least one other system 8862 of the following: the system comprises an internet of things system, a camera system, a networking monitoring system, an internet monitoring system, a mobile device system, a wearable device system, a user interface system and an interactive crowdsourcing system.
The status of the loan 8830 may be determined based on at least one of the status of the entity associated with the loan (e.g., user 8806) and the status of the performance of the loan condition. The fulfillment status of the condition may relate to at least one of payment fulfillment of the loan or satisfaction of the contract. The status of the loan may be determined based on the status of at least one entity associated with the loan and the performance status of the loan condition; the fulfillment of the condition may involve at least one of payment fulfillment or satisfaction of the loan contract. The data collection circuit 8812 may also be configured to determine whether the contract is met by monitoring at least one entity. When the at least one entity is a lending party, the data collection circuitry 8812 may monitor the financial condition of the at least one entity as a lending party. The condition of the loan may include a financial condition of the loan, and wherein the execution state of the financial condition may be determined based on one of the following attributes: a public valuation of at least one entity, a valuation of at least one entity's possession, as indicated by a public record, a valuation of at least one entity's possession, a bankruptcy condition of at least one entity, a redemption status of at least one entity, a contract breach status of at least one entity, a violation status of at least one entity, a crime status of at least one entity, an export regulation status of at least one entity, a banned status of at least one entity, a tariff status of at least one entity, a tax status of at least one entity, a credit report of at least one entity, a credit rating of at least one entity, a website rating of at least one entity, a plurality of customer reviews of a product of at least one entity, a social network rating of at least one entity, a plurality of credentials of at least one entity, a plurality of referrals of at least one entity, a plurality of credentials of at least one entity, a behavior of at least one entity, a location of at least one entity, a geographic location of at least one entity, and a relevant jurisdiction of at least one entity.
The loan principal may be selected from the following: primary borrowers, secondary borrowers, borrowing groups, corporate borrowers, government borrowers, banking borrowers, warranty borrowers, bond purchasers, non-warranty borrowers, warranty providers, borrowers, debtors, underwriters, inspectors, valuators, auditors, valuation professionals, government officers, and accountants.
The data monitoring circuitry 8812 may also be configured to monitor the status of the mortgage of the loan based on at least one of the following properties: the type of mortgage, the age of the mortgage, the condition of the mortgage, the history of the mortgage, the storage conditions of the mortgage, and the geographic location of the mortgage.
The controller 88101 may include a valuation circuit 8844 configured to use the valuation model 8852 to determine the value of a mortgage based on the status of the mortgage of the loan. The smart contract circuitry 8822 may initiate at least one substitution, removal, or addition operation of one or more items of the mortgage to maintain the value of the mortgage within a predetermined range.
The valuation circuit 8844 may also include a transaction outcome processing circuit 8864 configured to interpret outcome data 8810 associated with the mortgage transaction and iteratively refine (8850) the valuation model in response to the outcome data.
The valuation circuit 8844 may also include a market value data collection circuit 8848 configured to monitor and report market information related to the value of the mortgage. The market value data collection circuit 8848 can monitor pricing data or financial data for the countermortgage 8834 in at least one public market.
The market value data collection circuit 8848 is also configured to construct a set of countermortgages 8834 for determining the value of the mortgage using the clustering circuit 8832 of the controller 88101 based on the properties of the mortgage. The attributes may be selected from the following: the type of mortgage, the age of the mortgage, the condition of the mortgage, the history of the mortgage, the storage conditions of the mortgage, and the geographic location of the mortgage.
The terms and conditions 8824 of the loan may include at least one member of the following: liability principal amount, liability balance, fixed interest rate, variable interest rate, payment amount, payment plan, end-of-line clearing plan, mortgage description, mortgage substitutability description, principal, insured, guarantor, personal guarantor, retention, deadline, contract, redemption status, violation status, and outcome of the violation.
The smart contract circuitry may also include or be in communication with a loan management circuit 8860 configured to specify the terms and conditions of the smart loan contract 8831 that manages at least one of the loan terms and conditions, the loan related event 8839, or the loan related activity or action 8838.
Referring to FIG. 89, an example smart contract method for managing mortgages on a loan is shown. The example method may include monitoring a status of the loan and a status of the loan mortgage (step 8902); automatically initiating at least one of a replace, remove, or add operation for one or more items of a mortgage of the loan based on the information (step 8908); and interpreting a plurality of access control features corresponding to at least one party associated with the loan (step 8910) and recording at least one substitution, removal, or addition operation in a distributed ledger for the loan (step 8912). The status of the loan may be determined based on the status of at least one of the entities related to the loan and the execution status of the condition of the loan.
The method may also include interpreting the information from the monitoring (step 8914) and determining a value using a set of valuation models of the mortgage based on at least one of a status of the loan or the mortgage of the loan (step 8918). At least one substitution, removal, or addition operation may maintain the value of the mortgage within a predetermined range. The method may also include interpreting the outcome data associated with the transaction of one of the mortgages or countering the mortgage (step 8920), and iteratively refining the valuation model in response to the outcome data (step 8922). The method may also include monitoring and reporting market information related to the value of the mortgage (step 8924).
The method may also include monitoring pricing data or financial data for counteracting mortgages in at least one public marketplace (step 8928).
The method may further include specifying terms and conditions of an intelligent contract that governs at least one of the terms and conditions of the loan, the loan-related event, or the loan-related activity (step 8930).
Referring to FIG. 90, an illustrative and non-limiting example crowd-sourcing system (9000) for verifying the condition of a mortgage or guarantor of a loan is shown. The example system may include a controller 9001. The controller 9001 can comprise a data collection circuit 9012, a user interface 9054, and several artificial intelligence circuits including an intelligent contract circuit 9022, a robotic process automation circuit 9074, a crowdsourcing request circuit 9060, a crowdsourcing communication circuit 9062, a crowdsourcing issue circuit 9064, and a blockchain service circuit 9058.
The crowd-sourced request circuitry 9060 may be configured to configure at least one parameter of a crowd-sourced request 9068 that relates to obtaining information 9004 regarding a status 9011 of a mortgage 9002 of a loan 9030 or a status of a sponsor of the loan 9096. In addition, the crowdsourcing request circuit may enable a workflow by which a human user inputs at least one parameter to establish the crowdsourcing request. The at least one parameter includes a type of information requested, a reward, and a condition for receiving the reward. The consideration may be selected from the following consideration: financial remuneration, vouchers, tickets, contractual rights, cryptocurrency, multiple remuneration points, discounts on currency, products or services, and access rights.
The crowdsourcing circuitry 9064 may be used to issue crowdsourcing requests 9068 to a group of information providers.
The crowdsourcing communication circuitry 9062 may be configured to collect and process at least one response 9072 from the set of information providers 9070 and provide a reward 9080 to at least one of the set of information providers in response to a successful information provision event 9098.
The crowd-sourced communication circuit 9062 also includes a smart contract circuit 9022 configured to manage the reward 9080 by determining a successful information provision event 9098 in response to at least one parameter configured for the crowd-sourced request 9068, and automatically assign the reward 9080 to at least one of the set of information providers 9070 in response to the successful information provision event 9098. The smart contract circuitry is further configured to process at least one response 9072 and, in response, automatically take an action related to the loan. The action may be at least one of a redemption-stopping action, a lien management action, a rate adjustment action, a default initiation action, a mortgage replacement, or a loan receipt.
The loan 9030 may include at least one of the following loan types: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance arrangements, payday loans, refund expectations, learning-aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, and subsidy loans.
The crowd-sourced request circuitry 9060 may also be configured to configure at least one other parameter of the crowd-sourced request 9068 to obtain information regarding the status 9011 of a mortgage of a loan.
Mortgage 9002 can comprise at least one selected from: vehicles, ships, airplanes, buildings, houses, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, merchandise, securities, money, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, tools, machinery, and personal property.
The condition 9011 of the mortgage may be determined based on one of the following attributes: the quality of the mortgage, the condition of the mortgage, the ownership status of the mortgage, the possession status of the mortgage, and the lien status of the mortgage. When the mortgage is an item, the condition may be determined based on one of the following attributes: the new or used status of the item, the type of item, the category of the item, the description of the item, the product feature set of the item, the model of the item, the brand of the item, the manufacturer of the item, the status of the item, the environment of the item, the condition of the item, the value of the item, the storage location of the item, the geographic location of the item, the age of the item, the maintenance history of the item, the use history of the item, the accident history of the item, the malfunction history of the item, the ownership history of the item, the price of the item type, the value of the item type, the assessment of the item, and the valuation of the item.
The blockchain service circuitry 9058 may be configured to record in the distributed ledger 9040 identification information and at least one parameter of the crowdsourcing request, at least one response to the crowdsourcing request, and a reward description.
The robotic process automation circuit 9074 may be configured to configure the crowd-sourced request based on at least one attribute of the loan based on training with at least one of the crowd-sourced request circuit or the crowd-sourced communication circuit on the training data set 9078 including human user interactions. At least one attribute of the loan may be obtained from the smart contract circuit 9022 that manages the loan. Training data set 9078 may also include results from multiple crowdsourcing requests.
The robotic process automation circuit 9074 may also be configured to determine a reward 9080.
The robotic process automation circuit 9074 may be further configured to determine at least one domain to which the crowdsourcing issue circuit 9064 issues the crowdsourcing request 9068.
Referring to fig. 91, provided herein is a crowdsourcing method for verifying the condition of a mortgage or guarantor of a loan. At least one parameter of the crowd-sourced request may be used to obtain information regarding the condition of the loan mortgage or the condition of the loan insurer (step 9102). The crowdsourcing request may be issued to a group of information providers (step 9104). At least one response to the crowd-sourced request may be collected and processed (step 9108). At least one successful information provider in the set of information providers may be rewarded in response to a successful information provision event (step 9110). A description of the reward may be provided to at least a portion of the set of information providers in response to a successful information provision event (step 9112). At least one of the set of information providers may be automatically assigned a consideration in response to a successful information provision event (step 9130). The method may also include recording identification information and at least one parameter of the crowdsourcing request, at least one response to the crowdsourcing request, and a reward description in a distributed ledger for the crowdsourcing request (step 9114). The graphical user interface may be used to enable a workflow by which a human user enters at least one parameter to establish a crowd-sourced request (step 9118). Loan-related actions may be automatically taken in response to a successful information provision event (step 9120). The robotic process automation circuit may train the robotic process automation circuit based on a training data set comprising a plurality of results corresponding to the plurality of crowdsourcing requests, and operate the robotic process automation circuit to iteratively refine the crowdsourcing requests (step 9122). At least one attribute of the loan may be provided to the robotic process automation circuit to configure the crowd-sourced request (step 9124). Configuring the crowd-sourced request may include determining a reward. At least one attribute of the loan may be provided to the robotic process automation circuit to determine at least one domain to which to issue the crowd-sourced request (step 9128).
Referring to FIG. 92, an illustrative and non-limiting example smart contract system for modifying a loan 9200 is shown. The example system may include a controller 9201. The controller 9201 may include a data collection circuit 9212, a valuation circuit 9244, and a number of artificial intelligence circuits 9242 including an intelligent contract circuit 9222, a clustering circuit 9232, a jurisdiction definition circuit 9298, and a loan management circuit 9260. The data collection circuit 9212 may be configured to determine location information corresponding to each of a plurality of entities involved in the loan. The jurisdiction definition circuit 9298 may be configured to determine a jurisdiction of at least one of the plurality of entities in response to the location information. The smart contract circuitry 9222 may be configured to automatically take loan-related actions 9238 based at least in part on the jurisdiction of at least one of the plurality of entities.
The smart contract circuitry 9222 may also be configured to automatically take loan-related actions in response to a first of the plurality of entities being in the first jurisdiction and a second of the plurality of entities being in the second jurisdiction.
The smart contract circuitry 9222 may also be configured to automatically take loan-related actions in response to one of the plurality of entities moving from the first jurisdiction to the second jurisdiction.
Loan-related actions 9238 may include at least one of the following loan-related actions: providing a loan; accepting a loan; carrying out loan; setting the interest rate of loans; postponing payment requirements; modifying the interest rate of the loan; verifying ownership of the mortgage; recording the change of ownership; evaluating the value of the mortgage; initiating a check of the mortgage; collect loan; clearing loans; setting the terms and conditions of loans; providing a notification to be provided to the borrower; redemption-stopping property limited by the loan; and modifying the terms and conditions of the loan.
The smart contract circuitry 9222 may also be configured to process a plurality of jurisdiction-specific regulatory requirements 9268 (e.g., requirements related to notification) and provide appropriate notification to the borrower based on the jurisdiction corresponding to at least one of the following entities: a borrower, funds provided by a loan, repayment of a loan, or mortgage of a loan.
The smart contract circuitry 9222 may also be configured to process a plurality of jurisdiction-specific regulatory requirements 9268 (e.g., requirements relating to redemption) and provide appropriate redemption notification to the borrower based on the jurisdiction of at least one of the borrower, funds provided by the loan, repayment of the loan, and mortgage of the loan.
The smart contract circuitry 9222 may also be configured to process a plurality of jurisdiction-specific rules 9270 for setting the terms and conditions of the loan 9224 and configure the smart contract 9231 based on the jurisdiction corresponding to at least one of the following entities: borrowers, funds provided by loans, repayment of loans, and mortgages of loans.
The smart contract circuitry 9222 may also be configured to determine the interest rate of the loan to conform the loan to a maximum interest rate limit applicable to the jurisdiction corresponding to the selected one of the plurality of entities.
The data collection circuit 9212 may also be configured to monitor the condition of the mortgage of the loan, and wherein the smart contract circuit is further configured to determine the interest rate of the loan in response to the condition of the mortgage of the loan.
The data collection circuit 9212 may also be configured to monitor an attribute of at least one of the plurality of entities that are loan parties, and wherein the smart contract circuit is further configured to determine the interest rate of the loan in response to the attribute.
The smart contract circuitry 9222 may also include loan management circuitry 9260, the loan management circuitry 9260 being configured to specify terms and conditions of the smart contract, the terms and conditions managing at least one of loan terms and conditions 9224, loan related events 9239, or loan related activities 9272.
The loan may include at least one of the following loan types: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance management, payday loans, refund expectations, learning aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile fund loans, small business loans, agricultural loans, municipal bonds, and subsidy loans.
Each of the terms and conditions of the loan may include at least one member of the following: liability principal amount, liability balance, fixed interest rate, variable interest rate, payment amount, payment plan, end-of-line clearing plan, mortgage description, mortgage substitutability description, principal, insured, guarantor, personal guarantor, retention, deadline, contract, redemption status, violation status, and outcome of the violation.
The data collection circuit 9212 may also include at least one other system 9262 of the following: the system comprises an internet of things system, a camera system, a networking monitoring system, an internet monitoring system, a mobile device system, a wearable device system, a user interface system and an interactive crowdsourcing system.
The valuation circuit 9244 may be configured to determine a value of a mortgage of the loan based on the jurisdiction corresponding to at least one of the plurality of entities using the valuation model 9252. The rating model 9252 may be a jurisdiction-specific rating model, and wherein the jurisdiction corresponding to at least one of the plurality of entities includes a jurisdiction corresponding to at least one of the following entities: a borrower, funds offered on a loan, a delivery location of funds offered on a loan, repayment of a loan, and mortgage of a loan.
At least one of the terms and conditions of the loan may be based on the value of the mortgage of the loan.
The mortgage may include at least one of: vehicles, ships, airplanes, buildings, houses, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, merchandise, securities, money, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, tools, machinery, and personal property.
Rating circuit 9244 may also include a transaction result processing circuit 9264 configured to interpret result data related to the mortgage transaction and iteratively refine (9250) the rating model in response to the result data.
The valuation circuit 9244 may also include a market value data collection circuit 9248 configured to monitor and report market information related to the value of the mortgage. The market value data collection circuit may monitor pricing or financial data for counteracting mortgages in at least one public market. Clustering circuitry 9232 may be used to construct a set of offset mortgages 9234 for evaluating mortgages based on their properties. The attribute may be selected from: the type of mortgage, the age of the mortgage, the condition of the mortgage, the history of the mortgage, the storage conditions of the mortgage, and the geographic location of the mortgage.
Referring to fig. 93, provided herein is a smart contract method 9300 for modifying loans. An example method may include: monitoring location information corresponding to each of a plurality of entities involved in the loan (step 9302); location information about the entity is processed and a loan-related action is automatically taken on the loan based at least in part on the location information (step 9304). The example method includes processing a plurality of jurisdiction-specific regulatory notification requirements and providing appropriate notification to the borrower based on the borrower, funds provided by the loan, repayment of the loan, and/or location of the loan mortgage (step 9308). The example method includes processing a plurality of jurisdiction-specific rules for setting the terms and conditions of the loan and configuring an intelligent contract based on the borrower, funds provided by the loan, repayment of the loan, and/or location of the mortgage of the loan (step 9310). The example method also includes determining the interest rate of the loan such that the loan meets a maximum interest rate limit applicable to the jurisdiction (step 9312). The example method includes monitoring at least one of a condition of a plurality of mortgages of a loan or an attribute of one of the entities that are parties to the loan, wherein the condition or attribute is used to determine interest rate (step 9314). The example method includes specifying at least one of terms and conditions of the smart contract, smart contract management terms and conditions, a loan-related event, or a loan-related activity (step 9318). The example method includes interpreting the location information and using the valuation model to determine a value of a plurality of mortgages of the loan based on the location information (step 9320). The example method includes interpreting result data associated with a mortgage transaction and iteratively refining a valuation model in response to the result data (step 9322). The example method includes monitoring and reporting market information related to the value of the mortgage (step 9324).
A plurality of jurisdiction-specific requirements may be processed based on a jurisdiction of an associated one of the plurality of entities and at least one of the following operations is performed: providing appropriate notifications to the borrower in response to a plurality of jurisdiction-specific requirements including regulatory notification requirements; setting a specific rule for setting terms and conditions of the loan in response to a plurality of jurisdiction-specific requirements including the jurisdiction-specific rule for the terms and conditions of the loan; determining the interest rate of the loan such that the loan meets the maximum interest rate limit in response to a plurality of jurisdiction-specific requirements including the maximum interest rate limit; and wherein the associated one of the plurality of entities comprises at least one of the following entities: the borrower, funds offered on the basis of the loan, repayment of the loan, and mortgage of the loan (step 9308).
At least one of a condition of a plurality of mortgages of the loan or an attribute of at least one of a plurality of entities as a party to the loan may be monitored, wherein the condition or attribute is used to determine interest rate (step 9314).
The valuation model may be operated to determine the value of the mortgage of the loan based on the jurisdiction of at least one of the plurality of entities (step 9320).
The result data associated with the mortgage transaction may be interpreted and the valuation model iteratively improved in response to the result data (step 9322).
Referring now to FIG. 94, an illustrative and non-limiting example smart contract system 9400 for modifying loans is shown. The example system may include a controller 9401. The controller 94101 may include a data collection circuit 9412, a valuation circuit 9444, and a number of artificial intelligence circuits 9442 including an intelligent contract circuit 9422, a clustering circuit 9432, and a loan management circuit 9460.
The data collection circuit 9412 may be configured to monitor and collect information about at least one entity 9498 involved in the loan 9430. The smart contract circuitry 9422 may be configured to automatically reorganize liabilities associated with the loan based on the monitored and collected information about at least one entity involved in the loan. The monitored and collected information may include a condition 9411 of the mortgage for the loan, or according to at least one rule based on a contract of the loan and wherein reorganization occurs upon an event determined relative to at least one entity associated with the contract, or reorganization may be based on an attribute 9494 of the at least one entity monitored by the data collection circuit. The event may be that the mortgage of the loan fails to exceed a desired portion of the value of the remaining balance of the loan, or that the buyer violates a contract.
The smart contract circuit 9422 may also be configured to determine the occurrence of an event based on the contract of the loan and the monitored and collected information about at least one entity involved in the loan, and automatically reorganize the liabilities in response to the occurrence of the event.
The smart contract circuitry 9422 may also include loan management circuitry 9460 that may be configured to specify the terms and conditions of a smart contract that manages at least one of the loan terms and conditions 9424, the loan related event 9439, or the loan related activity 9472.
The loan may include at least one of the following loan types: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance arrangements, payday loans, refund expectations, learning-aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, and subsidy loans.
The terms and conditions of the loan may include at least one member of the following: liability principal amount, liability balance, fixed interest rate, variable interest rate, payment amount, payment plan, end-of-line clearing plan, mortgage description, mortgage substitutability description, principal, insured, guarantor, personal guarantor, retention, deadline, contract, redemption status, violation status, and outcome of the violation.
The data collection circuit 9412 may also include at least one other system 9462 of the following: the system comprises an internet of things system, a camera system, a networking monitoring system, an internet monitoring system, a mobile device system, a wearable device system, a user interface system and an interactive crowdsourcing system.
The valuation circuit 9444 may be configured to determine the value of the mortgage based on the monitored and collected information about at least one entity involved in the loan using the valuation model 9452. The smart contract circuitry may also be configured to automatically reorganize liabilities based on the value of the mortgage.
The mortgage may be at least one of: vehicles, ships, airplanes, buildings, houses, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, merchandise, securities, money, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, tools, machinery, and personal property.
The valuation circuit 9444 may also include a transaction outcome processing circuit 9464 configured to interpret outcome data 9410 associated with the mortgage transaction and iteratively refine 9450 the valuation model in response to the outcome data.
The valuation circuit 9444 may also include a market value data collection circuit 9448 configured to monitor and report market information related to the value of the mortgage. The market value data collection circuit 9448 monitors pricing data or financial data for counteracting mortgage 9434 in at least one public market. A set of counteracting mortgages 9434 for evaluating the mortgage may be constructed based on its properties using clustering circuitry 9432. The attribute may be selected from: the type of mortgage, the age of the mortgage, the condition of the mortgage, the history of the mortgage, the storage conditions of the mortgage, and the geographic location of the mortgage.
Referring now to FIG. 95, an illustrative and non-limiting example smart contract method 9500 for modifying loans is shown. The method includes monitoring and collecting information about at least one entity involved in the loan (step 9502); processing information from the monitoring at least one entity (step 9504); and automatically reorganizing the liabilities associated with the loan based on the monitored and collected information about the at least one entity (step 9508). Determining the occurrence of an event may determine the occurrence of an event based on the loan's contract and the monitored and collected information about at least one entity involved in the loan and automatically reorganize the debt in response to the occurrence of the event (step 9509).
The terms and conditions of the smart contract are specified, which manages at least one of loan terms and conditions, loan-related events, or loan-related activities (step 9510).
The valuation model is operated to determine the value of the mortgage based on the monitored and collected information about at least one entity involved in the loan (step 9512).
The result data associated with the mortgage transaction may be interpreted and the valuation model iteratively refined in response to the result data (step 9514).
The method may also include monitoring and reporting market information related to the value of the mortgage (step 9518).
Pricing or financial data for counteracting mortgages may be monitored in at least one public marketplace (step 9520).
A set of counteracting mortgages for evaluating the mortgage may be constructed using a similarity clustering algorithm based on the properties of the mortgage (step 9522).
Referring now to FIG. 96, an illustrative and non-limiting example smart contract system 9600 for modifying loans is shown. The example system may include a controller 9601. The controller 9601 may include data collection circuitry 9612, social network input circuitry 9644, social network data collection circuitry 9632, and a number of artificial intelligence circuitry 9642, including smart contract circuitry 9622, vouch for verification circuitry 9698, and robotic process automation circuitry 9648.
The social networking data collection circuit 9632 may be configured to collect data using a variety of algorithms for monitoring social networking information about the entities 9664 involved in the loan 9630 in response to the loan assurance parameters. Social network input circuit 9644 may be configured to interpret loan guarantee parameters. The vouching verification circuitry 9698 may be configured to verify vouching of the loan in response to the monitored social network information.
The loan guarantee parameter may include a financial status of an entity, wherein the entity is a guarantee of the loan.
The vouching verification circuit 9698 may be further configured to determine the financial condition based on at least one of the following attributes: public valuation of an entity, valuation of an entity as indicated by a public record, valuation of an entity, bankruptcy of an entity, redemption status of an entity, contract breach status of an entity, violation status of an entity, crime status of an entity, export regulation status of an entity, banned status of an entity, tariff status of an entity, tax status of an entity, credit reporting of an entity, credit rating of an entity, website rating of an entity, multiple customer reviews of a product of an entity, social network rating of an entity, multiple vouchers of an entity, multiple referrals of an entity, multiple proofs of an entity, multiple behaviors of an entity, location of an entity, jurisdiction of an entity, and geographic location of an entity.
The loan may include at least one of the following loan types: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance arrangements, payday loans, refund expectations, learning-aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, and subsidy loans.
The data collection circuit 9612 may be configured to obtain information regarding a status 9611 of a mortgage of the loan, wherein the mortgage includes at least one of: vehicles, ships, airplanes, buildings, homes, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, commodities, securities, money, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property, and wherein the guarantee verification circuitry is further configured to verify a guarantee of a loan in response to a condition of a mortgage of the loan.
The condition 9611 of the mortgage may include condition attributes in: the quality of the mortgage, the status of the ownership of the mortgage, the status of the lien of the mortgage, the new or used status, type, category, description, set of product features, model, brand, manufacturer, status, background, status, value, storage location, geographic location, age, maintenance history, use history, accident history, fault history, ownership history, price, assessment and valuation. The condition may be stored as mortgage data 9604.
Social network input circuit 9644 may also be configured to enable a workflow by which a human user enters loan assurance parameters to establish social network data collection and monitoring requests.
The smart contract circuitry 9622 may be configured to automatically take an action associated with the loan in response to a verification of the loan. The loan-related actions may be responsive to the loan assurance not being verified, and wherein the actions include at least one of the following actions: a redemption-stopping action, a lien management action, a rate adjustment action, a default initiation action, a mortgage replacement, a loan refund, and providing an alert to a second entity involved in the loan.
The robotic process automation circuit 9648 may be configured to configure the loan guarantee parameter based on at least one attribute of the loan based on iterative training with the social network data collection circuit on the training data set 9646 comprising human user interactions. At least one attribute of the loan 9630 may be obtained from the smart contract circuitry that manages the loan.
Training data set 9646 may also include results from multiple social network data collections and monitoring requests performed by the social network data collection circuitry.
The robotic process automation circuit 9648 may also be configured to determine at least one domain to which the social network data collection circuit is to apply.
Training may include training robotic process automation circuit 9648 to configure a variety of algorithms.
Referring now to FIG. 97, an illustrative and non-limiting example smart contract method 9700 for modifying loans is shown. The loan guarantee parameters may be interpreted (step 9701). Data may be collected using a variety of algorithms for monitoring social networking information about entities involved in a loan in response to the loan guarantee parameter (step 9702). The vouching of the loan may be verified in response to the monitored social networking information (step 9704). A workflow may be enabled through which a human user enters loan assurance parameters to establish social network data collection and monitoring requests (step 9708). In response to the validation of the loan, a loan-related action may be automatically performed (step 9710). The robotic process automation circuit may configure data collection and monitoring actions based on at least one attribute of the loan by iterative training, wherein the robotic process automation circuit trains using a plurality of algorithms based on a training data set including at least one of the results from the human user interaction (step 9712). At least one domain to which a variety of algorithms are to be applied may be determined (step 9714).
Referring to FIG. 98, an illustrative and non-limiting example monitoring system 9800 for verifying loan guarantee conditions is shown. The example system may include a controller 9801. The controller 9801 may include internet of things data collection input circuitry 9844, internet of things data collection circuitry 9832, and a number of artificial intelligence circuits 9842 including smart contract circuitry 9822, vouch for verification circuitry 9898, and robotic process automation circuitry 9848.
The internet of things data collection input circuit 9844 may be configured to interpret loan guarantee parameters 9892. The internet of things data collection circuit 9832 may be configured to collect data using at least one algorithm for collecting internet of things information from entities 9864 involved in the loan 9830 and about entities 9864 involved in the loan 9830 in response to the loan guarantee parameters. The vouching verification circuitry 9898 may be configured to verify the vouching of the loan in response to the monitored IoT information.
The loan guarantee parameter 9892 may include the financial status of an entity, where an entity is a guarantee of a loan. The monitored IoT information includes at least one of: public valuation of an entity, valuation of an entity as indicated by a public record, valuation of an entity, bankruptcy of an entity, redemption status of an entity, contract breach status of an entity, violation status of an entity, crime status of an entity, export regulation status of an entity, banned status of an entity, tariff status of an entity, tax status of an entity, credit reporting of an entity, credit rating of an entity, website rating of an entity, multiple customer reviews of a product of an entity, social network rating of an entity, multiple vouchers of an entity, multiple referrals of an entity, multiple proofs of an entity, multiple behaviors of an entity, location of an entity, jurisdiction of an entity, and geographic location of an entity.
The loan may include at least one of the following loan types: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance arrangements, payday loans, refund expectations, learning-aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, and subsidy loans.
The internet of things data collection circuit 9832 may be configured to obtain information regarding the status of mortgages on a loan, wherein the mortgage includes at least one of: vehicles, ships, airplanes, buildings, homes, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, commodities, securities, money, value certificates, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property, and wherein the guarantee verification circuitry 9898 is further configured to verify a guarantee of a loan in response to a condition of a mortgage of the loan.
The condition 9811 of a mortgage may include condition attributes in: the quality of the mortgage, the status of the ownership of the mortgage, the status of the lien of the mortgage, the new or used status, type, category, description, set of product features, model, brand, manufacturer, status, background, status, value, storage location, geographic location, age, maintenance history, use history, accident history, fault history, ownership history, price, assessment and valuation.
The internet of things data collection circuit 9844 may also be configured to enable a workflow by which a human user enters loan guarantee parameters 9892 to establish an internet of things data collection request.
The smart contract circuitry 9822 may be configured to automatically take an action associated with the loan in response to a verification of the loan. The loan-related actions may be responsive to the loan guarantee not being verified, and wherein the actions include at least one of the following actions: redemption-stopping actions, lien management actions, interest rate adjustment actions, default initiation actions, mortgage replacement, loan refund, and providing alerts to secondary entities involved in the loan.
The robotic process automation circuit 9848 may be configured to configure the loan guarantee parameter based on at least one attribute of the loan based on iterative training with the internet of things data collection circuit on a training data set comprising human user interactions. At least one attribute of the loan is obtained from an intelligent contract circuit that manages the loan. The training data set 9846 may also include results from multiple internet of things data collections and monitoring requests performed by the internet of things data collection circuit.
The robotic process automation circuit 9848 may also be configured to determine at least one domain to which the internet of things data collection circuit is to be applied.
Training may include training robotic process automation circuit 9848 to configure at least one algorithm.
Referring to FIG. 99, an illustrative and non-limiting example monitoring method 9900 for verifying the warranty condition of a loan is shown. The example method may include interpreting the loan guarantee parameters (step 9902); collecting data using a plurality of algorithms for monitoring information collected from entities involved in the loan and internet of things (internet of things) information about the entities involved in the loan in response to the loan guarantee parameter (step 9904); and validating the loan guarantee in response to the monitored internet of things information (step 9905).
The loan guarantee parameter may be used to obtain information about the financial status of the entity that is the guarantee of the loan (step 9908). At least one algorithm may be used to obtain information regarding the status of a mortgage of a loan (step 9910), wherein the mortgage includes at least one of: vehicles, ships, airplanes, buildings, houses, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, commodities, securities, money, value certificates, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property; the guarantee of the loan is further verified in response to the condition of the mortgage of the loan.
A workflow may be enabled through which a human user enters loan assurance parameters to establish an internet of things data collection request (step 9912).
A loan-related action may be automatically performed in response to the verification (step 9914).
The loan-related action may be in response to the loan guarantee not being verified, and wherein the action comprises a redemption-stopping action.
The loan-related actions may be responsive to the loan guarantee not being verified, and wherein the actions include lien management actions.
The loan-related actions may be responsive to the loan guarantee not being verified, and wherein the actions include an interest rate adjustment action.
The loan-related action may be in response to the loan guarantee not being verified, and wherein the action comprises a default initiating action.
The loan-related action may be in response to the loan guarantee not being verified, and wherein the action includes a mortgage replacement.
The loan-related action may be in response to the loan guarantee not being verified, and wherein the action includes loan earning.
The loan-related action may be in response to the loan guarantee not being verified, and wherein the action includes providing an alert to a second entity involved in the loan.
The robotic process automation circuit may be iteratively trained to configure the internet of things data collection and monitoring actions based on at least one attribute of the loan, wherein the robotic process automation circuit is trained using a plurality of algorithms based on a training data set comprising at least one of the results from the human user interaction (step 9918).
At least one domain to which at least one algorithm is to be applied may be determined (step 9920). Training may include training robotic process automation circuitry to configure a variety of algorithms.
The training data set may also include results from a set of IoT data collection and monitoring requests.
Referring now to fig. 100, an illustrative and non-limiting example robotic process automation system 10000 for negotiating loans is shown. The example system may include a controller 10001. The controller 10001 can include a data collection circuit 10012, a valuation circuit 10044, and a number of artificial intelligence circuits 10042 including an automated loan classification circuit 10032, a robotic process automation circuit 10060, an intelligent contract circuit 10084, and a clustering circuit 10082.
The data collection circuit 10012 may be configured to collect the interactive training set 10010 from at least one entity 10078 associated with at least one loan transaction. The automated loan classification circuit 10032 may train to classify at least one loan negotiation action based on the interactive training set 10010. The robotic process automation circuit 10060 may train to negotiate terms and conditions 10024 of the new loan on behalf of the principal of the new loan 10030 based on the training set of the plurality of loan negotiation actions 10074 and the plurality of loan transaction results 10039, as classified by the automatic loan classification circuit 10032.
The data collection circuit may also include at least one other system 10062 of the following: the system comprises an internet of things system, a camera system, a networking monitoring system, an internet monitoring system, a mobile device system, a wearable device system, a user interface system and an interactive crowdsourcing system. The at least one entity may be a party to the at least one loan transaction and may be selected from the following entities: primary borrowers, secondary borrowers, borrowing groups, corporate borrowers, government borrowers, banking borrowers, warranty borrowers, bond purchasers, non-warranty borrowers, warranty providers, borrowers, debtors, underwriters, inspectors, valuators, auditors, valuation professionals, government officers, and accountants.
The automated loan classification circuit 10032 may include one of the following systems: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, and simulation systems.
The robotic process automation circuit 10060 may also train based on a plurality of principal interactions with a plurality of user interfaces involved in a plurality of lending processes.
The smart contract circuitry 10084 may be configured to automatically configure the smart contract 8 for the new loan 10030 based on the result of the negotiation.
The distributed ledger 10080 may be associated with a new loan 10030, wherein the distributed ledger 10080 is structured to record at least one of the results of the negotiations and the negotiation events.
The new loan may include at least one of the following loan types: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance arrangements, payday loans, refund expectations, learning-aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, and subsidy loans.
The valuation circuit 10044 may be configured to determine the value of the mortgage of the new loan using the valuation model 10052. The mortgage may include at least one of: vehicles, ships, airplanes, buildings, houses, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, merchandise, securities, money, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, tools, machinery, and personal property.
The valuation circuit may also include market value data collection circuit 10048 configured to monitor and report market information related to the value of the mortgage. The market value data collection circuit 10048 may monitor pricing data or financial data for the countermortgage 10034 in at least one public market. A set of counteracting mortgages 10034 for evaluating the mortgage may be constructed using a clustering circuit 10082 based on the mortgage attributes. The attribute may be selected from: the type of mortgage, the age of the mortgage, the condition of the mortgage, the history of the mortgage, the storage conditions of the mortgage, and the geographic location of the mortgage. The terms and conditions 10024 of the new loan may include at least one member of the following: liability principal amount, liability balance, fixed interest rate, variable interest rate, payment amount, payment plan, end-of-line clearing plan, mortgage description, mortgage substitutability description, principal, insured, guarantor, personal guarantor, retention, deadline, contract, redemption status, violation status, and outcome of the violation.
Referring now to FIG. 101, an illustrative and non-limiting example robotic process automation method 10000 for negotiating loans is shown. The example method may include collecting a training set of interactions from at least one entity associated with at least one loan transaction (step 10102); training an automatic loan classification circuit on the interactive training set to classify the at least one loan negotiation action (step 10104); and training the robotic process automation circuit on the training set of the plurality of loan negotiation actions and the plurality of loan transaction results categorized by the automated loan classification circuit to negotiate terms and conditions of the new loan on behalf of the principal of the new loan (step 10108).
The robotic process automation circuit may be trained based on a plurality of principal interactions with a plurality of user interfaces involved in a plurality of lending processes (step 10110).
The smart contract for the new loan may be configured based on the result of the negotiation (step 10112).
At least one of the result of the negotiation and the negotiation event may be recorded in a distributed ledger associated with the new loan (step 10114).
The valuation model may be used to determine the value of the mortgage of the new loan (step 10118).
An example method may further include: market information relating to mortgage value is monitored and reported (step 10120).
A set of counteracting mortgages for valuation of the mortgage may be constructed using a similarity clustering algorithm based on the properties of the mortgage (step 10122).
Referring to fig. 102, an illustrative and non-limiting example system 10200 for adaptive intelligence and robotic process automation capability is shown. The example system may include a data collection circuit 10206 that may collect data, such as loan receipt results 10203, and a loan interaction training set 10204 that may include collections 10205, and the like. This data may be collected from loan transactions 10219, loan data 10201, entity information 10202, and the like. This data may be collected from various sources and systems, such as: the system comprises an internet of things system, a camera system, a networking monitoring system, an internet monitoring system, a mobile device system, a wearable device system, a user interface system and an interactive crowdsourcing system. The loan receipt result 10203 may include at least one of the following results, for example: responses to collection contact events, loan repayment, borrower loan violations, borrower bankruptcy of loans, collection litigation results, financial returns for a set of collection actions, return on investment for collection, reputation metrics of parties involved in collection, and the like.
The system may also include an artificial intelligence circuit 10210, which may be configured to categorize a set of loan collection actions 10209 based at least in part on the loan interaction training set 10204. The artificial intelligence circuit 10210 may include at least one of the following systems, for example: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, simulation systems, and the like.
The system may also include robotic process automation circuit 10213 configured to perform at least one loan collection action 10211 on behalf of the loan principal 10212 based at least in part on the loan interaction training set 10204 and the set of loan collection results 10203. The loan receipt action 10211 taken by the robotic process automation circuit 10213 may be at least one of: transferring the loan to the collection agency; configuring a receipt communication; arranging for a receipt communication; configuring the content of the checkout communication; configuring an offer for settling a loan; terminating the collection action; delay the collection of money; configuring an offer for an alternative payment plan; initiating litigation; redemption of the product is initiated; initiating a bankruptcy process; a re-occupation process; setting mortgage retention rights and the like. Loan principal 10212 may include at least one of the following, for example: primary borrowers, secondary borrowers, borrowing groups, corporate borrowers, government borrowers, banking borrowers, guaranteed borrowers, bond purchasers, non-guaranteed borrowers, guarantee providers, borrowers, debtors, underwriters, inspectors, valuators, auditors, valuation professionals, government officers, accountants, and the like. Loan 10201 may include at least one loan, such as: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance arrangements, payday loans, refund expectations, learning aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, subsidy loans, and the like.
The system may also include interface circuitry 10208 configured to receive interactions 10207 from one or more entities 10202. In some embodiments, the robotic process automation circuit 10213 may be trained based on interactions 10207. The system may also include a smart contract circuit 10218 configured to determine that the loan payment action 10211 negotiation is complete and modify the contract 10216 based on the result 10217 of the negotiation.
The system may also include a distributed ledger circuit 10215 configured to determine at least one of a payment result 10220 or an event 10221 associated with the loan payment action 10211. Distributed ledger circuit 10215 may be configured to record events 10221 and/or payment results 10220 in a distributed ledger 10214 associated with a loan.
Referring to fig. 103, an illustrative and non-limiting example method 10300 is shown. An example method 10300 may include a step 10301 of collecting a set of loan interaction training sets and a set of loan collection results between entities of a set of loan transactions, wherein the set of loan interaction training sets includes a set of payments to reclaim a loan. A set of loan collection actions is classified based at least in part on the loan interaction training set (step 10302). The method may also include a step 10303 of designating a loan receipt action on behalf of the lending party based at least in part on the loan interaction training set and the set of loan receipt results.
The method 10300 may further include a step 10304 of determining that the loan payment action negotiation is complete. In step 10305, the smart contract may be modified based on the result of the negotiation. The method may also include a step 10306 of determining at least one of a collection result or event associated with the loan payment action. In step 10307, at least one of the collection results or events may be recorded in a distributed ledger associated with the loan.
Referring to fig. 104, an illustrative and non-limiting example system 10400 for adaptive intelligence and robotic process automation capability is shown. The example system may include a data collection circuit 10406 configured to collect a loan interaction training set between entities 10402, where the loan interaction training set may include a set of loan repayment activities 10403 and a set of loan repayment results 10404. The system may include an artificial intelligence circuit 10410 configured to categorize a set of loan re-financing activities, wherein the artificial intelligence circuit is trained based on a loan interaction training set. The system may include robotic process automation circuit 10413 configured to perform a second loan repayment campaign 10411 on behalf of the principal 10412 of the second loan, wherein the robotic process automation circuit trains based on the set of loan repayment campaigns and the set of loan repayment results. The example system may include a data collection circuit 10406 that may collect data, such as a loan interaction training set between entities 10402. The data related to the loan interaction training set between entities 10402 may include data related to loan re-financing activities 10403 and loan re-financing results 10404. Data may be collected from loan data 10401, entity information 10402, and the like. This data may be collected from various sources and systems, such as: the system comprises an internet of things system, a camera system, a networking monitoring system, an internet monitoring system, a mobile device system, a wearable device system, a user interface system and an interactive crowdsourcing system. Loan re-financing activity 10403 may comprise at least one of the following: initiating a re-financing offer; initiating a re-financing request; configuring a re-financing rate; configuring a re-financing payment plan; configuring a re-financing balance; configuring a financing mortgage; managing the use of re-financing returns; removing or setting the lien right related to re-financing; verifying ownership of the re-financing; managing the inspection process; filling in an application; negotiating terms and conditions of re-financing; finishing the re-financing and the like.
The system may also include an artificial intelligence circuit 10410 that may be configured to categorize a set of loan re-financing activities 10409 based at least in part on the loan interaction training set 10405. The artificial intelligence circuit 10410 may include at least one of the following systems, for example: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, simulation systems, and the like.
The system may also include robotic process automation circuit 10413 configured to perform a second loan re-financing activity 10411 on behalf of a second loan principal 10412 based at least in part on the set of loan re-financing activities 10403 and the set of loan re-financing results 10404. The principal 10412 of the second loan may comprise at least one of the following, for example: primary borrowers, secondary borrowers, borrowing groups, corporate borrowers, government borrowers, banking borrowers, guaranteed borrowers, bond purchasers, non-guaranteed borrowers, guarantee providers, borrowers, debtors, underwriters, inspectors, valuators, auditors, valuation professionals, government officers, accountants, and the like.
The second loan 10419 may comprise at least one of the following, for example: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance arrangements, payday loans, refund expectations, learning aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, subsidy loans, and the like.
The system may also include an interface circuit 10408 configured to receive interactions 10407 from one or more entities 10402. In some embodiments, the robotic process automation circuit 10413 may be trained based on interactions 10407. The system may also include an intelligent contract circuit 10418 configured to determine that the second loan re-financing campaign 10411 is completed and modify the intelligent re-financing contract 10417 based on the result of the second loan re-financing campaign 10411.
The system may also include a distributed ledger circuit 10416 configured to determine an event 10415 associated with the second loan re-financing activity 10411. The distributed ledger circuit 10416 may be configured to record events 10415 associated with the second loan re-financing activity 10411 in the distributed ledger 10414 associated with the second loan 10419.
Referring to fig. 105, an illustrative and non-limiting example method 10500 is shown. An example method 10500 may include a step 10501 of collecting a loan inter-training set between entities, wherein the loan inter-training set includes a set of loan re-financing activities and a set of loan re-financing results. A set of loan re-financing actions is classified based at least in part on the loan interaction training set (step 10502). The method may also include a step 10503 of designating a second loan repayment on behalf of the party of the second loan based at least in part on the set of loan repayment activities and the set of loan repayment results.
The method 10500 may also include a step 10504 of determining that the second loan re-financing activity is complete. In step 10505, the intelligent resurf contract may be modified based on the results of the second loan resurf campaign. The method may also include a step 10506 of determining an event associated with the second loan re-financing activity. In step 10507, an event associated with the second loan re-financing campaign may be recorded in a distributed ledger associated with the second loan.
Referring to fig. 106, an illustrative and non-limiting example system 10600 for adaptive intelligence and robotic process automation capability is shown. The example system may include a data collection circuit 10605 that may collect data of a loan interaction training set 10604 among entities, etc., and the loan interaction training set 10604 among entities may include a set of loan merge transactions 10603, etc. This data may be collected from loan data 10601, entity information 10602, and so on. This data may be collected from various sources and systems, such as: the system comprises an internet of things system, a camera system, a networking monitoring system, an internet monitoring system, a mobile device system, a wearable device system, a user interface system and a crowdsourcing system.
The system may also include an artificial intelligence circuit 10610 that may be configured to classify a set of loans as merge candidates 10608 based at least in part on the loan interaction training set 10604. The artificial intelligence circuit 10610 may include at least one of the following systems, for example: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, simulation systems, and the like.
The system may also include robotic process automation circuitry 10613 configured to manage a merger 10611 of at least a subset of a set of loans on behalf of a loan merge principal 10612, based at least in part on the training set of loan merge transactions 10603. Managing the merge may include: identifying a loan in a set of candidate loans; compiling a merging offer; creating a merging plan; compiling content conveying a consolidated offer; arranging a merge offer; communicating a consolidated offer; negotiating a merge offer modification; compiling a merging protocol; executing a merging protocol; modifying a mortgage of a set of loans; processing a merging application workflow; managing and checking; managing and evaluating; setting interest rate; postponing payment requirements; setting up a payment plan or entering into a consolidated agreement.
The artificial intelligence circuit may also include a model 10609 that may be used to categorize loans as merge candidates 10608. Model 10609 can handle attributes of the entity, which can include identity of the principal, interest rate, payment balance, payment terms, payment plan, loan type, mortgage type, financial status of the principal, payment status, mortgage value, etc.
The loan merge principal 10612 may include at least one of the following, for example: primary borrowers, secondary borrowers, borrowing groups, corporate borrowers, government borrowers, banking borrowers, guaranteed borrowers, bond purchasers, non-guaranteed borrowers, guarantee providers, borrowers, debtors, underwriters, inspectors, valuators, auditors, valuation professionals, government officers, accountants, and the like.
Loan 10601 may include at least one loan, such as: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance arrangements, payday loans, refund expectations, learning aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, subsidy loans, and the like.
The system may also include an interface circuit 10607 configured to receive interactions 10606 from one or more entities 10602. In some embodiments, the robotic process automation circuit 10613 may be trained based on the interactions 10606. The system may also include a smart contract circuit 10620 configured to determine that the merge negotiation is complete and modify the contract 10618 based on the result 10619 of the negotiation.
The system may also include a distributed ledger circuit 10617 configured to determine at least one of the results 10615 or negotiation events 10616 associated with the merge. The distributed ledger circuit 10617 may be configured to record events 10616 and/or results 10615 in the distributed ledgers 10614 associated with the loan.
Referring to FIG. 107, an illustrative and non-limiting example method 10700 is shown. An example method 10700 may include a step 10701 of collecting training loan interaction sets between entities, wherein the loan interaction training sets include a set of loan combination transactions. A set of loans may be classified as merge candidates based at least in part on the loan interaction training set (step 10702). The method may also include a step 10703 of managing the merging of at least a subset of the set of loans on behalf of the merging party based at least in part on the set of loan merging transactions.
The method 10700 may further include a step 10704 of determining that the consolidated negotiation of at least one loan is complete from a subset of the set of loans. In step 10705, the smart contract may be modified based on the result of the negotiation. The method may also include a step 10706 of determining at least one of a result and a negotiation event associated with the merging of at least a subset of the set of loans. In step 10707, at least one of the results and negotiation events may be recorded in a distributed ledger associated with the merge.
Referring to FIG. 108, an illustrative and non-limiting example system 10800 for adaptive intelligence and robotic process automation capabilities is shown. The example system may include a data collection circuit 10805 that may collect data information about the entities 10802 involved in a set of warranty loans 10801 and an interaction training set 10804 between the entities of a set of warranty loan transactions 10803. This data may be collected from various sources and systems, such as: the system comprises an internet of things system, a camera system, a networking monitoring system, an internet monitoring system, a mobile device system, a wearable device system, a user interface system and a crowdsourcing system.
The system may also include an artificial intelligence circuit 10811 that may be configured to categorize 10808 the entities involved in the set of warranty loans based at least in part on the interactive training set 10804. Artificial intelligence circuit 10811 can include at least one of the following systems, for example: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, simulation systems, and the like.
The system may also include a robotic process automation circuit 10813 configured to manage the warranty loan 10812 based at least in part on the warranty loan transaction 10803. Managing the warranty loan may include managing at least one of a set of properties for warranty; identifying a warranty loan in a set of candidate loans; compiling a warranty offer; compiling a warranty plan; compiling content conveying an insurance offer; arranging a warranty offer; communicating an insurance offer; negotiating modifications to the policy offer; compiling a warranty agreement; executing a warranty protocol; modifying a set of mortgages of the warranty loan; processing a set of accounts receivable transfers; processing a warranty application workflow; managing and checking; managing an assessment of a set of assets to be secured; setting interest rate; postponing payment requirements; setting up a payment plan or entering into a consolidated agreement.
Artificial intelligence circuit 10811 may also include a model 10809 that may be used to process attributes of entities involved in a set of warranty loans, which may include: the property used for insurance, the identity of the principal, the interest rate, the payment difference, the payment terms, the payment plan, the loan type, the mortgage type, the principal's financial status, the payment status, the mortgage status, or the mortgage value. The assets for warranty may include a set of accounts receivable 10810. At least one of the entities 10802 may be a principal of at least one warranty loan transaction 10803. The principal may include at least one of the following, for example: primary borrowers, secondary borrowers, borrowing groups, corporate borrowers, government borrowers, banking borrowers, guaranteed borrowers, bond purchasers, non-guaranteed borrowers, guarantee providers, borrowers, debtors, underwriters, inspectors, valuators, auditors, valuation professionals, government officers, accountants, and the like.
The system may also include an interface circuit 10807 configured to receive interactions 10806 from the one or more entities 10802. In some embodiments, robotic process automation circuit 10813 may be trained based on interactions 10806.
The system may also include an intelligent contract circuit 10820 configured to determine that the warranty loan negotiation is complete and modify the contract 10818 based on the negotiated result 10819.
The system may also include a distributed ledger circuit 10817 configured to determine at least one of a result 10815 or a negotiation event 10816 associated with negotiating a warranty loan. The distributed ledger circuit 10817 may be configured to record events 10816 and/or results 10815 in the distributed ledgers 10814 associated with the warranty loans.
Referring to FIG. 109, an illustrative and non-limiting example method 10900 is shown. An example method 10900 may include a step 10901 of collecting information about entities involved in a set of warranty loans and a training set of interactions between entities of a set of warranty loan transactions. The entities involved in the set of warranty loans may be categorized based at least in part on the loan interaction training set (step 10902). The method may also include a step 10903 of managing the warranty loan based at least in part on the set of warranty loan interactions.
The method 10900 may also include a step 10904 of determining that the warranty loan negotiation is complete. In step 10905, the smart contract may be modified based on the result of the negotiation. The method may further include a step 10906 of determining at least one of a result associated with the negotiation of the warranty loan and a negotiation event. In step 10907, at least one of the result and the negotiation event may be recorded in a distributed ledger associated with the warranty loan.
Referring to fig. 110, an illustrative and non-limiting example system 11000 for adaptive intelligence and robotic process automation capability is shown. The example system may include a data collection circuit 11006 that may collect information about entities 11002 involved in a set of mortgage activities 11005 and a training set of interactions 11004 between entities of a set of warranty loan transactions 11003. This data may be collected from various sources and systems, such as: the system comprises an internet of things system, a camera system, a networking monitoring system, an internet monitoring system, a mobile device system, a wearable device system, a user interface system and a crowdsourcing system.
The system may also include an artificial intelligence circuit 11010 that may be configured to classify 11009 entities involved in a set of mortgage activities based at least in part on the interactive training set 11004. The artificial intelligence circuit 11010 may include at least one of the following systems, for example: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, simulation systems, and the like.
The system may also include robotic process automation circuitry 11012 configured to proxy mortgage 11011 based at least in part on at least one of the set of mortgage activities 11005 and the training interaction set 11004. The set of mortgage activities 11005 and/or the set of mortgage transactions 11003 may include the following activities: a marketing campaign; identifying a set of potential borrowers; identifying property; identifying a mortgage; ensuring that the borrower qualifies; searching for ownership; verifying ownership; evaluating property; checking property; valuating the property; verifying revenue; performing a demographic analysis on the borrower; identifying a sponsor; determining available interest rate; determining available payment terms and conditions; analyzing the existing mortgage loan; performing a comparative analysis on existing loan conditions and new mortgage loan terms; finishing the application workflow; filling an application field; compiling a mortgage protocol; finishing a mortgage protocol attached form; negotiating mortgage terms and conditions with the sponsor; negotiating mortgage terms and conditions with the borrower; transferring ownership; setting an indwelling right; or to achieve a mortgage protocol.
The artificial intelligence circuit 11010 may also include a model that may be used to process the attributes of the entities involved in a set of mortgage activities, which may be mortgage-limited attributes, the assets that act as mortgages, the identity of the principal, interest rates, payment balances, payment terms, payment plans, mortgage types, property types, the principal's financial status, payment status, the status of the property, or the value of the property. In an embodiment, the proxy mortgage includes at least one of the following activities, for example: managing at least one of the mortgage-limited properties; identifying candidate mortgages according to the current situation of a group of borrowers; compiling a mortgage offer; compiling content conveying mortgage offers; arranging a mortgage offer; communicating a mortgage offer; negotiating mortgage offer modifications; compiling a mortgage protocol; executing a mortgage protocol; modifying a mortgage of a set of mortgage offers; processing the retention right transfer; processing application workflow; managing and checking; managing an assessment of a set of assets to be mortgage; setting interest rate; postponing payment requirements; setting a payment plan; to achieve mortgage protocol, etc.
In an embodiment, at least one of the entities 11002 may be a party to at least one mortgage transaction in the set of mortgage transactions 11003. The principal may include at least one of the following, for example: primary borrowers, secondary borrowers, borrowing groups, corporate borrowers, government borrowers, banking borrowers, guaranteed borrowers, bond purchasers, non-guaranteed borrowers, guarantee providers, borrowers, debtors, underwriters, inspectors, valuators, auditors, valuation professionals, government officers, accountants, and the like.
The system may also include an interface circuit 11008 configured to receive interactions 11007 from one or more entities 11002. In some embodiments, the robotic process automation circuit 11012 may be trained based on the interactions 11007.
The system may also include a smart contract circuit 11019 configured to determine that mortgage negotiation is complete and modify the smart contract 11017 based on the result 11018 of the negotiation.
The system may also include a distributed ledger circuit 11016 configured to determine at least one of a result 11014 or a negotiation event 11015 associated with the negotiation of the mortgage. The distributed ledger circuit 11016 may be configured to record events 11015 and/or results 11014 in the distributed ledgers 11013 associated with mortgage loans.
Referring to FIG. 111, an illustrative and non-limiting example method 11100 is shown. Example method 11100 may include step 11101 of collecting information about entities involved in a set of mortgage activities and a set of interactive training sets between entities of a set of warranty loan transactions. The entities involved in the set of warranty loans may be categorized based at least in part on the loan interaction training set (step 11102). The method may also include a step 11103 of based at least in part on the set of mortgage activities and at least one proxy mortgage in the interactive training set.
Method 11100 may also include step 11104, which includes determining that mortgage negotiation is complete. In step 11105, the smart contract may be modified based on the result of the negotiation. The method may also include a step 11106 that includes determining at least one of a result associated with the negotiation of the mortgage and a negotiation event. In step 11107, at least one of the results and the negotiation event may be recorded in a distributed ledger associated with the mortgage.
Referring to FIG. 112, an illustrative and non-limiting example system 11200 for adaptive intelligence and robotic process automation capability is shown. The example system may include a data collection circuit 11208 that may collect data regarding entities 11205 involved in a set of liability transactions 11201, training data sets 11206 of results related to the entities, and training sets 11207 of liability management activities. Data may be collected from a variety of sources and systems, such as: an internet of things device, a set of environmental condition sensors, a set of crowdsourcing services, a set of social network analysis services, or a set of network domain query algorithms, etc.
The system may also include a condition classification circuit 11214, which may be configured to classify a condition 11211 of at least one of the entities 11205. The condition classification circuit 11214 may include a model 11212 and a set of artificial intelligence circuits 11213. The model 11212 may be trained using a training data set 11206 of results related to entities. The artificial intelligence circuit 11213 can include at least one of the following systems, for example: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, or simulation systems.
The system may also include an automatic liability management circuit 11216 configured to manage liability-related actions 11215. Automatic liability management circuit 11216 may be trained based on training set 11207 of liability management activities.
In an embodiment, at least one liability transaction in the set of liability transactions 11201 may include an automotive loan, an inventory loan, a capital equipment loan, a performance guarantee, a fixed property improvement loan, a construction loan, an account receivable guarantee loan, an invoice financing arrangement, a guarantee arrangement, a payday loan, a refund expectancy loan, an learning aid loan, a silver group loan, a ownership loan, a housing loan, a risk liability loan, an intellectual property loan, a contract liability loan, a mobile funds loan, a small business loan, an agricultural loan, a municipal bond, a subsidy loan, and the like.
In an embodiment, the entity 11205 involved in the set of liability transactions includes at least one of a set of principals 11202 and a set of assets 11204. Assets 11204 may include municipal assets, vehicles, ships, airplanes, buildings, residences, real estate, undeveloped properties, farms, crops, municipal facilities, warehouses, a group of inventory, goods, securities, currencies, value certificates, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, or personal property. The system may also include a set of sensors 11203 located on one of: at least one asset 11204 of the set of assets, a container for the at least one asset of the set of assets, and a package for the at least one asset of the set of assets, wherein the set of sensors is to associate sensor information sensed by the set of sensors with a unique identifier of the at least one asset of the set of assets. The sensor 11203 may include an image, temperature, pressure, humidity, speed, acceleration, rotation, torque, weight, chemical, magnetic field, electric field, or location.
In an embodiment, the system may further include a set of blockchain circuitry 11224 configured to receive information from the data collection circuitry 11208 and the sensor set 11203 and store the information in the blockchain 11226. Access to the blockchain 11226 may be provided via secure access control interface circuitry 11223.
The automated agent circuit 11225 may be configured to process an event related to at least one of a value, a status, or ownership of at least one asset in a group of assets, and further configured to take a set of actions related to liability transactions involving the asset.
The system may also include an interface circuit 11210 configured to receive interactions 11209 from at least one of the entities 11205. In an embodiment, automated liability management circuit 11216 may train based on interactions 11209. In some embodiments, the system may further include a market value data collection circuit 11218 configured to monitor and report market information 11217 related to the value of at least one asset in the set of assets 11204. The market value data collection circuit 11218 may also be configured to monitor at least one of pricing and financial data for items similar to at least one asset in a group of assets in at least one public market. A set of similar items for valuating at least one asset in a set of assets may be constructed using a similarity clustering algorithm based on attributes of the assets. In an embodiment, at least one of the attributes of the asset may include an asset class, an asset age, an asset condition, an asset history, an asset store, an asset geographic location, and the like.
In an embodiment, the system may further include a smart contract circuit 11222 configured for managing smart contracts 11219 of the liability transactions 11221. The smart contract circuitry 11222 may also be configured to establish a set of terms and conditions 11220 for the liability transaction 11221. At least one of the terms and conditions may include a bond principal amount, a bond balance, a fixed interest rate, a variable interest rate, a payment amount, a payment plan, a maximum end clearing plan, a mortgage description, a mortgage substitutability description, a principal, a insured, a guaranty, a personal guaranty, a retention, a deadline, a contract, a redemption prevention condition, a default outcome, and the like.
In an embodiment, the at least one action associated with debt 11215 may include: providing a liability transaction; underwriting liability transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing the checking and recording of changes of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint liabilities or combined liabilities. At least one liability management activity in liability management activity training set 11207 may include: offer liability transactions; underwriting liability transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint liabilities; or to consolidate liabilities.
Referring to FIG. 113, an illustrative and non-limiting example method 11300 is shown. Example method 11300 may include step 11301 of collecting information about entities involved in a set of liability transactions, training data sets for results related to the entities, and liability management activity training sets. The example method may also include classifying a condition of at least one of the entities based at least in part on the training data set of results associated with the entity (step 11302). The example method may also include managing liability-related actions based at least in part on the liability management activity training set (step 11303). The example method may also include receiving information from a set of sensors located on the at least one asset (step 11304). The example method may further include storing information in the blockchain, wherein access to the blockchain is provided to a principal of liability transactions involving at least one asset in the group of assets through the secure access control interface (step 11305). In step 11306, the method may include processing an event related to at least one of a value, a status, or ownership of at least one asset in the set of assets. Step 11307, the method may include processing a set of actions related to liability transactions involving the asset. In an embodiment, the method may further include receiving an interaction from at least one of the entities (step 11308); monitoring and reporting market information related to the value of at least one asset in a group of assets (step 11309); constructing a set of similar items for evaluating at least one asset from the set of assets using a similarity clustering algorithm based on the asset attributes (step 11310); managing smart contracts for liability transactions (step 11311); and establishing a set of terms and conditions for the intelligent contract for the liability transaction (step 11312).
Referring to FIG. 114, an illustrative and non-limiting example system 11400 for adaptive intelligence and robotic process automation capability is shown.
The example system may include crowd-sourced data collection circuitry 11405 configured to collect information about entities 11403 involved in a set of bond transactions 11402 and training data sets of results related to the entities 11403. The system may also include a condition classification circuit 11411 configured to classify the condition of a group of publishers 11408 using information from the crowd-sourced data collection circuit 11405 and the model 11409. Model 11409 may be trained using training dataset 11404 of results related to the set of publishers. The example system may further include an automated agent circuit 11419 configured to perform a liability transaction related action in response to the classification status of at least one of the group of issuers. In an embodiment, the at least one entity 11403 may include a set of publishers, a set of bonds, a set of principals, and/or a set of assets. The at least one distributor may include a municipality, a company, a contractor, a government entity, a non-government entity, or a non-profit entity. The at least one bond may include a municipal bond, a government bond, a national treasury bond, a vouchers bond, or a corporate bond.
In an embodiment, the conditions 11408 classified by the condition classification circuit 11411 may include a breach condition, a redemption-suppressing condition, a condition indicating a breach of a contract, a financial risk condition, a behavioral risk condition, a policy risk condition, a financial health condition, a physical defect condition, a physical health condition, an entity risk condition, an entity health condition, and the like. The crowdsourcing data collection circuit 11411 may be configured to enable a user interface 11407 through which a user may configure crowdsourcing requests 11406 for information related to the status of a group of publishers.
The system may also include a configurable data collection and monitoring circuit 11413 configured to monitor at least one of the set of publishers 11412. Configurable data collection and monitoring circuit 11413 may include the following systems, for example: an internet of things device, a set of environmental condition sensors, a set of social network analysis services, or a set of network domain query algorithms. The configurable data collection and monitoring circuit 11413 may be configured to monitor at least one of the following environments, for example: municipal environments, educational environments, corporate environments, securities trading environments, real estate environments, commercial facilities, warehouse facilities, transportation environments, manufacturing environments, storage environments, houses, or vehicles.
In an embodiment, a set of bonds associated with a set of bond transactions 11402 may be supported by a set of assets 11401. The at least one asset 11401 may include municipal assets, vehicles, ships, aircraft, buildings, houses, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a set of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrency, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contract rights, antiques, fixtures, furniture, equipment, tools, machinery, personal property, or the like.
In an embodiment, the system may further include an automated agent circuit 11419 configured to process an event related to at least one of a value, a status, or ownership of at least one asset of at least one of the set of publishers, and to perform a liability transaction related action in response to at least one of the processed events.
Acts 11418 may include: offer liability transactions; underwriting liability transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint liabilities; combining debts, etc. Condition classification circuit 11411 may include the following systems, for example: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, or simulation systems.
In an embodiment, the system may further comprise an automatic bond management circuit 11427 for managing bond-related actions 11424, the bond-related actions being related to at least one issuer of a group of issuers. The automated bond management circuit 11427 may train based on the training set 11426 of bond management activities. Automated bond management circuit 11427 may also train based on a set of principal interactions 11425 with a set of user interfaces involved in a set of bond transactions. The at least one bond transaction may include: offer liability transactions; underwriting liability transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint liabilities; combining debts, etc.
In an embodiment, the system may further include a market value data collection circuit 11417 configured to monitor and report market information 11414 related to the value of at least one of the publisher or the asset group. The report may include a report on: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farm, crop, municipal facility, warehouse, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrency, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, or personal property. The market value data collection circuit 11417 may be configured to monitor pricing 11416 or financial data 11415 for items similar to the assets in the at least one public market. The market value data collection circuit 11417 may also be configured to construct a set of similar items for valuating an asset using a similarity clustering algorithm based on the attributes of the asset. The at least one attribute from the attributes may be selected from: asset class, age of asset, asset condition, asset history, asset storage, or geographic location of the asset.
In an embodiment, the system may further include a smart contract circuit 11423 configured to manage smart contracts 11420 for bond transactions 11422 in response to a classification status of at least one issuer of a group of issuers. The smart contract circuit 11423 may be configured to determine terms and conditions 11421 of the bond. The at least one term and condition 11421 may include: the debt principal amount, the debt balance, the fixed interest rate, the variable interest rate, the payment amount, the payment plan, the end-of-line repayment plan, the bond guaranty asset description, the asset replaceability description, the principal, the issuer, the purchaser, the insured person, the insurer, the guaranty, the individual guaranty, the retention, the deadline, the contract, the redemption prevention condition, the default outcome, and the like.
Referring to FIG. 115, an illustrative and non-limiting example method 11500 is shown. Example method 11500 may include a step 11501 of collecting information about an entity of a training dataset related to a set of bond transactions for a set of bonds and results related to the entity. The method may also include a step 11502 of classifying a condition of the set of publishers using the collected information and the model, wherein the model is trained using a training dataset of results related to the set of publishers. The method may also include processing an event related to at least one of a value, a status, or ownership of at least one asset in the set of assets (step 11503). The method may also include a step 11504 of performing an action related to the liability transaction related to the asset; step 11505, which manages bond-related actions based at least in part on the bond management activity training set; a step 11506 of monitoring and reporting market information related to the value of at least one of the issuer and the set of assets; step 11507, which manages the smart contracts for bond transactions, and step 11508, which determines terms and conditions of the smart contracts for at least one bond.
Referring now to fig. 116, an illustrative and non-limiting example system 11600 for monitoring bond issuer status is shown. The example system may include a controller 11601. The controller 11601 may include a data collection circuit 11612, a market value data collection circuit 11656, a social network input circuit 11644, a social network data collection circuit 11632, and a number of artificial intelligence circuits 11642 including an intelligent contract circuit 11622, an automatic bond management circuit 11650, a condition classification circuit 11646, a clustering circuit 11662, and an event processing circuit 11652.
The social network data collecting circuit 11632 may be configured to collect information about at least one entity 11664 related to at least one transaction 11630 comprising at least one bond; the condition classification circuit 11646 may be configured to classify the condition of the at least one entity according to a model 11674 and based on information from the social network data collection circuit, wherein the model is trained using a training data set 11654 of a plurality of results associated with the at least one entity. The at least one entity may be selected from the following entities: bond issuers, bonds, parties, and assets. The bond issuer may be selected from the following: municipalities, companies, contractors, government entities, non-government entities and non-profit entities. The bond may be selected from the following entities: municipal bonds, government bonds, national treasury bonds, asset security bonds, and corporate bonds.
The conditions classified by the condition classification circuit 11648 may include at least one of: a default condition, a redemption-stopping condition, a condition indicating a contract violation, a financial risk condition, a behavioral risk condition, a policy risk condition, a financial health condition, a physical defect condition, a physical health condition, an entity risk condition, or an entity health condition.
The social network data gathering circuit 11632 may also include a social network input circuit 11644, which may be configured to receive input from a user for configuring a query for information about at least one entity.
The data collection circuit 11612 may be configured to monitor at least one of an internet of things device, an environmental condition sensor, a crowdsourcing request circuit, a crowdsourcing communication circuit, a crowdsourcing publishing circuit, and an algorithm for querying a network domain.
The data collection circuit 11612 may also be configured to monitor the following environment: municipal environments, corporate environments, securities trading environments, real estate environments, commercial facilities, warehouse facilities, transportation environments, manufacturing environments, storage environments, residences, and vehicles.
At least one bond is secured by at least one asset. The at least one asset may be selected from the following: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contract rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.
The event processing circuit 11652 may be configured to process events related to at least one of the value, status, and ownership of at least one asset and take actions related to at least one transaction. The action may be selected from the following actions: a bond transaction; underwriting bond transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint bonds; and merging bonds.
The condition classification circuit 11648 may also include one of the following systems: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, and simulation systems.
The automated bond management circuit 11650 may be configured to manage actions related to at least one bond, wherein the automated bond management circuit trains based on training data sets of a plurality of bond management activities.
Automated bond management circuit 11650 may be trained based on a plurality of interactions of a principal with a plurality of user interfaces involved in a plurality of bond transactions. The plurality of bond transactions may be selected from the following bond transactions: offer bond transactions; underwriting bond transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint bonds; and merging bonds.
The market value data collection circuit 11656 may be configured to monitor and report market information related to the value of at least one of the bond issuer, the at least one bond, and the asset. The asset may be selected from the following: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farm, crop, municipal facility, warehouse, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrency, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, or personal property.
The market value data collection circuit 11656 may also be configured to monitor pricing or financial data of counteracting assets in at least one public market.
Clustering circuitry 11662 may be used to construct a set of countermortgages 11658 for valuation of an asset based on its properties. The attribute may be selected from the following attributes: category, age of asset, status of asset, history of asset, storage of asset, and geographic location.
The smart contract circuitry 11622 may be configured to manage smart contracts for the at least one transaction. The smart contract circuitry may also be configured to determine terms and conditions of the at least one bond.
The terms and conditions may be selected from: the debt principal amount, the debt balance, the fixed interest rate, the variable interest rate, the payment amount, the payment plan, the end-of-line repayment plan, the warranty asset description of at least one bond, the asset replaceability description, the principal, the issuer, the purchaser, the insured, the insurer, the guaranty, the personal guaranty, the retention, the deadline, the contract, the redemption prevention condition, the default condition, and the outcome of the breach.
Referring now to fig. 117, an illustrative and non-limiting example method 11700 for monitoring the condition of a bond issuer is shown. An example method may include: collecting social network information (11702) about at least one entity related to at least one transaction comprising at least one bond; and classifying the condition of the at least one entity according to a model and based on the social network information, wherein the model is trained using a training dataset of a plurality of results related to the at least one entity (11704).
An event related to at least one of a value, a status, and ownership of the at least one asset may be processed (11708). An action associated with the at least one transaction may be taken in response to the event (11710). The automated bond management circuit may train to manage actions related to the at least one bond based on a training set of the plurality of bond management activities (11712). An example method may further include: market information (11714) relating to the value of at least one of the bond issuer, the at least one bond, and the asset is monitored and reported.
Referring now to fig. 118, an illustrative and non-limiting example system 11800 for monitoring bond issuer status is shown. The example system may include a controller 11801. The controller 11801 may include a data collection circuit 11812, a market value data collection circuit 11856, an internet of things input circuit 11844, an internet of things data collection circuit 11832, and several artificial intelligence circuits 11842 including an intelligent contract circuit 11822, an automatic bond management circuit 11850, a status classification circuit 11846, a clustering circuit 11862, and an event processing circuit 11852.
The internet of things data collection circuit 11832 may be configured to collect information about at least one entity 11864 related to at least one transaction 11830 including at least one bond; and the condition classification circuit 11846 may be configured to classify the condition of the at least one entity according to a model 11874 and based on information from the internet of things data collection circuit, wherein the model is trained using a training data set 11854 of a plurality of results associated with the at least one entity. The at least one entity may be selected from the following entities: bond issuers, bonds, parties, and assets. The bond issuer may be selected from the following: municipalities, companies, contractors, government entities, non-government entities and non-profit entities. The bond may be selected from the following entities: municipal bonds, government bonds, national treasury bonds, asset security bonds, and corporate bonds.
The conditions classified by the condition classification circuit 11848 may include at least one of: a default condition, a redemption-stopping condition, a condition indicating a contract violation, a financial risk condition, a behavioral risk condition, a policy risk condition, a financial health condition, a physical defect condition, a physical health condition, an entity risk condition, or an entity health condition.
The internet of things data collection circuit 11832 may further include an internet of things input circuit 11844, which may be configured to receive input from a user for configuring a query for information about at least one entity.
The data collection circuit 11812 may be configured to monitor at least one of an internet of things device, an environmental condition sensor, a crowdsourcing request circuit, a crowdsourcing communication circuit, a crowdsourcing publishing circuit, and an algorithm for querying a network domain.
The data collection circuit 11812 may also be configured to monitor the following environment: municipal environments, corporate environments, securities trading environments, real estate environments, commercial facilities, warehouse facilities, transportation environments, manufacturing environments, storage environments, residences, and vehicles.
At least one bond is secured by at least one asset. The at least one asset may be selected from the following: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contract rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.
The event processing circuitry 11852 may be configured to process events related to at least one of the value, status, and ownership of at least one asset and take actions related to at least one transaction. The action may be selected from the following actions: a bond transaction; underwriting bond transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint bonds; and merging bonds.
The condition classification circuit 11848 may also include one of the following systems: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, and simulation systems.
Automatic bond management circuit 11850 may be configured to manage actions related to at least one bond, wherein the automatic bond management circuit trains based on training data sets of a plurality of bond management activities.
Automated bond management circuit 11850 may train based on a plurality of interactions of the principal with a plurality of user interfaces involved in a plurality of bond transactions. The plurality of bond transactions may be selected from the following bond transactions: offer bond transactions; underwriting bond transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction;
providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint bonds; and merging bonds.
The market value data collection circuit 11856 may be configured to monitor and report market information related to the value of at least one of the bond issuer, the at least one bond, and the asset. The asset may be selected from the following: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farm, crop, municipal facility, warehouse, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrency, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, or personal property.
The market value data collection circuit 11856 may also be configured to monitor pricing or financial data of counteracting assets in at least one public market.
Clustering circuitry 11862 may be used to construct a set of countermortgages 11858 for valuation of an asset based on its properties. The attribute may be selected from the following attributes: category, age of asset, status of asset, history of asset, storage of asset, and geographic location.
The smart contract circuitry 11822 may be configured to manage smart contracts for the at least one transaction. The smart contract circuitry may also be configured to determine terms and conditions of the at least one bond.
The terms and conditions may be selected from: the debt principal amount, the debt balance, the fixed interest rate, the variable interest rate, the payment amount, the payment plan, the end-of-line repayment plan, the warranty asset description of at least one bond, the asset replaceability description, the principal, the issuer, the purchaser, the insured, the insurer, the guaranty, the personal guaranty, the retention, the deadline, the contract, the redemption prevention condition, the default condition, and the outcome of the breach.
Referring now to fig. 119, an illustrative and non-limiting example method 11900 for monitoring the condition of a bond issuer is shown. An example method may include: collecting internet of things information regarding at least one entity involved in at least one transaction including at least one bond (11902); and classifying a condition of the at least one entity according to a model and based on the internet of things information, wherein the model is trained using a training dataset of a plurality of results associated with the at least one entity (11904).
An event related to at least one of a value, a condition, and ownership of the at least one asset may be processed (11908). An action associated with the at least one transaction may be taken in response to the event (11910). The automated bond management circuit may train to manage actions related to the at least one bond based on the training set of the plurality of bond management activities (11912). An example method may further include: market information (11914) relating to the value of at least one of the bond issuer, the at least one bond, and the asset is monitored and reported.
Fig. 120 illustrates a system 12000 that includes an internet of things data collection circuit 12014 configured to collect information about an entity 12002 related to a subsidy loan transaction 12004 (e.g., where the entity may be a subsidy loan, a party, a subsidy, a guaranty, a subsidy party, a mortgage, etc., where the party may be at least one of a municipality, a company, a contractor, a government entity, a non-government entity, and a non-profit entity). In an embodiment, the internet of things data collection circuit may include a user interface 12016 configured to enable a user to configure a query for information about at least one entity. The system may include a condition classification circuit 12018, which may include a model 12020 configured to classify parameters 12006 of a subsidy loan 12008 (e.g., municipal subsidy loan, government subsidy loan, learning-aid loan, property-guarantee subsidy loan, or corporate subsidy loan) involved in a subsidy loan transaction, e.g., based on information from the internet of things data collection circuit. In an embodiment, the condition classification circuit may include: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, simulation systems, and the like. Subsidized loans may be guaranteed by: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farm, crop, municipal facility, warehouse, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, personal property, and the like. The conditions classified by the condition classification circuit may include a contraband condition, a redemption-stopping condition, a condition indicating a contraband contract, a financial risk condition, a behavioral risk condition, a contract performance condition, a policy risk condition, a financial health condition, a physical defect condition, a physical health condition, an entity risk condition, an entity health condition, and the like. The model may be trained using a training data set 12010 of a plurality of results related to the subsidized loan. For example, the subsidy loan may be an assisted loan, and the condition classification circuit may classify at least one of: the students get the progress of the academic position, the students participate in non-profit activities, the students participate in public-benefit activities, etc. The system may include an intelligent contract circuit 12022 configured to automatically modify the terms and conditions of the subsidy loan 12012, e.g., based on classification parameters from the condition classification circuit. The system may include a configurable data collection and circuit 12024 configured to monitor an entity, for example, further including social network analysis circuitry 12030, environmental condition circuitry 12032, crowdsourcing circuitry 12034, and algorithms for querying the network domain 12036, wherein the configurable data collection and circuit may monitor an environment selected from, for example, a municipal environment, educational environment, corporate environment, securities trading environment, real-estate environment, commercial facility, warehouse facility, transportation environment, manufacturing environment, storage environment, residential or vehicular environment, and the like. The system may include an automated agent 12026 configured to process events related to the value, status, and ownership of the property and perform actions related to subsidy loan transactions involving the property, wherein the actions may be: subsidy loan transaction; underwriting and subsidy loan transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint subsidy loans; merging patch loans, and the like. The system may include an automatic subsidy management circuit 12038 configured to manage actions related to at least one subsidy, wherein the automatic subsidy management circuit is trained based on a training set of subsidy management activities. For example, the automated subsidy loan management circuit may train based on a plurality of interactions of the principal with a plurality of user interfaces involved in a plurality of subsidy loan transaction activities, wherein the plurality of subsidy loan transaction activities are selected from the following activities: offer subsidy loan transactions; underwriting and subsidy loan transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; and combining the subsidy loans. The system may include a blockchain service circuit 12040 configured to record a modified set of terms and conditions of the subsidized loan, for example, in a distributed ledger 12042. The system may include a market value data collection circuit 12028 configured to monitor and report market information related to the value of a publisher, subsidized loan, property, etc., wherein properties selected from the following properties may be reported: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contract rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property. The market value data collection circuit may also be configured to monitor pricing or financial data of counteracting assets in the public marketplace. A set of counteracting assets for valuation of an asset may be constructed using clustering circuitry based on asset attributes, where the attributes may be categories, age of the asset, status of the asset, history of the asset, storage of the asset, geographic location, etc. The smart contract circuit may be configured to manage smart contracts for subsidy transactions, wherein the smart contract circuit may set terms and conditions of the subsidy, wherein the terms and conditions of the subsidy specified and managed by the smart contract circuit may include: the amount of liability principal, the balance of liability, fixed interest rate, variable interest rate, payment amount, payment plan, end-of-line repayment plan, warranty description of at least one subsidized loan, property substitutability description, principal, issuer, purchaser, insured, insurer, guaranty, personal guaranty, retention, deadline, contract, redemption prevention condition, default outcome, and the like.
Fig. 121 shows a method 12100 that includes collecting information about entities involved in subsidy loan transactions (12102). The method may include classifying parameters of the subsidy loan involved in the subsidy loan transaction based on the information using a model trained based on a training dataset of a plurality of results related to the at least one subsidy loan (12104). The method may include automatically modifying the terms and conditions of the subsidy loan based on the classification parameters (12108). The method may include processing at least one event related to at least one of a value, a condition, or an ownership of the property and taking an action related to at least one subsidy loan transaction related to the property (12110). The method may include recording (12112) the modified set of terms and conditions of the subsidized loan in a distributed ledger. The method may include monitoring and reporting market information related to the value of the publisher, subsidized loan, property, etc.
Fig. 122 illustrates a system 12200 that includes a social network-analysis data collection circuit 12214 configured to collect social network information about an entity 12202 related to a subsidy loan transaction 12204 (e.g., where the entity may be a subsidy loan, a party, a subsidy, a guaranty, a subsidy party, a mortgage, etc., where the party may be at least one of a municipality, a company, a contractor, a government entity, a non-government entity, and a non-profit entity). In an embodiment, the social network analysis data collection circuit may include a user interface 12216 configured to enable a user to configure a query for information about at least one entity, wherein the social network analysis data collection circuit may initiate at least one algorithm to respond to the query, the at least one algorithm searching and retrieving data from at least one social network based on the query. The system may include a condition classification circuit 12218, which may include a model 12220 configured to classify parameters 12206 of subsidy loans 12208 (e.g., municipal subsidy loans, government subsidy loans, learning-aid loans, property guarantee subsidy loans, or corporate subsidy loans) involved in subsidy loan transactions, e.g., based on social network information from a social network analysis data collection circuit. In an embodiment, the condition classification circuit may include: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, simulation systems, and the like. Subsidized loans may be guaranteed by: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farm, crop, municipal facility, warehouse, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, personal property, and the like. Parameters classified by the condition classification circuit may include a breach condition, a redemption-stopping condition, a condition indicating a breach of a contract, a financial risk condition, a behavioral risk condition, a contract performance condition, a policy risk condition, a financial health condition, a physical defect condition, a physical health condition, an entity risk condition, an entity health condition, and the like. The model may be trained using a training dataset 12210 of a plurality of results related to the subsidized loan. For example, the subsidy loan may be an assisted loan, and the condition classification circuit may classify at least one of: the students get the progress of the academic position, the students participate in non-profit activities, the students participate in public-benefit activities, etc. The system may include an intelligent contract circuit 12222 configured to automatically modify the terms and conditions of the subsidy loan 12212, e.g., based on the classification parameters. The system may include a configurable data collection and circuit 12224 configured to monitor an entity, for example, further including social network analysis circuit 12230, environmental condition circuit 12232, crowdsourcing circuit 12234, and algorithms for querying network domain 12236, wherein the configurable data collection and circuit may monitor an environment selected from, for example, a municipal environment, educational environment, corporate environment, securities trading environment, real-estate environment, commercial facility, warehouse facility, transportation environment, manufacturing environment, storage environment, residential or vehicular environment, and the like. The system may include an automated agent 12226 configured to process events related to the value, status, and ownership of a property and perform actions related to subsidy loan transactions involving the property, wherein the actions may be: subsidy loan transaction; underwriting and subsidy loan transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint subsidy loans; merging patch loans, and the like. The system may include an automatic subsidy management circuit 12238 configured to manage actions related to at least one subsidy, wherein the automatic subsidy management circuit is trained based on a training set of subsidy management activities. For example, the automated subsidy loan management circuit may train based on a plurality of interactions of the principal with a plurality of user interfaces involved in a plurality of subsidy loan transaction activities, wherein the plurality of subsidy loan transaction activities are selected from the following activities: offer subsidy loan transactions; underwriting and subsidy loan transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; and combining the subsidy loans. The system may include a blockchain service circuit 12240 configured to record a modified set of terms and conditions of the subsidized loan, for example, in a distributed ledger 12242. The system may include a market value data collection circuit 12228 configured to monitor and report market information related to the value of a publisher, subsidized loan, property, etc., wherein properties selected from the following properties may be reported: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contract rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property. The market value data collection circuit may also be configured to monitor pricing or financial data of counteracting assets in the public marketplace. A set of counteracting assets for valuation of an asset may be constructed using clustering circuitry based on asset attributes, where the attributes may be categories, age of the asset, status of the asset, history of the asset, storage of the asset, geographic location, etc. The smart contract circuit may be configured to manage a smart contract for a subsidy loan transaction, wherein the smart contract circuit may set terms and conditions of the subsidy loan, wherein the terms and conditions of the subsidy loan specified and managed by the smart contract circuit may include: the amount of liability principal, the balance of liability, fixed interest rate, variable interest rate, payment amount, payment plan, end-of-line repayment plan, warranty description of at least one subsidized loan, property substitutability description, principal, issuer, purchaser, insured, insurer, guaranty, personal guaranty, retention, deadline, contract, redemption prevention condition, default outcome, and the like.
Fig. 123 illustrates a method 12300 that includes collecting social networking information about an entity involved in subsidized loan transactions (12302). The method may include classifying parameters of the subsidy loan involved in the subsidy loan transaction based on the social networking information using a model trained based on a training dataset of a plurality of results related to the at least one subsidy loan (12304). The method may include automatically modifying the terms and conditions of the subsidy loan based on the classification parameters (12308). The method may include processing at least one event related to at least one of a value, a condition, or an ownership of the property and taking an action related to at least one subsidy loan transaction related to the property (12310). The method may include recording (12312) the modified set of terms and conditions of the subsidized loan in a distributed ledger. The method may include monitoring and reporting market information related to the value of the publisher, subsidized loan, property, etc.
Fig. 124 illustrates a system 12400 for automatically processing subsidized loans, which includes a crowdsourcing service circuit 12425 configured to gather information related to a set of entities 12402 involved in a set of subsidized loan transactions 12404. The set of entities may include, for example: a set of subsidized loans, a set of principals 12416, a set of subsidized, a set of guarantors, a set of subsidized principals, a set of mortgage, and so forth. A set of subsidized principals may include: municipalities, companies, contractors, government entities, non-profit entities, and the like. The loan may be a learning-aid loan, and the condition classification circuit classifies at least one of: the students can get the progress of the academic position, the students can participate in the non-profit activities, and the students can participate in the public-benefit activities. The crowdsourcing service circuitry may also be configured with a user interface 12420 through which a user can configure queries for information about a set of entities, and automatically configure crowdsourcing requests based on the queries. A set of subsidized loans may be guaranteed by a set of properties 12412: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farm, crop, municipal facility, warehouse, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, personal property, and the like. An example system may include: a condition classification circuit 12422, the condition classification circuit including a model 12424; and an artificial intelligence service circuit 12436 configured to classify a set of parameters 12406 of a set of subsidized loans 12410 involved in the transaction based on information from the crowd-sourced service circuit, wherein the model is trained using training data sets of results 12414 related to the subsidized loans. The set of subsidy loans may include at least one of municipal subsidy loans, government subsidy loans, learning-aid loans, property-guarantee subsidy loans, and corporate subsidy loans. The conditions classified by the condition classification circuit may include a contraband condition, a redemption-stopping condition, a condition indicating a contraband contract, a financial risk condition, a behavioral risk condition, a contract performance condition, a policy risk condition, a financial health condition, a physical defect condition, a physical health condition, an entity risk condition, an entity health condition, and the like. The artificial intelligence service circuit may include: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, simulation systems, and the like. An example system may include: smart contract circuitry 12426 for automatically modifying the terms and conditions 12418 of the subsidy loan based on a set of classification parameters from the condition classification circuitry. The smart contract service circuit may be used to manage smart contracts for subsidy loan transactions, set the terms and conditions of subsidy loans, and the like. In an embodiment, the set of terms and conditions of the liability transaction specified and managed by the smart contract service circuit may be selected from the group consisting of: the debt principal amount, the debt balance, the fixed interest rate, the variable interest rate, the payment amount, the payment plan, the end-of-line clearing plan, the warranty description of the subsidized loan, the property substitutability description, the principal, the issuer, the purchaser, the insured person, the insurer, the guaranty, the personal guaranty, the retention, the deadline, the contract, the redemption prevention condition, the default condition, and the outcome of the default. An example system may include: a configurable data collection and monitoring service circuit 12428 for monitoring entities such as a set of internet of things services, a set of environmental condition sensors, a set of social network analysis services, a set of algorithms for querying a network domain, and the like. The configurable data collection and monitoring service circuit may also be configured to monitor the following environments, for example: municipal environments, educational environments, corporate environments, securities trading environments, real estate environments, commercial facilities, warehouse facilities, transportation environments, manufacturing environments, storage environments, homes, vehicles, etc. An example system may include an automated agent circuit 12430 configured to process events related to at least one of a value, a condition, and an ownership of a property and to perform an action related to a subsidy loan transaction involving the property, for example, wherein the action may be: subsidy loan transaction; underwriting and subsidy loan transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint subsidy loans; merging patch loans, and the like. An example system may include: an automatic subsidy loan management circuit 12438 configured to manage actions related to the subsidy loan, wherein the automatic subsidy loan management circuit may be trained based on the subsidy loan management activity training set. The automated subsidy loan management circuit may train based on a set of interactions of the principal with a set of user interfaces involved in a set of subsidy loan transactions, such as: offer subsidy loan transactions; underwriting and subsidy loan transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint subsidy loans; merging patch loans, and the like. An example system may include: a blockchain service circuit 12440 configured to record a modified set of terms and conditions for a set of subsidized loans in a distributed ledger. An example system may include: market value data collection service circuitry 12432 configured to monitor and report market information 12434 related to the value of at least one of a principal, a set of subsidized loans, and a set of properties, wherein the report may be on a set of properties, such as: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contract rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property. The market value data collection service circuit may be further configured to monitor pricing or financial data for items in at least one public market that are similar to the asset. In an embodiment, a set of similar items for valuating an asset, such as asset class, age of asset, asset status, asset history, asset storage, geographic location of the asset, etc., may be constructed using a similarity clustering algorithm 12442 based on the following attributes of the asset.
Fig. 125 shows a method 12500 for automatically processing a subsidy loan, the method comprising: collecting information relating to a set of entities involved in a set of subsidy loan transactions (12502); classifying (12504) a set of parameters of a set of subsidized loans involved in the transaction based on the artificial intelligence service, a model, and information from the crowd-sourced service, wherein the model is trained based on a training dataset of results related to the subsidized loans; and modifying the terms and conditions of the subsidy loan based on the set of classification parameters (12508). The set of entities may include, for example: a set of subsidized loans, a set of parties, a set of subsidized, a set of insurers, a set of subsidized parties, and a set of mortgage (12510). A set of subsidized principals may include: municipalities, companies, contractors, government entities, non-government entities and non-profit entities (12512). The set of subsidy loans may include municipal subsidy loans, government subsidy loans, learning-aid loans, property-guarantee subsidy loans, and corporate subsidy loans (12514). The loan may be an assisted loan, wherein the condition classification system classifies at least one of: the student takes the progress of the academic, the student takes part in non-profit activities, and the student takes part in public welfare activities (12518).
Fig. 126 illustrates a system that includes an asset identification service circuit 12612 configured to interpret an asset 12624 corresponding to a financial entity 12622 for custody of the asset (e.g., identifying an asset that a bank may custody of), wherein an identity management service circuit 12614 may be configured to authenticate an identifier 12628 (e.g., including credentials 12630) corresponding to an executable action entity 12626 (e.g., owner, beneficiary, agent, delegate, trustee, etc.) that has authority to take an action with respect to the asset. For example, a group of financial entities may have rights with respect to taking actions with respect to an asset. The blockchain service circuitry 12616 may be configured to store a plurality of asset control features 12632 in a blockchain structure 12618, where the blockchain structure may include a distributed ledger configuration 12620. For example, transaction events may be stored in a distributed ledger in a blockchain structure, where financial entities and executable action entities may be accessed in a distributed manner through the blockchain structure to share and distribute asset events. The financial management circuitry 12610 may be configured to communicate the interpreted assets and authenticated identifiers to the blockchain service circuitry for storage in the blockchain structure as asset control features, wherein the asset control features are recorded in the distributed ledger configuration as asset events 12634 (e.g., transfer ownership, owner death, owner disability, owner bankruptcy, redemption prevention, setting up a retention right, use of the asset as a mortgage, designate a beneficiary, loan on the asset as a mortgage, provide notification about the asset, check the asset, evaluate the asset, report the asset for tax purposes, assign the asset ownership, dispose of the asset, sell the asset, purchase the asset, designate the ownership status, etc.). The data collection circuit 12602 may be configured to monitor the interpretation of the plurality of assets, the verification of the plurality of identifiers, and the recording of asset events, wherein the data collection circuit may be communicatively coupled with an internet of things system, a camera system, a networked monitoring system, an internet monitoring system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system. The smart contract circuitry 12604 may be configured to manage custody of assets, wherein asset events related to a plurality of assets may be managed by the smart contract circuitry based on terms and conditions 12608 implemented in the smart contract configuration 12606 and based on data collected by the data collection service circuitry. In an embodiment, the asset identification service circuit, the identity management service circuit, the blockchain service circuit, and the financial management circuit may include corresponding Application Programming Interface (API) components configured to facilitate communication between the system circuits, for example, wherein the corresponding API components of the circuits further include a user interface configured to interact with a system user.
Fig. 127 illustrates a method that includes interpreting assets corresponding to a financial entity for custody (12702) of a plurality of assets, for example, where the interpreting of the assets may include identifying a plurality of assets for which the financial entity is responsible for custody. The method may include authenticating an identifier (e.g., including credentials) corresponding to an executable action entity (e.g., owner, beneficiary, agent, trustee, and custodian) that is entitled to take action with respect to the plurality of assets, for example, wherein authenticating the identifier includes verifying the identifier corresponding to the executable action entity that is entitled to take action with respect to the asset (12704). The method may include storing a plurality of asset control features (12708) in a blockchain structure (e.g., including a distributed ledger configuration) (e.g., the blockchain structure may be provided in connection with a blockchain marketplace, utilize a blockchain-based automated transaction application, the blockchain structure may be a distributed blockchain structure across a plurality of asset nodes, etc.). The method may include communicating the interpreted asset and the authenticated identifier for storage in the blockchain structure as an asset control feature, wherein the asset control feature may be recorded in a distributed ledger configuration as an asset event (12710). The method may include monitoring an interpretation of the asset, authentication of the identifier, and recording (12712) of an asset event, for example, where the asset event may include: transfer ownership, owner death, owner disability, owner bankruptcy, redemption, setting up a deposit, using a property as a mortgage, designating a beneficiary, loaning a property as a mortgage, providing notification about a property, checking a property, evaluating a property, reporting a property for tax purposes, assigning ownership of a property, disposing of a property, selling a property, purchasing a property, and designating ownership status. In embodiments, monitoring may be performed by an internet of things system, a camera system, a networked monitoring system, an internet monitoring system, a mobile device system, a wearable device system, a user interface system, an interactive crowdsourcing system, or the like. The method may include managing custody of assets, wherein asset events related to a plurality of assets may be based on terms and conditions implemented in the smart contract configuration and on data collected by the data collection service circuit (12714). The method may include sharing and distributing asset events (12718) with a plurality of executable action entities. The method may include storing asset transaction data in a blockchain structure based on interactions between executable action entities (12720). The asset may include a virtual asset tag, where interpreting the asset includes identifying the virtual asset tag (e.g., storage of asset control features may include storing virtual asset tag data, e.g., where the virtual asset tag data is location data, tracking data, etc.). For example, an identifier corresponding to a financial entity or an actionable entity may be stored as virtual asset tag data.
Fig. 128 illustrates a system 12800 that includes a lending agreement storage circuit 12802 configured to store lending agreement data 12804 including lending agreement 12814, wherein the lending agreement may include lending condition data 12816. In an embodiment, the loan condition data may include at least one loan agreement term and condition data 12818 associated with a redemption-stopping condition 12822 of the asset 12820 that provides a mortgage condition 12824 associated with the mortgage asset 12826, such as a repayment obligation 12828 for warranting the loan agreement. The system may include a data collection service circuit 12806 configured to monitor the lending condition data and detect an default condition 12808 based on changes in the lending condition data. Further, the data collection service circuitry may include an internet of things system, a camera system, a networking monitoring system, an internet monitoring system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system. The system may include a smart contract service circuit 12810 configured to interpret the default condition 12812 and transmit a default condition indication 12830 when the data collection service circuit detects the default condition to initiate a redemption-stopping program 12832 based on the mortgage condition. For example, a redemption-stopping program may configure and initiate a listing of mortgage assets on a public auction website; configuring and transmitting a transport instruction set for the mortgage asset; configuring an instruction set for the drone to transport mortgage assets; configuring an instruction set for the robotic device to transport mortgage assets; initiating a process for automatically replacing a set of replacement mortgages; initiating a mortgage tracking process; initiating a mortgage valuation process; a message is sent to the borrower to initiate negotiations about redemption prevention, etc. An indication of the default condition may be communicated to the smart lock and smart container to lock the mortgage asset. The negotiations may be managed by a robotic process automation system trained based on a training set of redemption-stopping negotiations, and may involve modification of interest rates, payment terms, lending agreement mortgages, and the like. In an embodiment, each of the lending agreement storage circuit, the data collection service circuit, and the smart contract service circuit may further include a corresponding Application Programming Interface (API) component configured to facilitate communication between the system circuits, wherein the corresponding API component of the circuits may include a user interface configured to interact with multiple users of the system.
Fig. 129 shows a method 12900 of facilitating redemption-suppressing mortgage comprising storing debit-credit agreement data comprising a debit-credit agreement, wherein the debit-credit agreement may comprise debit-credit condition data, for example wherein the debit-credit condition data comprises terms and condition data of the debit-credit agreement, the terms and condition data of the debit-credit agreement being associated with an asset redemption-suppressing condition that provides a mortgage condition associated with a mortgage asset for warranting a repayment obligation of at least one debit-credit agreement (12902). The method may include monitoring the lending condition data and detecting an default condition based on a change in the lending condition data (12904). The method may include interpreting a default condition (12908) and communicating an indication of the default condition, the indication of the default condition initiating a redemption-stopping program based on the mortgage condition (12910). For example, a redemption-stopping program may configure and initiate a listing of mortgage assets on a public auction website; configuring and transmitting a transport instruction set for the mortgage asset; configuring an instruction set for the drone to transport mortgage assets; configuring an instruction set for the robotic device to transport mortgage assets; initiating a process for automatically replacing a set of replacement mortgages; initiating a mortgage tracking process; initiating a mortgage valuation process; a message is sent to the borrower initiating a negotiation regarding redemption (12914). An indication of the default condition may be communicated to the smart lock and smart container to lock the mortgage asset (12912). The negotiations may be managed (12918) by a robotic process automation system trained based on a training set of redemption-stopping negotiations, and may involve modification of interest rates, payment terms, loan agreement mortgages, and the like. In an embodiment, the communication may be provided (12920) by a corresponding Application Programming Interface (API), wherein the corresponding API may include a user interface configured to interact with a plurality of users.
Artificial intelligence embodiments
Referring to fig. 4-31, in embodiments of the invention (including embodiments involving artificial intelligence 3448, adaptive intelligence system 3304, robotic process automation 3422, expert systems, self-organization, machine learning, model training, etc.), use of neural networks may be benefited, such as training for pattern recognition, for prediction, for optimization based on a set of desired results, for classifying or identifying one or more parameters, feature characteristics or phenomena, for supporting autonomous control, and other objective neural networks. References throughout this disclosure to artificial intelligence, expert systems, models, adaptive intelligence, and/or neural networks should be understood to alternatively include the use of various different types of neural networks, machine learning systems, artificial intelligence systems, etc. as may be permitted by the particular embodiment, such as feed forward neural networks, radial basis function neural networks, self-organizing neural networks (e.g., kohonen self-organizing neural networks), recurrent neural networks, modular neural networks, artificial neural networks, physical neural networks, multi-layer neural networks, convolutional neural networks, mixtures of neural networks with other expert systems (e.g., hybrid fuzzy logic-neural network systems), self-encoding neural networks, probabilistic neural networks, time-lag neural networks, convolutional neural networks, regulatory feedback neural networks, radial basis function neural networks, recurrent neural networks, hopfield neural networks, boltzmann machine neural networks, self-organizing map (SOM) neural networks, learning Vector Quantization (LVQ) neural networks, full recurrent neural networks, simple recurrent neural networks, echo state neural networks, long-term short-term memory neural networks, two-way neural networks, layered neural networks, random neural networks, genetic scale RNN neural networks, machine neural networks committee, associative neural networks, physical neural networks, transient training neural networks, spike neural networks, new cognitive neural networks, dynamic neural networks, cascade neural networks, combined generating neural modes, automatic memory, time-based, depth-based, gate-controlled neural networks, fuzzy neural networks, and GCU (GCU) memory units, gate-based neural networks A variant automatic encoder neural network, a denoising automatic encoder neural network, a sparse automatic encoder neural network, a Markov chain neural network, a limited Boltzmann machine neural network, a deep belief neural network, a deep convolution neural network, a deconvolution neural network, a deep convolution inverse graph neural network, a generation countermeasure neural network, a liquid machine neural network, an extreme learning machine neural network, an echo state neural network, a deep residual neural network, a support vector machine neural network, a neural graph machine neural network, and/or a holographic associative memory neural network, or a mixture or combination of the foregoing neural networks, or a combination with other expert systems, such as a rule-based system, a model-based system (including a system based on a physical model, a statistical model, a flow-based model, a biological model, a bionic model, or the like).
The aforementioned neural network may have various nodes or neurons that may perform various functions upon input, such as input received from sensors or other data sources (including other nodes). The functions may relate to weights, features, feature vectors, etc. Neurons may include perceptron, neurons mimicking biological functions (e.g., human touch, vision, taste, hearing, and smell), and the like. Continuous neurons, e.g., with S-shaped activation, can be used in the context of various forms of neural networks, e.g., cases involving back propagation.
In many embodiments, the expert system or neural network may be trained, for example by a human operator or supervisor, or based on a dataset, model, or the like. Training may include presenting to a neural network one or more training data sets representing values, such as sensor data, event data, parameter data, and other types of data (including many of the types described in this disclosure), as well as one or more outcome metrics, such as the outcome of a process, the outcome of a calculation, the outcome of an event, the outcome of an activity, and so forth. Training may include optimization training, such as training a neural network to optimize one or more systems based on one or more optimization methods, such as bayesian methods, parametric bayesian classifier methods, k-nearest neighbor classifier methods, iterative methods, interpolation methods, pareto optimization methods, algorithmic methods, and the like. Feedback may be provided during the course of the change and selection, for example using a genetic algorithm that evolves one or more solutions based on feedback through a series of rounds.
In an embodiment, multiple neural networks may be deployed in a cloud platform that receives data streams and other inputs collected in one or more transaction environments (e.g., collected by a mobile data collector) and sent to the cloud platform over one or more networks (including using network coding to provide efficient transmission). In a cloud platform, multiple different types (including modular, architecture adaptive, hybrid, etc.) of neural networks can be used to undertake prediction, classification, control functions, and provide other outputs related to the expert systems disclosed herein, by optionally using massively parallel computing capabilities. Different neural networks may be configured to compete with each other (optionally including the use of evolutionary algorithms, genetic algorithms, etc.), such that an appropriate type of neural network with appropriate input sets, weights, node types, functions, etc. may be selected, e.g., by an expert system, for a particular task involved in a given context, workflow, environmental process, system, etc.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use a feed forward neural network that moves information in one direction through a series of neurons or nodes to an output, such as from a data input (e.g., a data source related to at least one resource or a parameter related to a transaction environment) or any of the data sources mentioned in this disclosure. The data may be moved from the input node to the output node, optionally through one or more hidden nodes, without looping. In an embodiment, the feed forward neural network may be constructed with various types of elements (e.g., binary McCulloch-Pitts neurons, the simplest of which is a sensor).
In embodiments, the methods and systems described herein that relate to expert systems or ad hoc capabilities may use a encapsulated neural network, for example, for predictive, classification, or control functions with respect to transaction environments, such as those related to one or more of the machines and automation systems described herein.
In embodiments, the methods and systems described herein that relate to expert systems or organizational capabilities may use Radial Basis Function (RBF) neural networks, which may be preferable in some cases involving interpolation in multidimensional space (e.g., where interpolation helps to optimize multidimensional functions, such as for optimizing the data market described herein, optimizing the efficiency or output of a power generation system, factory systems, etc., or other cases involving multiple dimensions.
In embodiments, the methods and systems described herein that relate to expert systems or ad hoc capabilities may use Radial Basis Function (RBF) neural networks, such as those employing distance criteria (e.g., gaussian functions) relative to the center. In a multi-layer sensor, radial basis functions may be applied as alternatives to hidden layers, such as an S-shaped hidden layer transform. The RBF network may have two layers, for example, with inputs mapped onto each RBF in the hidden layer. In an embodiment, the output layer may comprise a linear combination of hidden layer values, which represents, for example, an average prediction output. The output layer values may provide the same or similar output as the output of the regression model in the statistics. In the classification problem, the output layer may be an sigmoid function of a linear combination of hidden layer values, representing a posterior probability. Performance in both cases is typically improved by shrinkage techniques (e.g., ridge regression in classical statistics). This corresponds to a priori beliefs for small parameter values (and thus smooth output functions) in the bayesian framework. RBF networks can avoid local minima because the only parameter that is adjusted during learning is the linear mapping from the hidden layer to the output layer. Linearity ensures that the error surface is quadratic and therefore has a single minimum. In the regression problem, this can be found in a matrix operation. In classification problems, the fixed nonlinearities introduced by the sigmoid output function can be handled using iterative re-weighted least squares functions, or the like.
RBF networks may use kernel methods such as Support Vector Machines (SVMs) and gaussian processes (where RBF is a kernel function). The input data may be projected using a nonlinear kernel function into a space where the learning problem may be solved using a linear model.
In an embodiment, the RBF neural network may include an input layer, a hidden layer, and a summing layer. In the input layer, one neuron appears in the input layer for each prediction variable. In the case of a classification variable, N-1 neurons are used, where N is the number of classes. In an embodiment, the input neuron may normalize the range of values by subtracting the median and dividing by the quartile range. The input neurons may then feed back a value to each neuron in the hidden layer. A variable number of neurons (determined by the training process) may be used in the hidden layer. Each neuron may consist of a radial basis function centered on a point having as many dimensions as a number of predicted variables. The expansion (e.g., radius) of the RBF function may be different for each dimension. The center and extension may be determined by training. When presenting a vector of input values from the input layer, the hidden neurons can calculate the Euclidean distance of the test case from the center point of the neuron, and then apply the RBF kernel function to that distance, e.g., using an extension value. The resulting value may then be passed to a summation layer. In the summation layer, the values from neurons in the hidden layer may be multiplied by weights associated with neurons, and may be added to the weighted values of other neurons. This sum becomes the output. For classification problems, one output (with separate weight sets and summing units) is generated for each target class. The value output of a class is the probability that the evaluated situation has that class. In training of the RBF, various parameters may be determined, such as the number of neurons in the hidden layer, the center coordinates of each hidden layer function, the expansion of each function in each dimension, and the weights applied to the output when the weights are passed to the summing layer. Training may be used by clustering algorithms (e.g., k-means clustering), by evolutionary methods, and the like.
In an embodiment, the recurrent neural network may have a time-varying real value (not just 0 or 1) activation (output). Each connection may have a real-valued weight that may be modified. Some nodes are called marker nodes, some output nodes and other hidden nodes. For supervised learning in discrete time settings, the training sequence of real valued input vectors may become the activation sequence of input nodes, one input vector at a time. At each time step, each non-input unit may calculate its current activation as a non-linear function of the weighted sum of the activations of all units it receives the connection. The system may explicitly activate (independent of the input signal) certain output units at certain time steps.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use ad hoc neural networks, such as Kohonen ad hoc neural networks, for example, for visualization of data views, such as low-dimensional views as high-dimensional data. The ad hoc neural network may apply competition learning to a set of input data, such as from one or more sensors or other data inputs from or associated with a transaction environment, including any machines or components related to the transaction environment. In an embodiment, the ad hoc neural network may be used to identify structures in data, such as unlabeled data, such as data sensed from a series of data sources or sensors in a transaction environment, where the data sources are unknown (e.g., an event may come from any one of a series of unknown sources). The ad hoc neural network may organize structures or patterns in the data so that it may be identified, analyzed, and tagged, for example, to identify market behavioral structures as corresponding to other events and signals.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use recurrent neural networks, which may allow bi-directional flow of data, such as where connected units (e.g., neurons or nodes) form a directed loop. Such networks may be used to model or present dynamic time behavior, such as that involved in dynamic systems such as the various automated systems, machines and devices described in this disclosure, such as automated agents that interact with markets for collecting data, testing spot market transactions, executing transactions, etc., where dynamic system behavior involves complex interactions that a user may wish to understand, predict, control, and/or optimize. For example, recurrent neural networks may be used to predict market states, such as market states involving dynamic processes or actions that train or implement state changes of resources of a trading environment market, such as in the trading environment market. In embodiments, the recurrent neural network may use internal memory to process various types of input sequences described herein, such as from other nodes and/or from sensors or other data inputs provided by or related to the transaction environment. In embodiments, recurrent neural networks may also be used for pattern recognition, for example, to identify machines, components, agents, or other items based on behavioral signatures, profiles, a set of feature vectors (e.g., in an audio file or image), and so forth. In a non-limiting example, the recurrent neural network may identify transitions in the operating mode of the market or machine by learning to classify transitions from a training data set that includes data streams from one or more data sources applied to or about sensors of one or more resources.
In embodiments, the methods and systems described herein that relate to expert systems or ad hoc capabilities may use a modular neural network, which may include a series of independent neural networks (e.g., the various types of neural networks described herein) tuned by intermediaries. Each individual neural network in the modular neural network may work with a separate input to complete the sub-tasks that make up the task to be performed by the entire modular network. For example, the modular neural network may include a recurrent neural network for pattern recognition, e.g., to identify which type of machine or system is being sensed by one or more sensors provided as input channels to the modular network and the RBF neural network for optimizing the understood machine or system behavior. The intermediary may accept inputs from each individual neural network, process them, and create outputs for the modular neural network, such as appropriate control parameters, condition predictions, and the like.
Combinations between any two, three, or larger combinations of the various neural network types described herein are encompassed in the present invention. This may include a combination in which the expert system uses one neural network for identifying patterns (e.g., patterns indicative of problems or fault conditions) and uses a different neural network for self-organizing activities or workflows based on the identified patterns (e.g., providing output of management system autonomous control in response to the identified conditions or patterns). This may also include a combination in which the expert system uses one neural network for classifying the item (e.g., identifying a machine, component, or mode of operation) and a different neural network for predicting a condition of the item (e.g., fault condition, operating condition, expected condition, maintenance condition, etc.). The modular neural network may also include a situation in which the expert system uses one neural network for determining a situation or context (e.g., a situation of a machine, process, workflow, market, storage system, network, data collector, etc.) and a different neural network for self-organizing a process (e.g., a data storage process, network encoding process, network selection process, data market process, power generation process, manufacturing process, refining process, mining process, boring process, or other process described herein) related to the situation or context.
In embodiments, the methods and systems described herein that relate to expert systems or ad hoc capabilities may use a physical neural network in which neural behavior is performed or simulated using one or more hardware elements. In an embodiment, one or more hardware neurons may be used to stream voltage values, current values, etc. representing sensor data, e.g., by one or more machines providing energy or consuming energy for one or more transactions, calculating information from analog sensor inputs representing energy consumption, energy production, etc. One or more hardware nodes may be used to stream output data generated by the activity of the neural network. The hardware nodes may include one or more chips, microprocessors, integrated circuits, programmable logic controllers, application specific integrated circuits, field programmable gate arrays, etc., which may be used to optimize a machine that is generating or consuming energy, or to optimize another parameter of some portion of any type of neural network described herein. The hardware nodes may include hardware for accelerating computations (e.g., special purpose processors for performing basic or more complex computations on input data to provide output, special purpose processors for filtering or compressing data, special purpose processors for decompressing data, special purpose processors for compressing specific files or data types (e.g., for processing image data, video streams, acoustic signals, thermal images, heatmaps, etc.). The physical neural network may be embodied in a data collector, including a data collector that may be reconfigured by switching or routing inputs in a varying configuration, such as by providing different neural network configurations within the data collector for processing different types of inputs (with switching and configuration optionally under the control of an expert system, which may include a software-based neural network located on or remote from the data collector). A physical or at least partially physical neural network may include physical hardware nodes located in a storage system, such as input/output functions for storing data in a machine, a data storage system, a distributed ledger, a mobile device, a server, a cloud resource, or a transaction processing environment, for example, to accelerate one or more storage elements providing data to or retrieving data from the neural network. A physical or at least partially physical neural network may include physical hardware nodes located in the network, for example, for transmitting data within, to, or from an industrial environment, for example, for accelerating input/output functions of one or more network nodes in the network, for accelerating relay functions, and so forth. In embodiments of a physical neural network, an electrically tunable resistive material may be used to simulate the function of a nerve synapse. In an embodiment, the physical hardware simulates neurons and the software simulates neural networks between neurons. In an embodiment, the neural network supplements a conventional algorithm computer. They are versatile and can be trained to perform appropriate functions, such as classification functions, optimization functions, pattern recognition functions, control functions, selection functions, evaluation functions, etc., without requiring any instructions.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use a multi-layer feed forward neural network, such as complex pattern classification for one or more projects, phenomena, patterns, conditions, and the like. In an embodiment, the multi-layer feedforward neural network may be trained by optimization techniques such as genetic algorithms, e.g., exploring large-scale and complex option spaces to find optimal or near-optimal global solutions. For example, a multi-layer feedforward neural network may be trained using one or more genetic algorithms to classify complex phenomena, such as identifying complex modes of operation of the machine, such as modes involving complex interactions between machines (including interference effects, resonance effects, etc.), modes involving nonlinear phenomena, modes involving critical faults, such as where multiple faults occur simultaneously, making it difficult to analyze root causes, etc. In an embodiment, the multi-layer feed forward neural network may be used to categorize results from market monitoring, including, for example, monitoring systems operating within the market, such as automated agents, and monitoring resources implementing the market, such as computing, networking, energy sources, data storage, energy storage, and other resources.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use feed forward, back propagation multi-layer perceptive (MLP) neural networks, for example, for processing one or more remote sensing applications, for example, for taking input from sensors distributed in various transaction environments. In embodiments, the MLP neural network may be used for transactional and resource environment classification, such as lending markets, spot markets, forward markets, energy markets, renewable Energy Credit (REC) markets, networking markets, advertising markets, spectrum markets, ticketing markets, rewards markets, computing markets, and other environments mentioned in this disclosure, as well as physical resources and environments in which they are generated, such as energy resources (including renewable energy environments, mining environments, exploration environments, drilling environments, etc.), as well as for geologic formation (including subsurface and above-ground feature) classification, material (including fluid, mineral, metal, etc.) classification, and other issues. This may include fuzzy classification.
In embodiments, the methods and systems described herein that relate to expert systems or ad hoc capabilities may adapt a neural network using a structure, where the structure of the neural network is adapted based on, for example, rules, sensed conditions, environmental parameters, and the like. For example, if the neural network does not converge to a solution, such as classifying an item or predicting arrival, then when operating on a set of inputs after a certain amount of training, the neural network, such as from a feedforward to recurrent neural network, may be modified, such as by switching the data paths between some subset of nodes from unidirectional to bidirectional data paths. The structural adaptation may occur under the control of an expert system, for example, to trigger the adaptation in the event of a trigger, rule or event occurrence, for example to identify the occurrence of a threshold (e.g., no convergence to a solution for a given time) or to identify a phenomenon requiring a different or additional structure (e.g., to identify that the system is changing dynamically or in a nonlinear manner). In one non-limiting example, the expert system may switch from a simple neural network structure (e.g., feed forward neural network) to a more complex neural network structure (e.g., recurrent neural network, convolutional neural network, etc.) upon receiving an indication that the continuously variable transmission in the system being analyzed is being used to drive a generator, turbine, etc.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use an automatic encoder, automatic connector, or Diabolo neural network, which may be similar to a multi-layer perceptron (MLP) neural network, e.g., there may be an input layer, an output layer, and one or more hidden layers connecting them. However, the output layer in an auto-encoder may have the same number of cells as the input layer, where the purpose of the MLP neural network is to reconstruct its own input (rather than just issuing the target value).
Thus, the automatic encoder may operate as an unsupervised learning model. For example, an automatic encoder may be used for unsupervised learning efficient encoding, e.g., for dimension reduction, for learning a generation model of data, etc. In embodiments, the automatically encoded neural network may be used to self-learn an effective network encoding for transmitting analog sensor data from a machine or digital data from one or more data sources from the machine over one or more networks. In an embodiment, an automatically encoded neural network may be used to self-learn an efficient storage method for storing a data stream.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use Probabilistic Neural Networks (PNNs), which in embodiments may include a multi-layer (e.g., four-layer) feed-forward neural network, where each layer may include an input layer, a hidden layer, a mode/summing layer, and an output layer. In one embodiment of the PNN algorithm, the parent Probability Distribution Function (PDF) for each class may approximate, for example, a Parzen window function and/or a non-parametric function. Then, using the PDF of each class, the class probability of the new input is estimated, and bayesian rules may be employed, for example, to assign it to the class with the highest posterior probability. PNN may comprise a bayesian network and may use statistical algorithms or analysis techniques, such as nuclear Fisher discriminant analysis techniques. PNN may be used for classification and pattern recognition in any of the wide range of embodiments disclosed herein. In one non-limiting example, a probabilistic neural network may be used to predict a fault condition of an engine based on data input collection of sensors and instrumentation of the engine.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use Time Delay Neural Networks (TDNNs) that may include feed forward structures for identifying sequence data that is independent of the characteristics of the sequence position. In an embodiment, to account for time offsets in the data, a time delay is added to one or more inputs, or between one or more nodes, so that multiple data points (from different points in time) are analyzed together. The time-lapse neural network may form part of a larger pattern recognition system using, for example, a perceptron network. In an embodiment, TDNN may be trained using supervised learning, e.g., using back propagation or training connection weights under feedback. In an embodiment, TDNN may be used to process sensor data from different streams, such as velocity data streams, acceleration data streams, temperature data streams, pressure data streams, etc., where time delays are used to match the data streams in time, such as to help understand patterns related to various streams (e.g., changes in price patterns in spot or long-term markets).
In embodiments, the methods and systems described herein involving expert systems or self-organizing capabilities may use convolutional neural networks (in some cases referred to as CNN, convNet, translationally invariant neural networks, or spatially invariant neural networks) in which units are connected in a pattern similar to that of the visual cortex of the human brain. Neurons may respond to stimuli in a restricted spatial region (known as the receptive field). The experience fields may partially overlap such that they collectively cover the entire (e.g., visual) field. The node response may be calculated mathematically, for example, by convolution operations, using, for example, a multi-layer perceptron with minimal preprocessing. Convolutional neural networks may be used for identification in image and video streams, for example, to identify machine types in large environments using a camera system provided on a mobile data collector on, for example, a drone or mobile robot. In an embodiment, convolutional neural networks may be used to provide recommendations based on data inputs, including sensor inputs and other contextual information, such as recommended routes for mobile data collectors. In an embodiment, a convolutional neural network may be used to process input, such as natural language processing for instructions provided by one or more participants involved in a workflow in an environment. In an embodiment, a convolutional neural network may be deployed with a large number of neurons (e.g., 100,000, 500,000, or more), multiple (e.g., 4, 5, 6, or more) layers, and many (e.g., millions) of parameters. Convolutional neural networks may use one or more convolutional networks.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use a management feedback network, for example, for identifying incidents (e.g., new types of behavior that were not previously understood in a transactional environment).
In embodiments, the methods and systems described herein that relate to expert systems or ad hoc capabilities may use learning vector quantization neural networks (LVQ). Prototype representations of classes may be parameterized along with appropriate distance measures in a distance-based classification scheme.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use an Echo State Network (ESN), which may include a recurrent neural network with a sparsely connected random hidden layer. The weights of the output neurons may change (e.g., the weights may be trained based on feedback). In an embodiment, the ESN may be used to process a time series pattern, e.g., in an example, identify a pattern of events associated with the marketplace, e.g., a pattern of price changes in response to incentives.
In embodiments, the methods and systems described herein involving expert systems or self-organizing capabilities may use bi-directional recurrent neural networks (BRNNs), for example, using a finite sequence of values (e.g., voltage values from sensors) to predict or tag each element of a sequence based on past and future contexts of the element. This may be accomplished by adding the outputs of two RNNs, e.g., one processing sequence from left to right and the other processing sequence from right to left. The combined output is a prediction of the target signal, such as a signal provided by a teacher or supervisor. The bidirectional RNN may be combined with a long and short term memory RNN.
In embodiments, the methods and systems described herein that relate to expert systems or ad hoc capabilities may use a hierarchical RNN that connects elements in various ways to decompose hierarchical behavior, e.g., into useful subroutines. In an embodiment, the hierarchical RNN may be used to manage one or more hierarchical templates for data collection in a transaction environment.
In embodiments, the methods and systems described herein that relate to expert systems or ad hoc capabilities may use a random neural network that may introduce random variants into the network. This random variation can be seen as a form of statistical sampling, such as monte carlo sampling.
In embodiments, the methods and systems described herein that relate to expert systems or self-organizing capabilities may use genetic scale recurrent neural networks. In such an embodiment, RNNs (typically Long Short Term Memories (LSTM)) are used to break down sequences into several scales, where each scale forms a main length between two consecutive points. The first order scale consists of one normal RL' JN, the second order scale consists of all points separated by two exponentials, etc. The N-th order RNN connects the first node and the last node. The outputs from all the different scales may be considered as member committees and the associated scores may be used for genetic use for the next iteration.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use a machine committee (CoM) comprising a collection of different neural networks that collectively "vote" on a given example. Since neural networks may suffer from local minimisation, starting from the same architecture and training, but using randomly different initial weights often gives different results. CoM tends to stabilize the results.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use associative neural networks (ASNNs), such as extensions to the machine committee that involve combining multiple feed forward neural networks and k nearest neighbor technologies. In the case of analysis of KNN, correlation between integrated responses can be used as a measure of distance. This corrects for deviations in neural network integration. The associative neural network may have a memory coincident with the training set. If new data becomes available, the network immediately increases its predictive capability and provides data estimation (self-learning) without retraining. Another important feature of ASNN is that it is feasible to interpret neural network results by analyzing correlations between data instances in model space.
In embodiments, the methods and systems described herein involving expert systems or self-organizing capabilities may use an Instantaneous Trained Neural Network (ITNN) in which weights of the hidden layer and the output layer are mapped directly from training vector data.
In embodiments, the methods and systems described herein that relate to expert systems or ad hoc capabilities may use spiking neural networks, which may explicitly take into account the time of input. The network inputs and outputs may be represented as a series of spikes (e.g., a pulse function or a more complex shape). The sns may process information in the time domain (e.g., time-varying signals, such as signals related to dynamic behavior of a market or transaction environment). They are typically implemented as a recursive network.
In embodiments, the methods and systems described herein involving expert systems or self-organizing capabilities may use dynamic neural networks that handle nonlinear multivariate behavior and include learning of aging behavior, such as transient phenomena and time delay effects. Transients may include changing behavior of market variables such as price, available quantity, available opponents, etc.
In an embodiment, the cascading correlations may be used as an architecture and supervised learning algorithm, supplementing the adjustment of weights in a fixed topology network. The cascading correlations may start with a minimal network and then automatically train and add new hidden units one by one, creating a multi-layer structure. Once a new hidden unit is added to the network, its input side weights can be frozen. The unit then becomes a permanent feature detector in the network, which can be used to generate output or to create other more complex feature detectors. The cascade correlation architecture can learn quickly, determine its own size and topology, and preserve its built structure even if the training set changes and does not need to be back-propagated.
In embodiments, the methods and systems described herein that relate to expert systems or self-organizing capabilities may use a neural fuzzy network, such as a fuzzy inference system that relates to in the body of an artificial neural network. Depending on the type, several layers may simulate the processes involved in fuzzy reasoning, such as fuzzification, reasoning, aggregation, and defuzzification. The fuzzy system is embedded into the general structure of the neural network as a benefit of using the available training methods to find the parameters of the fuzzy system.
In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use a combined pattern to generate a network (CPPN), such as a variation of an Associated Neural Network (ANN), that is different from the set of activation functions and the manner in which they are applied. While a typical ANN generally contains only sigmoid functions (and sometimes Gaussian functions), CPPN may include both types of functions, as well as many others. Furthermore, CPPNs can be applied to the entire space of possible inputs so that they can represent a complete image. Because they are a combination of functions, CPPN encodes an image at virtually infinite resolution, and can sample a particular display regardless of whether the resolution is optimal or not.
This type of network can add new patterns without retraining. In embodiments, the methods and systems described herein involving expert systems or ad hoc capabilities may use a one-time associative memory network that assigns each new pattern to an orthogonal plane using a hierarchical array of adjacent connections, for example, by creating a specific memory structure.
In embodiments, the methods and systems described herein that relate to expert systems or self-organizing capabilities may use Hierarchical Time Memory (HTM) neural networks, such as those that relate to the structural and algorithmic characteristics of new cortex. The HTM may use a biomimetic model based on memory prediction theory. HTM can be used to discover and infer high-level causes of observed input patterns and sequences.
In embodiments, the methods and systems described herein involving expert systems or self-organizing capabilities may use Holographic Associative Memory (HAM) neural networks, which may include simulated, correlation-based, correlated stimulus response systems. The information may be mapped onto a complex phase orientation. The memory is effective for associative memory tasks, generalization, and pattern recognition with variable attention.
In embodiments, various embodiments involving network coding may be used to code transmission data between network nodes in a neural network, such as nodes located in one or more data collectors or machines in a transaction environment.
In embodiments, provided herein is a system for adaptive intelligence and robotic process automation capability for trading, finance, and marketing support. An example system may include: blockchain service circuitry configured to interpret a plurality of access control features corresponding to a plurality of parties associated with the loan; a data collection circuit configured to interpret entity information corresponding to a plurality of entities related to a loan transaction corresponding to a loan; an intelligent contract circuit configured to specify loan terms and conditions associated with a loan; loan management circuitry configured to: interpreting a loan-related event in response to the entity information, the plurality of access control features, and the loan terms and conditions, wherein the loan-related event is associated with the loan; executing a loan-related activity in response to the entity information, the plurality of access control features, and the loan terms and conditions, wherein the loan-related activity is associated with the loan; and wherein each of the blockchain service circuitry, the data collection circuitry, the smart contract circuitry, and the loan management circuitry further includes a corresponding Application Programming Interface (API) component configured to facilitate communication between the system circuitry.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example system may include: wherein each of the plurality of entities comprises at least one entity selected from the group consisting of: a borrower, insurer, loan related equipment, loan related goods, loan related systems, loan related fixtures, buildings, storage facilities, and mortgages.
An example system may include at least one of a plurality of entities including a mortgage, and wherein the data collection circuit is further configured to interpret a condition of the mortgage, wherein the mortgage includes at least one of: vehicles, ships, airplanes, buildings, houses, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, merchandise, vouchers, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, tools, machinery, and personal property.
An example system may include: wherein the data collection circuit further comprises at least one of the following: the system comprises an internet of things system, a camera system, a networking monitoring system, an internet monitoring system, a mobile device system, a wearable device system, a user interface system and an interactive crowdsourcing system.
An example system may include: wherein each of the loan-related events comprises at least one event selected from the group consisting of: requesting a loan; providing a loan; accepting a loan; providing loan underwriting information; providing a credit report; delay payment; requesting deferred payment; identifying a mortgage; verifying ownership of the mortgage; verifying ownership of the vouchers; checking the assets; altering a condition of at least one of the plurality of entities; altering the value of the entity; altering the value of the mortgage; changing the value of the guarantee; altering a professional reputation of at least one of the parties; altering a borrower's financial rating; providing insurance for loans; providing insurance evidence for the property; providing loan qualifications; identifying a loan guarantee; executing loan underwriting; paying a loan; loan violations; collect loan; clearing loans; altering the specified loan terms and conditions; specifying initial loan terms and conditions; and redemption-limited property.
An example system may include: wherein each of the loan terms and conditions includes at least one member of the following: loan principal amount, loan balance, fixed interest rate, variable interest rate description, payment amount, payment plan, end-of-line clearing plan, mortgage description, mortgage replacement description, description of at least one of the parties, insured description, insurer description, guaranty description, personal guaranty, lien, redemption prevention status, default outcome, contract relating to any of the foregoing, and deadline of any of the foregoing.
An example system may include: wherein at least one of the principals comprises at least one of the following principals: primary borrowers, secondary borrowers, borrowing groups, corporate borrowers, government borrowers, banking borrowers, warranty borrowers, bond purchasers, non-warranty borrowers, warranty providers, borrowers, debtors, underwriters, inspectors, valuators, auditors, valuation professionals, government officers, government authorities, and accountants.
An example system may include: wherein each of the loan-related activities includes at least one activity selected from the group consisting of: finding at least one of the parties interested in participating in the loan transaction; applying for loans; carrying out loan; legal contracts are made for loans; monitoring the performance of the loan; paying a loan; adjust or modify the loan; clearing loans; monitoring a loan mortgage; establishing a loan silver group; redemption-stopping loan; and closing the loan transaction, wherein the loan comprises at least one type selected from the following loan types: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance arrangements, payday loans, refund expectations, learning-aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, and subsidy loans.
An example system may include: wherein the smart contract circuitry is further configured to perform a contract-related loan action in response to the entity information.
An example system may include: wherein the contract-related loan actions include at least one action selected from the group consisting of: providing a loan; accepting a loan; carrying out loan; setting the interest rate of loans; the payment requirement of the delayed loan; modifying the interest rate of the loan; verifying ownership of the loan mortgage; recording the change of ownership; evaluating the value of the mortgage; initiating a check of the mortgage; collect loan; clearing loans; modifying the terms and conditions of the loan; providing a notification to one of the parties; providing necessary notification to the borrower; and redemption-limited property.
An example system may further include: an automated agency circuit configured to interpret the loan-related event and perform a loan-related action in response to the loan-related event, wherein the loan-related event comprises an event related to at least one of: the value of the loan, the condition of the loan mortgage, or the ownership of the loan mortgage, and wherein the loan-related actions include at least one of: modifying the terms and conditions of the loan; providing a notification to one of the parties; providing necessary notification to the borrower; and redemption-limited property.
An example system may include: wherein the corresponding API component of the circuit further comprises a user interface configured to interact with a plurality of users of the system.
An example system may include: wherein each of the plurality of users comprises one of one or more entities of the plurality of principals, and wherein at least one of the plurality of users comprises one of the intended principals or the intended entity.
An example system may include: wherein one of the user interfaces is for responding to a plurality of access control features.
In an embodiment, a method for providing access control to loan terms and conditions on a distributed ledger is provided herein. An example method may include: interpreting a plurality of access control features corresponding to a plurality of parties associated with a loan in a distributed ledger; interpreting entity information corresponding to a plurality of entities related to a loan transaction corresponding to a loan; designating loan terms and conditions related to the loan; the loan-related event is interpreted in response to the entity information, the plurality of access control features, and the loan terms and conditions, wherein the loan-related event is associated with the loan.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example method may include: wherein at least one of the plurality of entities comprises a mortgage, the method further comprising interpreting a condition of the mortgage.
An example method may further include: a contract-related loan action is performed in response to the entity information.
An example method may include: wherein a contract-related loan action is performed, the contract-related loan action comprising at least one action selected from the group consisting of: providing a loan; accepting a loan; carrying out loan; setting the interest rate of loans; the payment requirement of the delayed loan; modifying the interest rate of the loan; verifying ownership of the loan mortgage; recording the change of ownership; evaluating the value of the mortgage; initiating a check of the mortgage; collect loan; clearing loans; modifying the terms and conditions of the loan; providing a notification to one of the parties; providing necessary notification to the borrower; and redemption-limited property.
An example method may further include: interpreting the loan-related event and performing a loan-related action in response to the loan-related event, wherein the loan-related event comprises an event related to at least one of: the value of the loan, the condition of the loan mortgage, or the ownership of the loan mortgage, and wherein performing the loan-related action includes at least one of: modifying the terms and conditions of the loan; providing a notification to one of the parties; providing necessary notification to the borrower; and redemption-limited property.
An example method may further include: providing a user interface to a user, wherein the user comprises at least one of: one of the plurality of principals, one of the plurality of entities, the intended principal, or the intended entity, wherein providing the user interface is further responsive to the plurality of access control features.
An example method may further include: an intelligent loan contract for the loan is created and recorded as blockchain data.
In embodiments, provided herein is a system for adaptive intelligence and robotic process automation capability for trading, finance, and marketing support. An example platform or system may include: blockchain service circuitry configured to interpret a plurality of access control features corresponding to a plurality of parties associated with the guaranty loan; a data collection circuit configured to: receiving first mortgage data from at least one sensor associated with a mortgage for providing a guarantee for a loan; receiving second mortgage data about a mortgage environment from the internet of things circuit; associating mortgage data with a unique identifier associated with the mortgage, wherein the blockchain service circuitry is further configured to store the unique identifier and associated mortgage data as blockchain data. The example platform or system may further comprise: an intelligent contract circuit configured to create an intelligent lending contract; a secure access control circuit configured to receive access control instructions from a borrower securing a loan through the access control interface, wherein the secure access control circuit is further configured to provide instructions to the blockchain service circuit regarding accessing blockchain data associated with the mortgage, wherein each of the blockchain service circuit, the data collection circuit, the secure access control circuit, and the internet of things circuit further includes a corresponding Application Programming Interface (API) component configured to facilitate communication between the system circuits.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example system may include: wherein the sensor associated with the mortgage is placed in a position selected from the list consisting of: mortgage, mortgage container, and mortgage package.
An example system may include: wherein the data collection circuit is further configured to interpret a condition of the mortgage in response to a subset of the received mortgage data.
An example system may include: wherein the mortgage is selected from the list of items consisting of: vehicles, ships, airplanes, buildings, houses, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, merchandise, vouchers, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, tools, machinery, and personal property.
An example system may include: wherein the guaranty loan is at least one of: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance arrangements, payday loans, refund expectations, learning-aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, and subsidy loans.
An example system may include: wherein the mortgage environment is selected from the list of environments consisting of: real estate environments, commercial facilities, warehouse facilities, transportation environments, manufacturing environments, storage environments, homes, and vehicles.
An example system may include: wherein the at least one sensor is selected from: image capturing device, thermometer, pressure gauge, humidity sensor, speed sensor, acceleration sensor, rotation sensor, torque sensor, scale sensor, chemical sensor, magnetic field sensor, electric field sensor, and position sensor.
An example system may further include: reporting circuitry configured to report mortgage events related to mortgage aspects selected from a list of aspects consisting of: the value of the mortgage, the condition of the mortgage, and the ownership of the mortgage.
An example system may further include: an automated agent circuit configured to interpret a mortgage event and perform a loan-related action in response to the mortgage event.
An example system may further include: wherein the loan-related action is selected from actions consisting of: providing a loan; accepting a loan; carrying out loan; setting the interest rate of loans; postponing payment requirements; modifying the interest rate of the loan; verifying ownership of the mortgage; recording the change of ownership; evaluating the value of the mortgage; initiating a check of the mortgage; collect loan; clearing loans; setting the terms and conditions of loans; providing a notification to be provided to the borrower; redemption-stopping property limited by the loan; and modifying the terms and conditions of the loan.
An example system may further include: a mortgage sorting circuit configured to identify a set of countermortgages, wherein each member of the set of countermortgages shares a common attribute with the mortgage.
An example system may include: wherein the common attribute is selected from a list of attributes consisting of: mortgage category, mortgage age, mortgage status, mortgage history, mortgage ownership, mortgage administrator, mortgage warranty, mortgage owner status, mortgage lien, mortgage storage conditions, mortgage geographic location, and mortgage jurisdiction.
An example system may further include: market value data collection circuitry configured to monitor and report market information related to the value of at least one of the mortgage or the set of offset mortgages.
An example system may include: wherein the market value data collection circuit is further configured to monitor pricing or financial data of at least one of the mortgage or the set of counteracting mortgages in at least one public market.
An example system may include: wherein the market value data collection circuit is further configured to report one of pricing or financial data monitoring.
An example system may include: wherein the smart contract circuitry is further configured to modify terms or conditions of the loan based on the mortgage-counteracting market information related to the value of the mortgage.
An example system may further include: an intelligent contract service circuit is configured to manage intelligent contracts for guaranteeing loans.
An example system may include: wherein the smart contract service circuit is further configured to set terms and conditions associated with mortgages offering a loan guarantee.
An example system may include: wherein the terms and conditions are selected from the list consisting of: mortgage description, mortgage replaceability description, mortgage condition description, mortgage retention related description, mortgage warranty related description, and mortgage environment related description.
In an embodiment, provided herein is an intelligent contract method for mortgages for managing loans. An example method may include: monitoring the status of the loan and the status of the mortgage of the loan; automatically initiating at least one of a replacement, a removal, or an addition of one or more of the mortgages of the loan based on at least one of a status of the loan or a status of the mortgages of the loan; and interpreting a plurality of access control features corresponding to at least one principal associated with the loan and recording at least one substitution, removal, or addition in a distributed ledger of the loan.
Certain additional aspects of the example methods are described below, any one or more of which may be present in certain embodiments.
An example method may include: wherein the status of the loan is determined based on the status of at least one entity associated with the loan and the performance status of the condition of the loan.
An example method may include: a value is determined using a set of valuation models of the mortgage based on at least one of the status of the loan or the mortgage of the loan.
An example method may include: wherein at least one replacement, removal, or addition is initiated to maintain the value of the mortgage within a predetermined range.
An example method may include: interpreting outcome data relating to a transaction of one of the mortgages or the countering mortgage; and iteratively refining the valuation model in response to the outcome data.
An example method may include: market information related to the value of the mortgage is monitored and reported.
An example method may include: at least one of pricing data or financial data for counteracting mortgages in at least one public marketplace is monitored.
An example method may include: the terms and conditions of the smart contract that govern at least one of the terms and conditions of the loan, the loan-related event, or the loan-related activity are specified.
An example apparatus may include: a data collection circuit configured to monitor at least one of a status of the loan or a status of a mortgage of the loan; a smart contract circuit configured to interpret a smart contract of the loan and adjust at least one term or condition of the smart contract of the loan in response to at least one of a status of the loan or a status of a mortgage of the loan; and blockchain service circuitry configured to interpret a plurality of access control features corresponding to a plurality of parties associated with the loan and record at least one term or condition of the intelligent contract of the adjusted loan in the distributed ledger of the loan. The data collection circuit may monitor a status of a mortgage of the loan, the apparatus further comprising a valuation circuit configured to determine a value of the mortgage based on the status of the mortgage of the loan using the valuation model, and wherein the smart contract circuit is further configured to adjust at least one term or condition of the smart contract of the loan in response to the value of the mortgage.
In an embodiment, provided herein is a crowd sourcing system for verifying the condition of mortgages of a loan. An example platform, system, or apparatus may include: crowd-sourced request circuitry configured to configure at least one parameter of a crowd-sourced request, the parameter relating to obtaining information regarding a condition of a mortgage of a loan; a crowdsourcing issue circuit configured to issue crowdsourcing requests to a set of information providers; a crowdsourcing communication circuit configured to collect and process at least one response from the set of information providers and provide a reward to at least one of the set of information providers in response to a successful information provision event. A successful information provision event may be receipt of information identified as related to a mortgage that is the subject of a crowdsourcing request, and wherein the information relates to a condition of the mortgage. Information about identifying a mortgage feature (e.g., serial number or model number) may not be a successful information provision event.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example system may include: wherein the crowdsourcing circuitry is further operable to issue a reward description to at least a portion of the set of information providers in response to a successful information provision event. The description of the consideration may include the kind or type of consideration, the value of the consideration, the amount of the consideration, information about the effective use date of the consideration, information about the use of the consideration, and the like.
An example system may include: wherein the crowdsourcing communication circuit further comprises or communicates with a smart contract circuit configured to: managing rewards by determining successful information provision events in response to at least one parameter configured for the crowd-sourced request; and automatically assigning a reward to at least one of the set of information providers in response to a successful information provision event.
An example system may include: wherein the loan comprises at least one of the following loan types: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance arrangements, payday loans, refund expectations, learning-aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, and subsidy loans.
An example system may include: wherein the mortgage comprises at least one of the following: vehicles, ships, airplanes, buildings, houses, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, commodities, securities, currencies, value certificates, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.
An example system may include: wherein the condition of the mortgage is determined based on one of the following attributes: the quality of the mortgage, the condition of the mortgage, the status of the property of the mortgage, the possession of the mortgage, and the lien status of the mortgage.
An example system may include: wherein when the mortgage is an item, the condition of the mortgage is determined based on one of the following attributes: the new or used status of the item, the type of item, the category of the item, the description of the item, the product feature set of the item, the model of the item, the brand of the item, the manufacturer of the item, the status of the item, the environment of the item, the condition of the item, the value of the item, the storage location of the item, the geographic location of the item, the age of the item, the maintenance history of the item, the use history of the item, the accident history of the item, the malfunction history of the item, the ownership history of the item, the type of price of the item, the type of value of the item, the assessment of the item, and the valuation of the item.
An example system may further include: a blockchain service circuit is configured to record identification information and at least one parameter of the crowdsourcing request, at least one response to the crowdsourcing request, and a reward description in a distributed ledger of the crowdsourcing request.
An example system may include: wherein the crowdsourcing request circuit is further configured to enable a workflow by which a human user inputs at least one parameter to establish the crowdsourcing request.
An example system may include: wherein the at least one parameter includes a type of information requested, a reward, and a condition for receiving the reward.
An example system may include: wherein the consideration is selected from the following consideration: financial remuneration, vouchers, tickets, contractual rights, cryptocurrency amount, multiple remuneration points, currency amount, discounts on products or services, and access rights.
An example system may further include: an intelligent contract circuit configured to process at least one response and, in response, automatically take an action related to the loan.
An example system may include: wherein the action is at least one of a redemption-stopping action, a lien management action, a interest rate adjustment action, a default initiation action, a mortgage replacement, or a loan receipt.
An example system may further include: the robotic process automation circuit is configured to configure the crowd-sourced request based on at least one attribute of the loan based on training on a training dataset that includes interactions of a human user with at least one of the crowd-sourced request circuit or the crowd-sourced communication circuit.
An example system may include: wherein at least one attribute of the loan is available from an intelligent contract circuit that manages the loan.
An example system may include: wherein the training data set further comprises results from the plurality of crowd-sourced requests.
An example system may include: wherein the robotic process automation circuit is further configured to determine a reward.
An example system may include: wherein the robotic process automation circuit is further configured to determine at least one domain to which the crowdsourcing request is published by the crowdsourcing publication circuit.
In an embodiment, provided herein is a crowdsourcing method for verifying the condition of a mortgage of a loan. An example method may include: configuring at least one parameter of the crowd-sourced request, the parameter being related to obtaining information about a condition of a mortgage of a loan; issuing a crowd-sourced request to a group of information providers; collecting and processing at least one response to the crowd-sourced request; and providing a reward in response to a successful information provision event.
Certain additional aspects of the example methods are described below, any one or more of which may be present in certain embodiments.
An example method may further include: a reward description is issued to at least a portion of the set of information providers in response to a successful information provision event.
An example method may further include: wherein a consideration is automatically assigned to at least one of the set of information providers in response to a successful information provision event.
An example method may further include: the identification information and at least one parameter of the crowdsourcing request, at least one response to the crowdsourcing request, and a description of the reward are recorded in a distributed ledger of the crowdsourcing request.
An example method may further include: the graphical user interface is configured to enable a workflow by which a human user inputs at least one parameter to establish a crowd-sourced request.
An example method may further include: actions related to the loan are automatically taken in response to a successful information supply event.
An example method may further include: training a robotic process automation circuit based on a training dataset comprising a plurality of results corresponding to a plurality of crowd-sourced requests; and operating the robotic process automation circuit to iteratively refine the crowd-sourced requests.
An example method may further include: at least one attribute of the loan is provided to the robotic process automation circuit to configure the crowd-sourced request.
An example method may further include: configuring the crowd-sourced request includes determining a reward.
An example method may further include: at least one attribute of the loan is input to the robotic process automation circuit to determine at least one domain to which to issue the crowd-sourced request.
An example apparatus may include: crowd-sourced request circuitry configured to provide an interface to enable configuration of at least one parameter of a crowd-sourced request, the parameter being associated with obtaining information regarding a condition of a mortgage of a loan; a crowdsourcing circuitry to issue crowdsourcing requests to a group of information providers in response to the crowdsourcing requests; and crowd-sourced communication circuitry configured to provide an interface to collect at least one response to the crowd-sourced request from a member of the group of information providers and to provide a reward to at least one of the group of information providers in response to a successful information provision event.
The apparatus may further include a smart contract circuit configured to: managing rewards by determining successful information provision events in response to at least one parameter configured for the crowd-sourced request; and automatically assigning a reward to at least one of the set of information providers in response to a successful information provision event.
In an embodiment, provided herein is a crowdsourcing system for verifying the condition of a sponsor of a loan. An example platform, system, or apparatus may include: a crowd-sourced request circuit configured to configure at least one parameter of the crowd-sourced request, the parameter being related to acquiring information regarding a condition of a sponsor of the loan; a crowdsourcing issue circuit configured to issue crowdsourcing requests to a set of information providers; a crowdsourcing communication circuit configured to collect and process at least one response from the set of information providers and provide a reward to at least one of the set of information providers in response to a successful information provision event.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example system may include: wherein the condition is a financial condition of an entity, wherein the entity is a sponsor of the loan. An example system may include: wherein the financial condition is determined based at least in part on information about the entity selected from the group consisting of: public valuation of an entity, valuation of an entity as indicated by a public record, valuation of an entity, bankruptcy of an entity, redemption status of an entity, contract breach status of an entity, violation status of an entity, crime status of an entity, export regulation status of an entity, banned status of an entity, tariff status of an entity, tax status of an entity, credit reporting of an entity, credit rating of an entity, website rating of an entity, multiple customer reviews of a product of an entity, social network rating of an entity, multiple vouchers of an entity, multiple referrals of an entity, multiple proofs of an entity, multiple behaviors of an entity, location of an entity, geographic location of an entity, and jurisdiction of an entity.
The crowdsourcing communication circuit may further comprise a smart contract circuit configured to: managing rewards by determining successful information provision events in response to at least one parameter configured for the crowd-sourced request; and automatically assigning a reward to at least one of the set of information providers in response to a successful information provision event.
An example system may include: wherein the new loan comprises at least one of the following loan types: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance arrangements, payday loans, refund expectations, learning-aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, and subsidy loans.
An example system may include: wherein the crowd-sourced request circuit is further configured to configure at least one other parameter of the crowd-sourced request to obtain information regarding a condition of a mortgage of the loan.
An example system may include: wherein the mortgage comprises at least one of the following: vehicles, ships, airplanes, buildings, houses, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, commodities, securities, currencies, value certificates, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.
An example system may include: wherein when the mortgage is an item, the condition of the mortgage is determined based on one of the following attributes: the new or used status of the item, the type of item, the category of the item, the description of the item, the product feature set of the item, the model of the item, the brand of the item, the manufacturer of the item, the status of the item, the environment of the item, the condition of the item, the value of the item, the storage location of the item, the geographic location of the item, the age of the item, the maintenance history of the item, the use history of the item, the accident history of the item, the malfunction history of the item, the ownership history of the item, the type of price of the item, the type of value of the item, the assessment of the item, and the valuation of the item.
An example system may further include: a blockchain service circuit is configured to record identification information and at least one parameter of the crowdsourcing request, at least one response to the crowdsourcing request, and a reward description in a distributed ledger of the crowdsourcing request.
An example system may include: wherein the crowdsourcing request circuit is further configured to enable a workflow by which a human user inputs at least one parameter to establish the crowdsourcing request.
An example system may include: wherein the at least one parameter includes a type of information requested, a reward, and a condition for receiving the reward.
An example system may include: wherein the consideration is selected from the following consideration: financial remuneration, vouchers, tickets, contractual rights, cryptocurrency amount, multiple remuneration points, currency amount, discounts on products or services, and access rights.
An example system may further include: an intelligent contract circuit configured to process at least one response and, in response, automatically take an action related to the loan.
An example system may include: an intelligent contract circuit configured to process at least one response and, in response, automatically take an action related to the loan, wherein the action is at least one of a redemption-stopping action, a lien management action, an interest rate adjustment action, a default initiation action, a mortgage replacement, and a loan receipt.
An example system may further include: the robotic process automation circuit is configured to configure the crowd-sourced request based on at least one attribute of the loan based on training on a training dataset that includes interactions of a human user with at least one of the crowd-sourced request circuit or the crowd-sourced communication circuit.
An example system may include: wherein at least one attribute of the loan is available from an intelligent contract circuit that manages the loan.
An example system may include: wherein the training data set further comprises results from the plurality of crowd-sourced requests.
An example system may include: wherein the robotic process automation circuit is further configured to determine a reward.
An example system may include: wherein the robotic process automation circuit is further configured to determine at least one domain to which the crowdsourcing request is published by the crowdsourcing publication circuit.
In an embodiment, provided herein is a crowdsourcing method for verifying the condition of a mortgage of a loan. An example method may include: configuring at least one parameter of the crowdsourcing request, the parameter being related to acquiring information about the condition of the sponsor of the loan; issuing a crowd-sourced request to a group of information providers; collecting and processing at least one response to the crowd-sourced request; and providing a reward to at least one of the set of information providers in response to a successful information provision event.
Certain additional aspects of the example methods are described below, any one or more of which may be present in certain embodiments. An example method may further include: a reward description is issued to at least a portion of the set of information providers in response to a successful information provision event.
An example method may further include: wherein a consideration is automatically assigned to at least one of the set of information providers in response to a successful information provision event.
An example method may further include: the identification information and at least one parameter of the crowdsourcing request, at least one response to the crowdsourcing request, and a description of the reward are recorded in a distributed ledger of the crowdsourcing request.
An example method may further include: the graphical user interface is configured to enable a workflow by which a human user inputs at least one parameter to establish a crowd-sourced request.
An example method may further include: actions related to the loan are automatically taken in response to a successful information supply event.
An example method may further include: training a robotic process automation circuit based on a training dataset comprising a plurality of results corresponding to a plurality of crowd-sourced requests; and operating the robotic process automation circuit to iteratively refine the crowd-sourced requests.
An example method may further include: at least one attribute of the loan is provided to the robotic process automation circuit to configure the crowd-sourced request.
An example method may further include: configuring the crowd-sourced request includes determining a reward.
An example method may further include: at least one attribute of the loan is input to the robotic process automation circuit to determine at least one domain to which to issue the crowd-sourced request.
In embodiments, provided herein is a method for adaptive intelligence and robotic process automation capability for trading, finance, and marketing support. An example method may include: collecting information about entities involved in a set of warranty loans and a training set of interactions between entities of a set of warranty loan transactions; classifying entities involved in the set of warranty loans based at least in part on the interactive training set; and manage the warranty loan based at least in part on the set of warranty loan interactions.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example method may further include: wherein at least one managing the warranty loan comprises managing: managing at least one of a set of warranty assets; identifying a warranty loan in a set of candidate loans; compiling a warranty offer; compiling a warranty plan; compiling content conveying an insurance offer; arranging a warranty offer; communicating an insurance offer; negotiating a warranty offer modification; compiling a warranty agreement; executing a warranty protocol; modifying a set of mortgages of the warranty loan; processing a set of accounts receivable transfers; processing a warranty application workflow; managing and checking; managing an assessment of a set of assets to be secured; setting interest rate; postponing payment requirements; setting a payment plan; or to achieve a warranty agreement.
An example method may further include: wherein at least one of the entities is a principal of at least one of the set of warranty loan transactions.
An example method may include: wherein the principal is at least one of: primary borrowers, secondary borrowers, borrowing groups, corporate borrowers, government borrowers, banking borrowers, guaranteed borrowers, bond purchasers, non-guaranteed borrowers, guaranteed suppliers, borrowers, debtors, underwriters, inspectors, valuators, auditors, valuation professionals, government officers, or accountants.
An example method may further include: determining that the warranty loan negotiation is completed; and modifying the intelligent warranty loan contract based on the negotiated result.
An example method may further include: determining at least one of a result associated with negotiating a warranty loan and a negotiation event; and recording at least one of a result associated with the warranty loan and a negotiation event in a distributed ledger associated with the warranty loan.
In embodiments, a system for adaptive intelligence and robotic flow automation capability for trading, finance, and marketing support is provided herein.
An example apparatus or system may include: a data collection circuit configured to collect information about entities involved in a set of mortgage activities and a training set of interactions between entities for the set of warranty loan transactions. The apparatus or system may further comprise: an artificial intelligence circuit configured to categorize the set of mortgage entities, wherein the artificial intelligence circuit trains based on a trained set of interactions; and the robotic process automation circuit is configured to proxy a mortgage, wherein the robotic process automation circuit trains based on at least one of the set of mortgage activities and a set of interactions of training.
Certain additional aspects of the example systems or apparatus are described below, any one or more of which may be present in certain embodiments. An example apparatus or system may include: wherein at least one of the set of mortgage activities and the set of mortgage transactions includes activities in the group of: a marketing campaign; identifying a group of prospective borrowers; identifying property; identifying a mortgage; borrowing qualifications; searching ownership; ownership verification; evaluating property; checking property; property valuation; revenue verification; demographic analysis of borrowers; identifying a capital provider; determining available interest rate; determining available payment terms and conditions; analyzing the existing mortgage loan; comparing and analyzing the existing mortgage and new mortgage terms; finishing the application workflow; filling an application field; preparing a mortgage protocol; completing a mortgage protocol plan; negotiating mortgage terms and conditions with the capital provider; negotiating mortgage terms and conditions with the borrower; transferring ownership; setting the retention right or achieving the mortgage agreement.
An example apparatus or system may include: wherein the data collection circuit comprises at least one system selected from the following: an internet of things system that monitors the entity, a set of cameras that monitor the entity, a set of software services that extract information related to the entity from a public information site, a set of mobile devices that report information related to the entity, a set of wearable devices worn by a human entity, a set of user interfaces through which the entity provides information about the entity, and a set of crowdsourcing services for requesting and reporting information related to the entity.
An example apparatus or system may include: wherein the artificial intelligence circuit is further configured to use a model that processes attributes of entities involved in the set of mortgage activities; and wherein at least one attribute of the group comprising: the properties that are constrained by the mortgage, the asset that is acting as a mortgage, the identity of the principal, interest rate, payment balance, payment terms, payment plan, mortgage type, property type, financial status of the principal, payment status, property status, or property value.
An example apparatus or system may include: wherein the proxy mortgage includes at least one activity from the group of: managing at least one of the mortgage-constrained properties; identifying candidate mortgages from a set of borrower conditions; preparing a mortgage offer; preparing content conveying a mortgage offer; arranging a mortgage offer; communicating a mortgage offer; negotiating a modification to the mortgage offer; preparing a mortgage protocol; executing a mortgage protocol; modifying mortgages in a set of mortgage offers; processing the transfer of the retention right; processing an inspection workflow; managing and checking; managing an assessment of a set of assets to be mortgage constrained; setting interest rate; postponing payment requirements; setting up a payment plan or entering into a mortgage agreement.
An example apparatus or system may include: wherein at least one of the entities is a principal of at least one mortgage transaction in the set of mortgage transactions.
An example apparatus or system may include: wherein the principal comprises at least one of the following: primary borrowers, secondary borrowers, borrowing groups, corporate borrowers, government borrowers, banking borrowers, guaranteed borrowers, bond purchasers, non-guaranteed borrowers, guaranteed suppliers, borrowers, debtors, underwriters, inspectors, valuators, auditors, valuation professionals, government officers, and accountants.
An example apparatus or system may include: wherein the artificial intelligence circuit comprises at least one of the following: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, or simulation systems.
An example apparatus or system may further include: interface circuitry configured to receive interactions from at least one of the entities, and wherein the robotic process automation circuitry is further trained based on the interactions.
An example apparatus or system may further include: an intelligent contract circuit configured to determine that negotiation of a mortgage is complete; and modifying the intelligent warranty loan contract based on the negotiated result.
An example apparatus or system may further include: a distributed ledger circuit configured to determine at least one of a result associated with a negotiation of a mortgage and a negotiation event; and recording at least one of a result associated with the mortgage and a negotiation event in a distributed ledger associated with the mortgage.
In an embodiment, a method is provided herein for adaptive intelligence and robotic flow automation capability for trading, finance, and marketing support. An example method may include: collecting information about entities of a set of mortgages and an interactive training set between entities for a set of mortgage transactions; classifying entities involved in the set of mortgages based at least in part on the interactive training set; and at least one proxy mortgage in a set of interactions based at least in part on the set of mortgage activities and training.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example method may further include: classifying the entities involved in the set of mortgage activities is based on a model that processes attributes of the entities involved in the set of mortgage activities; and wherein the at least one attribute selected from the group consisting of: the properties that are constrained by the mortgage, the asset that is acting as a mortgage, the identity of the principal, interest rate, payment balance, payment terms, payment plan, mortgage type, property type, financial status of the principal, payment status, property status, or property value.
An example method may further include: at least one proxy mortgage includes an activity selected from the group consisting of: managing at least one of the mortgage-constrained properties; identifying candidate mortgages from a set of borrower conditions; preparing a mortgage offer; preparing content conveying a mortgage offer; arranging a mortgage offer; communicating a mortgage offer; negotiating a modification to the mortgage offer; preparing a mortgage protocol; executing a mortgage protocol; modifying mortgages in a set of mortgage offers; processing the transfer of the retention right; processing an inspection workflow; managing and checking; managing an assessment of a set of assets to be mortgage constrained; setting interest rate; postponing payment requirements; setting up a payment plan or entering into a mortgage agreement.
An example method may include: at least one of the entities is a party to at least one mortgage transaction in the set of mortgage transactions.
An example method may further include: the principal comprises at least one principal from the group: primary borrowers, secondary borrowers, borrowing groups, corporate borrowers, government borrowers, banking borrowers, guaranteed borrowers, bond purchasers, non-guaranteed borrowers, guaranteed suppliers, borrowers, debtors, underwriters, inspectors, valuators, auditors, valuation professionals, government officers, or accountants.
An example method may further include: determining that mortgage loan negotiation is completed; and modifying the intelligent warranty loan contract based on the negotiated result.
An example method may further include: determining at least one of a result associated with a negotiation of a mortgage and a negotiation event; and recording at least one of a result associated with the mortgage and a negotiation event in a distributed ledger associated with the mortgage.
In embodiments, a system for adaptive intelligence and robotic flow automation capability for trading, finance, and marketing support is provided herein.
An example system may include: a data collection circuit configured to collect information about entities involved in a set of liability transactions, a training set of outcome data related to the entities, and a training set of liability management activities. The system may further include a condition classification circuit configured to classify a condition of at least one of the entities, wherein the condition classification circuit includes a model and a set of artificial intelligence circuits, and wherein the training model is trained using result data associated with the entities; and an automatic liability management circuit configured to manage liability-related actions, wherein the automatic liability management circuit is trained based on the liability management activity training set.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example system may include: wherein the data collection circuit comprises at least one system from the group of: an internet of things device, a set of environmental condition sensors, a set of crowdsourcing services, a set of social network analysis services, or a set of network domain query algorithms.
An example system may include: wherein at least one of the liability transactions is selected from the group consisting of: automotive loans, inventory loans, capital equipment loans, performance guaranties, fixed property improvement loans, building loans, accounts receivable guaranty loans, invoice financing arrangements, insurance arrangements, payday loans, refund expectations, learning aid loans, silver group loans, ownership loans, housing loans, risk liability loans, intellectual property loans, contract liability loans, mobile funds loans, small business loans, agricultural loans, municipal bonds, or subsidy loans.
An example system may include: wherein the entities involved in the set of liability transactions include at least one of a set of parties and a set of assets.
An example system may include: wherein at least one asset in the group of assets comprises an asset in the group of: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farm, crop, municipal facility, warehouse, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment items, tools, machinery, or personal property.
An example system may further include: a set of sensors located on one of: at least one asset of the set of assets, a container for at least one asset of the set of assets, or a package for at least one asset of the set of assets, and at least one blockchain circuit configured to receive information from the data collection circuit and the set of sensors and store the information in the blockchain; and a secure access control interface circuit configured to provide the blockchain with access to a principal of liability transactions for at least one asset involved in the group of assets.
An example system may include: wherein at least one sensor of the set of sensors is selected from the group consisting of: image, temperature, pressure, humidity, speed, acceleration, rotation, torque, weight, chemicals, magnetic fields, electric fields or position sensors.
An example system may include: an automated agent circuit configured to process events related to at least one of value, status, or ownership of at least one asset in the set of assets and further configured to take a set of actions related to liability transactions related to the asset.
An example system may further include: wherein at least one action of the set of actions is selected from the group consisting of: providing a liability transaction; underwriting liability transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint liabilities or combined liabilities.
An example system may include: wherein at least one artificial intelligence circuit of the set of artificial intelligence circuits comprises at least one system of the group: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, or simulation systems.
An example system may further include: an interface circuit configured to receive interactions from at least one of the entities, and wherein the automated liability management circuit is further trained based on the interactions.
An example system may further include: wherein at least one of the trained liability management activities comprises an activity in the group of: providing a liability transaction; underwriting liability transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint liabilities or combined liabilities.
An example system may further include: market value data collection circuitry configured to monitor and report market information related to the value of at least one asset in a group of assets.
An example system may further include: wherein at least one asset of the group of assets is selected from the group consisting of: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farm, crop, municipal facility, warehouse, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment items, tools, machinery, or personal property.
An example system may further include: wherein the market value data collection circuit is further configured to monitor at least one of pricing and financial data for the item, the item being similar to at least one asset of the set of assets in the at least one public marketplace.
An example system may further include: wherein a set of similar items for evaluating at least one asset in the set of assets is constructed using a similarity clustering algorithm based on attributes of the assets.
An example system may further include: wherein at least one attribute of the attributes of the asset is selected from the group consisting of: asset class, age of asset, asset condition, asset history, asset storage, or asset geographic location.
An example system may further include: a smart contract circuit configured to manage smart contracts for liability transactions.
An example system may further include: a smart contract circuit configured to manage smart contracts for liability transactions.
An example system may further include: wherein at least one of the set of terms and conditions of the liability transaction is selected from the group consisting of: liability principal amount, liability balance, fixed interest rate, variable interest rate, payment amount, payment plan, end-of-line clearing plan, mortgage description, mortgage substitutability description, principal, insured, guarantor, personal guarantor, lien, duration, contract, redemption status, violation status, or outcome of the violation.
In an embodiment, a method is provided herein for adaptive intelligence and robotic flow automation capability for trading, finance, and marketing support. An example method may include: collecting information about entities involved in a set of liability transactions, training data sets of results related to the entities, and liability management activity training sets; classifying a condition of at least one of the entities based at least in part on a training dataset of results associated with the entity; and managing the liability-related actions based at least in part on the liability management activity training set.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example method may further include: the entities involved in the set of liability transactions include a set of parties and a set of assets.
An example method may further include: receiving information from a set of sensors located on at least one asset, wherein the set of sensors is to associate sensor information sensed by the set of sensors with a unique identifier for the at least one asset; and wherein a set of sensors is located on one of: at least one asset of the group of assets, a container for at least one asset of the group of assets, or a package for at least one asset of the group of assets; and storing the information in the blockchain, wherein access to the blockchain is provided via a secure access control interface for a principal of the liability transaction involving the at least one asset.
An example method may include: processing an event related to at least one of a value, a condition, or an ownership of at least one asset in the set of assets; and processing a set of actions related to liability transactions involving the asset.
An example method may include: an interaction is received from at least one of the entities.
An example method may further include: market information relating to the value of at least one asset in a group of assets is monitored and reported.
An example method may further include: monitoring also includes monitoring at least one pricing and financial data for the item, the item being similar to at least one asset in the set of assets in the at least one public marketplace.
An example method may further include: a set of similar items for evaluating at least one asset from the set of assets is constructed using a similarity clustering algorithm based on the asset attributes.
An example method may further include: managing smart contracts for liability transactions.
An example method may further include: a set of terms and conditions for an intelligent contract for a liability transaction is established.
In embodiments, a system for adaptive intelligence and robotic flow automation capability for trading, finance, and marketing support is provided herein.
An example method may include: crowd-sourced data collection circuitry configured to collect information about entities involved in a set of bond transactions and a training set of outcome data related to the entities. The system may also include a condition classification circuit configured to classify a condition of a group of publishers using information and models from the crowdsourcing data collection circuit, wherein the set of training models are trained using result data related to the group of publishers.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example system may include: at least one entity from the entities is selected from the group consisting of: a set of entities includes entities in a set of publishers, a set of bonds, a set of principals, and a set of assets.
An example system may include: wherein at least one of the group of publishers is selected from the group consisting of: municipal departments, companies, contractors, government entities, non-government entities, and non-profit entities.
An example system may include: wherein at least one bond in the set of bonds is selected from the group consisting of: municipal bonds, government bonds, national treasury bonds, asset security bonds, or corporate bonds.
An example system may include: wherein the condition classified by the condition classification circuit is selected from the group consisting of: the conditions include a default condition, a redemption-stopping condition, a condition indicating a contract violation, a financial risk condition, a behavioral risk condition, a policy risk condition, a financial health condition, a physical defect condition, a physical health condition, an entity risk condition, and an entity health condition.
An example system may include: wherein the crowdsourcing data collection circuitry is configured to enable a user interface by which a user can configure crowdsourcing requests for information related to the status of the set of publishers.
An example system may further include: a configurable data collection and monitoring circuit configured to monitor at least one of the set of publishers, wherein the configurable data collection and monitoring circuit comprises a system selected from the group consisting of: an internet of things device, a set of environmental condition sensors, a set of social network analysis services, or a set of network domain query algorithms.
An example system may include: wherein the configurable data collection and monitoring circuit is configured to monitor at least one environment from the group of: municipal environments, corporate environments, securities trading environments, real estate environments, commercial facilities, warehouse facilities, transportation environments, manufacturing environments, storage environments, residences, or vehicles.
An example system may include: wherein a set of bonds associated with the set of bond transactions are vouched for a set of assets.
An example system may include: wherein at least one asset in the group of assets comprises an asset in the group of: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farm, crop, municipal facility, warehouse, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment items, tools, machinery, or personal property.
An example system may include: an automated agent circuit configured to process events related to at least one of value, status, or ownership of at least one asset in the set of assets, and wherein the automated agent circuit is further configured to perform actions related to related liability transactions of the asset.
An example system may include: wherein the action is selected from the group consisting of: providing a liability transaction; underwriting liability transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint liabilities or combined liabilities.
An example system may include: wherein the condition classification circuit comprises a system of the group: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, and simulation systems.
An example system may further include: an automated bond management circuit for managing bond-related actions, wherein the automated bond management circuit trains on a bond management activity training set.
An example system may include: wherein the automated bond management circuit trains based on a set of principal interactions with a set of user interfaces involved in a set of bond transaction activities.
An example system may include: wherein at least one bond transaction in the set of bond transactions includes an activity in the group of: providing a liability transaction; underwriting liability transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint liabilities or combined liabilities.
An example system may further include: market value data collection circuitry configured to monitor and report market information related to the value of at least one of the issuer and the group of assets.
An example system may include: wherein the report is at least one asset from a group of assets from the group consisting of: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farm, crop, municipal facility, warehouse, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment items, tools, machinery, or personal property.
An example system may include: wherein the market value data collection circuit is configured to monitor pricing or financial data for items similar to at least one asset in the at least one public market.
An example system may include: wherein a set of similar items for evaluating the asset is constructed using a similarity clustering algorithm based on the attributes of the asset.
An example system may include: wherein at least one of the attributes is selected from the group consisting of: asset class, age of asset, asset condition, asset history, asset storage, or asset geographic location.
An example system may further include: a smart contract circuit configured to manage smart contracts for liability transactions.
An example system may include: wherein the smart contract circuitry is configured to determine terms and conditions of the bond.
An example system may include: wherein at least one term and condition of the set of terms and conditions of the liability transaction specified and managed by the set of smart contract circuits is selected from the group consisting of: the debt principal amount, the debt balance, the fixed interest rate, the variable interest rate, the payment amount, the payment plan, the end-of-line clearing plan, the bond's warranty asset description, the asset replacement description, the principal, the issuer, the purchaser, the insured person, the insurer, the guaranty, the personal guaranty, the lien, the duration, the contract, the redemption status, the default status, or the outcome of the default.
In an embodiment, a method is provided herein for adaptive intelligence and robotic flow automation capability for trading, finance, and marketing support. An example method may include: collecting information about entities of a training dataset relating to a set of bond transactions and results related to the entities of a set of bonds; the collected information and models are used to classify the status of a group of publishers, where the models are trained using a training dataset of results related to the group of publishers.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example method may further include: processing an event related to at least one of a value, a condition, or an ownership of at least one asset in the set of assets; and processing a set of actions related to liability transactions involving the asset.
An example method may further include: actions related to the bond are managed based at least in part on the bond management activity training set.
An example method may further include: market information relating to the value of at least one of the issuer and the set of assets is monitored and reported.
An example method may further include: managing smart contracts for bond transactions.
An example method may further include: the terms and conditions of the smart contract for the at least one bond are determined.
In an embodiment, a system for monitoring the condition of a bond issuer is provided herein. An example platform, system, or apparatus may include: social network data collection circuitry configured to collect information about at least one entity involved in at least one transaction including at least one bond; and a condition classification circuit configured to classify a condition of the at least one entity according to the model and based on information from the social network data collection circuit, wherein the training model is trained using a plurality of result data related to the at least one entity; and an automated bond management circuit configured to manage actions associated with the at least one bond in response to the classification status of the at least one entity.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example system may include: wherein at least one entity is selected from the following entities: bond issuers, bonds, parties, and assets.
An example system may include: wherein the at least one entity comprises a bond issuer of the bond issuer: municipal departments, companies, contractors, government entities, non-government entities, and non-profit entities.
An example system may include: wherein the at least one bond is selected from the following entities: municipal bonds, government bonds, national treasury bonds, asset vouchers and corporate bonds.
An example system may include: wherein the condition classified by the condition classification circuit includes at least one of the following conditions: the conditions include a default condition, a redemption-stopping condition, a condition indicating a contract violation, a financial risk condition, a behavioral risk condition, a policy risk condition, a financial health condition, a physical defect condition, a physical health condition, an entity risk condition, and an entity health condition.
An example system may include: wherein the social network data gathering circuit further comprises a social network input circuit configured to receive input from a user for configuring a query for information about at least one entity in response to the received input.
An example system may further include: a data collection circuit configured to monitor at least one of an internet of things device, an environmental condition sensor, a crowdsourcing request circuit, a crowdsourcing communication circuit, a crowdsourcing publishing circuit, and an algorithm for querying a network domain.
An example system may further include: wherein the condition classification circuit is further configured to classify the condition in response to information from the data collection circuit.
An example system may include: wherein the data collection circuit is further configured to monitor an environment in the group of: municipal environments, corporate environments, securities trading environments, real estate environments, commercial facilities, warehouse facilities, transportation environments, manufacturing environments, storage environments, residences, or vehicles.
An example system may further include: wherein the condition classification circuit is further configured to classify the condition in response to the monitored environment.
An example system may include: wherein at least one bond is vouched for by at least one asset.
An example system may include: wherein at least one asset is selected from the following: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farm, crop, municipal facility, warehouse, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment items, tools, machinery, or personal property.
An example system may further include: event processing circuitry configured to process events related to at least one of value, status, and ownership of the at least one asset and to take actions related to the at least one transaction in response to the events.
An example system may include: wherein the action is selected from the following actions: a bond transaction; underwriting bond transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint bonds and combined bonds.
An example system may include: wherein the condition classification circuit comprises a system selected from the following systems: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, and simulation systems.
An example system may further include: an automated bond management circuit configured to manage actions related to at least one bond, wherein the automated bond management circuit trains based on training data sets of a plurality of bond management activities.
An example system may include: wherein the automated bond management circuit trains based on a plurality of principal interactions with a plurality of user interfaces involved in a plurality of bond transaction activities.
An example system may include: wherein the plurality of bond transactions are selected from the group consisting of: providing a bond transaction; underwriting bond transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint liabilities or combined liabilities.
An example system may further include: market value data collection circuitry configured to monitor and report market information related to the value of at least one of the bond issuer, the at least one bond, and the asset associated with the at least one bond.
An example system may include: wherein the asset is selected from the following assets: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farm, crop, municipal facility, warehouse, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment items, tools, machinery, or personal property.
An example system may include: wherein the market value data collection circuit is further configured to monitor pricing or financial data for counteracting asset items in the at least one public market.
An example system may further include: a clustering circuit is included that is configured to construct a set of counteracting asset items for evaluating the asset based on the attributes of the asset using the clustering circuit.
An example system may include: wherein the attribute is selected from the following attributes: category, age of asset, status of asset, history of asset, asset storage, and geographic location.
An example system may further include: a smart contract circuit configured to manage smart contracts for at least one transaction.
An example system may include: wherein the smart contract circuitry is further configured to determine terms and conditions of the at least one bond.
An example system may include: wherein the terms and conditions are selected from the group consisting of: the debt principal amount, the debt balance, the fixed interest rate, the variable interest rate, the payment amount, the payment plan, the end-of-line repayment plan, the guarantee asset description of at least one bond, the asset replaceability description, the principal, the issuer, the purchaser, the insured, the insurer, the guarantor, the personal guaranty, the lien, the duration, the contract, the redemption stopping condition, the default condition, and the outcome of the violation. In an embodiment, a method is provided herein for monitoring conditions of a bond issuer. An example method may include: collecting social network information about at least one entity involved in at least one transaction including at least one bond; and classifying a condition of the at least one entity according to the model and based on the social network information, wherein the model is trained using a plurality of training result data sets associated with the at least one entity; and managing actions associated with the at least one bond in response to the classification status of the at least one entity.
Certain additional aspects of the example methods are described below, any one or more of which may be present in certain embodiments. An example method may further include: processing an event related to at least one of a value, a condition, or an ownership of at least one asset related to at least one bond, wherein the at least one asset is related to at least one bond; and taking an action associated with the at least one transaction in response to the event. An example method may further include: training an automated bond management circuit based on the plurality of training bond management active sets to manage actions related to the at least one bond, and wherein managing the actions includes operating the automated bond management circuit. An example method may further include: market information relating to the value of at least one of the bond issuer, the at least one bond, and the asset is monitored and reported.
In an embodiment, a system for monitoring the condition of a bond issuer is provided herein. An example platform, system, or apparatus may include: an internet of things data collection circuit configured to collect information about at least one entity involved in at least one transaction including at least one bond; and a condition classification circuit configured to classify a condition of the at least one entity according to the model and based on information from the internet of things data collection circuit, wherein the training model is trained using a plurality of result data related to the at least one entity; and event processing circuitry configured to take an action associated with the at least one transaction in response to the classification status of the at least one entity.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example system may include: wherein at least one entity is selected from the following entities: bond issuers, bonds, parties, and assets.
An example system may include: wherein the bond issuer is selected from the following bond issuers: municipal departments, municipalities, companies, contractors, government entities, non-government entities and non-profit entities. Non-profit entities.
An example system may include: wherein the at least one bond is selected from the following entities: municipal bonds, government bonds, national treasury bonds, asset vouchers and corporate bonds.
An example system may include: wherein the condition classified by the condition classification circuit includes at least one of a contraband condition, a redemption-stopping condition, a condition indicating a contraband contract, a financial risk condition, a behavioral risk condition, a policy risk condition, a financial health condition, a physical defect condition, a physical health condition, an entity risk condition, or an entity health condition.
An example system may include: wherein the internet of things data collection circuit further comprises an internet of things input circuit configured to receive input from a user for configuring a query for information about at least one entity.
An example system may further include: a data collection circuit configured to monitor at least one of an internet of things device, an environmental condition sensor, a crowdsourcing request circuit, a crowdsourcing communication circuit, a crowdsourcing publishing circuit, and an algorithm for querying a network domain.
An example system may further include: wherein the condition classification circuit is further configured to classify the condition in response to information from the data collection circuit.
An example system may include: wherein the data collection circuit is further configured to monitor an environment in the group of: municipal environments, corporate environments, securities trading environments, real estate environments, commercial facilities, warehouse facilities, transportation environments, manufacturing environments, storage environments, residences, or vehicles.
An example system may include: wherein the condition classification circuit is further configured to classify the condition in response to the monitored environment.
An example system may include: wherein at least one bond is vouched for by at least one asset.
An example system may include: wherein at least one asset is selected from the following: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farm, crop, municipal facility, warehouse, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment items, tools, machinery, or personal property.
An example system may further include: event processing circuitry configured to process events related to at least one of value, status, and ownership of the at least one asset and to take actions related to the at least one transaction to further respond to the events.
An example system may include: wherein the action is selected from the following actions: a bond transaction; underwriting bond transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint bonds and combined bonds.
An example system may include: wherein the condition classification circuit comprises a system selected from the following systems: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, and simulation systems.
An example system may further include: an automated bond management circuit configured to manage actions related to at least one bond, wherein the automated bond management circuit trains based on training data sets of a plurality of bond management activities.
An example system may include: wherein the automated bond management circuit trains based on a plurality of principal interactions with a plurality of user interfaces involved in a plurality of bond transaction activities.
An example system may include: wherein the plurality of bond transactions are selected from the group consisting of: providing a bond transaction; underwriting bond transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint liabilities or combined liabilities.
An example system may further include: market value data collection circuitry configured to monitor and report market information related to the value of at least one of the bond issuer, the at least one bond, and the asset associated with the at least one bond.
An example system may include: wherein the asset is selected from the following assets: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farm, crop, municipal facility, warehouse, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment items, tools, machinery, or personal property.
An example system may include: wherein the market value data collection circuit is further configured to monitor pricing or financial data for counteracting asset items in the at least one public market.
An example system may further include: clustering circuitry configured to construct a set of counteracting asset items for evaluating the asset based on the property of the asset using the clustering circuitry.
An example system may include: wherein the attribute is selected from the following attributes: category, age of asset, status of asset, history of asset, asset storage, and geographic location.
An example system may further include: a smart contract circuit configured to manage smart contracts for at least one transaction.
An example system may include: wherein the smart contract circuitry is further configured to determine terms and conditions of the at least one bond.
An example system may include: wherein the terms and conditions are selected from the group consisting of: the debt principal amount, the debt balance, the fixed interest rate, the variable interest rate, the payment amount, the payment plan, the end-of-line repayment plan, the guarantee asset description of at least one bond, the asset replaceability description, the principal, the issuer, the purchaser, the insured, the insurer, the guarantor, the personal guaranty, the lien, the duration, the contract, the redemption stopping condition, the default condition, and the outcome of the violation.
In an embodiment, a method is provided herein for monitoring conditions of a bond issuer. An example method may include: collecting internet of things information about at least one entity involved in at least one transaction comprising at least one bond; and classifying a condition of the at least one entity according to the model and based on the internet of things information, wherein the model is trained using a plurality of training result data sets associated with the at least one entity; and taking an action associated with the at least one transaction in response to the classification status of the at least one entity.
Certain additional aspects of the example methods are described below, any one or more of which may be present in certain embodiments. An example method may further include: processing an event related to at least one of a value, a condition, or an ownership of at least one asset, wherein the at least one asset is related to at least one bond; and taking an action associated with the at least one transaction in response to the event. An example method may further include: the automated bond management circuit is trained based on the plurality of training bond management active sets to manage actions associated with the at least one bond. An example method may further include: market information relating to the value of at least one of the bond issuer, the at least one bond, and the asset is monitored and reported.
In an embodiment, an example platform or system may include: an internet of things data collection circuit configured to collect information about at least one entity involved in at least one subsidy loan transaction; a condition classification circuit comprising a model configured to classify at least one parameter of at least one subsidy loan involved in at least one subsidy loan transaction based on information from the internet of things data collection circuit, wherein the training set model is trained using a plurality of outcome data related to the at least one subsidy loan:
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example system may include: wherein at least one entity is selected from the following entities: at least one subsidy loan, at least one different subsidy loan involved in at least one subsidy loan transaction, a principal, a subsidy, a guarantor, a subsidy principal, and a mortgage.
An example system may include: wherein the at least one entity comprises a principal of the following: at least one of a municipal department, a company, a contractor, a government entity, a non-government entity, and a non-profit entity.
An example system may include: wherein the at least one subsidy loan comprises at least one of a municipal subsidy loan, a government subsidy loan, an learning-aid loan, a property-guarantee subsidy loan, or a corporate subsidy loan.
An example system may include: wherein the condition classified by the condition classification circuit is selected from the following conditions: the conditions include a default condition, a redemption-stopping condition, a condition indicating a violation of a contract, a financial risk condition, a behavioral risk condition, a contract performance condition, a policy risk condition, a financial health condition, a physical defect condition, a physical health condition, an entity risk condition, and an entity health condition.
An example system may include: wherein the at least one subsidy loan is an assisted loan, and wherein the condition classification circuit classifies at least one of: the students take the progress of the academic position, the students participate in the non-profit activities and the students participate in the public interest activities.
An example system may include: a user interface of the internet of things data collection circuit is also included and is configured to enable a user to configure a query for information related to at least one entity.
An example system may include: also included is at least one configurable data collection and circuit configured to monitor at least one entity and selected from the group consisting of: social network analysis circuitry, environmental condition circuitry, crowd source circuitry, and algorithms for querying a network domain.
An example system may include: wherein the at least one configurable data collection and circuit monitors an environment of: municipal environments, educational environments, corporate environments, securities trading environments, real estate environments, business facilities, warehousing facilities, transportation environments, manufacturing environments, storage environments, homes, and vehicles.
An example system may include: wherein the at least one subsidy loan is guaranteed by the at least one property.
An example system may include: wherein at least one asset is selected from the following: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farm, crop, municipal facility, warehouse, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment items, tools, machinery, or personal property.
An example system may include: also included is an automated agent configured to process at least one event related to at least one of a value, a condition, and an ownership of the at least one property and to take an action related to at least one subsidy loan transaction involving the at least one property.
An example system may include: wherein the action is selected from the following actions: providing a subsidy loan transaction; underwriting and subsidy loan transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint subsidy loans or joint subsidy loans.
An example system may include: wherein the condition classification circuit comprises a system selected from the following systems: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, and simulation systems.
An example system may include: an automatic subsidy loan management circuit is also included and is configured to manage actions related to the subsidy loan, wherein the automatic subsidy loan management circuit is trained based on a set of subsidy loan management activities.
An example system may include: wherein the automated subsidy loan management circuit trains based on a plurality of principal interactions with a plurality of user interfaces involved in a plurality of subsidy loan transactions.
An example system may include: wherein the plurality of subsidy loan transaction activities are selected from the group consisting of: providing a subsidy loan transaction; underwriting and subsidy loan transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint subsidy loans or joint subsidy loans.
An example system may include: a blockchain service circuit is also included that is configured to record in the distributed ledger a modified set of terms and conditions of the at least one subsidized loan.
An example system may include: also included is a market value data collection circuit configured to monitor and report market information related to the value of at least one of the issuer, the at least one subsidy loan, and the at least one property.
An example system may include: wherein the report relates to at least one of the following assets: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farm, crop, municipal facility, warehouse, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment items, tools, machinery, or personal property.
An example system may include: wherein the market value data collection circuit is further configured to monitor pricing or financial data for counteracting asset items in the at least one public market.
An example system may include: a clustering circuit configured to construct a set of countering asset items for evaluating the at least one set of assets based on the attributes of the at least one set of assets using the clustering circuit.
An example system may include: wherein the attribute is selected from the following attributes: category, age of asset, status of asset, history of asset, asset storage, and geographic location.
An example system may include: also included is a smart contract circuit configured to manage smart contracts for at least one subsidized loan transaction.
An example system may include: wherein the smart contract circuitry is further configured to modify the smart contract in response to the classification parameter of the at least one subsidized loan.
An example system may include: wherein the terms and conditions of the at least one subsidy loan automatically modified by the smart contract circuit are selected from the group consisting of: the amount of liability principal, the balance of liability, fixed interest rate, variable interest rate, payment amount, payment plan, end-of-line repayment plan, warranty description of at least one subsidy loan, property substitutability description, principal, issuer, purchaser, insured, insurer, guaranty, personal guaranty, lien, duration, contract, redemption stopping condition, default condition, and post-violation outcome.
In an embodiment, an example method may include: collecting information about at least one entity involved in at least one subsidized loan transaction; classifying at least one parameter of the at least one subsidy loan involved in the at least one subsidy loan transaction based on the information using a model trained based on the plurality of result data sets associated with the at least one subsidy loan; and automatically modifying the terms and conditions of the at least one subsidy loan based on the classification parameters.
Certain additional aspects of the example methods are described below, any one or more of which may be present in certain embodiments. An example method may include: wherein further comprising processing at least one event related to at least one of a value, a condition, or an ownership of the at least one property and taking an action related to at least one subsidy loan transaction involving the at least one property.
An example method may include: also included is recording in the distributed ledger a modified set of terms and conditions for the at least one subsidy loan.
An example method may include: which also includes monitoring and reporting market information related to the value of at least one of the issuer, the at least one subsidy loan, or the at least one property related to the at least one subsidy loan.
In an embodiment, an example platform or system may include: a social network analysis data collection circuit configured to collect social network information about at least one entity involved in at least one subsidy loan transaction; a condition classification circuit comprising a model configured to classify at least one parameter of at least one subsidy loan involved in at least one subsidy loan transaction based on social network information from the social network analysis data collection circuit, wherein the training set model is trained using result data related to the at least one subsidy loan; and an intelligent contract circuit configured to automatically modify terms and conditions of the at least one subsidy loan based on the classified at least one parameter.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example system may include: wherein at least one entity is selected from the following entities: at least one subsidy loan, at least one different subsidy loan involved in at least one subsidy loan transaction, a principal, a subsidy, a guarantor, a subsidy principal, and a mortgage.
An example system may include: wherein the principal that subsidizes the at least one subsidized loan is selected from the group consisting of: municipal departments, companies, contractors, government entities, non-government entities, and non-profit entities.
An example system may include: wherein the at least one subsidy loan comprises at least one of a municipal subsidy loan, a government subsidy loan, an learning-aid loan, a property-guarantee subsidy loan, or a corporate subsidy loan.
An example system may include: wherein the parameters classified by the condition classification circuit are selected from the following conditions: the conditions include a default condition, a redemption-stopping condition, a condition indicating a violation of a contract, a financial risk condition, a behavioral risk condition, a contract performance condition, a policy risk condition, a financial health condition, a physical defect condition, a physical health condition, an entity risk condition, and an entity health condition.
An example system may include: wherein the at least one subsidy loan is an assisted loan, and wherein the condition classification circuit classifies at least one of: the students take the progress of the academic position, the students participate in the non-profit activities and the students participate in the public interest activities.
An example system may include: a user interface of the social network analysis data collection circuit is also included and is configured to enable a user to configure a query for relevant information of at least one entity, wherein the social network analysis data collection circuit initiates at least one algorithm to search for and retrieve data from at least one social network in response to the query.
An example system may include: also included is at least one configurable data collection and circuit configured to monitor at least one entity and selected from the group consisting of: social network analysis circuitry, environmental condition circuitry, crowd source circuitry, and algorithms for querying a network domain.
An example system may include: wherein the at least one configurable data collection and circuit monitors an environment of: municipal environments, educational environments, corporate environments, securities trading environments, real estate environments, business facilities, warehousing facilities, transportation environments, manufacturing environments, storage environments, homes, and vehicles.
An example system may include: wherein the at least one subsidy loan is guaranteed by the at least one property.
An example system may include: wherein at least one asset is selected from the following: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farm, crop, municipal facility, warehouse, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment items, tools, machinery, or personal property.
An example system may include: also included is an automated agent configured to process at least one event related to at least one of a value, a condition, or an ownership of the at least one property and take an action related to at least one subsidy loan transaction involving the at least one property.
An example system may include: wherein the action is selected from the following actions: providing a subsidy loan transaction; underwriting and subsidy loan transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint subsidy loans or joint subsidy loans.
An example system may include: wherein the condition classification circuit comprises a system selected from the following systems: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, and simulation systems.
An example system may include: wherein an automatic subsidy loan management circuit is further included that is configured to manage actions related to the at least one subsidy loan, and wherein the automatic subsidy loan management circuit is trained based on the set of subsidy loan management activities.
An example system may include: wherein the automated subsidy loan management circuit trains based on a plurality of principal interactions with a plurality of user interfaces involved in a plurality of subsidy loan transactions.
An example system may include: wherein the plurality of subsidy loan transaction activities are selected from the group consisting of: providing a subsidy loan transaction; underwriting and subsidy loan transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint subsidy loans or joint subsidy loans.
An example system may include: a blockchain service circuit is also included that is configured to record in the distributed ledger a modified set of terms and conditions of the at least one subsidized loan.
An example system may include: also included is a market value data collection circuit configured to monitor and report market information related to the value of at least one of the issuer, the at least one subsidized loan, or the at least one property.
An example system may include: wherein the report relates to at least one of the following assets: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farm, crop, municipal facility, warehouse, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment items, tools, machinery, or personal property.
An example system may include: wherein the market value data collection circuit is further configured to monitor pricing or financial data for counteracting asset items in the at least one public market.
An example system may further include: a clustering circuit configured to construct a set of countering asset items for evaluating the at least one set of assets based on the attributes of the at least one set of assets using the clustering circuit.
An example system may include: wherein the attribute is selected from the following attributes: category, age of asset, status of asset, history of asset, asset storage, and geographic location.
An example system may include: also included is a smart contract circuit configured to manage smart contracts for at least one subsidized loan transaction.
An example system may include: wherein the smart contract circuitry sets terms and conditions for at least one subsidized loan.
An example system may include: wherein the terms and conditions of the at least one subsidy loan specified and managed by the smart contract circuit are selected from the group consisting of: the amount of debt principal, the balance of debt, the fixed interest rate, the variable interest rate, the payment amount, the payment plan, the end-of-line repayment plan, the warranty description of at least one subsidized loan, the property replaceability description, the principal, the issuer, the purchaser, the insured, the insurer, the guaranty, the personal guaranty, the lien, the duration, the contract, the redemption stopping condition, the default condition, and the outcome of the default.
In an embodiment, an example method may include: collecting social network information about at least one entity involved in at least one subsidized loan transaction; classifying at least one parameter of the at least one subsidy loan involved in the at least one subsidy loan transaction based on the social network information using a model trained based on the result data training set related to the at least one subsidy loan; and automatically modifying the terms and conditions of the at least one subsidy loan based on the classified at least one parameter.
Certain additional aspects of the example methods are described below, any one or more of which may be present in certain embodiments. An example method may include: wherein further comprising processing at least one event related to at least one of a value, a status, and an ownership of the at least one property and taking an action related to at least one subsidy loan transaction involving the at least one property.
An example method may include: also included is recording in the distributed ledger a modified set of terms and conditions for the at least one subsidy loan.
An example method may include: also included is monitoring and reporting market information related to the value of at least one of the issuer, the at least one subsidized loan, or the at least one property.
In an embodiment, a system for automatically processing subsidized loans is provided herein. An example platform or system may include: crowd sourcing service circuitry configured to collect information related to a set of entities involved in a set of subsidized loan transactions; a condition classification circuit including a model and an artificial intelligence service circuit configured to classify a set of parameters of the set of subsidized loans involved in the transaction based on information from the crowd-sourced service circuit, wherein the set of training models are trained using result data related to the subsidized loans; and an intelligent contract circuit for automatically modifying the terms and conditions of the subsidy loan based on the classified set of parameters from the condition classification circuit.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example system may include: wherein the set of entities includes entities of the following entities: a set of subsidized loans, a set of parties, a set of subsidized, a set of guarantor, a set of subsidized parties, or a set of mortgage.
An example system may include: wherein each entity in the set of entities comprises an entity in the following entities: a subsidy from a set of subsidy loans corresponding to the set of subsidy loan transactions, a principal associated with at least one of the set of subsidy loan transactions, a subsidy corresponding to a subsidy loan from a set of subsidy loans corresponding to the set of subsidy loan transactions, a guarantor associated with at least one of the set of subsidy loan transactions, a subsidy corresponding to a subsidy loan from a set of subsidy loans corresponding to the set of subsidy loan transactions, a subsidy associated with at least one of the set of subsidy loan transactions, a subsidy associated with a subsidy from a set of subsidy loans corresponding to the set of subsidy loan transactions, and a mortgage associated with at least one of the set of subsidy loans corresponding to the set of subsidy transactions.
An example system may be: at least one entity of the set of entities includes a subsidizer associated with at least one of the set of subsidized loan transactions, wherein the subsidizer includes at least one of a municipal department, a company, a contractor, a government entity, a non-government entity, or a non-profit entity.
An example system may include: wherein each of the set of subsidy loans corresponding to the set of loan transactions includes at least one of a municipal subsidy loan, a government subsidy loan, an learning-aid loan, a property guarantee subsidy loan, or a corporate subsidy loan.
An example system may include: the conditions classified by the condition classification circuit are the following conditions: the conditions include a default condition, a redemption-stopping condition, a condition indicating a violation of a contract, a financial risk condition, a behavioral risk condition, a contract performance condition, a policy risk condition, a financial health condition, a physical defect condition, a physical health condition, an entity risk condition, and an entity health condition.
An example system may include: wherein the subsidy loan is an learning-aid loan, and wherein the condition classification circuit classifies at least one of: the students take the progress of the academic position, the students participate in the non-profit activities and the students participate in the public interest activities.
An example system may include; wherein the crowdsourcing service circuit is further configured with a user interface through which a user can configure queries for information about a set of entities, and wherein the crowdsourcing service circuit automatically configures crowdsourcing requests based on the queries.
An example system may include: further comprising a configurable data collection and monitoring service circuit for monitoring the entity, wherein the configurable data collection and monitoring service circuit comprises at least one of: an internet of things service, a set of environmental condition sensors, a set of social network analysis services, and a set of network domain query algorithms.
An example system may include: wherein the configurable data collection and monitoring service circuit is further configured to monitor an environment of: municipal environments, educational environments, corporate environments, securities trading environments, real estate environments, business facilities, warehousing facilities, transportation environments, manufacturing environments, storage environments, homes, and vehicles.
An example system may include: wherein the set of subsidized loans is guaranteed by a set of properties.
An example system may include: wherein the set of assets are each selected from the group consisting of: municipal assets, vehicles, vessels, aircraft, buildings, houses, real estate, undeveloped property, farm, crop, municipal facility, warehouse, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contractual rights, antiques, fixtures, furniture, equipment items, tools, machinery, or personal property.
An example system may include: also included is an automated agent circuit configured to process events related to at least one of value, status, or ownership of at least one property in the set of properties and take actions related to subsidy loan transactions involving the at least one property.
An example system may include: wherein the action is selected from the following actions: providing a subsidy loan transaction; underwriting and subsidy loan transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint subsidy loans or joint subsidy loans.
An example system may include: wherein the artificial intelligence service circuit comprises at least one of the following systems: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feedforward neural networks, feedback neural networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, bayesian systems, and simulation systems.
An example system may include: an automatic subsidy loan management circuit configured to manage actions related to the subsidy loan is also included, wherein the automatic subsidy loan management circuit is trained based on a set of subsidy loan management activities.
An example system may include: wherein the automated subsidy loan management circuit is further trained based on a set of principal interactions with a set of user interfaces, wherein the principal participates in a set of subsidy loan transaction activities.
An example system may include: wherein the set of subsidized loan transaction activities includes activities selected from the group consisting of: providing a subsidy loan transaction; underwriting and subsidy loan transactions; setting interest rate; postponing payment requirements; modifying the interest rate; verifying ownership; managing and checking; recording the change of ownership; evaluating the value of the asset; collect loan; ending the transaction; setting the terms and conditions of the transaction; providing a notice to be provided; redemption stopping a group of assets; modifying the clauses and conditions; setting a rating of the entity; joint subsidy loans or joint subsidy loans.
An example system may include: a blockchain service circuit is also included that is configured to record in the distributed ledger a modified set of terms and conditions for a set of subsidy loans corresponding to the set of subsidy loan transactions.
An example system may include: also included is a market value data collection service circuit configured to monitor and report market information related to the value of at least one of a principal related to the subsidy loan, a set of subsidy loans corresponding to the set of subsidy loan transactions, or a set of properties.
An example system may include: reports relate to a group of assets including municipal assets, vehicles, ships, airplanes, buildings, houses, real estate, undeveloped property, farms, crops, municipal facilities, warehouses, a group of inventory, merchandise, securities, currency, value documents, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, jewelry items, precious stones, intellectual property items, intellectual property, contract rights, antiques, fixtures, furniture, equipment items, tools, machinery, or personal property.
An example system may include: wherein the market value data collection service circuit is further configured to monitor pricing or financial data for items similar to the assets of the group of assets in the at least one public market.
An example system may include: wherein a set of similar items for evaluating the assets in the set of assets is constructed using a similarity clustering algorithm based on attributes of the assets.
An example system may include: wherein the attribute is selected from the following attributes: asset class, age of asset, asset condition, asset history, asset storage, or asset geographic location.
An example system may include: also included is an intelligent contract service circuit for managing intelligent contracts for subsidized loans.
An example system may include: wherein the smart contract service circuit is further configured to set terms and conditions of the subsidized loan.
An example system may include: wherein the set of terms and conditions for the subsidy loan transaction specified and managed by the smart contract service circuit are selected from the group consisting of: the amount of debt principal, the balance of debt, the fixed interest rate, the variable interest rate, the payment amount, the payment plan, the end-of-line repayment plan, the warranty description of at least one subsidized loan, the property replaceability description, the principal, the issuer, the purchaser, the insured, the insurer, the guaranty, the personal guaranty, the lien, the duration, the contract, the redemption stopping condition, the default condition, and the outcome of the default.
In an embodiment, a method for automatically processing a subsidy loan is provided herein. An example method may include: collecting information related to a set of entities involved in a set of subsidy loan transactions; classifying a set of parameters of a set of subsidy loans involved in a subsidy loan transaction based on the artificial intelligence service, the model, and information from the crowdsourcing service, wherein the model is trained using a training dataset of results related to the subsidy loan; and modifying the terms and conditions of the subsidy loan based on the classification parameter set.
Certain additional aspects of the example methods are described below, any one or more of which may be present in certain embodiments. An example method may include: wherein the set of entities includes entities of the following entities: a set of subsidized loans, a set of parties, a set of subsidized, a set of guarantor, a set of subsidized parties, or a set of mortgage.
An example method may include: wherein the set of entities includes a set of subsidized principals, and wherein each principal of the set of subsidized principals includes at least one of a municipal department, a company, a contractor, a government entity, a non-government entity, or a non-profit entity.
An example method may include: wherein the set of subsidy loans includes at least one of municipal subsidy loans, government subsidy loans, learning-aid loans, property-guarantee subsidy loans, or corporate subsidy loans.
An example method may include: wherein the subsidy loan is a learning-aid loan, and wherein at least one of the classification of the student making an advance in the degree, the student taking part in a non-profit activity, and the student participating in a public interest activity.
In an embodiment, an example platform or system may include: an asset identification service circuit configured to interpret a plurality of assets corresponding to a financial entity for keeping the plurality of assets; an identity management service circuit configured to authenticate a plurality of identifiers corresponding to executable action entities, the executable action entities being authorized to take actions on a plurality of assets, wherein the plurality of identifiers includes at least one credential; a blockchain service circuit configured to store a plurality of asset control features in a blockchain structure, wherein the blockchain structure includes a distributed ledger configuration; and a financial management circuit configured to communicate the interpreted plurality of assets and the authenticated plurality of identifiers to the blockchain service circuit for storage in the blockchain structure as asset control features, and wherein the blockchain service circuit is further configured to record the asset control features as asset events in a distributed ledger configuration.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example system may include: wherein the at least one credential includes an owner credential, a proxy credential, a beneficiary credential, a delegate credential, or a custodian credential.
An example system may include: wherein the asset event comprises an event of: ownership transfer, owner death, owner disability, owner bankruptcy, redemption prevention, setting liens, use of a property as a mortgage, designating a beneficiary, mortgage a property, providing notification about a property, checking a property, evaluating a property, reporting a property for tax purposes, assigning property ownership, disposing of a property, selling a property, purchasing a property, or designating ownership status.
An example system may include: a data collection circuit configured to monitor at least one of an interpretation of the plurality of assets, an authentication of the plurality of identifiers, and a recording of asset events.
An example system may include: wherein the executable action entities each include at least one of an owner, beneficiary, agent, trustee, or custodian.
An example system may include: an intelligent contract circuit configured to manage custody of the plurality of assets, and wherein at least one asset event associated with the plurality of assets is managed by the intelligent contract circuit based on a plurality of terms and conditions embodied in the intelligent contract configuration and based on data collected by the data collection service circuit.
An example system may include: wherein the at least one asset event associated with the plurality of assets comprises at least one of: ownership transfer, owner death, owner disability, owner bankruptcy, redemption prevention, setting liens, use of a property as a mortgage, designating a beneficiary, mortgage a property, providing notification about a property, checking a property, evaluating a property, reporting a property for tax purposes, assigning property ownership, disposing of a property, selling a property, purchasing a property, and designating ownership status.
An example system may include: wherein the data collection circuit further comprises at least one of the following: the system comprises an Internet of things system, a camera system, a networking monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system and an interactive crowdsourcing system.
An example system may include: wherein each of the asset identification service circuit, the identity management service circuit, the blockchain service circuit, and the financial management circuit further includes a respective Application Programming Interface (API) component configured to facilitate communication between the circuits of the system. The respective API components of these circuits also include a user interface configured to interact with a plurality of users of the system.
An example system may include: the blockchain service circuitry is further configured to share and distribute asset events with a plurality of executable action entities.
In an embodiment, an example method may include: interpreting a plurality of assets corresponding to a financial entity for keeping the plurality of assets; authenticating a plurality of identifiers corresponding to the executable action entity, the executable action entity being authorized to take actions on the plurality of assets, wherein the plurality of identifiers includes at least one credential; storing a plurality of asset control features in a blockchain structure, wherein the blockchain structure includes a distributed ledger configuration; and transmitting the interpreted plurality of assets and the authenticated plurality of identifiers for storage in the blockchain structure as asset control features, wherein the asset control features are recorded as asset events in a distributed ledger configuration.
An example method may include: wherein the at least one credential includes an owner credential, a proxy credential, a beneficiary credential, a delegate credential, or a custodian credential.
An example method may include: wherein the asset event comprises at least one of the following events: ownership transfer, owner death, owner disability, owner bankruptcy, redemption prevention, setting liens, use of a property as a mortgage, designating a beneficiary, mortgage a property, providing notification about a property, checking a property, evaluating a property, reporting a property for tax purposes, assigning property ownership, disposing of a property, selling a property, purchasing a property, or designating ownership status.
An example method may include: at least one of an interpretation of the plurality of assets, an authentication of the plurality of identifiers, or a recording of asset events is monitored.
An example method may include: wherein the executable action entities each include at least one of an owner, beneficiary, agent, trustee, or custodian.
An example method may include: managing custody of the plurality of assets, wherein at least one asset event related to the plurality of assets is based on a plurality of terms and conditions embodied in the smart contract configuration and based on data collected with respect to the plurality of asset data.
An example method may include: wherein each asset event associated with the plurality of assets comprises at least one of the following events: ownership transfer, owner death, owner disability, owner bankruptcy, redemption prevention, setting liens, use of a property as a mortgage, designating a beneficiary, mortgage a property, providing notification about a property, checking a property, evaluating a property, reporting a property for tax purposes, assigning property ownership, disposing of a property, selling a property, purchasing a property, or designating ownership status.
An example method may include: wherein monitoring is performed by at least one of the following systems: the system comprises an Internet of things system, a camera system, a networking monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system and an interactive crowdsourcing system.
An example method may include: including to share asset events with multiple litigation-capable entities and to distribute asset events.
An example method may include: wherein interpreting the plurality of assets includes identifying a plurality of assets that the financial entity is responsible for hosting.
An example method may include: wherein authenticating the plurality of identifiers includes verifying the plurality of identifiers corresponding to executable action entities authorized to take actions on the plurality of assets.
An example method may include: wherein the blockchain structure is provided in conjunction with a blockchain marketplace.
An example method may include: wherein the blockchain marketplace utilizes a blockchain-based automated transaction application.
An example method may include: including a blockchain structure is a distributed blockchain structure that spans multiple asset nodes.
An example method may include: wherein the blockchain structure is a distributed blockchain structure across a plurality of asset nodes.
An example method may include: wherein at least one of the plurality of assets is a virtual asset tag, and interpreting the plurality of assets includes identifying the virtual asset tag.
An example method may include: wherein storing the plurality of asset control features includes storing virtual asset tag data.
An example method may include: wherein the virtual asset tag data is at least one of location data or tracking data.
An example method may include: wherein an identifier corresponding to at least one of the financial entity or the actionable entity is stored as virtual asset tag data.
In an embodiment, provided herein is a system for facilitating redemption prevention of a mortgage. An example platform or system may include: a loan agreement storing circuit configured to store a plurality of loan agreement data including at least one loan agreement, wherein the at least one loan agreement includes loan condition data including terms and condition numbers of the at least one loan agreement, the terms and condition data of the at least one loan agreement being associated with redemption-stopping conditions on the at least one asset, the redemption-stopping conditions providing mortgage conditions associated with the mortgage asset to warrant repayment obligation of the at least one loan agreement; a data collection service circuit configured to monitor the lending condition data and detect an default condition based on a change in the lending condition data; and smart contract service circuitry configured to interpret the breach of contract conditions and transmit an indication of the breach of contract conditions, the indication of the breach of contract conditions initiating a redemption-stopping program based on the mortgage condition and the breach of contract conditions.
Certain additional aspects of the example systems are described below, any one or more of which may be present in certain embodiments. An example system may include: wherein the smart contract service circuit is further configured to communicate the detected breach condition to at least one of a smart lock or a smart container to lock the mortgage asset.
An example system may include: it aborts the redemption program configuration and initiates a list of mortgage assets on the public auction site.
An example system may include: it aborts the redemption program configuration and transmits a set of shipping instructions for the mortgage asset.
An example system may include: the abort redemption program configures the drone with an instruction set to transport the mortgage asset.
An example system may include: the abort redemption program configures the robotic device with an instruction set to ship the mortgage asset.
An example system may include: which aborts the redemption program to initiate a process that automatically replaces a set of replacement mortgages.
An example system may include: it aborts the redemption program and initiates the mortgage tracking program.
An example system may include: it aborts the redemption program initiating the mortgage valuation process.
An example system may include: it aborts the redemption program to send a message to the borrower initiating the negotiation regarding redemption.
An example system may include: wherein the negotiations are managed by a robotic process automation system trained based on a training set of redemption-stopping negotiations.
An example system may include: wherein negotiating involves modifying at least one of interest rate, payment terms, and mortgage of at least one lending agreement.
An example system may include: wherein the data collection service circuit further comprises at least one of the following systems: the system comprises an Internet of things system, a camera system, a networking monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system and an interactive crowdsourcing system.
An example system may include: wherein each of the lending agreement storage circuit, the data collection service circuit, and the smart contract service circuit further includes a respective Application Programming Interface (API) component configured to facilitate communication between the circuits of the system.
An example system may include: wherein the respective API component of the circuit further comprises a user interface configured to interact with a plurality of users of the system.
In an embodiment, provided herein is a method for facilitating redemption prevention of a mortgage. An example method may include: storing a plurality of lending agreement data including at least one lending agreement, wherein the at least one lending agreement includes lending condition data, the lending condition data including terms and condition numbers of the at least one lending agreement, the terms and condition data of the at least one lending agreement being associated with redemption-stopping conditions on the at least one asset, the redemption-stopping conditions providing mortgage conditions associated with the mortgage asset to warrant a repayment obligation of the at least one lending agreement; monitoring the lending condition data and detecting an default condition based on changes to the lending condition data; interpreting the default condition; and communicating an indication of a default condition that initiates a redemption-stopping program based on the mortgage condition.
Certain additional aspects of the example methods are described below, any one or more of which may be present in certain embodiments. An example method may include: the detected breach condition is communicated to at least one of a smart lock and a smart container to lock the mortgage asset.
An example method may include: it aborts the redemption program configuration and initiates a list of mortgage assets on the public auction site.
An example method may include: it aborts the redemption program configuration and transmits a set of shipping instructions for the mortgage asset.
An example method may include: the abort redemption program configures the drone with an instruction set to transport the mortgage asset.
An example method may include: the abort redemption program configures the robotic device with an instruction set to ship the mortgage asset.
An example method may include: which aborts the redemption program to initiate a process that automatically replaces a set of replacement mortgages.
An example method may include: it aborts the redemption program and initiates the mortgage tracking program.
An example method may include: it aborts the redemption program initiating the mortgage valuation process.
An example method may include: it aborts the redemption program to send a message to the borrower initiating the negotiation regarding redemption.
An example method may include: wherein the negotiations are managed by a robotic process automation system trained based on a training set of redemption-stopping negotiations.
An example method may include: wherein negotiating involves modifying at least one of interest rates, payment terms, or mortgages of at least one lending agreement.
An example method may include: wherein monitoring is performed by at least one of the following systems: the system comprises an Internet of things system, a camera system, a networking monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system and an interactive crowdsourcing system.
An example method may include: wherein the communication provided for monitoring, interpretation and communication is via an Application Programming Interface (API).
An example method may include: wherein a user interface incorporating an API is provided to interact with a plurality of users.
Detailed embodiments of the present invention are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary of the invention, which can be embodied in various forms. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present invention in virtually any appropriately detailed structure.
The terms "a" and "an", as used herein, are defined as "one or more". The term another, as used herein, is defined as "at least a second or more". The terms including and/or having, as used herein, are defined as comprising (i.e., open transition).
Although only a few embodiments of the present invention have been shown and described, it would be apparent to those skilled in the art that many changes and modifications can be made to the embodiments without departing from the spirit and scope of the invention as described in the following claims. All patent applications and patents (including foreign and domestic) cited herein and all other publications are incorporated herein in their entirety to the full extent permitted by law.
In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms. In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms; and has a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack. In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms; and a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportion licensing fees among the parties in the ledger. In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms; and has a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack. In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms; and has a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms. In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms; and has a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms; and has a distributed ledger that marks executable algorithm logic such that performing operations on the distributed ledger can provide provable access to the executable algorithm logic. In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms; and has a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms; and has a distributed ledger marking an instruction set for a coating process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms; and has a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing operations on the distributed ledger can provide provable access to the manufacturing process. In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms; and has a distributed ledger marking firmware programs such that performing operations on the distributed ledger can provide provable access to the firmware programs. In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms; and has a distributed ledger marking an instruction set for an FPGA such that performing operations on the distributed ledger can provide provable access to the FPGA. In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms; and has a distributed ledger that marks the serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic. In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms; and has a distributed ledger marking an instruction set for a crystal manufacturing system such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms; and has a distributed ledger marking an instruction set for a food preparation process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms; and has a distributed ledger marking an instruction set for a polymer production process such that performing operations on the distributed ledger can provide access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms; and has a distributed ledger marking an instruction set for a chemical synthesis process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms; and has a distributed ledger marking an instruction set for a bio-production process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms; and has a distributed ledger that marks a trade secret with an expert wrapper such that performing an operation on the distributed ledger can provide provable access to the trade secret and the wrapper provides for the verification of the trade secret by an expert. In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms; and has a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets. In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms; and having a distributed ledger marking an instruction set such that performing operations on the distributed ledger provides provable access to the instruction set, and executing the instruction set on a system results in logging transactions in the distributed ledger. In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms; and has a distributed ledger marking intellectual property items and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items. In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms; and a distributed ledger having an aggregate instruction set, wherein performing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set. In an embodiment, provided herein is a transaction-enabled system having an intelligent contract wrapper using a distributed ledger, wherein the intelligent contract embeds IP licensing terms for intellectual property embedded in the distributed ledger, and wherein performing operations on the distributed ledger provides access to the intellectual property and causes the executing party to commit to adhering to the IP licensing terms; and having a smart wrapper for managing a distributed ledger for aggregating instruction sets, wherein the smart wrapper manages allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportion licensing fees among the parties in the ledger. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger that marks executable algorithm logic such that performing operations on the distributed ledger can provide provable access to the executable algorithm logic. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking an instruction set for a coating process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing operations on the distributed ledger can provide provable access to the manufacturing process. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking firmware programs such that performing operations on the distributed ledger can provide provable access to the firmware programs. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking an instruction set for an FPGA such that performing operations on the distributed ledger can provide provable access to the FPGA. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger that marks the serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking an instruction set for a crystal manufacturing system such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking an instruction set for a food preparation process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking an instruction set for a polymer production process such that performing operations on the distributed ledger can provide access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking an instruction set for a chemical synthesis process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking an instruction set for a bio-production process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger that marks a trade secret with an expert wrapper such that performing an operation on the distributed ledger can provide provable access to the trade secret and the wrapper provides for the verification of the trade secret by an expert. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and having a distributed ledger marking an instruction set such that performing operations on the distributed ledger provides provable access to the instruction set, and executing the instruction set on a system results in logging transactions in the distributed ledger. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking intellectual property items and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and a distributed ledger having an aggregate instruction set, wherein performing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and having a smart wrapper for managing a distributed ledger for aggregating instruction sets, wherein the smart wrapper manages allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportionment of licensing fees among the parties in the ledger. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportionment of licensing fees among the parties in the ledger; and has a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportionment of licensing fees among the parties in the ledger; and has a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportionment of licensing fees among the parties in the ledger; and has a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportionment of licensing fees among the parties in the ledger; and has a distributed ledger that marks executable algorithm logic such that performing operations on the distributed ledger can provide provable access to the executable algorithm logic. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportionment of licensing fees among the parties in the ledger; and has a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportionment of licensing fees among the parties in the ledger; and has a distributed ledger marking an instruction set for a coating process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportionment of licensing fees among the parties in the ledger; and has a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing operations on the distributed ledger can provide provable access to the manufacturing process. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportionment of licensing fees among the parties in the ledger; and has a distributed ledger marking firmware programs such that performing operations on the distributed ledger can provide provable access to the firmware programs. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportionment of licensing fees among the parties in the ledger; and has a distributed ledger marking an instruction set for an FPGA such that performing operations on the distributed ledger can provide provable access to the FPGA. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportionment of licensing fees among the parties in the ledger; and has a distributed ledger that marks the serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportionment of licensing fees among the parties in the ledger; and has a distributed ledger marking an instruction set for a crystal manufacturing system such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportionment of licensing fees among the parties in the ledger; and has a distributed ledger marking an instruction set for a food preparation process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportionment of licensing fees among the parties in the ledger; and has a distributed ledger marking an instruction set for a polymer production process such that performing operations on the distributed ledger can provide access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportionment of licensing fees among the parties in the ledger; and has a distributed ledger marking an instruction set for a chemical synthesis process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportionment of licensing fees among the parties in the ledger; and has a distributed ledger marking an instruction set for a bio-production process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportionment of licensing fees among the parties in the ledger; and has a distributed ledger that marks a trade secret with an expert wrapper such that performing an operation on the distributed ledger can provide provable access to the trade secret and the wrapper provides for the verification of the trade secret by an expert. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportionment of licensing fees among the parties in the ledger; and has a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportionment of licensing fees among the parties in the ledger; and having a distributed ledger marking an instruction set such that performing operations on the distributed ledger provides provable access to the instruction set, and executing the instruction set on a system results in logging transactions in the distributed ledger. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportionment of licensing fees among the parties in the ledger; and has a distributed ledger marking intellectual property items and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportionment of licensing fees among the parties in the ledger; and a distributed ledger having an aggregate instruction set, wherein performing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to agree to apportionment of licensing fees among the parties in the ledger; and having a smart wrapper for managing a distributed ledger for aggregating instruction sets, wherein the smart wrapper manages allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger that marks executable algorithm logic such that performing operations on the distributed ledger can provide provable access to the executable algorithm logic. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking an instruction set for a coating process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing operations on the distributed ledger can provide provable access to the manufacturing process. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking firmware programs such that performing operations on the distributed ledger can provide provable access to the firmware programs. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking an instruction set for an FPGA such that performing operations on the distributed ledger can provide provable access to the FPGA. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger that marks the serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking an instruction set for a crystal manufacturing system such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking an instruction set for a food preparation process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking an instruction set for a polymer production process such that performing operations on the distributed ledger can provide access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking an instruction set for a chemical synthesis process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking an instruction set for a bio-production process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger that marks a trade secret with an expert wrapper such that performing an operation on the distributed ledger can provide provable access to the trade secret and the wrapper provides for the verification of the trade secret by an expert. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and having a distributed ledger marking an instruction set such that performing operations on the distributed ledger provides provable access to the instruction set, and executing the instruction set on a system results in logging transactions in the distributed ledger. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and has a distributed ledger marking intellectual property items and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and a distributed ledger having an aggregate instruction set, wherein performing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to add intellectual property to an intellectual property aggregation stack; and having a smart wrapper for managing a distributed ledger for aggregating instruction sets, wherein the smart wrapper manages allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms; and has a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms; and has a distributed ledger that marks executable algorithm logic such that performing operations on the distributed ledger can provide provable access to the executable algorithm logic. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms; and has a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms; and has a distributed ledger marking an instruction set for a coating process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms; and has a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing operations on the distributed ledger can provide provable access to the manufacturing process. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms; and has a distributed ledger marking firmware programs such that performing operations on the distributed ledger can provide provable access to the firmware programs. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms; and has a distributed ledger marking an instruction set for an FPGA such that performing operations on the distributed ledger can provide provable access to the FPGA. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms; and has a distributed ledger that marks the serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms; and has a distributed ledger marking an instruction set for a crystal manufacturing system such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms; and has a distributed ledger marking an instruction set for a food preparation process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms; and has a distributed ledger marking an instruction set for a polymer production process such that performing operations on the distributed ledger can provide access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms; and has a distributed ledger marking an instruction set for a chemical synthesis process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms; and has a distributed ledger marking an instruction set for a bio-production process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms; and has a distributed ledger that marks a trade secret with an expert wrapper such that performing an operation on the distributed ledger can provide provable access to the trade secret and the wrapper provides for the verification of the trade secret by an expert. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms; and has a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms; and having a distributed ledger marking an instruction set such that performing operations on the distributed ledger provides provable access to the instruction set, and executing the instruction set on a system results in logging transactions in the distributed ledger. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms; and has a distributed ledger marking intellectual property items and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms; and a distributed ledger having an aggregate instruction set, wherein performing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger for aggregating intellectual property licensing terms, wherein an intelligent contract wrapper on the distributed ledger allows operations to be performed on the ledger to promise parties to adhere to contract terms; and having a smart wrapper for managing a distributed ledger for aggregating instruction sets, wherein the smart wrapper manages allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
In an embodiment, provided herein is a transaction enabling system having a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger that marks executable algorithm logic such that performing operations on the distributed ledger can provide provable access to the executable algorithm logic. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a coating process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing operations on the distributed ledger can provide provable access to the manufacturing process. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking firmware programs such that performing operations on the distributed ledger can provide provable access to the firmware programs. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for an FPGA such that performing operations on the distributed ledger can provide provable access to the FPGA. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger that marks the serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a crystal manufacturing system such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a food preparation process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a polymer production process such that performing operations on the distributed ledger can provide access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a chemical synthesis process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a bio-production process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger that marks a trade secret with an expert wrapper such that performing an operation on the distributed ledger can provide provable access to the trade secret and the wrapper provides for the verification of the trade secret by an expert. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and having a distributed ledger marking an instruction set such that performing operations on the distributed ledger provides provable access to the instruction set, and executing the instruction set on a system results in logging transactions in the distributed ledger. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking intellectual property items and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and a distributed ledger having an aggregate instruction set, wherein performing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and having a smart wrapper for managing a distributed ledger for aggregating instruction sets, wherein the smart wrapper manages allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags executable algorithmic logic such that performing operations on the distributed ledger can provide provable access to the executable algorithmic logic. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags executable algorithmic logic such that performing operations on the distributed ledger can provide provable access to the executable algorithmic logic; and has a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags executable algorithmic logic such that performing operations on the distributed ledger can provide provable access to the executable algorithmic logic; and has a distributed ledger marking an instruction set for a coating process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags executable algorithmic logic such that performing operations on the distributed ledger can provide provable access to the executable algorithmic logic; and has a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing operations on the distributed ledger can provide provable access to the manufacturing process. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags executable algorithmic logic such that performing operations on the distributed ledger can provide provable access to the executable algorithmic logic; and has a distributed ledger marking firmware programs such that performing operations on the distributed ledger can provide provable access to the firmware programs. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags executable algorithmic logic such that performing operations on the distributed ledger can provide provable access to the executable algorithmic logic; and has a distributed ledger marking an instruction set for an FPGA such that performing operations on the distributed ledger can provide provable access to the FPGA. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags executable algorithmic logic such that performing operations on the distributed ledger can provide provable access to the executable algorithmic logic; and has a distributed ledger that marks the serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags executable algorithmic logic such that performing operations on the distributed ledger can provide provable access to the executable algorithmic logic; and has a distributed ledger marking an instruction set for a crystal manufacturing system such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags executable algorithmic logic such that performing operations on the distributed ledger can provide provable access to the executable algorithmic logic; and has a distributed ledger marking an instruction set for a food preparation process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags executable algorithmic logic such that performing operations on the distributed ledger can provide provable access to the executable algorithmic logic; and has a distributed ledger marking an instruction set for a polymer production process such that performing operations on the distributed ledger can provide access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags executable algorithmic logic such that performing operations on the distributed ledger can provide provable access to the executable algorithmic logic; and has a distributed ledger marking an instruction set for a chemical synthesis process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags executable algorithmic logic such that performing operations on the distributed ledger can provide provable access to the executable algorithmic logic; and has a distributed ledger marking an instruction set for a bio-production process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags executable algorithmic logic such that performing operations on the distributed ledger can provide provable access to the executable algorithmic logic; and has a distributed ledger that marks a trade secret with an expert wrapper such that performing an operation on the distributed ledger can provide provable access to the trade secret and the wrapper provides for the verification of the trade secret by an expert. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags executable algorithmic logic such that performing operations on the distributed ledger can provide provable access to the executable algorithmic logic; and has a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags executable algorithmic logic such that performing operations on the distributed ledger can provide provable access to the executable algorithmic logic; and having a distributed ledger marking an instruction set such that performing operations on the distributed ledger provides provable access to the instruction set, and executing the instruction set on a system results in logging transactions in the distributed ledger. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags executable algorithmic logic such that performing operations on the distributed ledger can provide provable access to the executable algorithmic logic; and has a distributed ledger marking intellectual property items and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags executable algorithmic logic such that performing operations on the distributed ledger can provide provable access to the executable algorithmic logic; and a distributed ledger having an aggregate instruction set, wherein performing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags executable algorithmic logic such that performing operations on the distributed ledger can provide provable access to the executable algorithmic logic; and having a smart wrapper for managing a distributed ledger for aggregating instruction sets, wherein the smart wrapper manages allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
In an embodiment, provided herein is a transaction enabling system having a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a coating process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing operations on the distributed ledger can provide provable access to the manufacturing process. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking firmware programs such that performing operations on the distributed ledger can provide provable access to the firmware programs. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for an FPGA such that performing operations on the distributed ledger can provide provable access to the FPGA. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger that marks the serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a crystal manufacturing system such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a food preparation process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a polymer production process such that performing operations on the distributed ledger can provide access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a chemical synthesis process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a bio-production process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger that marks a trade secret with an expert wrapper such that performing an operation on the distributed ledger can provide provable access to the trade secret and the wrapper provides for the verification of the trade secret by an expert. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and having a distributed ledger marking an instruction set such that performing operations on the distributed ledger provides provable access to the instruction set, and executing the instruction set on a system results in logging transactions in the distributed ledger. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking intellectual property items and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and a distributed ledger having an aggregate instruction set, wherein performing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking a 3D printer instruction set such that performing operations on the distributed ledger can provide provable access to the instruction set; and having a smart wrapper for managing a distributed ledger for aggregating instruction sets, wherein the smart wrapper manages allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for a coating process such that performing an operation on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a coating process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing operations on the distributed ledger can provide provable access to the manufacturing process. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a coating process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking firmware programs such that performing operations on the distributed ledger can provide provable access to the firmware programs. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a coating process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for an FPGA such that performing operations on the distributed ledger can provide provable access to the FPGA. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a coating process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger that marks the serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a coating process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a crystal manufacturing system such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a coating process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a food preparation process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a coating process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a polymer production process such that performing operations on the distributed ledger can provide access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a coating process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a chemical synthesis process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a coating process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a bio-production process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a coating process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and having a distributed ledger marking a trade secret with an expert wrapper such that performing an operation on the distributed ledger can provide provable access to the trade secret and the wrapper provides expert verification of the trade secret, in embodiments, a transaction enabling system is provided having a distributed ledger marking an instruction set for a coating process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a coating process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and having a distributed ledger marking an instruction set such that performing operations on the distributed ledger provides provable access to the instruction set, and executing the instruction set on a system results in logging transactions in the distributed ledger. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a coating process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking intellectual property items and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a coating process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and a distributed ledger having an aggregate instruction set, wherein performing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a coating process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and having a smart wrapper for managing a distributed ledger for aggregating instruction sets, wherein the smart wrapper manages allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing operations on the distributed ledger can provide provable access to the manufacturing process. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing operations on the distributed ledger can provide provable access to the manufacturing process; and has a distributed ledger marking firmware programs such that performing operations on the distributed ledger can provide provable access to the firmware programs. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing operations on the distributed ledger can provide provable access to the manufacturing process; and has a distributed ledger marking an instruction set for an FPGA such that performing operations on the distributed ledger can provide provable access to the FPGA. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing operations on the distributed ledger can provide provable access to the manufacturing process; and has a distributed ledger that marks the serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing operations on the distributed ledger can provide provable access to the manufacturing process; and has a distributed ledger marking an instruction set for a crystal manufacturing system such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing operations on the distributed ledger can provide provable access to the manufacturing process; and has a distributed ledger marking an instruction set for a food preparation process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing operations on the distributed ledger can provide provable access to the manufacturing process; and has a distributed ledger marking an instruction set for a polymer production process such that performing operations on the distributed ledger can provide access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing operations on the distributed ledger can provide provable access to the manufacturing process; and has a distributed ledger marking an instruction set for a chemical synthesis process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing operations on the distributed ledger can provide provable access to the manufacturing process; and has a distributed ledger marking an instruction set for a bio-production process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing operations on the distributed ledger can provide provable access to the manufacturing process; and having a distributed ledger marking a trade secret with an expert wrapper such that performing an operation on the distributed ledger can provide provable access to the trade secret and the wrapper provides expert verification of the trade secret, in embodiments, a transaction enabling system is provided herein having a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing an operation on the distributed ledger can provide provable access to the manufacturing process; and has a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing operations on the distributed ledger can provide provable access to the manufacturing process; and having a distributed ledger marking an instruction set such that performing operations on the distributed ledger provides provable access to the instruction set, and executing the instruction set on a system results in logging transactions in the distributed ledger. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing operations on the distributed ledger can provide provable access to the manufacturing process; and has a distributed ledger marking intellectual property items and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing operations on the distributed ledger can provide provable access to the manufacturing process; and a distributed ledger having an aggregate instruction set, wherein performing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a semiconductor manufacturing process such that performing operations on the distributed ledger can provide provable access to the manufacturing process; and having a smart wrapper for managing a distributed ledger for aggregating instruction sets, wherein the smart wrapper manages allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
In an embodiment, a transaction-enabled system is provided herein having a distributed ledger that tags a firmware program such that performing operations on the distributed ledger can provide provable access to the firmware program. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags a firmware program such that performing operations on the distributed ledger can provide provable access to the firmware program; and has a distributed ledger marking an instruction set for an FPGA such that performing operations on the distributed ledger can provide provable access to the FPGA. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags a firmware program such that performing operations on the distributed ledger can provide provable access to the firmware program; and has a distributed ledger that marks the serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags a firmware program such that performing operations on the distributed ledger can provide provable access to the firmware program; and has a distributed ledger marking an instruction set for a crystal manufacturing system such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags a firmware program such that performing operations on the distributed ledger can provide provable access to the firmware program; and has a distributed ledger marking an instruction set for a food preparation process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags a firmware program such that performing operations on the distributed ledger can provide provable access to the firmware program; and has a distributed ledger marking an instruction set for a polymer production process such that performing operations on the distributed ledger can provide access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags a firmware program such that performing operations on the distributed ledger can provide provable access to the firmware program; and has a distributed ledger marking an instruction set for a chemical synthesis process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags a firmware program such that performing operations on the distributed ledger can provide provable access to the firmware program; and has a distributed ledger marking an instruction set for a bio-production process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags a firmware program such that performing operations on the distributed ledger can provide provable access to the firmware program; and has a distributed ledger that marks a trade secret with an expert wrapper such that performing an operation on the distributed ledger can provide provable access to the trade secret and the wrapper provides for the verification of the trade secret by an expert. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags a firmware program such that performing operations on the distributed ledger can provide provable access to the firmware program; and has a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags a firmware program such that performing operations on the distributed ledger can provide provable access to the firmware program; and having a distributed ledger marking an instruction set such that performing operations on the distributed ledger provides provable access to the instruction set, and executing the instruction set on a system results in logging transactions in the distributed ledger. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags a firmware program such that performing operations on the distributed ledger can provide provable access to the firmware program; and has a distributed ledger marking intellectual property items and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags a firmware program such that performing operations on the distributed ledger can provide provable access to the firmware program; and a distributed ledger having an aggregate instruction set, wherein performing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that tags a firmware program such that performing operations on the distributed ledger can provide provable access to the firmware program; and having a smart wrapper for managing a distributed ledger for aggregating instruction sets, wherein the smart wrapper manages allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for an FPGA such that performing operations on the distributed ledger can provide provable access to the FPGA. In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for an FPGA such that performing an operation on the distributed ledger can provide provable access to the FPGA; and has a distributed ledger that marks the serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic. In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for an FPGA such that performing an operation on the distributed ledger can provide provable access to the FPGA; and has a distributed ledger marking an instruction set for a crystal manufacturing system such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for an FPGA such that performing an operation on the distributed ledger can provide provable access to the FPGA; and has a distributed ledger marking an instruction set for a food preparation process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for an FPGA such that performing an operation on the distributed ledger can provide provable access to the FPGA; and has a distributed ledger marking an instruction set for a polymer production process such that performing operations on the distributed ledger can provide access to the instruction set. In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for an FPGA such that performing an operation on the distributed ledger can provide provable access to the FPGA; and has a distributed ledger marking an instruction set for a chemical synthesis process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for an FPGA such that performing an operation on the distributed ledger can provide provable access to the FPGA; and has a distributed ledger marking an instruction set for a bio-production process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for an FPGA such that performing an operation on the distributed ledger can provide provable access to the FPGA; and having a distributed ledger marking a trade secret with an expert wrapper such that performing an operation on the distributed ledger can provide provable access to the trade secret and the wrapper provides expert verification of the trade secret, in embodiments, a transaction enabling system is provided having a distributed ledger marking an instruction set for an FPGA such that performing an operation on the distributed ledger can provide provable access to the FPGA; and has a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets. In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for an FPGA such that performing an operation on the distributed ledger can provide provable access to the FPGA; and having a distributed ledger marking an instruction set such that performing operations on the distributed ledger provides provable access to the instruction set, and executing the instruction set on a system results in logging transactions in the distributed ledger. In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for an FPGA such that performing an operation on the distributed ledger can provide provable access to the FPGA; and has a distributed ledger marking intellectual property items and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items. In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for an FPGA such that performing an operation on the distributed ledger can provide provable access to the FPGA; and a distributed ledger having an aggregate instruction set, wherein performing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set. In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for an FPGA such that performing an operation on the distributed ledger can provide provable access to the FPGA; and having a smart wrapper for managing a distributed ledger for aggregating instruction sets, wherein the smart wrapper manages allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that marks serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that marks serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic; and has a distributed ledger marking an instruction set for a crystal manufacturing system such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that marks serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic; and has a distributed ledger marking an instruction set for a food preparation process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that marks serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic; and has a distributed ledger marking an instruction set for a polymer production process such that performing operations on the distributed ledger can provide access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that marks serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic; and has a distributed ledger marking an instruction set for a chemical synthesis process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that marks serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic; and has a distributed ledger marking an instruction set for a bio-production process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that marks serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic; and has a distributed ledger that marks a trade secret with an expert wrapper such that performing an operation on the distributed ledger can provide provable access to the trade secret and the wrapper provides for the verification of the trade secret by an expert. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that marks serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic; and has a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that marks serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic; and having a distributed ledger marking an instruction set such that performing operations on the distributed ledger provides provable access to the instruction set, and executing the instruction set on a system results in logging transactions in the distributed ledger. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that marks serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic; and has a distributed ledger marking intellectual property items and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that marks serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic; and a distributed ledger having an aggregate instruction set, wherein performing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that marks serverless code logic such that performing operations on the distributed ledger can provide provable access to the serverless code logic; and having a smart wrapper for managing a distributed ledger for aggregating instruction sets, wherein the smart wrapper manages allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for a crystal manufacturing system such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for a crystal manufacturing system such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a food preparation process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for a crystal manufacturing system such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a polymer production process such that performing operations on the distributed ledger can provide access to the instruction set. In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for a crystal manufacturing system such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a chemical synthesis process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for a crystal manufacturing system such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a bio-production process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for a crystal manufacturing system such that performing operations on the distributed ledger can provide provable access to the instruction set; and having a distributed ledger marking a trade secret with an expert wrapper such that performing an operation on the distributed ledger can provide provable access to the trade secret and the wrapper provides expert verification of the trade secret, in embodiments, a transaction enabling system is provided having a distributed ledger marking an instruction set for a crystal manufacturing system such that performing an operation on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets. In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for a crystal manufacturing system such that performing operations on the distributed ledger can provide provable access to the instruction set; and having a distributed ledger marking an instruction set such that performing operations on the distributed ledger provides provable access to the instruction set, and executing the instruction set on a system results in logging transactions in the distributed ledger. In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for a crystal manufacturing system such that performing operations on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking intellectual property items and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items. In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for a crystal manufacturing system such that performing operations on the distributed ledger can provide provable access to the instruction set; and a distributed ledger having an aggregate instruction set, wherein performing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set. In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for a crystal manufacturing system such that performing operations on the distributed ledger can provide provable access to the instruction set; and having a smart wrapper for managing a distributed ledger for aggregating instruction sets, wherein the smart wrapper manages allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a food preparation process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a food preparation process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a polymer production process such that performing operations on the distributed ledger can provide access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a food preparation process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a chemical synthesis process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a food preparation process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a bio-production process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a food preparation process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and having a distributed ledger marking a trade secret with an expert wrapper such that performing an operation on the distributed ledger can provide provable access to the trade secret and the wrapper provides expert verification of the trade secret, in embodiments, a transaction enabling system is provided having a distributed ledger marking an instruction set for a food preparation process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a food preparation process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and having a distributed ledger marking an instruction set such that performing operations on the distributed ledger provides provable access to the instruction set, and executing the instruction set on a system results in logging transactions in the distributed ledger. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a food preparation process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking intellectual property items and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a food preparation process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and a distributed ledger having an aggregate instruction set, wherein performing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a food preparation process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and having a smart wrapper for managing a distributed ledger for aggregating instruction sets, wherein the smart wrapper manages allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
In an embodiment, provided herein is a transaction enabled system having a distributed ledger marking an instruction set for a polymer production process such that performing operations on the distributed ledger can provide access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a polymer production process such that performing operations on the distributed ledger can provide access to the instruction set; and has a distributed ledger marking an instruction set for a chemical synthesis process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a polymer production process such that performing operations on the distributed ledger can provide access to the instruction set; and has a distributed ledger marking an instruction set for a bio-production process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a polymer production process such that performing operations on the distributed ledger can provide access to the instruction set; and having a distributed ledger marking a trade secret with an expert wrapper such that performing an operation on the distributed ledger can provide provable access to the trade secret and the wrapper provides expert verification of the trade secret, in embodiments, a transaction enabling system is provided having a distributed ledger marking an instruction set for a polymer production process such that performing an operation on the distributed ledger can provide access to the instruction set; and has a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a polymer production process such that performing operations on the distributed ledger can provide access to the instruction set; and having a distributed ledger marking an instruction set such that performing operations on the distributed ledger provides provable access to the instruction set, and executing the instruction set on a system results in logging transactions in the distributed ledger. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a polymer production process such that performing operations on the distributed ledger can provide access to the instruction set; and has a distributed ledger marking intellectual property items and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a polymer production process such that performing operations on the distributed ledger can provide access to the instruction set; and a distributed ledger having an aggregate instruction set, wherein performing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a polymer production process such that performing operations on the distributed ledger can provide access to the instruction set; and having a smart wrapper for managing a distributed ledger for aggregating instruction sets, wherein the smart wrapper manages allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a chemical synthesis process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a chemical synthesis process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking an instruction set for a bio-production process such that performing operations on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a chemical synthesis process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and having a distributed ledger marking a trade secret with an expert wrapper such that performing an operation on the distributed ledger can provide provable access to the trade secret and the wrapper provides expert verification of the trade secret, in embodiments, a transaction enabling system is provided having a distributed ledger marking an instruction set for a chemical synthesis process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a chemical synthesis process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and having a distributed ledger marking an instruction set such that performing operations on the distributed ledger provides provable access to the instruction set, and executing the instruction set on a system results in logging transactions in the distributed ledger. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a chemical synthesis process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking intellectual property items and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a chemical synthesis process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and a distributed ledger having an aggregate instruction set, wherein performing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a chemical synthesis process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and having a smart wrapper for managing a distributed ledger for aggregating instruction sets, wherein the smart wrapper manages allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a bioproduction process such that performing an operation on the distributed ledger can provide provable access to the instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a bioproduction process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and having a distributed ledger marking a trade secret with an expert wrapper such that performing an operation on the distributed ledger can provide provable access to the trade secret and the wrapper provides expert verification of the trade secret, in embodiments, a transaction enabling system is provided having a distributed ledger marking an instruction set for a bioproduction process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a bioproduction process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and having a distributed ledger marking an instruction set such that performing operations on the distributed ledger provides provable access to the instruction set, and executing the instruction set on a system results in logging transactions in the distributed ledger. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a bioproduction process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and has a distributed ledger marking intellectual property items and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a bioproduction process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and a distributed ledger having an aggregate instruction set, wherein performing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking an instruction set for a bioproduction process such that performing an operation on the distributed ledger can provide provable access to the instruction set; and having a smart wrapper for managing a distributed ledger for aggregating instruction sets, wherein the smart wrapper manages allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
In an embodiment, a transaction enabling system is provided herein having a distributed ledger marking trade secrets with expert wrappers such that performing operations on the distributed ledger can provide provable access to the trade secrets and the wrappers provide for the verification of the trade secrets by an expert. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking a trade secret with an expert wrapper such that performing an operation on the distributed ledger can provide provable access to the trade secret, and the wrapper provides for the verification of the trade secret by an expert; and has a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking a trade secret with an expert wrapper such that performing an operation on the distributed ledger can provide provable access to the trade secret, and the wrapper provides for the verification of the trade secret by an expert; and having a distributed ledger marking an instruction set such that performing operations on the distributed ledger provides provable access to the instruction set, and executing the instruction set on a system results in logging transactions in the distributed ledger. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking a trade secret with an expert wrapper such that performing an operation on the distributed ledger can provide provable access to the trade secret, and the wrapper provides for the verification of the trade secret by an expert; and has a distributed ledger marking intellectual property items and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking a trade secret with an expert wrapper such that performing an operation on the distributed ledger can provide provable access to the trade secret, and the wrapper provides for the verification of the trade secret by an expert; and a distributed ledger having an aggregate instruction set, wherein performing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking a trade secret with an expert wrapper such that performing an operation on the distributed ledger can provide provable access to the trade secret, and the wrapper provides for the verification of the trade secret by an expert; and having a smart wrapper for managing a distributed ledger for aggregating instruction sets, wherein the smart wrapper manages allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
In an embodiment, a transaction-enabled system is provided herein with a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets; and having a distributed ledger marking an instruction set such that performing operations on the distributed ledger provides provable access to the instruction set, and executing the instruction set on a system results in logging transactions in the distributed ledger. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets; and has a distributed ledger marking intellectual property items and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets; and a distributed ledger having an aggregate instruction set, wherein performing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger that aggregates views of trade secrets into a chain that proves which and how many parties have viewed the trade secrets; and having a smart wrapper for managing a distributed ledger for aggregating instruction sets, wherein the smart wrapper manages allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
In an embodiment, a transaction enabling system is provided herein having a distributed ledger that marks an instruction set such that performing an operation on the distributed ledger can provide provable access to the instruction set, and executing the instruction set on the system results in a transaction being recorded in the distributed ledger. In an embodiment, provided herein is a transaction enabling system having a distributed ledger marking a set of instructions such that performing an operation on the distributed ledger can provide provable access to the set of instructions, and executing the set of instructions on the system results in a transaction being recorded in the distributed ledger; and has a distributed ledger marking intellectual property items and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items. In an embodiment, provided herein is a transaction enabling system having a distributed ledger marking a set of instructions such that performing an operation on the distributed ledger can provide provable access to the set of instructions, and executing the set of instructions on the system results in a transaction being recorded in the distributed ledger; and a distributed ledger having an aggregate instruction set, wherein performing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set. In an embodiment, provided herein is a transaction enabling system having a distributed ledger marking a set of instructions such that performing an operation on the distributed ledger can provide provable access to the set of instructions, and executing the set of instructions on the system results in a transaction being recorded in the distributed ledger; and having a smart wrapper for managing a distributed ledger for aggregating instruction sets, wherein the smart wrapper manages allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking intellectual property items and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking intellectual property items, and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items; and a distributed ledger having an aggregate instruction set, wherein performing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger marking intellectual property items, and a reporting system reporting analysis results based on operations performed on the distributed ledger or the intellectual property items; and having a smart wrapper for managing a distributed ledger for aggregating instruction sets, wherein the smart wrapper manages allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
In an embodiment, provided herein is a transaction-enabled system having a distributed ledger aggregating instruction sets, wherein executing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set. In an embodiment, provided herein is a transaction-enabled system having a distributed ledger aggregating instruction sets, wherein performing operations on the distributed ledger adds at least one instruction to a pre-existing instruction set to provide a modified instruction set; and having a smart wrapper for managing a distributed ledger for aggregating instruction sets, wherein the smart wrapper manages allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
In an embodiment, provided herein is a transaction enabled system having an intelligent wrapper for managing a distributed ledger of an aggregated instruction set, wherein the intelligent wrapper manages the allocation of instruction subsets to the distributed ledgers and access to the instruction subsets.
The methods and systems described herein may be deployed in part or in whole by a machine executing computer software, program code, and/or instructions on a processor. The invention may be implemented as a method on a machine, as a system or apparatus associated with a machine or as part of a machine, or as a computer program product in a computer readable medium executing on one or more machines. In an embodiment, the processor may be part of a server, cloud server, client, network infrastructure, mobile computing platform, fixed computing platform, or other computing platform. A processor may be any type of computing or processing device capable of executing program instructions, code, binary instructions, etc. A processor may be or include a signal processor, a digital processor, an embedded processor, a microprocessor, or any variation thereof, such as a coprocessor (math coprocessor, graphics coprocessor, communications coprocessor, etc.), or the like, that may directly or indirectly facilitate the execution of program code or program instructions stored thereon. Further, a processor may enable execution of multiple programs, threads, and code. Threads may be executed simultaneously to enhance the performance of the processor and facilitate the simultaneous execution of applications. As an implementation, the methods, program code, program instructions, etc. described herein may be implemented in one or more threads. Threads may spawn other threads that may have assigned priorities associated therewith; the processor may execute the threads based on priority or based on any other order of instructions provided in the program code. The processor or any machine utilizing the same may include a non-transitory memory storing methods, code, instructions, and programs as described herein and elsewhere. The processor may access the non-transitory storage medium through an interface that may store the methods, code, and instructions as described herein and elsewhere. A storage medium associated with a processor for storing methods, programs, code, program instructions or other types of instructions that can be executed by a computing or processing device may include, but is not limited to, one or more of CD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache, etc.
A processor may include one or more cores that may enhance the speed and performance of the multiprocessor. In embodiments, the processor may be a dual-core processor, a quad-core processor, other chip-level multiprocessors combining two or more independent cores (referred to as a wafer bulk), and the like.
The methods and systems described herein may be deployed in part or in whole by a machine executing computer software on a server, client, firewall, gateway, hub, router, or other such computer and/or network hardware. The software programs may be associated with servers, which may include file servers, print servers, domain servers, internet servers, intranet servers, cloud servers, and other variations such as auxiliary servers, host servers, distributed servers, and the like. The server may include one or more of memory, processors, computer-readable media, storage media, ports (physical and virtual), communication devices, and interfaces that enable access to other servers, clients, machines, and devices through wired or wireless media, etc. The methods, programs, or code described herein and elsewhere may be executed by a server. In addition, other devices required to perform the methods described herein may be considered part of the infrastructure associated with the server.
The server may provide interfaces to other devices including, but not limited to, clients, other servers, printers, database servers, print servers, file servers, communication servers, distributed servers, social networks, and the like. In addition, such coupling and/or connection may facilitate executing programs remotely across a network. Networking of some or all of these devices may facilitate parallel processing of programs or methods at one or more locations without departing from the scope of the present invention. In addition, any device attached to a server through an interface may include at least one storage medium capable of storing methods, programs, code, and/or instructions. The central repository may provide program instructions to be executed on different devices. In this embodiment, the remote store may act as a storage medium for program code, instructions, and programs.
The software program may be associated with a client, which may include a file client, a print client, a domain client, an internet client, an intranet client, and other variations such as a secondary client, a host client, a distributed client, and the like. Clients may include one or more of memory, processors, computer-readable media, storage media, ports (physical and virtual), communication devices, and interfaces that enable access to other clients, servers, machines, and devices through wired or wireless media, etc. The methods, programs, or code described herein and elsewhere may be executed by a client. In addition, other devices required to perform the methods described herein may be considered part of the infrastructure associated with the client.
The client may provide an interface to other devices including, but not limited to, servers, other clients, printers, database servers, print servers, file servers, communication servers, distributed servers, and the like. In addition, such coupling and/or connection may facilitate executing programs remotely across a network. Networking of some or all of these devices may facilitate parallel processing of programs or methods at one or more locations without departing from the scope of the present invention. In addition, any device attached to the client through the interface may include at least one storage medium capable of storing methods, programs, applications, code, and/or instructions. The central repository may provide program instructions to be executed on different devices. In this embodiment, the remote store may act as a storage medium for program code, instructions, and programs.
The methods and systems described herein may be deployed, in part or in whole, through a network infrastructure. The network infrastructure may include elements such as computing devices, servers, routers, hubs, firewalls, clients, personal computers, communication devices, routing devices, and other active and passive devices, modules, and/or components known in the art. One or more computing and/or non-computing devices associated with the network infrastructure may include storage media such as flash memory, buffers, stacks, RAM, ROM, etc., among other components. The processes, methods, program code, instructions described herein and elsewhere may be executed by one or more network infrastructure elements. The methods and systems described herein may be used with any type of private, community, or hybrid cloud computing network or cloud computing environment, including those involving features of software-as-a-service (SaaS), platform-as-a-service (PaaS), and/or infrastructure-as-a-service (LaaS).
The methods, program code, and instructions described herein and elsewhere may be implemented on a cellular network having a plurality of cells. The cellular network may be a Frequency Division Multiple Access (FDMA) network or a Code Division Multiple Access (CDMA) network. The cellular network may include mobile devices, cellular sites, base stations, repeaters, antennas, towers, and the like. The cellular network may be GSM, GPRS, 3G, EVDO, mesh or other network types.
The methods, program code, and instructions described herein and elsewhere may be implemented on or by a mobile device. The mobile device may include a navigation device, a cellular telephone, a mobile personal digital assistant, a laptop computer, a palmtop computer, a netbook, a pager, an electronic book reader, a music player, etc. These devices may include storage media such as flash memory, buffers, RAM, ROM, and one or more computing devices, among other components. Computing devices associated with the mobile devices may be enabled to execute program code, methods, and instructions stored thereon. Alternatively, the mobile device may be used to execute instructions in cooperation with other devices. The mobile device may communicate with a base station that interfaces with the server and is used to execute program code. The mobile device may communicate over a point-to-point network, a mesh network, or other communication network. The program code may be stored on a storage medium associated with a server and executed by a computing device embedded within the server. The base station may include a computing device and a storage medium. The storage device may store program code and instructions for execution by a computing device associated with a base station.
Computer software, program code, and/or instructions may be stored and/or accessed on a machine readable medium, which may include: computer components, devices, and recording media that retain calculated digital data for certain time intervals; a semiconductor memory, which is called a Random Access Memory (RAM); mass storage, such as in the form of optical disks, magnetic storage (e.g., hard disks, magnetic tapes, drums, cards, and other types), typically used for more permanent storage; processor registers, cache memory, volatile memory, and non-volatile memory; optical storage such as CD, DVD; removable media such as flash memory (e.g., a U disk or a key), floppy disks, magnetic tape, paper tape, punch cards, stand-alone RAM disk, zip drives, removable mass storage, offline storage, etc.; other computer memory, such as dynamic memory, static memory, read/write memory, variable memory, read only memory, random access memory, sequential access memory, location addressable memory, file addressable memory, content addressable memory, network attached storage devices, storage area networks, bar codes, magnetic ink, and the like.
The methods and systems described herein may transition physical and/or intangible articles from one state to another. The methods and systems described herein may also transition data representing physical and/or intangible items from one state to another.
The elements described and depicted herein, including the flowchart and block diagrams throughout the figures, imply logical boundaries between the elements. However, according to software or hardware engineering practices, the illustrated elements and their functions may be implemented by computer-executable media on a machine having a processor capable of executing program instructions stored thereon as a monolithic software structure, as stand-alone software modules, or as modules employing external routines, code, services, etc., or any combination of these, and all such implementations may be within the scope of the present invention. Examples of such machines may include, but are not limited to, personal digital assistants, laptop computers, personal computers, cell phones, other handheld computing devices, medical devices, wired or wireless communication devices, transducers, chips, calculators, satellites, tablets, electronic books, gadgets, electronic devices, devices with artificial intelligence, computing devices, network devices, servers, routers, and the like. Furthermore, the elements shown in the flowcharts and block diagrams, or any other logical components, may be implemented on a machine capable of executing program instructions. Thus, while the foregoing figures and description set forth functional aspects of the disclosed systems, no particular arrangement of software for implementing such functional aspects should be inferred from such description unless explicitly stated or otherwise clear from the context. Similarly, it should be appreciated that the various steps identified and described above may be varied, and that the order of the steps may be adapted to the specific application of the techniques disclosed herein. All such variations and modifications are intended to be within the scope of the present invention. Thus, the order in which individual steps are explained and/or described should not be construed as requiring that the steps be performed in a specific order unless specifically identified by a particular application or by explicitly described or readily apparent from the context.
The methods and/or processes described above and the steps associated therewith may be implemented in hardware, software, or any combination of hardware and software as appropriate for the particular application. The hardware may include a general purpose computer, and/or a special purpose computing device, or a particular computing device, or particular aspects or components of a particular computing device. These processes may be implemented in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable devices, and internal and/or external memory. These processes may also or alternatively be embodied in application specific integrated circuits, programmable gate arrays, programmable array logic, or any other device or combination of devices that can be used to process electronic signals. It should also be understood that one or more of the processes may be implemented as computer executable code capable of being executed on a machine readable medium.
Computer-executable code may be created using a structured programming language (such as C), an object oriented programming language (such as c++), or any other high-level or low-level programming language (including assembly languages, hardware description languages, and database programming languages) that may be stored, compiled or interpreted to run on one of the above devices, as well as heterogeneous combinations of processors, processor architectures, or combinations of different hardware and software, or any other machine capable of executing program instructions.
Thus, in one aspect, the above-described methods and combinations thereof can be embodied in computer-executable code that, when executed on one or more computing devices, performs the steps thereof. In another aspect, the method may be embodied in a system that performs its steps and may be distributed across devices in a number of ways, or all of the functions may be integrated into a dedicated, stand-alone device or other hardware. In another aspect, the means for performing the steps associated with the processes described above may include any of the hardware and/or software described above. All such permutations and combinations are intended to fall within the scope of the invention.
While the invention has been disclosed in conjunction with the preferred embodiments shown and described in detail, various modifications and improvements will readily occur to those skilled in the art. Accordingly, the spirit and scope of the present invention is not limited by the foregoing examples, but should be construed in the broadest sense permitted under law.
The use of the terms "a" and "an" and "the" and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. Unless otherwise indicated, the terms "comprising," "having," "including," and "containing" are to be construed as open-ended terms (i.e., meaning "including, but not limited to"). Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., "such as") provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
While the foregoing written description enables one to make and use what is presently considered to be the best mode thereof, those skilled in the art will understand and appreciate the existence of variations, combinations, and equivalents of the specific embodiments, methods, and examples herein. Thus, the present invention should not be limited by the above-described embodiments, methods, and examples, but by all embodiments and methods within the scope and spirit of the present invention.
Any element of a claim that does not explicitly state a "means" for performing a specified function or a "step" for performing a specified function should not be construed as a "means" or a "step" clause as specified in the united states code 35 u.s.c. ≡112 (f). In particular, any use of "step" in the claims is not intended to recite the provision of american act 35 u.s.c. ζ112 (f). The term "set" as used herein refers to a group having one or more members.
Those skilled in the art will appreciate that there are numerous design configurations that may be used to enjoy the functional benefits of the present system. Accordingly, in view of the various configurations and arrangements of the embodiments of the present invention, the scope of the invention is reflected in the following claims and is not limited to the above-described embodiments.

Claims (85)

1. A knowledge distribution system for controlling digital knowledge-related rights, the system comprising:
an input system for receiving an instance of digital knowledge from a user;
a tagging system for tagging the digital knowledge such that an instance of the digital knowledge operates as a tag;
the ledger management system is used for:
creating and managing a distributed ledger; and
storing the tagged digital knowledge by the distributed ledger; and
an intelligent contract system in communication with the distributed ledger, the intelligent contract system to:
implementing a smart contract through the distributed ledger, wherein the smart contract includes marked digital knowledge, trigger events, and corresponding smart contract actions;
performing a smart contract action against the marked digital knowledge in response to the occurrence of the trigger event;
processing commitments of a plurality of parties to the smart contract;
managing control and access rights to the marked digital knowledge according to the intelligent contract; and
managing the smart contract actions in response to the trigger event;
wherein the distributed ledger comprises a plurality of encrypted link blocks distributed over a plurality of nodes of a network.
2. The knowledge distribution system of claim 1, wherein the tagged digital knowledge comprises intellectual property of an intellectual property holder, wherein the smart contract system is further to:
embedding intellectual property licensing terms for the intellectual property in the distributed ledger; and
performing operations on the distributed ledger in response to the trigger event to: 1) Providing access to the intellectual property rights; or 2) process commitments of one of the plurality of principals to the intelligent contract and corresponding intellectual property licensing terms.
3. The knowledge distribution system of claim 1, wherein the smart contract further comprises a smart contract wrapper for adding intellectual property to an intellectual property aggregation stack.
4. The knowledge distribution system of claim 3, wherein the smart contract further comprises a smart contract wrapper to perform operations on the distributed ledger to add intellectual property and to have parties in the distributed ledger promise to allocate licensing fees for the added intellectual property.
5. The knowledge distribution system of claim 4, wherein the smart contract wrapper is further to add the added intellectual property to an intellectual property aggregation stack in the distributed ledger and to have parties in the distributed ledger promise to allocate licensing fees for the intellectual property aggregation stack.
6. The knowledge distribution system of claim 1, wherein the smart contract further comprises a smart contract wrapper for handling commitments of parties to contract terms on the distributed ledger.
7. The knowledge distribution system of claim 1, wherein the tagged digital knowledge comprises a set of instructions.
8. The knowledge distribution system of claim 7, wherein the ledger administration system is further to:
providing provable access to the instruction set; and
executing the set of instructions on a system;
wherein providing provable access includes logging access transactions in the distributed ledger.
9. The knowledge distribution system of claim 1, wherein the tagged digital knowledge comprises executable algorithm logic.
10. The knowledge distribution system of claim 1, wherein the marked digital knowledge comprises a three-dimensional (3D) printer instruction set.
11. The knowledge distribution system of claim 1, wherein the tagged digital knowledge comprises an instruction set for a coating process.
12. The knowledge distribution system of claim 1, wherein the tagged digital knowledge comprises a set of instructions for a semiconductor manufacturing process.
13. The knowledge distribution system of claim 1, wherein the tagged digital knowledge comprises a firmware program.
14. The knowledge distribution system of claim 1, wherein the tagged digital knowledge comprises an instruction set for a field programmable gate array.
15. The knowledge distribution system of claim 1, wherein the tagged digital knowledge comprises serverless code logic.
16. The knowledge distribution system of claim 1, wherein the tagged digital knowledge comprises an instruction set for a crystal manufacturing system.
17. The knowledge distribution system of claim 1, wherein the tagged digital knowledge comprises a set of instructions for a food preparation process.
18. The knowledge distribution system of claim 1, wherein the tagged digital knowledge comprises a set of instructions for a polymer production process.
19. The knowledge distribution system of claim 1, wherein the tagged digital knowledge comprises a set of instructions for a chemical synthesis process.
20. The knowledge distribution system of claim 1, wherein the tagged digital knowledge comprises a set of instructions for a bio-production process.
21. The knowledge distribution system of claim 1, wherein the tagged digital knowledge comprises a dataset for digital twinning.
22. The knowledge distribution system of claim 1, wherein the tagged digital knowledge comprises a set of instructions for executing a trade secret.
23. The knowledge distribution system of claim 1, wherein the ledger administration system is further to aggregate views of trade secrets into a chain that records which knowledge recipients of the plurality of principals have viewed the trade secrets.
24. The knowledge distribution system of claim 1, further comprising a reporting system for reporting analysis results based on a plurality of operations performed on the distributed ledger or based on the tagged digital knowledge.
25. The knowledge distribution system of claim 1, wherein the smart contract system is further to aggregate instruction sets, and wherein performing operations on the distributed ledger comprises adding at least one instruction to a pre-existing instruction set to provide a modified instruction set.
26. The knowledge distribution system of claim 25, wherein the smart contract system is further to:
managing the allocation of subsets of instructions to the distributed ledgers; and
access to the subset of instructions is managed.
27. The knowledge distribution system of claim 1, wherein the ledger administration system is further configured to record at least one of the plurality of parties contributing to the digital knowledge, and wherein recording the at least one of the plurality of parties comprises storing data related to the at least one of the plurality of parties in at least one of the plurality of encrypted link blocks of the distributed ledger.
28. The knowledge distribution system of claim 1, wherein the smart contract system is further for recording a source of the instance of digital knowledge by storing data related to the source in at least one of the plurality of cryptographically linked blocks.
29. The knowledge distribution system of claim 1, wherein the distributed ledger is further operable to enable a private network of authorized participants to establish encryption-based consensus requirements to verify new encrypted link blocks to be added to the plurality of encrypted link blocks.
30. The knowledge distribution system of claim 1, wherein the ledger administration system further comprises a crowdsourcing module to obtain crowdsourcing information for blocks to be added to the plurality of encrypted link blocks.
31. The knowledge distribution system of claim 30, wherein the crowdsourcing information includes comments on an instance of the digital knowledge; and wherein the distributed ledger is further configured to store the comments in the block of the plurality of cryptographically linked blocks.
32. The knowledge distribution system of claim 30, wherein the crowdsourcing information further comprises a signature associated with an instance of crowdsourcing information; and wherein the ledger administration system is further configured to store the signature in a block of the plurality of cryptographically linked blocks.
33. The knowledge distribution system of claim 30, wherein the crowdsourcing information includes verification of an instance of the digital knowledge; and wherein the distributed ledger is further configured to store the verification in a block of the plurality of cryptographically linked blocks.
34. The knowledge distribution system of claim 1, wherein the ledger management system is further to establish a plurality of cryptocurrency tokens configured to be transactable between users of the distributed ledger.
35. The knowledge distribution system of claim 1, further comprising an account management system in communication with the distributed ledger, the account management system to facilitate creation and management of a plurality of user accounts corresponding to a plurality of users of the knowledge distribution system.
36. The knowledge distribution system of claim 35, further comprising a user interface system in communication with the distributed ledger for presenting a user interface to a user of the knowledge distribution system, wherein the user interface enables the user to view data related to an instance of the digital knowledge.
37. The knowledge distribution system of claim 1, further comprising a marketplace system in communication with the distributed ledger, the marketplace system to:
establishing and maintaining a digital market; and
data corresponding to the instance of digital knowledge is visually presented to a user of the knowledge distribution system.
38. The knowledge distribution system of claim 1, further comprising a knowledge data store in communication with the distributed ledger, the knowledge data store to store data related to the digital knowledge.
39. The knowledge distribution system of claim 1, further comprising a client data store in communication with the distributed ledger, wherein the client data store is to store data related to a plurality of users of the knowledge distribution system.
40. The knowledge distribution system of claim 1, further comprising a smart contract data store in communication with the distributed ledger, wherein the smart contract data store is to store data related to the smart contract.
41. The knowledge distribution system of claim 1, further comprising a reporting system in communication with the distributed ledger, the reporting system to:
analyzing the digital knowledge of the tag to produce an analysis result; and reporting the analysis result.
42. The knowledge distribution system of claim 1, wherein implementing the smart contract comprises generating the smart contract using a parameterizable smart contract template.
43. The knowledge distribution system of claim 1, wherein the smart contract includes parameters based on a type of digital knowledge to be tagged.
44. The knowledge distribution system of claim 43, wherein the parameters comprise: financial parameters, license fee parameters, usage parameters, yield parameters, price allocation parameters, identity parameters, or access condition parameters.
45. A computer-implemented method for controlling digital knowledge-related rights, the computer-implemented method comprising:
creating and managing a distributed ledger, wherein the distributed ledger comprises a plurality of blocks linked by encryption distributed over a plurality of nodes of a network;
implementing and managing a smart contract, wherein the smart contract includes a triggering event and a corresponding smart contract action and is stored in the distributed ledger;
Receiving an instance of the digital knowledge;
marking the digital knowledge;
storing the tagged digital knowledge by the distributed ledger;
processing commitments of a plurality of parties to the smart contract;
managing control and access rights to the marked digital knowledge according to the intelligent contract; and
the corresponding smart contract action is performed against the marked digital knowledge in response to the occurrence of the trigger event.
46. The computer-implemented method of claim 45, further comprising orchestrating an exchange of new digital knowledge based on the intelligent totals about the tagged digital knowledge.
47. The computer-implemented method of claim 46, further comprising integrating the knowledge exchange with a separate exchange, wherein the knowledge exchange facilitates exchange of at least one of valuable knowledge and sensitive knowledge related to a topic of the separate exchange.
48. A knowledge distribution system for controlling digital knowledge-related rights, the system comprising:
an input system for receiving an instance of digital knowledge from a knowledge provider device, the instance of digital knowledge comprising a 3D printer instruction set for a three-dimensional (3D) print object;
A tagging system for tagging the digital knowledge such that an instance of the digital knowledge operates as a tag;
the ledger management system is used for:
creating and managing a distributed ledger;
storing, by the distributed ledger, a smart contract; and
storing the tagged digital knowledge by the distributed ledger;
an intelligent contract system in communication with the distributed ledger for:
implementing and managing a smart contract, wherein the smart contract includes a triggering event and a corresponding smart contract action;
performing a smart contract action against the digital knowledge in response to the occurrence of the trigger event;
processing commitments of the intelligent contract by the knowledge provider and knowledge receiver of the 3D printer instruction set;
managing control and access rights to the marked digital knowledge according to the intelligent contract; and
managing the smart contract actions according to conditions and the trigger event;
wherein the distributed ledger comprises a plurality of encrypted link blocks distributed over a plurality of nodes of a network.
49. The knowledge distribution system of claim 48, wherein the 3D printer instruction set comprises a 3D print schematic.
50. The knowledge distribution system of claim 48, wherein the object comprises a custom part, a custom product, a manufacturing part, a replacement part, a toy, a medical device, or a tool.
51. The knowledge distribution system of claim 48, wherein the smart contract actions include providing the 3D printer instruction set to a knowledge receiver device for downloading and using the 3D printer instruction set, wherein the knowledge receiver device is a computing device, a server, a 3D printer, or a manufacturing device.
52. The knowledge distribution system of claim 48, wherein the smart contract actions comprise: a purchase request is received from or performed by a knowledge receiver device, wherein the purchase request includes a request to purchase the tagged digital knowledge corresponding to the 3D printer instruction set.
53. The knowledge distribution system of claim 48, further comprising an event monitoring module for monitoring an Application Programming Interface (API) for providing a connection between the knowledge distribution system and a knowledge receiver device of the knowledge receiver.
54. The knowledge distribution system of claim 48, wherein the triggering event comprises transmitting the 3D printer instruction or using the 3D instruction; and wherein the smart contract action includes generating a payment request for the knowledge recipient based on the control rights and the access rights to the tagged digital knowledge.
55. The knowledge distribution system of claim 48, wherein the control rights and the access rights to the tagged digital knowledge comprise allowing a user to 3D print using multiple instances of the 3D printer instruction set.
56. The knowledge distribution system of claim 48, wherein the control rights and the access rights to the tagged digital knowledge comprise: the 3D printer asks for, the period of time that the object can be 3D printed, whether the marked digital knowledge is transferred to a downstream knowledge receiver, guarantees, disclaimers, reimbursements or authentication on the object.
57. The knowledge distribution system of claim 48, wherein the triggering event is transmitting the 3D printer instruction or using the 3D instruction; and wherein the smart contract action modifies a purchase, download, or use time of the 3D printer instruction set on the distributed ledger based on the control rights and the access rights to the digital knowledge of the tag.
58. The knowledge distribution system of claim 48, wherein the 3D printer instruction set comprises: source, date of creation, name of contributing person, group or company, price, market trend of related schematic, serial number or part identifier.
59. The knowledge distribution system of claim 48, wherein the smart contract actions comprise: assigning a serial number to the 3D printed object; monitoring the trigger event; verifying performance of the obligation based on the condition; verifying payment or transfer of the marked digital knowledge; transferring the digital knowledge of the tag; recording one or more transactions in the distributed ledger; performing one or more operations on the distributed ledger; or create one or more new blocks in the distributed ledger.
60. The knowledge distribution system of claim 48, wherein the smart contract action comprises verifying that the condition is satisfied, wherein the condition is: printer requirements, payments received, money transferred from the knowledge receiver device of the knowledge receiver or digital knowledge of the tag is transferred to the knowledge receiver device.
61. The knowledge distribution system of claim 48, further comprising a smart contract generator for parameterizing a smart contract template based on information provided by the knowledge provider, the conditions, or the trigger events.
62. A computer-implemented method for controlling digital knowledge-related rights, comprising:
creating and managing a distributed ledger, wherein the distributed ledger comprises a plurality of blocks linked by encryption distributed over a plurality of nodes of a network;
implementing and managing a smart contract, wherein the smart contract includes a triggering event;
performing a smart contract action against the digital knowledge in response to the occurrence of the trigger event;
receiving an instance of the digital knowledge from a knowledge provider device, the instance of the digital knowledge comprising a 3D printer instruction set for a three-dimensional (3D) print object;
tagging the digital knowledge such that the instance of the digital knowledge operates as a tag on the distributed ledger;
storing the tagged digital knowledge on the distributed ledger;
processing commitments of the intelligent contract by the knowledge provider and knowledge receiver of the 3D printer instruction set;
managing control and access rights to the marked digital knowledge according to the intelligent contract; and
and managing the intelligent contract action according to the condition and the trigger event.
63. The computer-implemented method of claim 62, further comprising crowd-sourcing elements of the instance of the digital knowledge through the smart contract, wherein the elements of the instance of the digital knowledge are managed by a smart contract system in accordance with the smart contract.
64. The computer-implemented method of claim 62, further comprising:
crowd sourcing information about: an element of the instance of the digital knowledge, the knowledge provider, or a knowledge receiver; and
the smart contract is updated in response to the crowd-sourced information.
65. The computer-implemented method of claim 64, further comprising updating a condition or a smart contract action based at least in part on the crowd-sourced information.
66. A knowledge distribution system for controlling digital knowledge-related rights, the system comprising:
an input system for receiving an instance of digital knowledge from a user;
a tagging system for tagging the digital knowledge such that an instance of the digital knowledge operates as a tag;
the ledger management system is used for:
creating and managing a distributed ledger;
storing the tagged digital knowledge by the distributed ledger; and
providing provable access to the digital knowledge, wherein providing provable access includes recording an access transaction in the distributed ledger; and
an intelligent contract system in communication with the distributed ledger, the intelligent contract system to:
Implementing a smart contract through the distributed ledger, wherein the smart contract includes marked digital knowledge and triggering events;
performing a smart contract action against the marked digital knowledge in response to the occurrence of the trigger event;
managing the smart contract actions in response to the trigger event;
processing commitments of a plurality of parties to the smart contract; and
and managing control rights and access rights to the marked digital knowledge according to the intelligent contract.
67. The knowledge distribution system of claim 66, wherein the smart contract further comprises a smart contract wrapper for adding intellectual property to an intellectual property aggregation stack.
68. The knowledge distribution system of claim 66, wherein the smart contract further comprises a smart contract wrapper for:
performing operations on the distributed ledger to add intellectual property;
causing parties in the distributed ledger to promise to apportion licensing fees for the added intellectual property rights; and
a commitment of a principal to contract terms on the distributed ledger is processed.
69. The knowledge distribution system of claim 66, further comprising an account management system in communication with the distributed ledger, the account management system to facilitate creation and management of a plurality of user accounts corresponding to a plurality of users of the knowledge distribution system.
70. The knowledge distribution system of claim 66, further comprising a user interface system in communication with the distributed ledger, the user interface system to present a user interface to a user of the knowledge distribution system, wherein the user interface enables the user to view data related to an instance of the digital knowledge.
71. The knowledge distribution system of claim 66, further comprising a marketplace system in communication with the distributed ledger, the marketplace system to:
establishing and maintaining a digital market; and
data corresponding to the instance of digital knowledge is visually presented to a user of the knowledge distribution system.
72. The knowledge distribution system of claim 66, further comprising a knowledge data store in communication with the distributed ledger, the knowledge data store to store data related to the digital knowledge.
73. The knowledge distribution system of claim 66, further comprising a client data store in communication with the distributed ledger, wherein the client data store is to store data related to a plurality of users of the knowledge distribution system.
74. The knowledge distribution system of claim 66, further comprising a smart contract data store in communication with the distributed ledger, wherein the smart contract data store is to store data related to the smart contracts.
75. The knowledge distribution system of claim 66, further comprising a reporting system in communication with the distributed ledger, the reporting system to:
analyzing the digital knowledge of the tag to produce an analysis result; and
reporting the analysis results.
76. The knowledge distribution system of claim 66, wherein implementing the smart contract comprises generating the smart contract using a parameterizable smart contract template.
77. The knowledge distribution system of claim 76, wherein the smart contract includes parameters based on the type of digital knowledge to be tagged.
78. A computer-implemented method for controlling digital knowledge-related rights, the computer-implemented method comprising:
creating and managing a distributed ledger, wherein the distributed ledger comprises a plurality of blocks linked by encryption distributed over a plurality of nodes of a network;
marking the digital knowledge;
storing the tagged digital knowledge by the distributed ledger;
implementing and managing a smart contract, wherein the smart contract includes trigger events, knowledge of the tags, and corresponding smart contract actions, and is stored in the distributed ledger;
Receiving an instance of the digital knowledge;
processing commitments of a plurality of parties to the smart contract;
managing control and access rights to the marked digital knowledge according to the intelligent contract;
performing the corresponding smart contract action for the marked digital knowledge in response to the occurrence of the trigger event; and
the smart contract actions are managed in response to the trigger event.
79. The computer-implemented method of claim 78, further comprising:
crowd sourcing information about: elements of the instance of the digital knowledge; and
the smart contract is updated in response to the crowd-sourced information.
80. The computer-implemented method of claim 79, wherein the crowdsourcing information comprises information regarding: knowledge provider or knowledge receiver.
81. The computer-implemented method of claim 78, further comprising:
adding intellectual property to the distributed ledger;
committing the principal to allocate a licensing fee for the added intellectual property; and
the principal's promise to agree to the terms of the agreement is handled.
82. The computer-implemented method of claim 78, further comprising:
Creating a user account;
receiving a request from a user account to display data related to an instance of the digital knowledge;
confirm access to the instance of the digital knowledge allowed by the user account; and
a user interface is presented for displaying the data related to the instance of the digital knowledge.
83. The computer-implemented method of claim 82, further comprising:
creating a user account; and
issuing a public access key and a private access key to the user account;
wherein the access key corresponds to a respective access level.
84. The computer-implemented method of claim 78, further comprising purchasing or selling the digital knowledge.
85. The computer-implemented method of claim 78, further comprising creating and issuing a monetary token associated with the distributed ledger.
CN202180063278.1A 2020-07-16 2021-07-16 System and method for controlling digital knowledge dependent rights Pending CN116113967A (en)

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