EP4264533A2 - Système d'orchestration de marché pour faciliter les transactions de places de marché électroniques - Google Patents

Système d'orchestration de marché pour faciliter les transactions de places de marché électroniques

Info

Publication number
EP4264533A2
EP4264533A2 EP21907893.8A EP21907893A EP4264533A2 EP 4264533 A2 EP4264533 A2 EP 4264533A2 EP 21907893 A EP21907893 A EP 21907893A EP 4264533 A2 EP4264533 A2 EP 4264533A2
Authority
EP
European Patent Office
Prior art keywords
knowledge
digital
role
marketplace
smart contract
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP21907893.8A
Other languages
German (de)
English (en)
Inventor
Charles Howard CELLA
Andrew Cardno
Taylor D. Charon
Teymour S. EL-TAHRY
Richard A. SPITZ
Jenna Lynn Parenti
JR. Leon R. FORTIN
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Strong Force Tx Portfolio 2018 LLC
Original Assignee
Strong Force Tx Portfolio 2018 LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from PCT/US2021/042050 external-priority patent/WO2022016102A1/fr
Application filed by Strong Force Tx Portfolio 2018 LLC filed Critical Strong Force Tx Portfolio 2018 LLC
Publication of EP4264533A2 publication Critical patent/EP4264533A2/fr
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/18—Legal services
    • G06Q50/184—Intellectual property management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N10/00—Quantum computing, i.e. information processing based on quantum-mechanical phenomena
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
    • B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
    • B29C64/00—Additive manufacturing, i.e. manufacturing of three-dimensional [3D] objects by additive deposition, additive agglomeration or additive layering, e.g. by 3D printing, stereolithography or selective laser sintering
    • B29C64/30—Auxiliary operations or equipment
    • B29C64/386—Data acquisition or data processing for additive manufacturing
    • B29C64/393—Data acquisition or data processing for additive manufacturing for controlling or regulating additive manufacturing processes
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B33—ADDITIVE MANUFACTURING TECHNOLOGY
    • B33Y—ADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL [3-D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3-D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING
    • B33Y50/00—Data acquisition or data processing for additive manufacturing
    • B33Y50/02—Data acquisition or data processing for additive manufacturing for controlling or regulating additive manufacturing processes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q2220/00—Business processing using cryptography
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y30/00—IoT infrastructure
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/32—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials

Definitions

  • Exchanges and other marketplaces provide a range of critical functions for their stakeholders, including the ability to find counterparties who are willing to engage in transactions involving a wide range of asset classes.
  • exchange transactions allow parties to unlock liquidity, execute financial strategies (such as with arbitrage), manage risk (such as with options and futures contracts), aggregate capital, convert value from one asset class to another, participate in gains from trade, influence behavior, and obtain insight (such as from data streams about transactions).
  • Successful marketplaces like the New York Stock Exchange (NYSE) and the Chicago Mercantile Exchange (CME) are fundamental components of the global economy, and new exchanges emerge regularly for new categories.
  • Some of the information is subject to access and control restrictions, such as restrictions on who can view, edit, change, use, transmit, sell, buy, rent, review, license, and source the digital information (e.g., vis-a-vis patent licenses, trademark licenses, contract agreements, copyright licenses, and the like).
  • access and control restrictions such as restrictions on who can view, edit, change, use, transmit, sell, buy, rent, review, license, and source the digital information (e.g., vis-a-vis patent licenses, trademark licenses, contract agreements, copyright licenses, and the like).
  • Setting and enforcing access and control restrictions is difficult, as any computer- based system for doing so has potential flaws, such as risks of impropriety or unreliability of an owner or maintainer of the system, or risks of other parties gaining unauthorized access and illegitimately accessing, copying, editing, or otherwise tampering with the digital knowledge.
  • a cryptographically secure blockchain for knowledge system capable of storing digital knowledge and providing convenient
  • Lending transactions provide financing for a wide variety of needs, ranging from housing and education to corporate and government projects, among many others, while enabling lenders to earn financial returns.
  • lending transactions are plagued by a number of problems, including opacity and asymmetry of information, moral hazard induced by shifting of the consequences of risky or inappropriate behavior, complexity of application and negotiation processes, burdensome regulatory and policy regimes, difficulty in determining the value of property that is used as collateral or backing for obligations, difficulty in determining the reliability or financial health of entities, and others.
  • Machines and automated agents are increasingly involved in market activities, including for data collection, forecasting, planning, transaction execution, and other activities.
  • energy consumption has become a major factor in utilization of computing resources, such that energy resources and computing resources (or simply “energy and compute”) have begun to converge from various standpoints, such as requisitioning, purchasing, provisioning, configuration, and management of inputs, activities, outputs, and the like. Projects have been undertaken, for example, to place large scale computing resource facilities, such as BitcoinTM or other cryptocurrency mining operations, in close proximity to large-scale hydropower sources, such as Niagara Falls.
  • large scale computing resource facilities such as BitcoinTM or other cryptocurrency mining operations
  • a major challenge for facility owners and operators is the uncertainty involved in optimizing a facility, such as resulting from volatility in the cost and availability of inputs (in particular where less stable renewable resources are involved), variability in the cost and availability of computing and networking resources (such as where network performance varies), and volatility and uncertainty in various end markets to which energy and compute resources can be applied (such as volatility in cryptocurrencies, volatility in energy markets, volatility in pricing in various other markets, and uncertainty in the utility of artificial intelligence in a wide range of applications), among other factors.
  • Market orchestration enables the identification, configuration, and execution of a wide range of marketplace types where goods, services or other items or assets are exchanged. These marketplaces may support traditional asset classes (such as equities, commodities, and bonds) and nontraditional asset classes (such as human-delivered services and entertainment experiences). While traditional marketplaces are established for long periods of time and operate with a consistent set of assets, market orchestration allows for the configuration and execution of a variety of marketplaces with customizable parameters, bringing much needed flexibility to trading.
  • traditional asset classes such as equities, commodities, and bonds
  • nontraditional asset classes such as human-delivered services and entertainment experiences
  • the method may include identifying, by a processing system, an opportunity to facilitate a new marketplace.
  • the method may include receiving, by the processing system, marketplace opportunity data.
  • the marketplace opportunity data may include information related to one or more assets.
  • the method may include determining, by the processing system, configuration parameters to be implemented in the new marketplace.
  • the method may include determining, by the processing system, the feasibility of implementing the configuration parameters in the new marketplace.
  • the method may include determining, by the processing system, databases to support the new marketplace.
  • the method may include determining, by the processing system, architecture of the new marketplace.
  • the method may include determining, by the processing system, the design of data within the selected database environment.
  • the method may include configuring, by the processing system, a marketplace object.
  • the method may include connecting, by the processing system, the new marketplace
  • the method may include generating, by a processing system, a digital twin of a marketplace.
  • the digital twin may be a digital representation of the structure of the marketplace.
  • the method may include determining, by the processing system, a set of relationships between different roles within the set of roles based on the marketplace.
  • the method may include determining, by the processing system, a set of settings for a role from the set of roles based on the inferred relationships.
  • the method may include using the inferred relationships within the marketplace structure to provide a set of settings for a set of roles.
  • the method may include linking a set of identities to roles.
  • the method may include determining, by the processing system, a configuration of a presentation layer of a role-based market orchestration digital twin corresponding to the role based on the settings of the at least one role that is linked to the identity.
  • the configuration system of the presentation layer may define a set of states that is depicted in the role-based market orchestration digital twin associated with the role.
  • the method may include determining, by a processing system, a set of data sources that provide data corresponding to the set of states. Each data source may provide one or more respective types of data.
  • the method may include configuring one or more data structures that store the data that is received from the one or more data sources.
  • the one or more data structures may be configured to provide data used to populate one or more of the set of states in the role-based market orchestration digital twin.
  • the method may include linking a set of identities to the set of roles.
  • each identity may correspond to a respective role from the set of roles.
  • a role-based market orchestration digital twin may integrate with a market orchestration platform that operates on the role-based market orchestration digital twin such that changes in the market orchestration platform are automatically reflected in the role-based market orchestration digital twin.
  • the set of settings for the set of roles includes role-based permission settings.
  • the set of settings for the set of roles includes role-based preference settings.
  • the role-based preference settings are configured based on a set of role-specific templates.
  • the set of templates includes at least one of a trader template, a marketplace host template, a broker template, a buyer template, and a seller template.
  • the set of settings for the set of roles includes role-based taxonomy settings.
  • the taxonomy settings identify a taxonomy that is used to characterize data that is presented in the role-based digital twin, such that the data is presented in a taxonomy that is linked to the role corresponding to the role-based digital twin.
  • the set of taxonomies includes at least one of a trader template, a marketplace host template, a broker template, a buyer template, and a seller template.
  • At least one role is selected from among a trader role, a marketplace host role, a broker role, a buyer role, and a seller role.
  • the at least one role is selected from among a market maker role, a market analyst role, an exchange manager role, a broker-dealer role, a trading role, a reconciliation role, a contract counterparty role, an exchange rate setting role, a market orchestration role, a market configuration role, and a contract configuration role.
  • the role is selected from among a chief marketing officer role, a product development role, a supply chain manager role, a customer role, a supplier role, a vendor role, a demand management role, a marketing manager role, a sales manager role, a service manager role, a demand forecasting role, a retail manager role, a warehouse manager role, a salesperson role, and a distribution center manager role.
  • the method may include receiving digital twin data from a set of data sources.
  • the digital data may include sensor data that is received from a set of sensors that monitor a set of monitored physical entities associated with a marketplace, the sensor data transported by a set of network entities.
  • the digital data may include marketplace data streams generated by a set of marketplace assets, wherein the marketplace assets include at least one of physical entities associated with the marketplace and digital entities associated with the marketplace.
  • the method may include structuring the digital twin data into a set of digital twin data structures that are configured to serve a plurality of different role-based digital twins.
  • the method may include receiving a request for a role-based digital twin from a client application.
  • the role-based digital twin is configured with respect to a defined role within the marketplace.
  • the method may include determining a subset of the structured digital twin data to corresponds to a set of states that are depicted in the role-based digital twin.
  • the method may include providing the subset of the structured digital twin data to the client application.
  • the method may include receiving intelligent agent training data sets from the client application, each intelligent agent training data set indicating a respective action taken by a user using the client application and one or more features that correspond to the respective action.
  • the method may include training an intelligent agent on behalf of the user based on the intelligent agent training data sets.
  • the intelligent agent is configured to determine actions to be performed on behalf of the user.
  • the determined actions are either recommended to the user or automatically performed on behalf of the user.
  • the role is selected from among a trader role, a marketplace host role, a broker role, a buyer role, and a seller role.
  • the role is selected from among a market maker role, an exchange manager role, a broker-dealer role, a trading role, a reconciliation role, a contract counterparty role, an exchange rate setting role, a market orchestration role, a market configuration role, and a contract configuration role.
  • the role is selected from among a chief marketing officer role, a product development role, a supply chain manager role, a customer role, a supplier role, a vendor role, a demand management role, a marketing manager role, a sales manager role, a service manager role, a demand forecasting role, a retail manager role, a warehouse manager role, a salesperson role, and a distribution center manager role.
  • the intelligent agent training data includes interactions training data that indicates a set of interactions with a set of experts by the user during performance of the role.
  • the set of interactions used to train the intelligent agent includes interactions of the user with the physical entities.
  • the set of interactions used to train the intelligent agent includes interactions of the user with the role-based digital twin.
  • the set of interactions used to train the intelligent agent includes interactions of the user with the sensor data as depicted in the role-based digital twin.
  • the set of interactions used to train the artificial intelligence system includes interactions of the experts with the data streams generated by the physical entities.
  • the set of interactions used to train the intelligent agent system includes interactions of the experts with one or more computational entities.
  • the set of interactions used to train the intelligent agent includes interactions of the user with one or more network entities.
  • the intelligent agent is trained to determine an action selected from the group comprising: selection of an asset, pricing of an asset, configuring a marketplace, listing an asset in a marketplace, uploading information related to an asset listed in a marketplace, identifying counterparties, selecting counterparties, identifying opportunities, selecting opportunities, performing a negotiation, identifying the need for a new marketplace, digitally inspecting an asset, physically inspecting an asset, configuring a digital twin, placing an order request, generating a smart contract, physically delivering an asset, physically retrieving an asset, selection of a trading strategy, brokering a trade, valuation of an asset, and order matching.
  • the intelligent agent is trained on a training set of outcomes resulting from the actions taken by the user.
  • the training set of outcomes includes data relating to at least one of a financial outcome, an operational outcome, a fault outcome, a success outcome, a performance indicator outcome, an output outcome, a consumption outcome, an energy utilization outcome, a resource utilization outcome, a cost outcome, a profit outcome, a revenue outcome, a sales outcome, and a production outcome.
  • the intelligent agent is trained to perform an action selected from among determining an architecture for a system, reporting on a status, reporting on an event, reporting on a context, reporting on a condition, determining a model, configuring a model, populating a model, designing a system, designing a process, designing an apparatus, engineering a system, engineering a device, engineering a process, engineering a product, maintaining a system, maintaining a device, maintaining a process, maintaining a network, maintaining a computational resource, maintaining equipment, maintaining hardware, repairing a system, repairing a device, repairing a process, repairing a network, repairing a computational resource, repairing equipment, repairing hardware, assembling a system, assembling a device, assembling a process, assembling a network, assembling a computational resource, assembling equipment, assembling hardware, setting a price, physically securing a system, physically securing a device, physically securing a process, physically securing a network, physically securing a computational resource, setting
  • the intelligent agent is at least one of trained and configured via feedback from at least one expert in the defined role regarding a set of outputs of the intelligent agent.
  • the set of outputs of the intelligent agent upon which the expert provides feedback includes at least one of a recommendation, a classification, a prediction, a control instruction, an input selection, a protocol selection, a communication, an alert, a target selection for a communication, a data storage selection, a computational selection, a configuration, an event detection, and a forecast.
  • the feedback of the at least one expert is solicited to train the intelligent agent to replicate the expertise of the expert in the role.
  • the feedback of the at least one expert is used to modify the set of inputs to the intelligent agent.
  • the feedback of the at least one expert is used to identify and characterize at least one error by the intelligent agent.
  • a report on a set of errors is provided to a user of the intelligent agent to enable reconfiguring of the intelligent agent based on the feedback from the expert.
  • the artificial intelligence system includes at least one of removing an input that is the source of the error, reconfiguring a set of nodes of the artificial intelligence system, reconfiguring a set of weights of the artificial intelligence system, reconfiguring a set of outputs of the artificial intelligence system, reconfiguring a processing flow within the artificial intelligence system, and augmenting the set of inputs to the artificial intelligence system.
  • the intelligent agent is trained upon a training set of outcomes and to provide at least one of training and guidance to an individual who performs a defined role in a marketplace.
  • the training set of outcomes includes data relating to at least one of a financial outcome, an operational outcome, a fault outcome, a success outcome, a performance indicator outcome, an output outcome, a consumption outcome, an energy utilization outcome, a resource utilization outcome, a cost outcome, a profit outcome, a revenue outcome, a sales outcome, and a production outcome.
  • the method further includes parsing the training data set of interactions to identify a type of processing undertaken by the user in analyzing the set of interactions.
  • the type of processing is selected from the set of audio processing in analyzing audio information, tactile processing, textual information processing, motion processing, visual processing, spatiotemporal processing, mathematical processing, and analytic processing.
  • the method may include taking an information technology architecture that supports a digital twin of a set of physical entities.
  • the information technology architecture includes a set of sensors that provide sensor data about the set of physical entities; a set of data streams generated by at least a subset of the set of physical entities; a set of computational entities for processing data and a set of network entities for transporting data that is derived from the set of sensors and the set of data streams; and a set of data processing systems for extracting.
  • the method may include transforming and loading the data that is transported by the network entities into a set of resources that are sources for the digital twin.
  • the method may include integrating an artificial intelligence system with the information technology architecture, wherein the artificial intelligence system is configured to operate as a double of an expert worker for a defined role of marketplace.
  • the expert worker is provided with a benefit for training the artificial intelligence system.
  • the benefit is a reward based on the outcomes of the use of the artificial intelligence system.
  • the benefit is a reward based on the productivity of the artificial intelligence system.
  • the benefit is a reward based on a measure of the expertise of the artificial intelligence system.
  • the benefit is a share of revenue or profit generated by the work of the artificial intelligence system.
  • the reward is administered via a smart contract operating on the blockchain.
  • the artificial intelligence system is trained upon a training set of data that includes a set of interactions by a specific expert worker during performance of the defined role.
  • the set of interactions used to train the artificial intelligence system includes interactions of the expert with the physical entities.
  • the set of interactions used to train the artificial intelligence system includes interactions of the expert with the digital twin.
  • the set of interactions used to train the artificial intelligence system includes interactions of the expert with the sensor data.
  • the set of interactions used to train the artificial intelligence system includes interactions of the expert with the data streams generated by the physical entities.
  • the set of interactions used to train the artificial intelligence system includes interactions of the expert with the computational entities.
  • the set of interactions used to train the artificial intelligence system includes interactions of the expert with the network entities.
  • the artificial intelligence system is trained based on the interactions to determine an action selected from the group consisting of selection of an asset, pricing of an asset, configuring a marketplace, listing an asset in a marketplace, uploading information related to an asset listed in a marketplace, identifying counterparties, selecting counterparties, identifying opportunities, selecting opportunities, performing a negotiation, identifying the need for a new marketplace, digitally inspecting an asset, physically inspecting an asset, configuring a digital twin, placing an order request, generating a smart contract, physically delivering an asset, physically retrieving an asset, selection of a trading strategy, brokering a trade, valuation of an asset, and order matching.
  • the training set of interactions is parsed to identify a chain of reasoning of the expert worker upon a set of information and the chain of reasoning is embodied in the configuration of the artificial intelligence system.
  • the chain of reasoning is parsed to identify a type of reasoning of the expert worker and the type of reasoning is used as a basis for configuration of the artificial intelligence system.
  • the chain of reasoning is a deductive chain of reasoning from a set of data.
  • the artificial intelligence system is trained to perform an action selected from among determining an architecture for a system, reporting on a status, reporting on an event, reporting on a context, reporting on a condition, determining a model, configuring a model, populating a model, designing a system, designing a process, designing an apparatus, engineering a system, engineering a device, engineering a process, engineering a product, maintaining a system, maintaining a device, maintaining a process, maintaining a network, maintaining a computational resource, maintaining equipment, maintaining hardware, repairing a system, repairing a device, repairing a process, repairing a network, repairing a computational resource, repairing equipment, repairing hardware, assembling a system, assembling a device, assembling a process, assembling a network, assembling a computational resource, assembling equipment, assembling hardware, setting a price, physically securing a system, physically securing a device, physically securing a process, physically securing a network, physically securing a computational resource,
  • the method may include taking an information technology architecture that supports a digital twin of a set of physical entities.
  • the architecture includes a set of sensors that provide sensor data about the set of physical entities; a set of data streams generated by at least a subset of the set of physical entities; a set of computational entities for processing data and a set of network entities for transporting data that is derived from the set of sensors and the set of data streams; and a set of data processing systems for extracting, transforming and loading the data that is transported by the network entities into a set of resources that are sources for the digital twin.
  • the method may include integrating an artificial intelligence system with the information technology architecture.
  • the artificial intelligence system is configured to operate as a double of a defined market orchestration workforce involving a defined set of roles of a marketplace.
  • the artificial intelligence system is trained upon a training set of data that includes a set of interactions by members of the defined workforce during performance of the defined set of roles.
  • the set of interactions used to train the artificial intelligence system includes interactions of the workforce with the physical entities.
  • the set of interactions used to train the artificial intelligence system includes interactions of the workforce with the digital twin.
  • the set of interactions used to train the artificial intelligence system includes interactions of the workforce with the sensor data.
  • the set of interactions used to train the artificial intelligence system includes interactions of the workforce with the data streams generated by the physical entities.
  • the set of interactions used to train the artificial intelligence system includes interactions of the workforce with the computational entities. In embodiments, the set of interactions used to train the artificial intelligence system includes interactions of the workforce with the network entities. In embodiments, the training set of interactions is parsed to identify a chain of operations of the workforce upon a set of information and the chain of reasoning is embodied in the configuration of the artificial intelligence system. In embodiments, the training set of interactions is parsed to identify a type of processing of the workforce upon a set of information and the type of processing is embodied in the configuration of the artificial intelligence system.
  • the artificial intelligence system is trained based on the interactions to determine an action selected from the group consisting of selection of an asset, pricing of an asset, configuring a marketplace, listing an asset in a marketplace, uploading information related to an asset listed in a marketplace, identifying counterparties, selecting counterparties, identifying opportunities, selecting opportunities, performing a negotiation, identifying the need for a new marketplace, digitally inspecting an asset, physically inspecting an asset, configuring a digital twin, placing an order request, generating a smart contract, physically delivering an asset, physically retrieving an asset, selection of a trading strategy, brokering a trade, valuation of an asset, and order matching.
  • the artificial intelligence system is trained on a training set of outcomes.
  • the training set of outcomes includes data relating to at least one of a financial outcome, an operational outcome, a fault outcome, a success outcome, a performance indicator outcome, an output outcome, a consumption outcome, an energy utilization outcome, a resource utilization outcome, a cost outcome, a profit outcome, a revenue outcome, a sales outcome, and a production outcome.
  • the artificial intelligence system is at least one of trained and configured via feedback from members of the workforce regarding a set of outputs of the artificial intelligence system.
  • the set of outputs of the artificial intelligence system upon which the workforce members provide feedback includes at least one of a recommendation, a classification, a prediction, a control instruction, an input selection, a protocol selection, a communication, an alert, a target selection for a communication, a data storage selection, a computational selection, a configuration, an event detection, and a forecast.
  • the feedback of the workforce members is solicited to train the artificial intelligence system to replicate the operation of the workforce in the defined set of roles.
  • the feedback of the workforce members is used to modify the set of inputs to the artificial intelligence system.
  • the feedback of the workforce members is used to identify and characterize at least one error by the artificial intelligence system.
  • a report on a set of errors is provided to a manager of the artificial intelligence system to enable reconfiguring of the artificial intelligence system based on the feedback.
  • reconfiguring the artificial intelligence system includes at least one of removing an input that is the source of the error, reconfiguring a set of nodes of the artificial intelligence system, reconfiguring a set of weights of the artificial intelligence system, reconfiguring a set of outputs of the artificial intelligence system, reconfiguring a processing flow within the artificial intelligence system, and augmenting the set of inputs to the artificial intelligence system.
  • the artificial intelligence system is configured to provide at least one of training and guidance to enable the other worker to perform a role within the defined set of roles of the workforce.
  • the artificial intelligence system learns on a training set of outcomes to enhance the training and guidance.
  • the training set of outcomes includes data relating to at least one of a financial outcome, an operational outcome, a fault outcome, a success outcome, a performance indicator outcome, an output outcome, a consumption outcome, an energy utilization outcome, a resource utilization outcome, a cost outcome, a profit outcome, a revenue outcome, a sales outcome, and a production outcome.
  • the artificial intelligence system is trained to perform an action selected from among determining an architecture for a system, reporting on a status, reporting on an event, reporting on a context, reporting on a condition, determining a model, configuring a model, populating a model, designing a system, designing a process, designing an apparatus, engineering a system, engineering a device, engineering a process, engineering a product, maintaining a system, maintaining a device, maintaining a process, maintaining a network, maintaining a computational resource, maintaining equipment, maintaining hardware, repairing a system, repairing a device, repairing a process, repairing a network, repairing a computational resource, repairing equipment, repairing hardware, assembling a system, assembling a device, assembling a process, assembling a network, assembling a computational resource, assembling equipment, assembling hardware, setting a price, physically securing a system, physically securing a device, physically securing a process, physically securing a network, physically securing a computational resource,
  • the artificial intelligence system is configured to provide at least one of training and guidance to the workforce to enable the workforce to perform the defined role.
  • the artificial intelligence system learns on a training set of outcomes to enhance the training and guidance.
  • the training set of outcomes includes data relating to at least one of a financial outcome, an operational outcome, a fault outcome, a success outcome, a performance indicator outcome, an output outcome, a consumption outcome, an energy utilization outcome, a resource utilization outcome, a cost outcome, a profit outcome, a revenue outcome, a sales outcome, and a production outcome.
  • outcomes are compared between a set of actions of the workforce and a set of outputs of the artificial intelligence system. In embodiments, the comparison is used to train the workforce.
  • the comparison is used to improve the artificial intelligence system.
  • at least one role within the set of roles of the workforce is selected from among a trader role, a marketplace host role, a broker role, a buyer role, and a seller role.
  • the workforce is a supply chain management workforce.
  • the workforce is a demand planning workforce.
  • the workforce is a logistics planning workforce.
  • the workforce is a vendor management workforce.
  • the workforce is a brokering workforce for a marketplace.
  • the workforce is a trading workforce for a marketplace.
  • the workforce is a trade reconciliation workforce for a marketplace.
  • the workforce is a transactional execution workforce for a marketplace.
  • the computational entities and the network entities are integrated as a converged computational and network entity.
  • the method may include representing an enterprise organizational structure in a digital twin of a digital marketplace orchestration enterprise.
  • the method may include parsing the structure to infer relationships among a set of roles within the organizational structure, the relationships and the roles defining a workforce of the digital marketplace orchestration enterprise.
  • the method may include configuring the presentation layer of a digital twin to represent the digital marketplace orchestration enterprise as a set of workforces having a set of attributes and relationships.
  • the digital twin integrates with a market orchestration platform that operates on a data structure representing a set of roles in the marketplace, such that changes in the market orchestration platform are automatically reflected in the digital twin.
  • the workforce is a brokering workforce for a marketplace.
  • the workforce is a trading workforce for a marketplace.
  • the workforce is a trade reconciliation workforce for a marketplace.
  • the workforce is a transactional execution workforce for a marketplace.
  • at least one workforce role is selected from among a trader role, a marketplace host role, a broker role, a buyer role, and a seller role.
  • at least one workforce role is selected from among a market maker role, an exchange manager role, a broker-dealer role, a trading role, a reconciliation role, a contract counterparty role, an exchange rate setting role, a market orchestration role, a market configuration role, and a contract configuration role.
  • the digital twin represents a recommendation for training for the workforce. In embodiments, the digital twin provides a recommendation for augmentation of the workforce. In embodiments, the digital twin provides a recommendation for configuration of a set of operations involving the workforce. In embodiments, the digital twin provides a recommendation for configuration of the workforce.
  • a market orchestration platform leverages machine learning and artificial intelligence trained to recognize the stage of a marketplace.
  • a machine learning system trains a set of machine-learned models to output a determination related to the stage of a marketplace using training data comprising marketplace features and outcomes.
  • an artificial intelligence system receives a request for a determination related to the stage of the marketplace and generates the determination related to the stage of the marketplace based on the set of machine-learned models and the request.
  • the determination related to the stage of the marketplace is leveraged, at least in part, to automatically adjust parameters of the marketplace.
  • the set of machine- learned models employ a convolutional neural network, a recurrent neural network, a feed forward neural network, a long-term/short-term memory (LTSM) neural network, a self-organizing neural network, and hybrids and combinations of the foregoing.
  • a convolutional neural network e.g., a convolutional neural network, a recurrent neural network, a feed forward neural network, a long-term/short-term memory (LTSM) neural network, a self-organizing neural network, and hybrids and combinations of the foregoing.
  • LTSM long-term/short-term memory
  • the method may include receiving a request to update one or more properties of one or more digital twins.
  • the method may include retrieving the one or more digital twins required to fulfill the request.
  • the method may include selecting data sources from a set of available data sources.
  • the method may include retrieving data from selected data sources.
  • the method may include updating one or more properties of the one or more digital twins based on the retrieved data.
  • the digital twins are selected from the set of marketplace digital twins, asset digital twins, trader digital twins, broker digital twins, environment digital twins, and marketplace host digital twins.
  • the one or more properties of the one or more digital twins relates to asset ownership.
  • the data source is selected from the set of an Internet of Things connected device, a machine vision system, an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a tri-axial vibration sensor, a single axis vibration sensor, an optical vibration sensor, and a crosspoint switch.
  • Another aspect of the invention relates to a method for generating a fairness score for a set of transactions.
  • the method may include receiving, by a fairness engine, transaction data from an execution engine.
  • the method may include calculating, by the fairness engine, a fairness score representing the fairness of a transaction.
  • the fairness engine may include an execution timing fairness engine that determines or receives a set of measures of latency for a set of users.
  • the execution timing fairness engine automatically orchestrates a set of configuration parameters or other features that mitigate unfairness that may be caused by disparate latency.
  • the set of measures of latency are determined by testing network return times.
  • testing network return times may include determining a ping, an upload speed, or a download speed.
  • the set of transactions are executed based upon the fairness score exceeding a predetermined threshold.
  • the quantum computing system is configured to determine a rate of exchange between, among, or across a set of marketplaces by simulating a set of trading activities involving the set of marketplaces.
  • the quantum computing system supports models selected from the set of quantum circuit model, quantum Turing model, adiabatic quantum computer, oneway quantum computer, quantum annealing, quantum cellular autonoma, and hybrids and combinations of the foregoing.
  • the counterparty strategy engine includes a machine learning system that trains a set of machine- learned models to output a determination related to trading strategies employed by a set of counterparties using training data comprising marketplace features and outcomes.
  • the counterparty strategy engine includes an artificial intelligence system that receives a request for a determination related to the trading strategies employed by the set of counterparties and generates the determination related to the strategies employed by the set of counterparties based on the set of machine-learned models and the request.
  • the trading strategies are selected from the set of buy and hold, long/short equity, asset allocation, intertemporal portfolio choice, pairs trading, swing trading, scalping, day trading, news-based, market timing, social trading, front-running, chart-based, computer- science based, automated/algorithmic, and hybrids and combinations of the foregoing.
  • a machine learning system trains a set of machine-learned models to identify a set of similar buyers for a cohort-targeted marketplace using training data comprising marketplace features and outcomes.
  • an artificial intelligence system receives a request to identify the group of similar buyers, identifies the group of similar buyers, and generates the cohort-targeted marketplace based on the identification.
  • the set of machine- learned models employ a convolutional neural network, a recurrent neural network, a feed forward neural network, a long-term/short-term memory (LTSM) neural network, a self-organizing neural network, and hybrids and combinations of the foregoing.
  • content from a set of product websites may be fed to the set of machine-learned models, which are trained to identify new product or service offerings relevant to the cohort-targeted marketplace.
  • Another aspect of the present disclosure relates to the quantum computing system configured to connect and direct a neural network development or selection process.
  • the quantum computing system directly programs the weights of a neural network such that the neural network produces desired outputs.
  • the quantum computing system supports models selected from the set of quantum circuit model, quantum Turing model, adiabatic quantum computer, one-way quantum computer, quantum annealing, quantum cellular autonoma, and hybrids and combinations of the foregoing.
  • Example embodiments herein disclose systems, procedures, and aspects that provide cryptographically secure blockchains for knowledge systems capable of storing digital knowledge for providing convenient and secure control of the same.
  • Example methods and systems herein provide for improvements in determining property valuation, reliability of financial health of entities, transparency, symmetry of information, and application and negotiation processes in the lending environment.
  • Example methods and systems herein provide for improvements to the machines that enable markets, providing for increased efficiency, speed, and/or reliability for participants in such markets.
  • Example methods and systems herein provide for improvements to data collection, storage and processing, automated configuration of inputs, resource, and outputs, and means for facility optimization for an energy and compute facility.
  • a knowledge distribution system for controlling rights related to digital knowledge may be a blockchain for knowledge system that allows for storage of digital knowledge, buying and selling of digital knowledge, tokenization of digital knowledge, and/or reviewing/auditing of the digital knowledge via a cryptographically secure distributed ledger. Smart contracts may be implemented on the distributed ledger and controlling of rights to digital knowledge, transferring digital knowledge, and adherence of parties to agreements related to the digital knowledge.
  • the blockchain for knowledge system can also facilitate third parties reviewing, auditing, or verifying information related to digital knowledge.
  • a platform exists for a digital knowledge distribution system that facilitates orchestration of the sharing of knowledge by providing a high degree of control over the extent to which counterparties can access shared knowledge. Even where knowledge is secure and well- controlled, some types of knowledge are so sensitive that an owner may be unwilling to share the entire set of knowledge with a single counterparty.
  • a platform is disclosed for a digital knowledge distribution system that facilitates handling and control of subsets of knowledge, including automated handling of aggregation of knowledge, or related outputs, that result from division of knowledge subsets.
  • the knowledge distribution system may include a ledger management system configured to create and manage a distributed ledger where the distributed ledger may be distributed over nodes of a network and may include blocks linked via cryptography.
  • a smart contract system may be communication with the distributed ledger and may be configured to implement and manage a smart contract via the distributed ledger.
  • the smart contract may be stored in the distributed ledger and may include a triggering event.
  • the smart contract may be configured to perform a smart contract action with respect to the digital knowledge in response to an occurrence of the triggering event.
  • the knowledge distribution system may be configured to receive from a user an instance of the digital knowledge.
  • the digital knowledge may be tokenized such that the instance of the digital knowledge can be manipulated as a token on the distributed ledger.
  • the tokenized digital knowledge may be stored via the distributed ledger. Commitments of parties to the smart contract may be processed.
  • the knowledge distribution system may be configured to manage rights of control of and access to the tokenized digital knowledge according to the smart contract and manage the smart contract action in response to the triggering event.
  • the digital knowledge may include intellectual property where the smart contract embeds intellectual property licensing terms for intellectual property embedded in the distributed ledger, and where executing an operation on the distributed ledger may provide access to the intellectual property and may process a commitment of a party to the smart contract to the intellectual property licensing terms.
  • a smart contract wrapper on the distributed ledger may allow an operation on the ledger to add intellectual property to an aggregate stack of intellectual property, may allow an operation on the ledger to add intellectual property to agree to an apportionment of royalties among the parties in the ledger, may allow an operation on the ledger to add intellectual property to an aggregate stack of intellectual property, and/or may allow an operation on the ledger to process a commitment of a party to a contract term.
  • the tokenized digital knowledge may include an instruction set.
  • the distributed ledger may be configured to provide provable access to the instruction set and execute the instruction set on a system resulting in recording a transaction in the distributed ledger.
  • the tokenized digital knowledge may include executable algorithmic logic, a three-dimensional (3D) printer instruction set, an instruction set for a coating process, an instruction set for a semiconductor fabrication process, a firmware program, an instruction set for a field-programmable gate array, serverless code logic, an instruction set for a crystal fabrication 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 biological production process, a data set for a digital twin, and/or a trade secret with an expert wrapper.
  • the system may be configured to aggregate views of a trade secret into a chain that proves which knowledge recipients of the parties have viewed the trade secret.
  • the knowledge distribution system may include a reporting system configured to report an analytic result based on operations performed on the distributed ledger or the digital knowledge.
  • the distributed ledger may be configured to aggregate a set of instructions where an operation on the distributed ledger may add at least one instruction to a pre-existing set of instructions to provide a modified set of instructions.
  • the smart contract may be configured to manage allocation of instruction sub-sets to the distributed ledger and access to the instruction sub-sets.
  • the distributed ledger may be configured to log parties who have contributed to an instance of the digital knowledge by storing data related to the parties in at least one of the blocks.
  • the knowledge distribution system may be configured to log a source of an instance of the digital knowledge by storing data related to the source in at least one of the blocks.
  • the distributed ledger may be configured such that a private network of authorized participants may establish cryptography-based consensus required for verification of new blocks to be added to the blocks.
  • the ledger management system may be configured to facilitate crowdsourcing of information added to a block of the blocks of the distributed ledger.
  • the distributed ledger may be configured such to store a review of an instance of the digital knowledge by a crowdsourcer in a block of the blocks.
  • the distributed ledger may be configured such to store a signature of an instance of the digital knowledge by a crowdsourcer in a block of the blocks.
  • the distributed ledger may be configured such to store a verification of an instance of the digital knowledge by a crowdsourcer in a block of the blocks.
  • the ledger management system may be configured to establish cryptographic currency tokens that may be tradeable among users of the distributed ledger.
  • the knowledge distribution system may include an account management system in communication with the distributed ledger that may be configured 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 and may be configured to present a user interface to a user of the knowledge distribution system where 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 and may be configured to establish and maintain a digital marketplace that may be configured to visually present data related to an instance of the digital knowledge to a user of the knowledge distribution system.
  • the knowledge distribution system may include a knowledge datastore in communication with the distributed ledger and may be configured to store data related to the digital knowledge.
  • the knowledge distribution system may include a client datastore in communication with the distributed ledger and may be configured to store data related to users of the knowledge distribution system.
  • the knowledge distribution system may include a smart contract datastore in communication with the distributed ledger and may be configured to store data related to the smart contract.
  • the knowledge distribution system may include a reporting system in communication with the distributed ledger and may be configured to analyze said tokenized digital knowledge and report an analytic result based on the analysis of the tokenized digital knowledge.
  • the smart contract may be generated using a parameterizable smart contract template.
  • the smart contract may include parameters based on type of digital knowledge to be tokenized.
  • the parameters may include financial parameters, royalty parameters, usage parameters, output produced parameters, allocation of consideration parameters, identity parameters, and/or access condition parameters.
  • a knowledge distribution system may use a distributed ledger and smart contracts to facilitate management and exchange of access, licensing, and ownership rights of digital knowledge.
  • a computer-implemented method for controlling rights related to digital knowledge may include creating and managing a distributed ledger that is distributed over nodes of a network and includes blocks linked via cryptography.
  • a smart contract may be implemented and managed via the distributed ledger where the smart contract may be stored in the distributed ledger and may include a triggering event.
  • a smart contract action may be performed with respect to the digital knowledge in response to an occurrence of the triggering event.
  • An instance of the digital knowledge may be received.
  • the digital knowledge may be tokenized such that the instance of the digital knowledge can be manipulated as a token on the distributed ledger.
  • the tokenized digital knowledge may be stored via the distributed ledger. Commitments of parties to the smart contract may be processed.
  • the method may include management of rights over control of and access to the tokenized digital knowledge according to the smart contract and management of the smart contract action in response to the triggering event.
  • a knowledge exchange for the exchange of the tokenized digital knowledge based on the smart contract may be orchestrated.
  • the knowledge exchange of the tokenized digital knowledge may be integrated with another exchange where the knowledge exchange facilitates exchange of valuable and/or sensitive knowledge related to a subject matter of the other exchange.
  • a knowledge distribution system for controlling rights related to digital knowledge.
  • the knowledge distribution system may include a ledger management system configured to create and manage a distributed ledger.
  • the distributed ledger may be distributed over nodes of a network and may include blocks linked via cryptography.
  • a smart contract system may be in communication with the distributed ledger and may be configured to implement and manage a smart contract via the distributed ledger.
  • the smart contract may be stored in the distributed ledger and may include a triggering event.
  • the smart contract may be configured to perform a smart contract action with respect to the digital knowledge in response to an occurrence of the triggering event.
  • the knowledge distribution system may be configured to receive from a knowledge provider device an instance of the digital knowledge including a three-dimensional (3D) printer instruction set for 3D printing an object.
  • the digital knowledge may be tokenized such that the instance of the digital knowledge may be manipulated as a token on the distributed ledger.
  • the tokenized digital knowledge may be stored via the distributed ledger. Commitments of the knowledge provider and a knowledge recipient of the 3D printer instruction set to the smart contract may be processed.
  • the knowledge distribution system may be configured to manage rights of control of and access to the tokenized digital knowledge according to the smart contract and may manage the smart contract action according to a condition and the triggering event.
  • the 3D printer instruction set may include a 3D printing schematic.
  • the object may be at least one of a custom part, a custom product, a manufacturing part, a replacement part, a toy, a medical device, and a tool.
  • the knowledge recipient may use a knowledge recipient device to download and use the 3D printer instruction set.
  • the knowledge recipient device may be at least one of a computing device, a server, a 3D printer, and a manufacturing device.
  • the knowledge recipient may use a knowledge recipient device to purchase the tokenized digital knowledge corresponding to the 3D printer instruction set.
  • the knowledge distribution system may include an event listener configured to listen to an application programming interface (API) that may provide a connection between the knowledge distribution system and a knowledge recipient device of the knowledge recipient.
  • API application programming interface
  • the smart contract may be configured to trigger the condition of the knowledge recipient to make a payment when the 3D printer instruction set may be transferred or used based on the rights of control of and access to the tokenized digital knowledge.
  • the rights of control of and access to the tokenized digital knowledge may include a permission for a user to 3D print using multiple instances of the 3D printer instruction set.
  • the rights of control of and access to the tokenized digital knowledge may include at least one of 3D printer requirements, a time period during which the object can be 3D printed, whether the tokenized digital knowledge is transferred to a downstream knowledge recipient, warranties, disclaimers, indemnifications, and certifications with respect to the object.
  • Information related to the 3D printer instruction set of the tokenized digital knowledge may be modified on the distributed ledger when the 3D printer instruction set is at least one of purchased, downloaded, and used.
  • information related to the 3D printer instruction set may include at least one of origin, date of creation, names of one or more contributing individuals, groups, and/or companies, pricing, market trends for related schematics, serial numbers, and part identifiers.
  • the smart contract action may be one of an assignment of a serial number to the object that is 3D printed, monitoring for the triggering event, verifying fulfillment of an obligation based on the condition, verifying payment and/or transfer of the tokenized digital knowledge, transferring the tokenized digital knowledge, logging one or more transactions in the distributed ledger, performing one or more operations with respect to the distributed ledger, and creating one or more new blocks in the distributed ledger.
  • the smart contract action may include verifying that the condition is met as defined in the smart contract where the condition may be one of printer requirements, payment received or currency transferred from a knowledge recipient device of the knowledge recipient, and transfer of the tokenized digital knowledge to the knowledge recipient device.
  • a 3D printer may be configured to print the object according to the 3D printer instruction set.
  • the knowledge distribution system may include a smart contract generator that may be configured to parametrize a smart contract template based on at least one of information provided by the knowledge provider, the condition, and the triggering event.
  • a computer-implemented method for controlling rights related to digital knowledge may include creating and managing a distributed ledger that is distributed over nodes of a network and includes blocks linked via cryptography.
  • a smart contract may be implemented and managed via the distributed ledger where the smart contract may be stored in the distributed ledger and may include a triggering event.
  • a smart contract action may be performed with respect to the digital knowledge in response to an occurrence of the triggering event.
  • the method may include receiving from a knowledge provider device an instance of the digital knowledge that includes a three- dimensional (3D) printer instruction set for 3D printing an object.
  • the digital knowledge may be tokenized such that the instance of the digital knowledge can be manipulated as a token on the distributed ledger.
  • the tokenized digital knowledge may be stored via the distributed ledger. Commitments of the knowledge provider and a knowledge recipient of the 3D printer instruction set to the smart contract may be processed.
  • the method may include management of rights of control of and access to the tokenized digital knowledge according to the smart contract, and management of the smart contract action according to a condition and the triggering event.
  • An element of the instance of the digital knowledge via the smart contract may be crowdsourced.
  • the element of the instance of the digital knowledge may be managed by a smart contract system according to the smart contract.
  • a lending transaction enablement platform having a set of data- integrated microservices including data collection and monitoring services, blockchain services, and smart contract services for handling lending entities and transactions.
  • the platform is capable of enabling a wide range of dedicated solutions, which may share data collection and storage infrastructure, and which may share or exchange inputs, events, activities, and outputs, such as to reinforce learning, enable automation, and enable adaptive intelligence across the various solutions.
  • the method may include receiving an access request from a licensee to obtain approval to license personality rights from a set of available licensors.
  • the method may include selectively granting access to the licensee based on the access request.
  • the method may include receiving confirmation of a deposit of an amount of funds from the licensee.
  • the method may include issuing an amount of cryptocurrency 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 governing the licensing of the one or more personality rights of the licensor by the licensee.
  • the smart contract request may indicate one or more terms including a consideration amount of cryptocurrency to be paid to the licensor in exchange for one or more obligations on the licensor.
  • the method may include generating the smart contract based on the smart contract request.
  • the method may include escrowing the consideration amount of cryptocurrency 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 performed the one or more obligations.
  • the method may include, in response to receiving verification that the licensor has performed the one or more obligations, releasing at least a portion of the consideration amount of cryptocurrency into a licensor account of the licensor.
  • the method may include outputting a record indicating a completion of a licensing transaction defined by the smart contract to the distributed ledger.
  • the system may include one or more hardware processors configured by machine-readable instructions.
  • the processor(s) may be configured to receive an access request from a licensee to obtain approval to license personality rights from a set of available licensors.
  • the processor(s) may be configured to selectively grant access to the licensee based on the access request.
  • the processor(s) may be configured to receive confirmation of a deposit of an amount of funds from the licensee.
  • the processor(s) may be configured to issue an amount of cryptocurrency corresponding to the amount of funds deposited by the licensee to an account of the licensee.
  • the processor(s) may be configured to receive a smart contract request to create a smart contract governing the licensing of the one or more personality rights of the licensor by the licensee.
  • the smart contract request may indicate one or more terms including a consideration amount of cryptocurrency to be paid to the licensor in exchange for one or more obligations on the licensor.
  • the processor(s) may be configured to generate the smart contract based on the smart contract request.
  • the processor(s) may be configured to escrow the consideration amount of cryptocurrency from the account of the licensee.
  • the processor(s) may be configured to deploy the smart contract to a distributed ledger.
  • the processor(s) may be configured to verify, by the smart contract, that the licensor has performed the one or more obligations.
  • the processor(s) may be configured to, in response to receiving verification that the licensor has performed the one or more obligations, release at least a portion of the consideration amount of cryptocurrency into a licensor account of the licensor.
  • the processor(s) may be configured to output a record indicating a completion of a licensing transaction defined by the smart contract to the distributed ledger.
  • FIG. 1 is a schematic diagram of components of a platform for enabling intelligent transactions in accordance with embodiments of the present disclosure.
  • FIGs. 2 A and 2B are schematic diagrams of additional components of a platform for enabling intelligent transactions in accordance with embodiments of the present disclosure.
  • Fig. 3 is a schematic diagram of additional components of a platform for enabling intelligent transactions in accordance with embodiments of the present disclosure.
  • Figs. 4 to Fig. 31 are schematic diagrams of embodiments of neural net systems that may connect to, be integrated in, and be accessible by the platform for enabling intelligent transactions including ones involving expert systems, self-organization, machine learning, artificial intelligence and including neural net systems trained for pattern recognition, for classification of one or more parameters, characteristics, or phenomena, for support of autonomous control, and other purposes in accordance with embodiments of the present disclosure.
  • Fig. 32 is a schematic diagram of components of an environment including an intelligent energy and compute facility, a host intelligent energy and compute facility resource management platform, a set of data sources, a set of expert systems, interfaces to a set of market platforms and external resources, and a set of user or client systems and devices in accordance with embodiments of the present disclosure.
  • Fig. 33 depicts components and interactions of a transactional, financial and marketplace enablement system.
  • Fig. 34 depicts components and interactions of a set of data handling layers of a transactional, financial and marketplace enablement system.
  • Fig. 35 depicts adaptive intelligence and robotic process automation capabilities of a transactional, financial and marketplace enablement system.
  • Fig. 36 depicts opportunity mining capabilities of a transactional, financial and marketplace enablement system.
  • Fig. 37 depicts adaptive edge computation management and edge intelligence capabilities of a transactional, financial and marketplace enablement system.
  • Fig. 38 depicts protocol adaptation and adaptive data storage capabilities of a transactional, financial and marketplace enablement system.
  • Fig. 39 depicts robotic operational analytic capabilities of a transactional, financial and marketplace enablement system.
  • Fig. 40 depicts a blockchain and smart contract platform for a forward market for access rights to events.
  • Fig. 41 depicts an algorithm and a dashboard of a blockchain and smart contract platform for a forward market for access rights to events.
  • Fig. 42 depicts a blockchain and smart contract platform for forward market demand aggregation.
  • Fig. 43 depicts an algorithm and a dashboard of a blockchain and smart contract platform for forward market demand aggregation.
  • Fig. 44 depicts a blockchain and smart contract platform for crowdsourcing for innovation.
  • Fig. 45 depicts an algorithm and a dashboard of a blockchain and smart contract platform for crowdsourcing for innovation.
  • Fig. 46 depicts a blockchain and smart contract platform for crowdsourcing for evidence.
  • Fig. 47 depicts an algorithm and a dashboard of a blockchain and smart contract platform for crowdsourcing for evidence.
  • Fig. 48 depicts components and interactions of an embodiment of a lending platform having a set of data-integrated microservices including data collection and monitoring services for handling lending entities and transactions.
  • Fig. 49 depicts components and interactions of an embodiment of a lending platform in which a set of lending solutions are supported by a data-integrated set of data collection and monitoring services, adaptive intelligent systems, and data storage systems.
  • Fig. 50 depicts components and interactions of an embodiment of a lending platform having a set of data integrated blockchain services, smart contract services, social network analytic services, crowdsourcing services and Internet of Things data collection and monitoring services for collecting, monitoring, and processing information about entities involved in or related to a lending transaction.
  • Fig. 51 depicts components and interactions of a lending platform having an Internet of Things and sensor platform for monitoring at least one of a set of assets, a set of collateral, and a guarantee for a loan, a bond, or a debt transaction.
  • Fig. 52 depicts components and interactions of a lending platform having a crowdsourcing system for collecting information related to entities involved in a lending transaction.
  • Fig. 53 depicts an embodiment of a crowdsourcing workflow enabled by a lending platform.
  • Fig. 54 depicts components and interactions of an embodiment of a lending platform having a smart contract system that automatically adjusts an interest rate for a loan based on information collected via at least one of an Internet of Things system, a crowdsourcing system, a set of social network analytic services and a set of data collection and monitoring services.
  • Fig. 55 depicts components and interactions of an embodiment of a lending platform having a smart contract that automatically restructures debt based on a monitored condition.
  • Fig. 56 depicts components and interactions of a lending platform having a set of data collection and monitoring systems for validating the reliability of a guarantee for a loan, including an Internet of Things system and a social network analytics system.
  • Fig. 57 depicts components and interactions of a lending platform having a robotic process automation system for negotiation of a set of terms and conditions for a loan.
  • Fig. 58 depicts components and interactions of a lending platform having a robotic process automation system for loan collection.
  • Fig. 59 depicts components and interactions of a lending platform having a robotic process automation system for consolidating a set of loans.
  • Fig. 60 depicts components and interactions of a lending platform having a robotic process automation system for managing a factoring loan.
  • Fig. 61 depicts components and interactions of a lending platform having a robotic process automation system for brokering a mortgage loan.
  • Fig. 62 depicts components and interactions of a lending platform having a crowdsourcing and automated classification system for validating condition of an issuer for a bond, a social network monitoring system with artificial intelligence for classifying a condition about a bond, and an Internet of Things data collection and monitoring system with artificial intelligence for classifying a condition about a bond.
  • Fig. 63 depicts components and interactions of a lending platform having a system that manages the terms and conditions of a loan based on a parameter monitored by the loT, by a parameter determined by a social network analytic system, or a parameter determined by a crowdsourcing system.
  • Fig. 64 depicts components and interactions of a lending platform having an automated blockchain custody service for managing a set of custodial assets.
  • Fig. 65 depicts components and interactions of a lending platform having an underwriting system for a loan with a set of data-integrated microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting lending entities and transactions.
  • Fig. 66 depicts components and interactions of a lending platform having a loan marketing system with a set of data-integrated microservices including data collection and monitoring services, blockchain services, artificial intelligence services and smart contract services for marketing a loan to a set of prospective parties.
  • Fig. 67 depicts components and interactions of a lending platform having a rating system with a set of data-integrated microservices 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 depicts components and interactions of a lending platform having a regulatory and/or compliance system with a set of data-integrated microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for automatically facilitating compliance with at least one of a law, a regulation and a policy that applies to a lending transaction.
  • Fig. 69 depicts a system for automated loan management.
  • Fig. 70 depicts a system.
  • Fig. 71 depicts a method for handling a loan.
  • Fig. 72 depicts a system for adaptive intelligence and robotic process automation capabilities of a transactional, financial and marketplace enablement.
  • Fig. 73 depicts a method for automated smart contract creation and collateral assignment.
  • Fig. 74 depicts a system for handling a loan.
  • Fig. 75 depicts a method for handling a loan.
  • Fig. 76 depicts a system for adaptive intelligence and robotic process automation.
  • Fig. 77 depicts a method for loan creation and management.
  • Fig. 78 depicts a system for adaptive intelligence and robotic process automation capabilities of a transactional, financial and marketplace enablement.
  • Fig. 79 depicts a method for robotic process automation of transactional, financial and marketplace activities.
  • Fig. 80 depicts a system for adaptive intelligence and robotic process automation.
  • Fig. 81 depicts a method for automated transactional, financial and marketplace activities.
  • Fig. 82 depicts a system for adaptive intelligence and robotic process.
  • Fig. 83 depicts a method for performing loan related actions.
  • Fig. 84 depicts a system for adaptive intelligence and robotic process.
  • Fig. 85 depicts a method for performing loan related actions.
  • Fig. 86 depicts a system for adaptive intelligence and robotic process.
  • Fig. 87 depicts a method for performing loan related actions.
  • Fig. 88 depicts a smart contract system for managing collateral for a loan.
  • Fig. 89 depicts a smart contract method for managing collateral for a loan.
  • Fig. 90 depicts a system for validating conditions of collateral or a guarantor for a loan.
  • Fig. 91 depicts a crowdsourcing method for validating conditions of collateral or a guarantor for a loan.
  • Fig. 92 depicts a smart contract system for modifying a loan.
  • Fig. 93 depicts a smart contract method for modifying a loan.
  • Fig. 94 depicts a smart contract system for modifying a loan.
  • Fig. 95 depicts a smart contract method for modifying a loan.
  • Fig. 96 depicts a smart contract system for modifying a loan.
  • Fig. 97 depicts a smart contract method for modifying a loan.
  • Fig. 98 depicts a monitoring system for validating conditions of a guarantee for a loan.
  • Fig. 99 depicts a monitoring method for validating conditions of a guarantee for a loan.
  • Fig. 100 depicts a robotic process automation system for negotiating a loan.
  • Fig. 101 depicts a robotic process automation method for negotiating a loan.
  • Fig. 102 depicts a system for adaptive intelligence and robotic process automation.
  • Fig. 103 depicts a loan collection method.
  • Fig. 104 depicts a system for adaptive intelligence and robotic process automation.
  • Fig. 105 depicts a loan refinancing method.
  • Fig. 106 depicts a system for adaptive intelligence and robotic process automation.
  • Fig. 107 depicts a for loan consolidation method.
  • Fig. 108 depicts a system for adaptive intelligence and robotic process automation.
  • Fig. 109 depicts a loan factoring method.
  • Fig. 110 depicts a system for adaptive intelligence and robotic process automation.
  • Fig. Ill depicts a mortgage brokering method.
  • Fig. 112 depicts a system for adaptive intelligence and robotic process automation.
  • Fig. 113 depicts a method for debt management.
  • Fig. 114 depicts a system for adaptive intelligence and robotic process automation.
  • Fig. 115 depicts a method for bond management.
  • Fig. 116 depicts a system for monitoring a condition of an issuer for a bond.
  • Fig. 117 depicts a method for monitoring a condition of an issuer for a bond
  • Fig. 118 depicts a system for monitoring a condition of an issuer for a bond.
  • Fig. 119 depicts a method for monitoring a condition of an issuer for a bond.
  • Fig. 120 depicts a system for automatic subsidized loan management.
  • Fig. 121 depicts a method for automatically modifying subsidized loan terms and conditions.
  • Fig. 122 depicts a system to automatically modify terms and conditions of a loan.
  • Fig. 123 depicts a method for collecting social network information about an entity involved in a subsidized loan transaction.
  • Fig. 124 depicts a system for automating handling of a subsidized loan using crowdsourcing.
  • Fig. 125 depicts a method for automating handling of a subsidized loan.
  • Fig. 126 depicts a system for asset access control.
  • Fig. 127 depicts a method for asset access control.
  • Fig. 128 depicts a system automated handling of loan foreclosure.
  • Fig. 129 depicts a method for facilitating foreclosure on collateral.
  • Fig. 130 depicts an example energy and computing resource platform.
  • Fig. 131 depicts an example facility data record.
  • Fig. 132 depicts an example schema of a person data record.
  • Fig. 133 depicts a cognitive processing system.
  • Fig. 134 depicts a process for a lead generation system to generate a lead list.
  • Fig. 135 depicts a process for a lead generation system to determine facility outputs for identified leads.
  • Fig. 136 depicts a process to generate and output personalized content.
  • Fig. 137 depicts a schematic illustrating an example of a portion of an information technology system for transaction artificial intelligence leveraging digital twins according to some embodiments of the present disclosure.
  • Fig. 138 depicts a schematic illustrating a compliance system that facilitates the licensing of personality rights according to some embodiments of the present disclosure.
  • Fig. 139 depicts a schematic illustrating an example set of components of a compliance system according to some embodiments of the present disclosure.
  • Fig. 140 depicts a set of operations of a method for vetting a potential licensee for purposes of licensing personality rights of a licensor according to some embodiments of the present disclosure.
  • Fig. 141 depicts a set of operations of a method for facilitating the licensing of personality rights of a licensor by a licensee according to some embodiments of the present disclosure.
  • Fig. 142 depicts a set of operations of a method for detecting potential circumvention of rules or regulations by a licensor and/or licensee according to some embodiments of the present disclosure.
  • Fig. 143 depicts a method for selecting an Al solution.
  • Fig. 144 depicts a method for selecting an Al solution.
  • Fig. 145 depicts an example of an assembled Al solution.
  • Fig. 146 depicts a method for selecting an Al solution.
  • Fig. 147 depicts a method for selecting an Al solution.
  • Fig. 148 depicts an Al solution selection and configuration system.
  • Fig. 149 depicts an Al solution selection and configuration system.
  • Fig. 150 depicts an Al solution selection and configuration system.
  • Fig. 151 depicts a component configuration circuit.
  • Fig. 152 depicts an Al solution selection and configuration system.
  • Fig. 153 depicts a system for selecting and configuring an artificial intelligence model.
  • Fig. 154 depicts a method of selecting and configuring an artificial intelligence model.
  • Fig. 155 is a schematic illustrating examples of architecture of a digital twin system according to embodiments of the present disclosure.
  • Fig. 156 is a schematic illustrating exemplary components of a digital twin management system according to embodiments of the present disclosure.
  • Fig. 157 is a schematic illustrating examples of a digital twin I/O system that interfaces with an environment, the digital twin system, and/or components thereof to provide bidirectional transfer of data between coupled components according to embodiments of the present disclosure.
  • Fig. 158 is a schematic illustrating an example set of identified states related to industrial environments that the digital twin system may identify and/or store for access by intelligent systems (e.g., a cognitive intelligence system) or users of the digital twin system according to embodiments of the present disclosure.
  • intelligent systems e.g., a cognitive intelligence system
  • Fig. 159 is a schematic illustrating example embodiments of methods for updating a set of properties of a digital twin of the present disclosure on behalf of a client application and/or one or more embedded digital twins.
  • Fig. 160 illustrates example embodiments of a display interface of the present disclosure that renders a digital twin of a dryer centrifuge with information relating to the dryer centrifuge.
  • Fig. 161 is a schematic illustrating an example embodiment of a method for updating a set of vibration fault level states of machine components such as bearings in the digital twin of an industrial machine, on behalf of a client application.
  • Fig. 162 is a schematic illustrating an example embodiment of a method for updating a set of vibration severity unit values of machine components such as bearings in the digital twin of a machine on behalf of a client application.
  • Fig. 163 is a schematic illustrating an example embodiment of a method for updating a set of probability of failure values in the digital twins of machine components on behalf of a client application.
  • Fig. 164 is a schematic illustrating an example embodiment of a method for updating a set of probability of downtime values of machines in the digital twin of a manufacturing facility on behalf of a client application.
  • Fig. 165 is a schematic illustrating an example embodiment of a method for updating a set of probability of shutdown values of manufacturing facilities in the digital twin of an enterprise on behalf of a client application.
  • Fig. 166 is a schematic illustrating an example embodiment of a method for updating a set of cost of downtime values of machines in the digital twin of a manufacturing facility.
  • Fig. 167 is a schematic illustrating an example embodiment of a method for updating one or more manufacturing KPI values in a digital twin of a manufacturing facility, on behalf of a client application.
  • Fig. 168 is a schematic diagram of components of a knowledge distribution system and a communication network for facilitating management of digital knowledge in accordance with embodiments of the present disclosure.
  • Fig. 169 is a schematic diagram of a ledger network of the knowledge distribution system in accordance with embodiments of the present disclosure.
  • Fig. 170 is a schematic diagram of the knowledge distribution system of FIG. 168 including details of a smart contract and a smart contract system of the knowledge distribution system in accordance with embodiments of the present disclosure.
  • Fig. 171 is a schematic diagram of a plurality of datastores of the knowledge distribution system in accordance with embodiments of the present disclosure.
  • Fig. 172 illustrates a method of deploying a knowledge token and related smart contract via the knowledge distribution system in accordance with embodiments of the present disclosure.
  • Fig. 173 illustrates a method of performing high level process flow of a smart contract that distributes digital knowledge via the knowledge distribution system in accordance with embodiments of the present disclosure.
  • Fig. 174 is a schematic diagram of another embodiment of components of the knowledge distribution system and a communication network for facilitating management of digital knowledge in accordance with embodiments of the present disclosure.
  • Fig. 175 depicts a knowledge distribution system for controlling rights related to digital knowledge.
  • Fig. 176 depicts a computer-implemented method for controlling rights related to digital knowledge.
  • Fig. 177 depicts a computer-implemented method for controlling rights related to digital knowledge.
  • Fig. 178 depicts a knowledge distribution system for controlling rights related to digital knowledge.
  • Fig. 179 depicts possible components of a 3D printer instruction set.
  • Fig. 180 depicts possible content of tokenized digital knowledge.
  • Fig. 181 depicts possible smart contract actions.
  • Fig. 182 depicts possible conditions relating to triggering events.
  • Fig. 183 depicts possible control and access rights.
  • Fig. 184 depicts possible triggering events.
  • Fig. 185 depicts a computer-implemented method for controlling rights related to digital knowledge.
  • Fig. 186 depicts a computer-implemented method for controlling rights related to digital knowledge.
  • Fig. 187 depicts possible crowdsourced information.
  • Fig. 188 depicts possible contents of a distributed ledger.
  • Fig. 189 depicts possible parameters.
  • Fig. 190 depicts an embodiment of a knowledge distribution system for controlling rights related to digital knowledge.
  • FIGs. 191-196 depict embodiments of operations for controlling rights related to digital knowledge.
  • Fig. 197 is a diagrammatic view illustrating an example implementation of the knowledge distribution system including a trust network for identifying the likelihood of fraudulent transactions using a consensus trust score and preventing such fraudulent transactions according to some embodiments of the present disclosure.
  • FIG. 198 illustrates an example method that describes operation of an example trust network illustrated in FIG. 197 according to some embodiments of the present disclosure.
  • FIG. 199 is a diagrammatic view illustrating a transaction being processed by the ledger network including a plurality of node computing devices according to some embodiments of the present disclosure.
  • Fig. 200 is a diagrammatic view illustrating an example implementation of the knowledge distribution system including a digital marketplace configured to provide an environment allowing knowledge providers and knowledge recipients to engage in commerce relating to the transfer of digital knowledge according to some embodiments of the present disclosure.
  • FIG. 201 is a diagrammatic view illustrating an example user interface of a digital marketplace configured to enable transactions and commerce between various users of the knowledge distribution system according to some embodiments of the present disclosure.
  • FIG. 202 is a schematic view of an exemplary embodiment of the market orchestration system according to some embodiments of the present disclosure.
  • FIG. 203 is a schematic view of an exemplary embodiment of the market orchestration system including a marketplace configuration system for configuring and launching a marketplace.
  • FIG. 204 is a schematic illustrating an example embodiment of a method of configuring and launching a marketplace according to some embodiments of the present disclosure.
  • FIG. 205 is a schematic view of an exemplary embodiment of the market orchestration system including a robotic process automation system configured to automate internal marketplace workflows based on robotic process automation.
  • FIG. 206 is a schematic view of an exemplary embodiment of the market orchestration system including an edge device configured to perform edge computation and intelligence.
  • FIG. 207 is a schematic view of an exemplary embodiment of the market orchestration system including a digital twin system configured to integrate a set of adaptive edge computing systems with a market orchestration digital twin.
  • FIG. 208 is a schematic view of a digital twin system. DETAILED DESCRIPTION
  • a service/microservice includes any system (or platform) configured to functionally perform the operations of the service, where the system may be data-integrated, including data collection circuits, blockchain circuits, artificial intelligence circuits, and/or smart contract circuits for handling lending entities and transactions.
  • Services/microservices may facilitate data handling and may include facilities for data extraction, transformation and loading; data cleansing and deduplication facilities; data normalization facilities; data synchronization facilities; data security facilities; computational facilities (e.g., for performing pre-defined calculation operations on data streams and providing an output stream); compression and decompression facilities; analytic facilities (such as providing automated production of data visualizations), data processing facilities, and/or data storage facilities (including storage retention, formatting, compression, migration, etc.), and others.
  • Services/microservices may include controllers, processors, network infrastructure, input/output devices, servers, client devices (e.g., laptops, desktops, terminals, mobile devices, and/or dedicated devices), sensors (e.g., loT sensors associated with one or more entities, equipment, and/or collateral), actuators (e.g., automated locks, notification devices, lights, camera controls, etc.), virtualized versions of any one or more of the foregoing (e.g., outsourced computing resources such as a cloud storage, computing operations; virtual sensors; subscribed data to be gathered such as stock or commodity prices, recordal logs, etc.), and/or include components configured as computer readable instructions that, when performed by a processor, cause the processor to perform one or more functions of the service, etc. Services may be distributed across a number of devices, and/or functions of a service may be performed by one or more devices cooperating to perform the given function of the service.
  • client devices e.g., laptops, desktops, terminals, mobile devices, and/or dedicated devices
  • Services/ microservices may include application programming interfaces that facilitate connection among the components of the system performing the service (e.g., microservices) and between the system to entities (e.g., programs, web sites, user devices, etc.) that are external to the system.
  • entities e.g., programs, web sites, user devices, etc.
  • example microservices that may be present in certain embodiments include (a) a multi-modal set of data collection circuits that collect information about and monitor entities related to a lending transaction; (b) blockchain circuits for maintaining a secure historical ledger of events related to a loan, the blockchain circuits having access control features that govern access by a set of parties involved in a loan; (c) a set of application programming interfaces, data integration services, data processing workflows and user interfaces for handling loan-related events and loan-related activities; and (d) smart contract circuits for specifying terms and conditions of smart contracts that govern at least one of loan terms and conditions, loan-related events, and loan-related activities. Any of the services/microservices may be controlled by or have control over a controller.
  • Certain systems may not be considered to be a service/microservice.
  • a point of sale device that simply charges a set cost for a good or service may not be a service.
  • a service that tracks the cost of a good or service and triggers notifications when the value changes may not be a valuation service itself, but may rely on valuation services, and/or may form a portion of a valuation service in certain embodiments.
  • a given circuit, controller, or device may be a service or a part of a service in certain embodiments, such as when the functions or capabilities of the circuit, controller, or device are configured to support a service or microservice as described herein, but may not be a service or part of a service for other embodiments (e.g., where the functions or capabilities of the circuit, controller, or device are not relevant to a service or microservice as described herein).
  • a mobile device being operated by a user may form a portion of a service as described herein at a first point in time (e.g., when the user accesses a feature of the service through an application or other communication from the mobile device, and/or when a monitoring function is being performed via the mobile device), but may not form a portion of the service at a second point in time (e.g., after a transaction is completed, after the user uninstalls an application, and/or when a monitoring function is stopped and/or passed to another device).
  • a first point in time e.g., when the user accesses a feature of the service through an application or other communication from the mobile device, and/or when a monitoring function is being performed via the mobile device
  • a second point in time e.g., after a transaction is completed, after the user uninstalls an application, and/or when a monitoring function is stopped and/or passed to another device.
  • service in the listing following
  • the balance of capital costs versus operating costs in implementing and operating the service includes, without limitation: the balance of capital costs versus operating costs in implementing and operating the service; the availability, speed, and/or bandwidth of network services available for system components, service users, and/or other entities that interact with the service; the response time of considerations for the service (e.g., how quickly decisions within the service must be implemented to support the commercial function of the service, the operating time for various artificial intelligence or other high computation operations) and/or the capital or operating cost to support a given response time; the location of interacting components of the service, and the effects of such locations on operations of the service (e.g., data storage locations and relevant regulatory schemes, network communication limitations and/or costs, power costs as a function of the location, support availability for time zones relevant to the service, etc.); the availability of certain sensor types, the related support for those sensors, and the availability of sufficient
  • certain operations performed by services herein include: performing real-time alterations to a loan based on tracked data; utilizing data to execute a collateral-backed smart contract; re-evaluating debt transactions in response to a tracked condition or data, and the like. While specific examples of services/microservices and considerations are described herein for purposes of illustration, any system benefitting from the disclosures herein, and any considerations understood to one of skill in the art having the benefit of the disclosures herein, are specifically contemplated within the scope of the present disclosure.
  • services include a financial service (e.g., a loan transaction service), a data collection service (e.g., a data collection service for collecting and monitoring data), a blockchain service (e.g., a blockchain service to maintain secure data), data integration services (e.g., a data integration service to aggregate data), smart contract services (e.g., a smart contract service to determine aspects of smart contracts), software services (e.g., a software service to extract data related to the entities from publicly available information sites), crowdsourcing services (e.g., a crowdsourcing service to solicit and report information), Internet of Things services (e.g., an Internet of Things service to monitor an environment), publishing services (e.g., a publishing services to publish data), microservices (e.g., having a set of application programming interfaces that facilitate connection among the microservices), valuation services (e.g., that use a valuation model to set a value for collateral based on information), artificial intelligence services, market value data collection services (e.g., that
  • Example services to perform one or more functions herein include computing devices; servers; networked devices; user interfaces; inter-device interfaces such as communication protocols, shared information and/or information storage, and/or application programming interfaces (APIs); sensors (e.g., loT sensors operationally coupled to monitored components, equipment, locations, or the like); distributed ledgers; circuits; and/or computer readable code configured to cause a processor to execute one or more functions of the service.
  • One or more aspects or components of services herein may be distributed across a number of devices, and/or may consolidated, in whole or part, on a given device.
  • aspects or components of services herein may be implemented at least in part through circuits, such as, in non-limiting examples, a data collection service implemented at least in part as a data collection circuit structed to collect and monitor data, a blockchain service implemented at least in part as a blockchain circuit structured to maintain secure data, data integration services implemented at least in part as a data integration circuit structured to aggregate data, smart contract services implemented at least in part as a smart contract circuit structed to determine aspects of smart contracts, software services implemented at least in part as a software service circuit structured to extract data related to the entities from publicly available information sites, crowdsourcing services implemented at least in part as a crowdsourcing circuit structured to solicit and report information, Internet of Things services implemented at least in part as an Internet of Things circuit structured to monitor an environment, publishing services implemented at least in part as a publishing services circuit structured to publish data, microservice service implemented at least in part as a microservice circuit structured to interconnect a plurality of service circuits, valuation service implemented at least in part as valuation services circuit structured to access a valuation model to set
  • the benefits of the present disclosure may be applied in a wide variety of systems, and any such systems may be considered with respect to items and services herein, while in certain embodiments a given system may not be considered with respect to items and services herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system, and/or how to combine processes and systems from the present disclosure to enhance operations of the contemplated system.
  • a configuration for a particular service include: the distribution and access devices available to one or more parties to a particular transaction; jurisdictional limitations on the storage, type, and communication of certain types of information; requirements or desired aspects of security and verification of information communication for the service; the response time of information gathering, inter-party communications, and determinations to be made by algorithms, machine learning components, and/or artificial intelligence components of the service; cost considerations of the service, including capital expenses and operating costs, as well as which party or entity will bear the costs and availability to recover costs such as through subscriptions, service fees, or the like; the amount of information to be stored and/or communicated to support the service; and/or the processing or computing power to be utilized to support the service.
  • items and service include any items and service, including, without limitation, items and services used as a reward, used as collateral, become the subject of a negotiation, and the like, such as, without limitation, an application for a warranty or guarantee with respect to an item that is the subject of a loan, collateral for a loan, or the like, such as a product, a service, an offering, a solution, a physical product, software, a level of service, quality of service, a financial instrument, a debt, an item of collateral, performance of a service, or other items.
  • items and service include any items and service, including, without limitation, items and services as applied to physical items (e.g., a vehicle, a ship, a plane, a building, a home, real estate property, undeveloped land, a farm, a crop, a municipal facility, a warehouse, a set of inventory, an antique, a fixture, an item of furniture, an item of equipment, a tool, an item of machinery, and an item of personal property), a financial item (e.g., a commodity, a security, a currency, a token of value, a ticket, a cryptocurrency), a consumable item (e.g., an edible item, a beverage), a highly valued item (e.g., a precious metal, an item of jewelry, a gemstone), an intellectual item (e.g., an item of intellectual property, an intellectual property right, a contractual right), and the like.
  • physical items e.g., a vehicle, a ship, a plane, a building, a home, real estate property
  • the benefits of the present disclosure may be applied in a wide variety of systems, and any such systems may be considered with respect to items and services herein, while in certain embodiments a given system may not be considered with respect to items and services herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system, and/or how to combine processes and systems from the present disclosure to enhance operations of the contemplated system.
  • agent automated agent
  • an agent or automated agent may process events relevant to at least one of the value, the condition, and the ownership of items of collateral or assets.
  • the agent or automated agent may also undertake an action related to a loan, debt transaction, bond transaction, subsidized loan, or the like to which the collateral or asset is subject, such as in response to the processed events.
  • the agent or automated agent may interact with a marketplace for purposes of collecting data, testing spot market transactions, executing transactions, and the like, where dynamic system behavior involves complex interactions that a user may desire to understand, predict, control, and/or optimize. Certain systems may not be considered an agent or an automated agent.
  • the system may not be an agent or automated agent.
  • a loan-related action is undertaken not in response to a processed event, it may not have been undertaken by an agent or automated agent.
  • marketplace information and market value describe a status or value of an asset, collateral, food, or service at a defined point or period in time.
  • Market value may refer to the expected value placed on an item in a marketplace or auction setting, or pricing or financial data for items that are similar to the item, asset, or collateral in at least one public marketplace.
  • market value may be the number of its outstanding shares multiplied by the current share price.
  • Valuation services may include market value data collection services that monitor and report on marketplace information relevant to the value (e.g., market value) of collateral, the issuer, a set of bonds, and a set of assets, a set of subsidized loans, a party, and the like.
  • Market values may be dynamic in nature because they depend on an assortment of factors, from physical operating conditions to economic climate to the dynamics of demand and supply. Market value may be affected by, and marketplace information may include, proximity to other assets, inventory or supply of assets, demand for assets, origin of items, history of items, underlying current value of item components, a bankruptcy condition of an entity, a foreclosure status of an entity, a contractual default status of an entity, a regulatory violation status of an entity, a criminal status of an entity, an export controls status of an entity, an embargo 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 for 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 testimonials for an entity, a set of behavior of an entity, a location of an entity, and a geolocation of an entity.
  • a market value may include information such as a volatility of a value, a sensitivity of a value (e.g., relative to other parameters having an uncertainty associated therewith), and/or a specific value of the valuated object to a particular party (e.g., an object may have more value as possessed by a first party than as possessed by a second party).
  • Certain information may not be marketplace information or a market value.
  • variables related to a value may be a value-in-use or an investment value.
  • an investment value may be considered a market value (e.g., when the valuating party intends to utilize the asset as an investment if acquired), and not a market value in other embodiments (e.g., when the valuating party intends to immediately liquidate the investment if acquired).
  • One of skill in the art having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit from marketplace information or a market value.
  • apportion value describes a proportional distribution or allocation of value proportionally, or a process to divide and assign value according to a rule of proportional distribution.
  • Apportionment of the value may be to several parties (e.g., each of the several parties is a beneficiary of a portion of the value), to several transactions (e.g., each of the transactions utilizes a portion of the value), and/or in a many-to-many relationship (e.g., a group of objects has an aggregate value that is apportioned between a number of parties and/or transactions).
  • the value may be a net loss and the apportioned value is the allocation of a liability to each entity.
  • apportioned value may refer to the distribution or allocation of an economic benefit, real estate, collateral, or the like.
  • apportionment may include a consideration of the value relative to the parties - for example, a $10 million asset apportioned 50/50 between two parties, where the parties have distinct value considerations for the asset, may result in one party crediting the apportionment differing resulting values from the apportionment.
  • apportionment may include a consideration of the value relative to given transactions - for example, a first type of transaction (e.g., a long-term loan) may have a different valuation of a given asset than a second type of transaction (e.g., a short-term line of credit).
  • a first type of transaction e.g., a long-term loan
  • a second type of transaction e.g., a short-term line of credit
  • apportioned value includes, without limitation: the currency of the principal sum, the anticipated transaction type (loan, bond or debt), the specific type of collateral, the ratio of the loan to value, the ratio of the collateral to the loan, the gross transaction/loan amount, the amount of the principal sum, the number of entities owed, the value of the collateral, and the like. While specific examples of apportioned values are described herein for purposes of illustration, any embodiment benefitting from the disclosures herein, and any considerations understood to one of skill in the art having the benefit of the disclosures herein are specifically contemplated within the scope of the present disclosure.
  • financial condition describes a current status of an entity's assets, liabilities, and equity positions at a defined point or period in time.
  • the financial condition may be memorialized in financial statement.
  • the financial condition may further include an assessment of the ability of the entity to survive future risk scenarios or meet future or maturing obligations.
  • Financial condition may be based on a set of attributes of the entity selected from among a publicly stated valuation of the entity, a set of property owned by the entity as indicated by public records, a valuation of a set of property owned by the entity, a bankruptcy condition of an entity, a foreclosure status of an entity, a contractual default status of an entity, a regulatory violation status of an entity, a criminal status of an entity, an export controls status of an entity, an embargo 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 for 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 testimonials for an entity, a set of behavior of an entity, a location of an entity, and a geolocation of an entity.
  • a financial condition may also describe a requirement or threshold for an agreement or loan.
  • conditions for allowing a developer to proceed may be various certifications and their agreement to a financial payout. That is, the developer's ability to proceed is conditioned upon a financial element, among others.
  • Certain conditions may not be a financial condition.
  • a credit card balance alone may be a clue as to the financial condition, but may not be the financial condition on its own.
  • a payment schedule may determine how long a debt may be on an entity's balance sheet, but in a silo may not accurately provide a financial condition.
  • interest rate includes an amount of interest due per period, as a proportion of an amount lent, deposited, or borrowed.
  • the total interest on an amount lent or borrowed may depend on the principal sum, the interest rate, the compounding frequency, and the length of time over which it is lent, deposited, or borrowed.
  • interest rate is expressed as an annual percentage but can be defined for any time period.
  • the interest rate relates to the amount a bank or other lender charges to borrow its money, or the rate a bank or other entity pays its savers for keeping money in an account. Interest rate may be variable or fixed.
  • an interest rate may vary in accordance with a government or other stakeholder directive, the currency of the principal sum lent or borrowed, the term to maturity of the investment, the perceived default probability of the borrower, supply and demand in the market, the amount of collateral, the status of an economy, or special features like call provisions.
  • an interest rate may be a relative rate (e.g., relative to a prime rate, an inflation index, etc.).
  • an interest rate may further consider costs or fees applied (e.g., "points") to adjust the interest rate.
  • a nominal interest rate may not be adjusted for inflation while a real interest rate takes inflation into account. Certain examples may not be an interest rate for purposes of particular embodiments.
  • a bank account growing by a fixed dollar amount each year, and/or a fixed fee amount may not be an example of an interest rate for certain embodiments.
  • One of skill in the art, having the benefit of the disclosure herein and knowledge about interest rates, can readily determine the characteristics of an interest rate for a particular embodiment.
  • an interest rate includes, without limitation: the currency of the principal sum, variables for setting an interest rate, criteria for modifying an interest rate, the anticipated transaction type (loan, bond or debt), the specific type of collateral, the ratio of the loan to value, the ratio of the collateral to the loan, the gross transaction/loan amount, the amount of the principal sum, the appropriate lifespans of transactions and/or collateral for a particular industry, the likelihood that a lender will sell and/or consolidate a loan before the term, and the like. While specific examples of interest rates are described herein for purposes of illustration, any embodiment benefitting from the disclosures herein, and any considerations understood to one of skill in the art having the benefit of the disclosures herein are specifically contemplated within the scope of the present disclosure.
  • valuation services (and similar terms) as utilized herein should be understood broadly. Without limitation to any other aspect or description of the present disclosure, a valuation service includes any service that sets a value for a good or service.
  • Valuation services may use a valuation model to set a value for collateral based on information from data collection and monitoring services. Smart contract services may process output from the set of valuation services and assign items of collateral sufficient to provide security for a loan and/or apportion value for an item of collateral among a set of lenders and/or transactions.
  • Valuation services may include artificial intelligence services that may iteratively improve the valuation model based on outcome data relating to transactions in collateral.
  • Valuation services may include market value data collection services that may monitor and report on marketplace information relevant to the value of collateral.
  • Certain processes may not be considered to be a valuation service.
  • a point of sale device that simply charges a set cost for a good or service may not be a valuation service.
  • a service that tracks the cost of a good or service and triggers notifications when the value changes may not be a valuation service itself, but may rely on valuation services and/or form a part of a valuation service. Accordingly, the benefits of the present disclosure may be applied in a wide variety of processes systems, and any such processes or systems may be considered a valuation service herein, while in certain embodiments a given service may not be considered a valuation service herein.
  • a contemplated system is a valuation service and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation: perform real-time alterations to a loan based on a value of a collateral; utilize marketplace data to execute a collateral-backed smart contract; re-evaluate collateral based on a storage condition or geolocation; the tendency of the collateral to have a volatile value, be utilized, and/or be moved; and the like. While specific examples of valuation services and considerations are described herein for purposes of illustration, any system benefitting from the disclosures herein, and any considerations understood to one of skill in the art having the benefit of the disclosures herein, are specifically contemplated within the scope of the present disclosure.
  • collateral attributes include any identification of the durability (ability of the collateral to withstand wear or the useful life of the collateral), value, identification (does the collateral have definite characteristics that make it easy to identify or market), stability of value (does the collateral maintain value over time), standardization, grade, quality, marketability, liquidity, transferability, desirability, trackability, deliverability (ability of the collateral be delivered or transfer without a deterioration in value), market transparency (is the collateral value easily verifiable or widely agreed upon), physical or virtual.
  • Collateral attributes may be measured in absolute or relative terms, and/or may include qualitative (e.g., categorical descriptions) or quantitative descriptions. Collateral attributes may be different for different industries, products, elements, uses, and the like. Collateral attributes may be assigned quantitative or qualitative values. Values associated with collateral attributes may be based on a scale (such as 1-10) or a relative designation (high, low, better, etc.). Collateral may include various components; each component may have collateral attributes. Collateral may, therefore, have multiple values for the same collateral attribute. In some embodiments, multiple values of collateral attributes may be combined to generate one value for each attribute. Some collateral attributes may apply only to specific portions of collateral.
  • collateral attributes even for a given component of the collateral, may have distinct values depending upon the party of interest (e.g., a party that values an aspect of the collateral more highly than another party) and/or depending upon the type of transaction (e.g., the collateral may be more valuable or appropriate for a first type of loan than for a second type of loan). Certain attributes associated with collateral may not be collateral attributes as described herein depending upon the purpose of the collateral attributes herein.
  • a product may be rated as durable relative to similar products; however, if the life of the product is much lower than the term of a particular loan in consideration, the durability of the product may be rated differently (e.g., not durable) or irrelevant (e.g., where the current inventory of the product is attached as the collateral, and is expected to change out during the term of the loan).
  • the benefits of the present disclosure may be applied to a variety of attributes, and any such attributes may be considered collateral attributes herein, while in certain embodiments a given attribute may not be considered a collateral attribute herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about contemplated collateral attributes ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular collateral attribute.
  • a contemplated attribute is a collateral attribute and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation: the source of the attribute and the source of the value of the attribute (e.g. does the attribute and attribute value comes from a reputable source), the volatility of the attribute (e.g.
  • blockchain services include any service related to the processing, recordation, and/or updating of a blockchain, and may include services for processing blocks, computing hash values, generating new blocks in a blockchain, appending a block to the blockchain, creating a fork in the blockchain, merging of forks in the blockchain, verifying previous computations, updating a shared ledger, updating a distributed ledger, generating cryptographic keys, verifying transactions, maintaining a blockchain, updating a blockchain, verifying a blockchain, generating random numbers.
  • the services may be performed by execution of computer readable instructions on local computers and/or by remote servers and computers.
  • Certain services may not be considered blockchain services individually but may be considered blockchain services based on the final use of the service and/or in a particular embodiment - for example, a computing a hash value may be performed in a context outside of a blockchain such in the context of secure communication.
  • Some initial services may be invoked without first being applied to blockchains, but further actions or services in conjunction with the initial services may associate the initial service with aspects of blockchains. For example, a random number may be periodically generated and stored in memory; the random numbers may initially not be generated for blockchain purposes but may be utilized for blockchains. Accordingly, the benefits of the present disclosure may be applied in a wide variety of services, and any such services may be considered blockchain services herein, while in certain embodiments a given service may not be considered a blockchain service herein.
  • a contemplated service is a blockchain service and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation: the application of the service, the 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 through utilization), cost of the service, the amount of data requested for the service, and/or the amount of data generated by the service (blocks of blockchain or keys associated with blockchains may be a specific size or a specific range of sizes).
  • a blockchain may be understood broadly to describe a cryptocurrency ledger that records, administrates, or otherwise processes online transactions.
  • a blockchain may be public, private, or a combination thereof, without limitation.
  • a blockchain may also be used to represent a set of digital transactions, agreement, terms, or other digital value.
  • a blockchain may also be used in conjunction with investment applications, token-trading applications, and/or digital/cryptocurrency based marketplaces.
  • a blockchain can also be associated with rendering consideration, such as providing goods, services, items, fees, access to a restricted area or event, data, or other valuable benefit.
  • Blockchains in various forms may be included where discussing a unit of consideration, collateral, currency, cryptocurrency, or any other form of value.
  • One of skill in the art, having the benefit of the disclosure herein and knowledge ordinarily available about a contemplated system, can readily determine the value symbolized or represented by a blockchain. While specific examples of blockchains are described herein for purposes of illustration, any embodiment benefitting from the disclosures herein, and any considerations understood to one of skill in the art having the benefit of the disclosures herein, are specifically contemplated within the scope of the present disclosure.
  • ledger and distributed ledger should be understood broadly. Without limitation to any other aspect or description of the present disclosure, a ledger may be a document, file, computer file, database, book, and the like which maintains a record of transactions. Ledgers may be physical or digital. Ledgers may include records related to sales, accounts, purchases, transactions, assets, liabilities, incomes, expenses, capital, and the like. Ledgers may provide a history of transactions that may be associated with time. Ledgers may be centralized or decentralized/distributed.
  • a centralized ledger may be a document that is controlled, updated, or viewable by one or more selected entities or a clearinghouse and wherein changes or updates to the ledger are governed or controlled by the entity or clearinghouse.
  • a distributed ledger may be a ledger that is distributed across a plurality of entities, participants or regions which may independently, concurrently, or consensually, update, or modify their copies of the ledger.
  • Ledgers and distributed ledgers may include security measures and cryptographic functions for signing, concealing, or verifying content.
  • blockchain technology may be used.
  • the ledger may be Merkle trees comprising a linked list of nodes in which each node contains hashed or encrypted transactional data of the previous nodes. Certain records of transactions may not be considered ledgers.
  • a file, computer file, database, or book may or may not be a ledger depending on what data it stores, how the data is organized, maintained, or secured. For example, a list of transactions may not be considered a ledger if it cannot be trusted or verified, and/or if it is based on inconsistent, fraudulent, or incomplete data.
  • Data in ledgers may be organized in any format such as tables, lists, binary streams of data, or the like which may depend on convenience, source of data, type of data, environment, applications, and the like.
  • a ledger that is shared among various entities may not be a distributed ledger, but the distinction of distributed may be based on which entities are authorized to make changes to the ledger and/or how the changes are shared and processed among the different entities. Accordingly, the benefits of the present disclosure may be applied in a wide variety of data, and any such data may be considered ledgers herein, while in certain embodiments a given data may not be considered a ledger herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about contemplated ledgers and distributed ledger ordinarily available to that person, can readily determine which aspects of the present disclosure can be utilized to implement, and/or will benefit a particular ledger.
  • a contemplated data is a ledger and/or whether aspects of the present disclosure can benefit or enhance the contemplated ledger include, without limitation: the security of the data in the ledger (can the data be tampered or modified), the time associated with making changes to the data in the ledger, cost of making changes (computationally and monetarily), detail of data, organization of data (does the data need to be processed for use in an application), who controls the ledger (can the party be trusted or relied to manage the ledger), confidentiality of the data (who can see or track the data in the ledger), size of the infrastructure, communication requirements (distributed ledgers may require a communication interface or specific infrastructure), resiliency.
  • loan and similar terms as utilized herein should be understood broadly. Without limitation to any other aspect or description of the present disclosure, a loan may be an agreement related to an asset that is borrowed, and that is expected to be returned in kind (e.g., money borrowed, and money returned) or as an agreed transaction (e.g., a first good or service is borrowed, and money, a second good or service, or a combination, is returned).
  • kind e.g., money borrowed, and money returned
  • an agreed transaction e.g., a first good or service is borrowed, and money, a second good or service, or a combination, is returned.
  • Assets may be money, property, time, physical objects, virtual objects, services, a right (e.g., a ticket, a license, or other rights), a depreciation amount, a credit (e.g., a tax credit, an emissions credit, etc.), an agreed assumption of a risk or liability, and/or any combination thereof.
  • a loan may be based on a formal or informal agreement between a borrower and a lender wherein a lender may provide an asset to the borrower for a predefined amount of time, a variable period of time, or indefinitely.
  • Lenders and borrowers may be individuals, entities, corporations, governments, groups of people, organizations, and the like.
  • loan types may include mortgage loans, personal loans, secured loans, unsecured loans, concessional loans, commercial loans, microloans, and the like.
  • the agreement between the borrower and the lender may specify terms of the loan.
  • the borrower may be required to return an asset or repay with a different asset than was borrowed.
  • a loan may require interest to be repaid on the borrowed asset.
  • Borrowers and lenders may be intermediaries between other entities and may never possess or use the asset.
  • a loan may not be associated with direct transfer of goods but may be associated with usage rights or shared usage rights.
  • the agreement between the borrower and the lender may be executed between the borrower and the lender, and/or executed between an intermediary (e.g., a beneficiary of a loan right such as through a sale of the loan).
  • the agreement between the borrower and the lender may be executed through services herein, such as through a smart contract service that determines at least a portion of the terms and conditions of the loans, and in certain embodiments may commit the borrower and/or the lender to the terms of the agreement, which may be a smart contract.
  • the smart contract service may populate the terms of the agreement, and present them to the borrower and/or lender for execution.
  • the smart contract service may automatically commit one of the borrower or the lender to the terms (at least as an offer) and may present the offer to the other one of the borrower or the lender for execution.
  • a loan agreement may include multiple borrowers and/or multiple lenders, for example where a set of loans includes a number of beneficiaries of payment on the set of loans, and/or a number of borrowers on the set of loans.
  • the risks and/or obligations of the set of loans may be individualized (e.g., each borrower and/or lender is related to specific loans of the set of loans), apportioned (e.g., a default on a particular loan has an associated loss apportioned between the lenders), and/or combinations of these (e.g., one or more subsets of the set of loans is treated individually and/or apportioned).
  • Certain agreements may not be considered a loan.
  • An agreement to transfer or borrow assets may not be a loan depending on what assets are transferred, how the assets were transferred, or the parties involved.
  • the transfer of assets may be for an indefinite time and may be considered a sale of the asset or a permanent transfer.
  • an asset is borrowed or transferred without clear or definite terms or lack of consensus between the lender and the borrower it may, in some cases, not be considered a loan.
  • An agreement may be considered a loan even if a formal agreement is not directly codified in a written agreement as long as the parties willingly and knowingly agreed to the arrangement, and/or ordinary practices (e.g., in a particular industry) may treat the transaction as a loan.
  • the benefits of the present disclosure may be applied in a wide variety of agreements, and any such agreement may be considered a loan herein, while in certain embodiments a given agreement may not be considered a loan herein.
  • Any such agreement may be considered a loan herein, while in certain embodiments a given agreement may not be considered a loan herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about contemplated loans ordinarily available to that person, can readily determine which aspects of the present disclosure implement a loan, utilize a loan, or benefit a particular loan transaction.
  • Certain considerations for the person of skill in the art, in determining whether a contemplated data is a loan and/or whether aspects of the present disclosure can benefit or enhance the contemplated loan include, without limitation: the value of the assets involved, the ability of the borrower to return or repay the loan, the types of assets involved (e.g., whether the asset is consumed through utilization), the repayment time frame associated with the loan, the interest on the loan, how the agreement of the loan was arranged, formality of the agreement, detail of the agreement, the detail of the agreements of the loan, the collateral attributes associated with the loan, and/or the ordinary business expectations of any of the foregoing in a particular context. While specific examples of loans and considerations are described herein for purposes of illustration, any system benefitting from the disclosures herein, and any considerations understood to one of skill in the art having the benefit of the disclosures herein, are specifically contemplated within the scope of the present disclosure.
  • loan related event(s) (and similar terms, including loan-related events) as utilized herein should be understood broadly.
  • a loan related events may include any event related to terms of the loan or events triggered by the agreement associated with the loan.
  • Loan-related events may include default on loan, breach of contract, fulfillment, repayment, payment, change in interest, late fee assessment, refund assessment, distribution, and the like.
  • Loan-related events may be triggered by explicit agreement terms; for example - an agreement may specify a rise in interest rate after a time period has elapsed from the beginning of the loan; the rise in interest rate triggered by the agreement may be a loan related event.
  • Loan-related events may be triggered implicitly by related loan agreement terms.
  • any occurrence that may be considered relevant to assumptions of the loan agreement, and/or expectations of the parties to the loan agreement, may be considered an occurrence of an event. For example, if collateral for a loan is expected to be replaceable (e.g., an inventory as collateral), then a change in inventory levels may be considered an occurrence of a loan related event. In another example, if review and/or confirmation of the collateral is expected, then a lack of access to the collateral, the disablement or failure of a monitoring sensor, etc. may be considered an occurrence of a loan related event. In certain embodiments, circuits, controllers, or other devices described herein may automatically trigger the determination of a loan-related events.
  • loan-related events may be triggered by entities that manage loans or loan-related contracts.
  • Loan-related events may be conditionally triggered based on one or more conditions in the loan agreement.
  • Loan related events may be related to tasks or requirements that need to be completed by the lender, borrower, or a third party.
  • Certain events may be considered loan- related events in certain embodiments and/or in certain contexts, but may not be considered a loan-related event in another embodiment or context.
  • Many events may be associated with loans but may be caused by external triggers not associated with a loan.
  • an externally triggered event e.g., a commodity price change related to a collateral item
  • loan-related events may be loan-related events.
  • renegotiation of loan terms initiated by a lender may not be considered a loan related event if the terms and/or performance of the existing loan agreement did not trigger the renegotiation.
  • the benefits of the present disclosure may be applied in a wide variety of events, and any such event may be considered a loan related event herein, while in certain embodiments given events may not be considered a loan related event herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure may be considered a loan- related event for the contemplated system and/or for particular transactions supported by the system.
  • the impact of the related event on the loan events that cause default or termination of the loan may have higher impact
  • loan-related activities should be understood broadly. Without limitation to any other aspect or description of the present disclosure, a loan related activity may include activities related to the generation, maintenance, termination, collection, enforcement, servicing, billing, marketing, ability to perform, or negotiation of a loan.
  • Loan-related activity may include activities related to the signing of a loan agreement or a promissory note, review of loan documents, processing of payments, evaluation of collateral, evaluation of compliance of the borrower or lender to the loan terms, renegotiation of terms, perfection of security or collateral for the loan, and/or a negation of terms.
  • Loan- related activities may relate to events associated with a loan before formal agreement on the terms, such as activities associated with initial negotiations.
  • loan-related activities may relate to events during the life of the loan and after the termination of a loan.
  • Loan-related activities may be performed by a lender, borrower, or a third party.
  • Certain activities may not be considered loan related activities services individually but may be considered loan related activities based on the specificity of the activity to the loan lifecycle- for example, billing or invoicing related to outstanding loans may be considered a loan related activity, however when the invoicing or billing of loans is combined with billing or invoicing for non loan-related elements the invoicing may not be considered a loan related activity.
  • Some activities may be performed in relation to an asset regardless of whether a loan is associated with the asset; in these cases, the activity may not be considered a loan related activity.
  • regular audits related to an asset may occur regardless of whether the asset is associated with a loan and may not be considered a loan related activity.
  • a regular audit related to an asset may be required by a loan agreement and would not typically occur but for the association with a loan, in this case, the activity may be considered a loan related activity.
  • activities may be considered loan-related activities if the activity would otherwise not occur if the loan is not active or present, but may still be considered a loan-related activity in some instances (e.g., if auditing occurs normally, but the lender does not have the ability to enforce or review the audit, then the audit may be considered a loan-related activity even though it already occurs otherwise).
  • any such event may be considered a loan related event herein, while in certain embodiments given events may not be considered a loan related events herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine a loan related activity for the purposes of the contemplated system.
  • a contemplated data is a loan related activity and/or whether aspects of the present disclosure can benefit or enhance the contemplated loan include, without limitation: the necessity of the activity for the loan (can the loan agreement or terms be satisfied without the activity), the cost of the activity, the specificity of the activity to the loan (is the activity similar or identical to other industries), time involved in the activity, the impact of the activity on a loan life cycle, entity performing the activity, amount of data required for the activity (does the activity require confidential information related to the loan, or personal information related to the entities), and/or the ability of parties to enforce and/or review the activity.
  • loan terms may relate to conditions, rules, limitations, contract obligations, and the like related to the timing, repayment, origination, and other enforceable conditions agreed to by the borrower and the lender of the loan.
  • loan terms may be specified in a formal contract between a borrower and the lender.
  • Loan terms may specify aspects of an interest rate, collateral, foreclose conditions, consequence of debt, payment options, payment schedule, a covenant, and the like.
  • Loan terms may be negotiable or may change during the life of a loan.
  • loan terms may be change or be affected by outside parameters such as market prices, bond prices, conditions associated with a lender or borrower, and the like. Certain aspects of a loan may not be considered loan terms. In certain embodiments, aspects of loan that have not been formally agreed upon between a lender and a borrower, and/or that are not ordinarily understood in the course of business (and/or the particular industry) may not be considered loan terms. Certain aspects of a loan may be preliminary or informal until they have been formally agreed or confirmed in a contract or a formal agreement. Certain aspects of a loan may not be considered loan terms individually but may not be considered loan terms based on the specificity of the aspect to a specific loan.
  • Certain aspects of a loan may not be considered loan terms at a particular time during the loan, but may be considered loan terms at another time during the loan (e.g., obligations and/or waivers that may occur through the performance of the parties, and/or expiration of a loan term).
  • an interest rate may generally not be considered a loan term until it is defined in relation of a loan and defined as to how the interest compounded (annual, monthly), calculated, and the like.
  • An aspect of a loan may not be considered a term if it is indefinite or unenforceable.
  • Some aspects may be manifestations or related to terms of a loan but may themselves not be the terms.
  • a loan term may be the repayment period of a loan, such as one year.
  • the term may not specify how the loan is to be repaid in the year.
  • the loan may be repaid with 12 monthly payments or one annual payment.
  • a monthly payment plan in this case may not be considered a loan term as it can be just one or many options for repayment not directly specified by a loan.
  • the benefits of the present disclosure may be applied in a wide variety of loan aspects, and any such aspect may be considered a loan term herein, while in certain embodiments given aspects may not be considered loan terms herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure are loan terms for the contemplated system.
  • Certain considerations for the person of skill in the art, in determining whether a contemplated data is a loan term and/or whether aspects of the present disclosure can benefit or enhance the contemplated loan include, without limitation: the enforceability of the terms (can the conditions be enforced by the lender or the lender or the borrower), the cost of enforcing the terms (amount of time, or effort required ensure the conditions are being followed), the complexity of the terms (how easily can they be followed or understood by the parties involved, are the terms error prone or easily misunderstood), entities responsible for the terms, fairness of the terms, stability of the terms (how often do they change), observability of the terms (can the terms be verified by a another party), favorability of the terms to one party (do the terms favor the borrower or the lender), risk associated with the loan (terms may depend on the probability that the loan may not be repaid), characteristics of the borrower or lender (their ability to meet the terms), and/or ordinary expectations for the loan and/or related industry.
  • loan conditions may relate to rules, limits, and/or obligations related to a loan.
  • loan conditions may relate to rules or necessary obligations for obtaining a loan, for maintaining a loan, for applying for a loan, for transferring a loan, and the like.
  • Loan conditions may include principal amount of debt, a balance of debt, a fixed interest rate, a variable interest rate, a payment amount, a payment schedule, a balloon payment schedule, a specification of collateral, a specification of substitutability of collateral, treatment of collateral, access to collateral, a party, a guarantee, a guarantor, a security, a personal guarantee, a lien, a duration, a covenant, a foreclose condition, a default condition, conditions related to other debts of the borrower, and a consequence of default.
  • Certain aspects of a loan may not be considered loan conditions. Aspects of loan that have not been formally agreed upon between a lender and a borrower, and/or that are not ordinarily understood in the course of business (and/or the particular industry), may not be considered loan conditions. Certain aspects of a loan may be preliminary or informal until they have been formally agreed or confirmed in a contract or a formal agreement. Certain aspects of a loan may not be considered loan conditions individually but may be considered loan conditions based on the specificity of the aspect to a specific loan.
  • Certain aspects of a loan may not be considered loan conditions at a particular time during the loan, but may be considered loan conditions at another time during the loan (e.g., obligations and/or waivers that may occur through the performance of the parties, and/or expiration of a loan condition). Accordingly, the benefits of the present disclosure may be applied in a wide variety of loan aspects, and any such aspect may be considered loan conditions herein, while in certain embodiments given aspects may not be considered loan conditions herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure are loan conditions for the contemplated system.
  • a contemplated data is a loan condition and/or whether aspects of the present disclosure can benefit or enhance the contemplated loan include, without limitation: the enforceability of the condition (can the conditions be enforced by the lender or the lender or the borrower), the cost of enforcing the condition (amount of time, or effort required ensure the conditions are being followed), the complexity of the condition (how easily can they be followed or understood by the parties involved, are the conditions error prone or easily misunderstood), entities responsible for the conditions, fairness of the conditions, observability of the conditions (can the conditions be verified by a another party), favorability of the conditions to one party (do the conditions favor the borrower or the lender), risk associated with the loan (conditions may depend on the probability that the loan may not be repaid), and/or ordinary expectations for the loan and/or related industry.
  • loan collateral may relate to any asset or property that a borrower promises to a lender as backup in exchange for a loan, and/or as security for the loan.
  • Collateral may be any item of value that is accepted as an alternate form of repayment in case of default on a loan.
  • Collateral may include any number of physical or virtual items such as a vehicle, a ship, a plane, a building, a home, real estate property, undeveloped land, a farm, a crop, a municipal facility, a warehouse, a set of inventory, a commodity, a security, a currency, a token of value, a ticket, a cryptocurrency, a consumable item, an edible item, a beverage, a precious metal, an item of jewelry, a gemstone, an item of intellectual property, an intellectual property right, a contractual right, an antique, a fixture, an item of furniture, an item of equipment, a tool, an item of machinery, and an item of personal property.
  • Collateral may include more than one item or types of items.
  • a collateral item may describe an asset, a property, a value, or other item defined as a security for a loan or a transaction.
  • a set of collateral items may be defined, and within that set substitution, removal or addition of collateral items may be affected.
  • a collateral item may be, without limitation: a vehicle, a ship, a plane, a building, a home, real estate property, undeveloped land, a farm, a crop, a municipal facility, a warehouse, a set of inventory, a commodity, a security, a currency, a token of value, a ticket, a cryptocurrency, a consumable item, an edible item, a beverage, a precious metal, an item of jewelry, a gemstone, an item of intellectual property, an intellectual property right, a contractual right, an antique, a fixture, an item of furniture, an item of equipment, a tool, an item of machinery, or an item of personal property, or the like.
  • collateral item or set of collateral items may also be used in conjunction with other terms to an agreement or loan, such as a representation, a warranty, an indemnity, a covenant, a balance of debt, a fixed interest rate, a variable interest rate, a payment amount, a payment schedule, a balloon payment schedule, a specification of collateral, a specification of substitutability of collateral, a security, a personal guarantee, a lien, a duration, a foreclose condition, a default condition, and a consequence of default.
  • a smart contract may calculate whether a borrower has satisfied conditions or covenants and in cases where the borrower has not satisfied such conditions or covenants, may enable automated action, or trigger another conditions or terms that may affect the status, ownership, or transfer of a collateral item, or initiate the substitution, removal, or addition of collateral items to a set of collateral for a loan.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about collateral items, can readily determine the purposes and use of collateral items in various embodiments and contexts disclosed herein, including the substitution, removal, and addition thereof.
  • a smart contract service includes any service or application that manages a smart contract or a smart lending contract.
  • the smart contract service may specify terms and conditions of a smart contract, such as in a rules database, or process output from a set of valuation services and assign items of collateral sufficient to provide security for a loan.
  • Smart contract services may automatically execute a set of rules or conditions that embody the smart contract, wherein the execution may be based on or take advantage of collected data.
  • Smart contract services may automatically initiate a demand for payment of a loan, automatically initiate a foreclosure process, automatically initiate an action to claim substitute or backup collateral or transfer ownership of collateral, automatically initiate an inspection process, automatically change a payment, or interest rate term that is based on the collateral, and may also configure smart contracts to automatically undertake a loan-related action.
  • Smart contracts may govern at least one of loan terms and conditions, loan-related events, and loan-related activities.
  • Smart contracts may be agreements that are encoded as computer protocols and may facilitate, verify, or enforce the negotiation or performance of a smart contract. Smart contracts may or may not be one or more of partially or fully self-executing, or partially or fully selfenforcing.
  • Certain processes may not be considered to be smart-contract related individually, but may be considered smart-contract related in an aggregated system - for example automatically undertaking a loan-related action may not be smart contract-related in one instance, but in another instance, may be governed by terms of a smart contract. Accordingly, the benefits of the present disclosure may be applied in a wide variety of processes systems, and any such processes or systems may be considered a smart contract or smart contract service herein, while in certain embodiments a given service may not be considered a smart contract service herein.
  • loT system (and similar terms) as utilized herein should be understood broadly. Without limitation to any other aspect or description of the present disclosure, an loT system includes any system of uniquely identified and interrelated computing devices, mechanical and digital machines, sensors, and objects that are able to transfer data over a network without intervention. Certain components may not be considered an loT system individually, but may be considered an loT system in an aggregated system - for example, a single networked.
  • the sensor, smart speaker, and/or medical device may be not an loT system, but may be a part of a larger system and/or be accumulated with a number of other similar components to be considered an loT system and/or a part of an loT system.
  • a system may be considered an loT system for some purposes but not for other purposes - for example, a smart speaker may be considered part of an loT system for certain operations, such as for providing surround sound, or the like, but not part of an loT system for other operations such as directly streaming content from a single, locally networked source.
  • otherwise similar looking systems may be differentiated in determining whether such systems are loT systems, and/or which type of loT system.
  • one group of medical devices may not, at a given time, be sharing to an aggregated HER database, while another group of medical devices may be sharing data to an aggregate HER for the purposes of a clinical study, and accordingly one group of medical devices may be an loT system, while the other is not. Accordingly, the benefits of the present disclosure may be applied in a wide variety of systems, and any such systems may be considered an loT system herein, while in certain embodiments a given system may not be considered an loT system herein.
  • a contemplated system is an loT system and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation: the transmission environment of the system (e.g., availability of low power, inter-device networking); the shared data storage of a group of devices; establishment of a geofence by a group of devices; service as blockchain nodes; the performance of asset, collateral, or entity monitoring; the relay of data between devices; ability to aggregate data from a plurality of sensors or monitoring devices, and the like.
  • the transmission environment of the system e.g., availability of low power, inter-device networking
  • the shared data storage of a group of devices e.g., establishment of a geofence by a group of devices; service as blockchain nodes; the performance of asset, collateral, or entity monitoring; the relay of data between devices; ability to aggregate data from a plurality of sensors or monitoring devices, and the like.
  • a data collection service includes any service that collects data or information, including any circuit, controller, device, or application that may store, transmit, transfer, share, process, organize, compare, report on and/or aggregate data.
  • the data collection service may include data collection devices (e.g., sensors) and/or may be in communication with data collection devices.
  • the data collection service may monitor entities, such as to identify data or information for collection.
  • the data collection service may be event-driven, run on a periodic basis, or retrieve data from an application at particular points in the application's execution.
  • Certain processes may not be considered to be a data collection service individually, but may be considered a data collection service in an aggregated system - for example, a networked storage device may be a component of a data collection service in one instance, but in another instance, may have stand-alone functionality. Accordingly, the benefits of the present disclosure may be applied in a wide variety of processes systems, and any such processes or systems may be considered a data collection service herein, while in certain embodiments a given service may not be considered a data collection service herein.
  • a contemplated system is a data collection service and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation: ability to modify a business rule on the fly and alter a data collection protocol; perform real-time monitoring of events; connection of a device for data collection to a monitoring infrastructure, execution of computer readable instructions that cause a processor to log or track events; use of an automated inspection system; occurrence of sales at a networked point-of-sale; need for data from one or more distributed sensors or cameras; and the like.
  • a data integration service includes any service that integrates data or information, including any device or application that may extract, transform, load, normalize, compress, decompress, encode, decode, and otherwise process data packets, signals, and other information.
  • the data integration service may monitor entities, such as to identify data or information for integration.
  • the data integration service may integrate data regardless of required frequency, communication protocol, or business rules needed for intricate integration patterns.
  • any such processes or systems may be considered a data integration service herein, while in certain embodiments a given service may not be considered a data integration service herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system and how to combine processes and systems from the present disclosure to implement a data integration service and/or enhance operations of the contemplated system.
  • a contemplated system is a data integration service and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation: ability to modify a business rule on the fly and alter a data integration protocol; communication with third party databases to pull in data to integrate with; synchronization of data across disparate platforms; connection to a central data warehouse; data storage capacity, processing capacity, and/or communication capacity distributed throughout the system; the connection of separate, automated workflows; and the like.
  • computational services should be understood broadly. Without limitation to any other aspect or description of the present disclosure, computational services may be included as a part of one or more services, platforms, or microservices, such as blockchain services, data collection services, data integration services, valuation services, smart contract services, data monitoring services, data mining, and/or any service that facilitates collection, access, processing, transformation, analysis, storage, visualization, or sharing of data. Certain processes may not be considered to be a computational service. For example, a process may not be considered a computational service depending on the sorts of rules governing the service, an end product of the service, or the intent of the service.
  • any such processes or systems may be considered a computational service herein, while in certain embodiments a given service may not be considered a computational service herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system and how to combine processes and systems from the present disclosure to implement one or more computational service, and/or to enhance operations of the contemplated system.
  • a contemplated system is a computational service and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation: agreement-based access to the service; mediate an exchange between different services; provides on demand computational power to a web service; accomplishes one or more of monitoring, collection, access, processing, transformation, analysis, storage, integration, visualization, mining, or sharing of data. While specific examples of computational services and considerations are described herein for purposes of illustration, any system benefitting from the disclosures herein, and any considerations understood to one of skill in the art having the benefit of the disclosures herein, are specifically contemplated within the scope of the present disclosure.
  • a sensor may be a device, module, machine, or subsystem that detects or measures a physical quality, event, or change. In embodiments, may record, indicate, transmit, or otherwise respond to the detection or measurement.
  • sensors may be sensors for sensing movement of entities, for sensing temperatures, pressures or other attributes about entities or their environments, cameras that capture still or video images of entities, sensors that collect data about collateral or assets, such as, for example, regarding the location, condition (health, physical, or otherwise), quality, security, possession, or the like.
  • sensors may be sensitive to, but not influential on, the property to be measured but insensitive to other properties. Sensors may be analog or digital.
  • Sensors may include processors, transmitters, transceivers, memory, power, sensing circuit, electrochemical fluid reservoirs, light sources, and the like.
  • sensors contemplated for use in the system include biosensors, chemical sensors, black silicon sensor, IR sensor, acoustic sensor, induction sensor, motion sensor, optical sensor, opacity sensor, proximity sensor, inductive sensor, Eddy-current sensor, passive infrared proximity sensor, radar, capacitance sensor, capacitive displacement sensor, hall-effect sensor, magnetic sensor, GPS sensor, thermal imaging sensor, thermocouple, thermistor, photoelectric sensor, ultrasonic sensor, infrared laser sensor, inertial motion sensor, MEMS internal motion sensor, ultrasonic 3D motion sensor, accelerometer, inclinometer, force sensor, piezoelectric sensor, rotary encoders, linear encoders, ozone sensor, smoke sensor, heat sensor, magnetometer, carbon dioxide detector, carbon monoxide detector, oxygen sensor, glucose sensor, smoke detector, metal detector, rain sensor, altimeter, GPS, detection of
  • a sensor may be a virtual sensor - for example determining a parameter of interest as a calculation based on other sensed parameters in the system.
  • a sensor may be a smart sensor - for example reporting a sensed value as an abstracted communication (e.g., as a network communication) of the sensed value.
  • a sensor may provide a sensed value directly (e.g., as a voltage level, frequency parameter, etc.) to a circuit, controller, or other device in the system.
  • storage condition includes an environment, physical location, environmental quality, level of exposure, security measures, maintenance description, accessibility description, and the like related to the storage of an asset, collateral, or an entity specified and monitored in a contract, loan, or agreement or backing the contract, loan or other agreement, and the like.
  • actions may be taken to, maintain, improve, and/or confirm a condition of the asset or the use of that asset as collateral.
  • actions may be taken to alter the terms or conditions of a loan or bond.
  • Storage condition may be classified in accordance with various rules, thresholds, conditional procedures, workflows, model parameters, and the like and may be based on self-reporting or on data from Internet of Things devices, data from a set of environmental condition sensors, data from a set of social network analytic services and a set of algorithms for querying network domains, social media data, crowdsourced data, and the like.
  • the storage condition may be tied to a geographic location relating to the collateral, the issuer, the borrower, the distribution of the funds or other geographic locations. Examples of loT data may include images, sensor data, location data, and the like.
  • Examples of social media data or crowdsourced data may include behavior of parties to the loan, financial condition of parties, adherence to a party’s a term or condition of the loan, or bond, or the like.
  • Parties to the loan may include issuers of a bond, related entities, lender, borrower, 3rd parties with an interest in the debt.
  • Storage condition may relate to an asset or type of collateral such as a municipal asset, a vehicle, a ship, a plane, a building, a home, real estate property, undeveloped land, a farm, a crop, a municipal facility, a warehouse, a set of inventory, a commodity, a security, a currency, a token of value, a ticket, a cryptocurrency, a consumable item, an edible item, a beverage, a precious metal, an item of jewelry, a gemstone, an item of intellectual property, an intellectual property right, a contractual right, an antique, a fixture, an item of furniture, an item of equipment, a tool, an item of machinery, and an item of personal property.
  • an asset or type of collateral such as a municipal asset, a vehicle, a ship, a plane, a building, a home, real estate property, undeveloped land, a farm, a crop, a municipal facility, a warehouse, a set of inventory, a commodity, a security, a currency, a token of
  • the storage condition may include an environment where environment may include an environment selected from among a municipal environment, a corporate environment, a securities trading environment, a real property environment, a commercial facility, a warehousing facility, a transportation environment, a manufacturing environment, a storage environment, a home, and a vehicle.
  • Actions based on the storage condition of a collateral, an asset or an entity may include managing, reporting on, altering, syndicating, consolidating, terminating, maintaining, modifying terms and/or conditions, foreclosing an asset, or otherwise handling a loan, contract, or agreement.
  • Certain considerations for the person of skill in the art, or embodiments of the present disclosure in choosing an appropriate storage condition to manage and/or monitor include, without limitation: the legality of the condition given the jurisdiction of the transaction, the data available for a given collateral, the anticipated transaction type (loan, bond or debt), the specific type of collateral, the ratio of the loan to value, the ratio of the collateral to the loan, the gross trans action/loan amount, the credit scores of the borrower and the lender, ordinary practices in the industry, and other considerations. While specific examples of storage conditions are described herein for purposes of illustration, any embodiment benefitting from the disclosures herein, and any considerations understood to one of skill in the art having the benefit of the disclosures herein are specifically contemplated within the scope of the present disclosure.
  • geolocation includes the identification or estimation of the real-world geographic location of an object, including the generation of a set of geographic coordinates (e.g. latitude and longitude) and/or street address.
  • a geolocation of a collateral an asset, or entity, actions may be taken to maintain or improve a condition of the asset or the use of that asset as collateral.
  • actions may be taken to alter the terms or conditions of a loan or bond.
  • Geolocations may be determined in accordance with various rules, thresholds, conditional procedures, workflows, model parameters, and the like and may be based on self-reporting or on data from Internet of Things devices, data from a set of environmental condition sensors, data from a set of social network analytic services and a set of algorithms for querying network domains, social media data, crowdsourced data, and the like.
  • Examples of geolocation data may include GPS coordinates, images, sensor data, street address, and the like.
  • Geolocation data may be quantitative (e.g., longitude/latitude, relative to a plat map, etc.) and/or qualitative (e.g., categorical such as “coastal”, “rural”, etc.; “within New York City”, etc.). Geolocation data may be absolute (e.g., GPS location) or relative (e.g., within 100 yards of an expected location). Examples of social media data or crowdsourced data may include behavior of parties to the loan as inferred by their geolocation, financial condition of parties inferred by geolocation, adherence of parties to a term or condition of the loan, or bond, or the like.
  • Geolocation may be determined for an asset or type of collateral such as a municipal asset, a vehicle, a ship, a plane, a building, a home, real estate property, undeveloped land, a farm, a crop, a municipal facility, a warehouse, a set of inventory, a commodity, a security, a currency, a token of value, a ticket, a consumable item, an edible item, a beverage, a precious metal, an item of jewelry, a gemstone, an antique, a fixture, an item of furniture, an item of equipment, a tool, an item of machinery, and an item of personal property.
  • Geolocation may be determined for an entity such as one of the parties, a third-party (e.g., an inspection service, maintenance service, cleaning service, etc.
  • the geolocation may include an environment selected from among a municipal environment, a corporate environment, a securities trading environment, a real property environment, a commercial facility, a warehousing facility, a transportation environment, a manufacturing environment, a storage environment, a home, and a vehicle.
  • Actions based on the geolocation of a collateral, an asset or an entity may include managing, reporting on, altering, syndicating, consolidating, terminating, maintaining, modifying terms and/or conditions, foreclosing an asset, or otherwise handling a loan, contract, or agreement.
  • Certain considerations for the person of skill in the art, or embodiments of the present disclosure in choosing an appropriate geolocation to manage include, without limitation: the legality of the geolocation given the jurisdiction of the transaction, the data available for a given collateral, the anticipated transaction type (loan, bond or debt), the specific type of collateral, the ratio of the loan to value, the ratio of the collateral to the loan, the gross trans action/loan amount, the frequency of travel of the borrower to certain jurisdictions and other considerations, the mobility of the collateral, and/or a likelihood of location-specific event occurrence relevant to the transaction (e.g., weather, location of a relevant industrial facility, availability of relevant services, etc.). While specific examples of geolocation are described herein for purposes of illustration, any embodiment benefitting from the disclosures herein, and any considerations understood to one of skill in the art having the benefit of the disclosures herein are specifically contemplated within the scope of the present disclosure.
  • jurisdictional location refers to the laws and legal authority governing a loan entity.
  • the jurisdictional location may be based on a geolocation of an entity, a registration location of an entity (e.g. a ship's flag state, a state of incorporation for a business, and the like), a granting state for certain rights such as intellectual priority, and the like.
  • a jurisdictional location may be one or more of the geolocations for an entity in the system.
  • a jurisdictional location may not be the same as the geolocation of any entity in the system (e.g., where an agreement specifies some other jurisdiction).
  • a jurisdictional location may vary for entities in the system (e.g., borrower at A, lender at B, collateral positioned at C, agreement enforced at D, etc.). In certain embodiments, a jurisdictional location for a given entity may vary during the operations of the system (e.g., due to movement of collateral, related data, changes in terms and conditions, etc.). In certain embodiments, a given entity of the system may have more than one jurisdictional location (e.g., due to operations of the relevant law, and/or options available to one or more parties), and/or may have distinct jurisdictional locations for different purposes.
  • jurisdictional location of an item of collateral, an asset, or entity, actions may dictate certain terms or conditions of a loan or bond, and/or may indicate different obligations for notices to parties, foreclosure and/or default execution, treatment of collateral and/or debt security, and/or treatment of various data within the system. While specific examples of jurisdictional location are described herein for purposes of illustration, any embodiment benefitting from the disclosures herein, and any considerations understood to one of skill in the art having the benefit of the disclosures herein are specifically contemplated within the scope of the present disclosure.
  • token of value may be understood broadly to describe either: (a) a unit of currency or cryptocurrency (e.g. a cryptocurrency token), and (b) may also be used to represent a credential that can be exchanged for a good, service, data or other valuable consideration (e.g. a token of value).
  • a token may also be used in conjunction with investment applications, token-trading applications, and token-based marketplaces.
  • a token can also be associated with rendering consideration, such as providing goods, services, fees, access to a restricted area or event, data, or other valuable benefit.
  • Tokens can be contingent (e.g. contingent access token) or not contingent.
  • a token of value may be exchanged for accommodations, (e.g. hotel rooms), dining/food goods and services, space (e.g. shared space, workspace, convention space, etc.), fitness/wellness goods or services, event tickets or event admissions, travel, flights or other transportation, digital content, virtual goods, license keys, or other valuable goods, services, data, or consideration.
  • Tokens in various forms may be included where discussing a unit of consideration, collateral, or value, whether currency, cryptocurrency, or any other form of value such as goods, services, data, or other benefits.
  • One of skill in the art, having the benefit of the disclosure herein and knowledge about a token can readily determine the value symbolized or represented by a token, whether currency, cryptocurrency, good, service, data, or other value. While specific examples of tokens are described herein for purposes of illustration, any embodiment benefitting from the disclosures herein, and any considerations understood to one of skill in the art having the benefit of the disclosures herein, are specifically contemplated within the scope of the present disclosure.
  • pricing data may be understood broadly to describe a quantity of information such as a price or cost, of one or more items in a marketplace. Without limitation to any other aspect or description of the present disclosure, pricing data may also be used in conjunction with spot market pricing, forward market pricing, pricing discount information, promotional pricing, and other information relating to the cost or price of items. Pricing data may satisfy one or more conditions, or may trigger application of one or more rules of a 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, claims data or other forms of data.
  • Pricing data may be adjusted for the context of the valued item (e.g., condition, liquidity, location, etc.) and/or for the context of a particular party.
  • the context of the valued item e.g., condition, liquidity, location, etc.
  • pricing data can readily determine the purposes and use of pricing data in various embodiments and contexts disclosed herein.
  • a token includes any token including, without limitation, a token of value, such as collateral, an asset, a reward, such as in a token serving as representation of value, such as a value holding voucher that can be exchanged for goods or services.
  • a token of value such as collateral
  • an asset such as an asset
  • a reward such as in a token serving as representation of value, such as a value holding voucher that can be exchanged for goods or services.
  • Certain components may not be considered tokens individually, but may be considered tokens in an aggregated system - for example, a value placed on an asset may not be in itself be a token, but the value of an asset may be placed in a token of value, such as to be stored, exchanged, traded, and the like.
  • a blockchain circuit may be structured to provide lenders a mechanism to store the value of assets, where the value attributed to the token is stored in a distributed ledger of the blockchain circuit, but the token itself, assigned the value, may be exchanged, or traded such as through a token marketplace.
  • a token may be considered a token for some purposes but not for other purposes - for example, a token may be used as an indication of ownership of an asset, but this use of a token would not be traded as a value where a token including the value of the asset might.
  • the benefits of the present disclosure may be applied in a wide variety of systems, and any such systems may be considered a token herein, while in certain embodiments a given system may not be considered a token herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system, and/or how to combine processes and systems from the present disclosure to enhance operations of the contemplated system.
  • a contemplated system is a token and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation, access data such as relating to rights of access, tickets, and tokens; use in an investment application such as for investment in shares, interests, and tokens; a token-trading application; a token-based marketplace; forms of consideration such as monetary rewards and tokens; translating the value of a resources in tokens; a cryptocurrency token; indications of ownership such as identity information, event information, and token information; a blockchain-based access token traded in a marketplace application; pricing application such as for setting and monitoring pricing for contingent access rights, underlying access rights, tokens, and fees; trading applications such as for trading or exchanging contingent access rights or underlying access rights or tokens; tokens created and stored on a blockchain for contingent access rights resulting in an ownership (e.g., a ticket); and the like.
  • access data such as relating to rights of access, tickets, and tokens
  • use in an investment application such as for investment in shares, interests,
  • financial data may be understood broadly to describe a collection of financial information about an asset, collateral or other item or items.
  • Financial data may include revenues, expenses, assets, liabilities, equity, bond ratings, default, return on assets (ROA), return on investment (ROI), past performance, expected future performance, earnings per share (EPS), internal rate of return (IRR), earnings announcements, ratios, statistical analysis of any of the foregoing (e.g. moving averages), and the like.
  • ROI return on assets
  • EPS earnings per share
  • IRR internal rate of return
  • earnings announcements e.g. moving averages
  • ratios e.g. moving averages
  • 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, claims data or other forms of data.
  • market value data pricing data
  • accounting data access data
  • asset and facility data access data
  • worker data event data
  • underwriting data claims data or other forms of data.
  • a covenant may be understood broadly to describe a term, agreement, or promise, such as performance of some action or inaction.
  • a covenant may relate to behavior of a party or legal status of a party.
  • a covenant may also be used in conjunction with other related terms to an agreement or loan, such as a representation, a warranty, an indemnity, a balance of debt, a fixed interest rate, a variable interest rate, a payment amount, a payment schedule, a balloon payment schedule, a specification of collateral, a specification of substitutability of collateral, a party, a guarantee, a guarantor, a security, a personal guarantee, a lien, a duration, a foreclose condition, a default condition, and a consequence of default.
  • a covenant or lack of performance of a covenant may satisfy one or more conditions, or may trigger collection, breach or other terms and conditions.
  • a smart contract may calculate whether a covenant is satisfied and in cases where the covenant is not satisfied, may enable automated action, or trigger other conditions or terms.
  • entity may be understood broadly to describe a party, a third-party (e.g., an auditor, regulator, service provider, etc.), and/or an identifiable related object such as an item of collateral related to a transaction.
  • Example entities include an individual, partnership, corporation, limited liability company or other legal organization.
  • Other example entities include an identifiable item of collateral, offset collateral, potential collateral, or the like.
  • an entity may be a given party, such as an individual, to an agreement or loan.
  • Data or other terms herein may be characterized as having a context relating to an entity, such as entity-oriented data.
  • An entity may be characterized with a specific context or application, such as a human entity, physical entity, transactional entity, or a financial entity, without limitation.
  • An entity may have representatives that represent or act on its behalf. Without limitation to any other aspect or description of the present disclosure, an entity may also be used in conjunction with other related entities or terms to an agreement or loan, such as a representation, a warranty, an indemnity, a covenant, a balance of debt, a fixed interest rate, a variable interest rate, a payment amount, a payment schedule, a balloon payment schedule, a specification of collateral, a specification of substitutability of collateral, a party, a guarantee, a guarantor, a security, a personal guarantee, a lien, a duration, a foreclose condition, a default condition, and a consequence of default.
  • a representation such as a representation, a warranty, an indemnity, a covenant, a balance of debt, a fixed interest rate, a variable interest rate, a payment amount, a payment schedule, a balloon payment schedule, a specification of collateral, a specification of substitutability of collateral, a party, a guarantee, a gua
  • An entity may have a set of attributes such as: a publicly stated valuation, a set of property owned by the entity as indicated by public records, a valuation of a set of property owned by the entity, a bankruptcy condition, a foreclosure status, a contractual default status, a regulatory violation status, a criminal status, an export controls status, an embargo status, a tariff status, a tax status, a credit report, a credit rating, a website rating, a set of customer reviews for a product of an entity, a social network rating, a set of credentials, a set of referrals, a set of testimonials, a set of behavior, a location, and a geolocation, without limitation.
  • attributes such as: a publicly stated valuation, a set of property owned by the entity as indicated by public records, a valuation of a set of property owned by the entity, a bankruptcy condition, a foreclosure status, a contractual default status, a regulatory violation status, a criminal status, an export controls status, an embargo status, a tariff status,
  • a smart contract may calculate whether an entity has satisfied conditions or covenants and in cases where the entity has not satisfied such conditions or covenants, may enable automated action, or trigger other conditions or terms.
  • the term party as utilized herein may be understood broadly to describe a member of an agreement, such as an individual, partnership, corporation, limited liability company or other legal organization.
  • a party may be a primary lender, a secondary lender, a lending syndicate, a corporate lender, a government lender, a bank lender, a secured lender, a bond issuer, a bond purchaser, an unsecured lender, a guarantor, a provider of security, a borrower, a debtor, an underwriter, an inspector, an assessor, an auditor, a valuation professional, a government official, an accountant or other entities having rights or obligations to an agreement, transaction or loan.
  • a party may characterize a different term, such as transaction as in the term multi-party transaction, where multiple parties are involved in a transaction, or the like, without limitation.
  • a party may have representatives that represent or act on its behalf.
  • the term party may reference a potential party or a prospective party - for example, an intended lender or borrower interacting with a system, that may not yet be committed to an actual agreement during the interactions with the system.
  • an party may also be used in conjunction with other related parties or terms to an agreement or loan, such as a representation, a warranty, an indemnity, a covenant, a balance of debt, a fixed interest rate, a variable interest rate, a payment amount, a payment schedule, a balloon payment schedule, a specification of collateral, a specification of substitutability of collateral, an entity, a guarantee, a guarantor, a security, a personal guarantee, a lien, a duration, a foreclose condition, a default condition, and a consequence of default.
  • a representation such as a representation, a warranty, an indemnity, a covenant, a balance of debt, a fixed interest rate, a variable interest rate, a payment amount, a payment schedule, a balloon payment schedule, a specification of collateral, a specification of substitutability of collateral, an entity, a guarantee, a guarantor, a security, a personal guarantee, a lien, a duration, a foreclose condition,
  • a party may have a set of attributes such as: an identity, a creditworthiness, an activity, a behavior, a business practice, a status of performance of a contract, information about accounts receivable, information about accounts payable, information about the value of collateral, and other types of information, without limitation.
  • a smart contract may calculate whether a party has satisfied conditions or covenants and in cases where the party has not satisfied such conditions or covenants, may enable automated action, or trigger other conditions or terms.
  • party attribute, entity attribute, or party/entity attribute may be understood broadly to describe a value, characteristic, or status of a party or entity.
  • attributes of a party or entity may be, without limitation: value, quality, location, net worth, price, physical condition, health condition, security, safety, ownership, identity, creditworthiness, activity, behavior, business practice, status of performance of a contract, information about accounts receivable, information about accounts payable, information about the value of collateral, and other types of information, and the like.
  • a smart contract may calculate values, status or conditions associated with attributes of a party or entity, and in cases where the party or entity has not satisfied such conditions or covenants, may enable automated action, or trigger other conditions or terms.
  • the term lender as utilized herein may be understood broadly to describe a party to an agreement offering an asset for lending, proceeds of a loan, and may include an individual, partnership, corporation, limited liability company, or other legal organization.
  • a lender may be a primary lender, a secondary lender, a lending syndicate, a corporate lender, a government lender, a bank lender, a secured lender, an unsecured lender, or other party having rights or obligations to an agreement, transaction or loan offering a loan to a borrower, without limitation.
  • a lender may have representatives that represent or act on its behalf.
  • an party may also be used in conjunction with other related parties or terms to an agreement or loan, such as a borrower, a guarantor, a representation, a warranty, an indemnity, a covenant, a balance of debt, a fixed interest rate, a variable interest rate, a payment amount, a payment schedule, a balloon payment schedule, a specification of collateral, a specification of substitutability of collateral, a security, a personal guarantee, a lien, a duration, a foreclose condition, a default condition, and a consequence of default.
  • an agreement or loan such as a borrower, a guarantor, a representation, a warranty, an indemnity, a covenant, a balance of debt, a fixed interest rate, a variable interest rate, a payment amount, a payment schedule, a balloon payment schedule, a specification of collateral, a specification of substitutability of collateral, a security, a personal guarantee, a lien, a duration, a foreclose condition, a default condition
  • a smart contract may calculate whether a lender has satisfied conditions or covenants and in cases where the lender has not satisfied such conditions or covenants, may enable automated action, a notification or alert, or trigger other conditions or terms.
  • crowdsourcing services may be understood broadly to describe services offered or rendered in conjunction with a crowdsourcing model or transaction, wherein a large group of people or entities supply contributions to fulfill a need, such as a loan, for the transaction.
  • Crowdsourcing services may be provided by a platform or system, without limitation.
  • a crowdsourcing request may be communicated to a group of information suppliers and by which responses to the request may be collected and processed to provide a reward to at least one successful information supplier.
  • the request and parameters may be configured to obtain information related to the condition of a set of collateral for a loan.
  • the crowdsourcing request may be published.
  • crowdsourcing services may be performed by a smart contract, wherein the reward is managed by a smart contract that processes responses to the crowdsourcing request and automatically allocates a reward to information that satisfies a set of parameter configured for the crowdsourcing request.
  • a smart contract that processes responses to the crowdsourcing request and automatically allocates a reward to information that satisfies a set of parameter configured for the crowdsourcing request.
  • publishing services may be understood to describe a set of services to publish a crowdsourcing request.
  • Publishing services may be provided by a platform or system, without limitation.
  • publishing services may be performed by a smart contract, wherein the crowdsourcing request is published, or publication is initiated by the smart contract.
  • One of skill in the art, having the benefit of the disclosure herein and knowledge about publishing services, can readily determine the purposes and use of publishing services in various embodiments and contexts disclosed herein.
  • an interface may be understood broadly to describe a component by which interaction or communication is achieved, such as a component of a computer, which may be embodied in software, hardware, or a combination thereof.
  • an interface may serve a number of different purposes or be configured for different applications or contexts, such as, without limitation: an application programming interface, a graphic user interface, user interface, software interface, marketplace interface, demand aggregation interface, crowdsourcing interface, secure access control interface, network interface, data integration interface or a cloud computing interface, or combinations thereof.
  • An interface may serve to act as a way to enter, receive or display data, within the scope of lending, refinancing, collection, consolidation, factoring, brokering or foreclosure, without limitation.
  • An interface may serve as an interface for another interface.
  • an interface may be used in conjunction with applications, processes, modules, services, layers, devices, components, machines, products, sub-systems, interfaces, connections, or as part of a system.
  • an interface may be embodied in software, hardware, or a combination thereof, as well as stored on a medium or in memory.
  • graphical user interface as utilized herein may be understood as a type of interface to allow a user to interact with a system, computer, or other interfaces, in which interaction or communication is achieved through graphical devices or representations.
  • a graphical user interface may be a component of a computer, which may be embodied in computer readable instructions, hardware, or a combination thereof.
  • a graphical user interface may serve a number of different purposes or be configured for different applications or contexts. Such an interface may serve to act as a way to receive or display data using visual representation, stimulus or interactive data, without limitation.
  • a graphical user interface may serve as an interface for another graphical user interface or other interfaces.
  • a graphical user interface may be used in conjunction with applications, processes, modules, services, layers, devices, components, machines, products, sub-systems, interfaces, connections, or as part of a system.
  • a graphical user interface may be embodied in computer readable instructions, hardware, or a combination thereof, as well as stored on a medium or in memory.
  • Graphical user interfaces may be configured for any input types, including keyboards, a mouse, a touch screen, and the like.
  • Graphical user interfaces may be configured for any desired user interaction environments, including for example a dedicated application, a web page interface, or combinations of these.
  • user interface as utilized herein may be understood as a type of interface to allow a user to interact with a system, computer, or other apparatus, in which interaction or communication is achieved through graphical devices or representations.
  • a user interface may be a component of a computer, which may be embodied in software, hardware, or a combination thereof.
  • the user interface may be stored on a medium or in memory.
  • User interfaces may include drop-down menus, tables, forms, or the like with default, templated, recommended, or pre-configured conditions.
  • a user interface may include voice interaction.
  • a user interface may be used in conjunction with applications, circuits, controllers, processes, modules, services, layers, devices, components, machines, products, sub-systems, interfaces, connections, or as part of a system.
  • User interfaces may serve a number of different purposes or be configured for different applications or contexts.
  • a lender-side user interface may include features to view a plurality of customer profiles, but may be restricted from making certain changes.
  • a debtor-side user interface may include features to view details and make changes to a user account.
  • a 3rd party neutral-side interface (e.g.
  • a 3rd party not having an interest in an underlying transaction such as a regulator, auditor, etc.
  • a 3rd party interested-side interface e.g. a 3rd party that may have an interest in an underlying transaction, such as a collector, debtor advocate, investigator, partial owner, etc.
  • a 3rd party interested-side interface may include features enabling a view of particular user data with restrictions on making changes. Many more features of these user interfaces may be available to implements embodiments of the systems and/or procedures described throughout the present disclosure.
  • the benefits of the present disclosure may be applied in a wide variety of processes and systems, and any such processes or systems may be considered a service herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about a user interface, can readily determine the purposes and use of a user interface in various embodiments and contexts disclosed herein.
  • Certain considerations for the person of skill in the art, in determining whether a contemplated interface is a user interface and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation: configurable views, ability to restrict manipulation or views, report functions, ability to manipulate user profile and data, implement regulatory requirements, provide the desired user features for borrowers, lenders, and 3rd parties, and the like.
  • Interfaces and dashboards as utilized herein may further be understood broadly to describe a component by which interaction or communication is achieved, such as a component of a computer, which may be embodied in software, hardware, or a combination thereof.
  • Interfaces and dashboards may acquire, receive, present, or otherwise administrate an item, service, offering or other aspects of a transaction or loan.
  • interfaces and dashboards may serve a number of different purposes or be configured for different applications or contexts, such as, without limitation: an application programming interface, a graphic user interface, user interface, software interface, marketplace interface, demand aggregation interface, crowdsourcing interface, secure access control interface, network interface, data integration interface or a cloud computing interface, or combinations thereof.
  • An interface or dashboard may serve to act as a way to receive or display data, within the context of lending, refinancing, collection, consolidation, factoring, brokering or foreclosure, without limitation.
  • An interface or dashboard may serve as an interface or dashboard for another interface or dashboard.
  • an interface may be used in conjunction with applications, circuits, controllers, processes, modules, services, layers, devices, components, machines, products, sub-systems, interfaces, connections, or as part of a system.
  • an interface or dashboard may be embodied in computer readable instructions, hardware, or a combination thereof, as well as stored on a medium or in memory.
  • a domain may serve a number of different purposes or be configured for different applications or contexts, such as, without limitation: a domain for execution, a domain for a digital asset, domains to which a request will be published, domains to which social network data collection and monitoring services will be applied, domains to which Internet of Things data collection and monitoring services will be applied, network domains, geolocation domains, jurisdictional location domains, and time domains.
  • a domain for execution a domain for a digital asset
  • domains to which a request will be published domains to which social network data collection and monitoring services will be applied
  • domains to which Internet of Things data collection and monitoring services will be applied domains to which Internet of Things data collection and monitoring services will be applied
  • network domains geolocation domains
  • jurisdictional location domains jurisdictional location domains
  • time domains may be utilized relative to any applications, circuits, controllers, processes, modules, services, layers, devices, components, machines, products, subsystems, interfaces, connections, or as part of a system.
  • a domain may be embodied in computer readable instructions, hardware, or a combination thereof, as well as stored on a medium or in memory.
  • request may be understood broadly to describe the action or instance of initiating or asking for a thing (e.g. information, a response, an object, and the like) to be provided.
  • a specific type of request may also serve a number of different purposes or be configured for different applications or contexts, such as, without limitation: a formal legal request (e.g. a subpoena), a request to refinance (e.g. a loan), or a crowdsourcing request.
  • Systems may be utilized to perform requests as well as fulfill requests. Requests in various forms may be included where discussing a legal action, a refinancing of a loan, or a crowdsourcing service, without limitation.
  • a specific type of reward may also serve a number of different purposes or be configured for different applications or contexts, such as, without limitation: a reward event, claims for rewards, monetary rewards, rewards captured as a data set, rewards points, and other forms of rewards.
  • Rewards may be triggered, allocated, generated for innovation, provided for the submission of evidence, requested, offered, selected, administrated, managed, configured, allocated, conveyed, identified, without limitation, as well as other actions.
  • Systems may be utilized to perform the aforementioned actions.
  • Rewards in various forms may be included where discussing a particular behavior, or encouragement of a particular behavior, without limitation.
  • a reward may be utilized as a specific incentive (e.g., rewarding a particular person that responds to a crowdsourcing request) or as a general incentive (e.g., providing a reward responsive to a successful crowdsourcing request, in addition to or alternatively to a reward to the particular person that responded).
  • a specific incentive e.g., rewarding a particular person that responds to a crowdsourcing request
  • a general incentive e.g., providing a reward responsive to a successful crowdsourcing request, in addition to or alternatively to a reward to the particular person that responded.
  • robotic process automation system as utilized herein may be understood broadly to describe a system capable of performing tasks or providing needs for a system of the present disclosure.
  • a robotic process automation system can be configured for: negotiation of a set of terms and conditions for a loan, negotiation of refinancing of a loan, loan collection, consolidating a set of loans, managing a factoring loan, brokering a mortgage loan, training for foreclosure negotiations, configuring a crowdsourcing request based on a set of attributes for a loan, setting a reward, determining a set of domains to which a request will be published, configuring the content of a request, configuring a data collection and monitoring action based on a set of attributes of a loan, determining a set of domains to which the Internet of Things data collection and monitoring services will be applied, and iteratively training and improving based on a set of outcomes.
  • a robotic process automation system may include: a set of data collection and monitoring services, an artificial intelligence system, and another robotic process automation system which is a component of the higher level robotic process automation system.
  • the robotic process automation system may include: at least one of the set of mortgage loan activities and the set of mortgage loan interactions includes activities among marketing activity, identification of a set of prospective borrowers, identification of property, identification of collateral, qualification of borrower, title search, title verification, property assessment, property inspection, property valuation, income verification, borrower demographic analysis, identification of capital providers, determination of available interest rates, determination of available payment terms and conditions, analysis of existing mortgage, comparative analysis of existing and new mortgage terms, completion of application workflow, population of fields of application, preparation of mortgage agreement, completion of schedule to mortgage agreement, negotiation of mortgage terms and conditions with capital provider, negotiation of mortgage terms and conditions with borrower, transfer of title, placement of lien and closing of mortgage agreement.
  • Example and non-limiting robotic process automation systems may include one or more user interfaces, interfaces with circuits and/or controllers throughout the system to provide, request, and/or share data, and/or one or more artificial intelligence circuits configured to iteratively improve one or more operations of the robotic process automation system.
  • One of skill in the art having the benefit of the disclosure herein and knowledge ordinarily available about a contemplated robotic process automation system, can readily determine the circuits, controllers, and/or devices to include to implement a robotic process automation system performing the selected functions for the contemplated system. While specific examples of robotic process automation systems are described herein for purposes of illustration, any embodiment benefitting from the disclosures herein, and any considerations understood.
  • loan-related action and other related terms such as loan-related event and loan-related activity
  • loan-related event and loan-related activity are utilized herein and may be understood broadly to describe one or multiple actions, events or activities relating to a transaction that includes a loan within the transaction.
  • the action, event or activity may occur in many different contexts of loans, such as lending, refinancing, consolidation, factoring, brokering, foreclosure, administration, negotiating, collecting, procuring, enforcing and data processing (e.g. data collection), or combinations thereof, without limitation.
  • a loan-related action may be used in the form of a noun (e.g. a notice of default has been communicated to the borrower with formal notice, which could be considered a loan-related action).
  • a loan-related action, event, or activity may refer to a single instance, or may characterize a group of actions, events, or activities.
  • a single action such as providing a specific notice to a borrower of an overdue payment may be considered a loan-related action.
  • a group of actions from start to finish relating to a default may also be considered a single loan-related action.
  • Appraisal, inspection, funding, and recording may all also be considered loan-related actions that have occurred, as well as events relating to the loan, and may also be loan-related events.
  • these activities of completing these actions may also be considered loan-related activities (e.g. appraising, inspecting, funding, recording, etc.), without limitation.
  • a smart contract or robotic process automation system may perform loan-related actions, loan- related events, or loan-related activities for one or more of the parties, and process appropriate tasks for completion of the same.
  • the smart contract or robotic process automation system may not complete a loan-related action, and depending upon such outcome this may enable an automated action or may trigger other conditions or terms.
  • loan-related action, events, and activities may also more specifically be utilized to describe a context for calling of a loan.
  • a calling of a loan is an action wherein the lender can demand the loan be repaid, usually triggered by some other condition or term, such as delinquent payment(s).
  • a loan-related action for calling of the loan may occur when a borrower misses three payments in a row, such that there is a severe delinquency in the loan payment schedule, and the loan goes into default.
  • a lender may be initiating loan-related actions for calling of the loan to protect its rights.
  • the borrower pays a sum to cure the delinquency and penalties, which may also be considered as a loan-related action for calling of the loan.
  • a smart contract or robotic process automation system may initiate, administrate, or process loan- related actions for calling of the loan, which without limitation, may including providing notice, researching, and collecting payment history, or other tasks performed as a part of the calling of the loan.
  • loan-related actions for calling of the loan or other forms of the term and its various forms, can readily determine the purposes and use of this term in the context of an event or other various embodiments and contexts disclosed herein.
  • loan-related action, events, and activities may also more specifically be utilized to describe a context for payment of a loan.
  • a loan is repaid on a payment schedule.
  • Various actions may be taken to provide a borrower with information to pay back the loan, as well as actions for a lender to receive payment for the loan. For example, if a borrower makes a payment on the loan, a loan-related action for payment of the loan may occur.
  • such a payment may comprise several actions that may occur with respect to the payment on the loan, such as: the payment being tendered to the lender, the loan ledger or accounting reflecting that a payment has been made, a receipt provided to the borrower of the payment made, and the next payment being requested of the borrower.
  • a smart contract or robotic process automation system may initiate, administrate, or process such loan-related actions for payment of the loan, which without limitation, may including providing notice to the lender, researching and collecting payment history, providing a receipt to the borrower, providing notice of the next payment due to the borrower, or other actions associated with payment of the loan.
  • loan-related action, events, and activities may also more specifically be utilized to describe a context for a payment schedule or alternative payment schedule.
  • a loan is repaid on a payment schedule, which may be modified over time. Or, such a payment schedule may be developed and agreed in the alternative, with an alternative payment schedule.
  • Various actions may be taken in the context of a payment schedule or alternate payment schedule for the lender or the borrower, such as: the amount of such payments, when such payments are due, what penalties or fees may attach to late payments, or other terms. For example, if a borrower makes an early payment on the loan, a loan-related action for payment schedule and alternative payment schedule of the loan may occur; in such case, perhaps the payment is applied as principal, with the regular payment still being due.
  • loan-related actions for a payment schedule and alternative payment schedule may comprise several actions that may occur with respect to the payment on the loan, such as: the payment being tendered to the lender, the loan ledger or accounting reflecting that a payment has been made, a receipt provided to the borrower of the payment made, a calculation if any fees are attached or due, and the next payment being requested of the borrower.
  • an activity to determine a payment schedule or alternative payment schedule may be a loan-related action, event, or activity.
  • an activity to communicate the payment schedule or alternative payment schedule (e.g., to the borrower, the lender, or a 3rd party) may be a loan- related action, event, or activity.
  • a smart contract circuit or robotic process automation system may initiate, administrate, or process such loan-related actions for payment schedule and alternative payment schedule, which without limitation, may include providing notice to the lender, researching and collecting payment history, providing a receipt to the borrower, calculating the next due date, calculating the final payment amount and date, providing notice of the next payment due to the borrower, determining the payment schedule or an alternate payment schedule, communicating the payment scheduler or an alternate payment schedule, or other actions associated with payment of the loan.
  • regulatory notice requirement may be understood broadly to describe an obligation or condition to communicate a notification or message to another party or entity.
  • the regulatory notice requirement may be required under one or more conditions that are triggered, or generally required.
  • a lender may have a regulatory notice requirement to provide notice to a borrower of a default of a loan, or change of an interest rate of a loan, or other notifications relating to a transaction or loan.
  • the regulatory aspect of the term may be attributed to jurisdiction-specific laws, rules, or codes that require certain obligations of communication.
  • a policy directive may be treated as a regulatory notice requirement - for example where a lender has an internal notice policy that may exceed the regulatory requirements of one or more of the jurisdictional locations related to a transaction.
  • the notice aspect generally relates to formal communications, which may take many different forms, but may specifically be specified as a particular form of notice, such as a certified mail, facsimile, email transmission, or other physical or electronic form, a content for the notice, and/or a timing requirement related to the notice.
  • the requirement aspect relates to the necessity of a party to complete its obligation to be in compliance with laws, rules, codes, policies, standard practices, or terms of an agreement or loan.
  • a smart contract may process or trigger regulatory notice requirements and provide appropriate notice to a borrower.
  • This may be based on location of at least one of: the lender, the borrower, the funds provided via the loan, the repayment of the loan, and the collateral of the loan, or other locations as designated by the terms of the loan, transaction, or agreement.
  • certain changes in the rights or obligations between the parties may be triggered - for example where a lender provides a non-compliant notice to the borrower, an automated action or trigger based on the terms and conditions of the loan, and/or based on external information (e.g., a regulatory prescription, internal policy of the lender, etc.) may be affected by a smart contract circuit and/or robotic process automation system may be implemented.
  • a smart contract circuit and/or robotic process automation system may be implemented.
  • regulatory notice requirement may also be utilized herein to describe an obligation or condition to communicate a notification or message to another party or entity based upon a general or specific policy, rather than based on a particular jurisdiction, or laws, rules, or codes of a particular location (as in regulatory notice requirement that may be jurisdiction-specific).
  • the regulatory notice requirement may be prudent or suggested, rather than obligatory or required, under one or more conditions that are triggered, or generally required.
  • a lender may have a regulatory notice requirement that is policy based to provide notice to a borrower of a new informational website, or will experience a change of an interest rate of a loan in the future, or other notifications relating to a transaction or loan that are advisory or helpful, rather than mandatory (although mandatory notices may also fall under a policy basis).
  • a smart contract circuit may process or trigger regulatory notice requirements and provide appropriate notice to a borrower which may or may not necessarily be required by a law, rule, or code.
  • the basis of the notice or communication may be out of prudence, courtesy, custom, or obligation.
  • the term regulatory notice may also be utilized herein to describe an obligation or condition to communicate a notification or message to another party or entity specifically, such as a lender or borrower.
  • the regulatory notice may be specifically directed toward any party or entity, or a group of parties or entities.
  • a particular notice or communication may be advisable or required to be provided to a borrower, such as on circumstances of a borrower's failure to provide scheduled payments on a loan resulting in a default.
  • a regulatory notice directed to a particular user such as a lender or borrower, may be as a result of a regulatory notice requirement that is jurisdiction-specific or policy-based, or otherwise.
  • a smart contract may process or trigger a regulatory notice and provide appropriate notice to a specific party such as a borrower, which may or may not necessarily be required by a law, rule, or code, but may otherwise be provided out of prudence, courtesy or custom.
  • a party or entity has not satisfied such regulatory notice requirements to a specific party or parties, it may create circumstances where certain rights may be forgiven by one or more parties or entities, or may enable automated action or trigger other conditions or terms.
  • One of skill in the art having the benefit of the disclosure herein and knowledge ordinarily available about a contemplated system, can readily determine the purposes and use of regulatory notice requirements based in various embodiments and contexts disclosed herein.
  • regulatory foreclosure requirement (and any derivatives) as utilized herein may be understood broadly to describe an obligation or condition in order to trigger, process or complete default of a loan, foreclosure, or recapture of collateral, or other related foreclosure actions.
  • the regulatory foreclosure requirement may be required under one or more conditions that are triggered, or generally required.
  • a lender may have a regulatory foreclosure requirement to provide notice to a borrower of a default of a loan, or other notifications relating to the default of a loan prior to foreclosure.
  • the regulatory aspect of the term may be attributed to jurisdiction-specific laws, rules, or codes that require certain obligations of communication.
  • the foreclosure aspect generally relates to the specific remedy of foreclosure, or a recapture of collateral property and default of a loan, which may take many different forms, but may be specified in the terms of the loan.
  • the requirement aspect relates to the necessity of a party to complete its obligation in order to be in compliance or performance of laws, rules, codes or terms of an agreement or loan.
  • a smart contract circuit may process or trigger regulatory foreclosure requirements and process appropriate tasks relating to such a foreclosure action. This may be based on a jurisdictional location of at least one of the lender, the borrower, the fund provided via the loan, the repayment of the loan, and the collateral of the loan, or other locations as designated by the terms of the loan, transaction, or agreement.
  • regulatory foreclosure requirement may also be utilized herein to describe an obligation or in order to trigger, process or complete default of a loan, foreclosure, or recapture of collateral, or other related foreclosure actions, based upon a general or specific policy rather than based on a particular jurisdiction, or laws, rules, or codes of a particular location (as in regulatory foreclosure requirement that may be jurisdiction-specific).
  • the regulatory foreclosure requirement may be prudent or suggested, rather than obligatory or required, under one or more conditions that are triggered, or generally required.
  • a lender may have a regulatory foreclosure requirement that is policy based to provide notice to a borrower of a default of a loan, or other notifications relating to a transaction or loan that are advisory or helpful, rather than mandatory (although mandatory notices may also fall under a policy basis).
  • a smart contract may process or trigger regulatory foreclosure requirements and provide appropriate notice to a borrower which may or may not necessarily be required by a law, rule, or code.
  • the basis of the notice or communication may be out of prudence, courtesy, custom, industry practice, or obligation.
  • the term regulatory foreclosure requirements may also be utilized herein to describe an obligation or condition that is to be performed with regard to a specific user, such as a lender or a borrower.
  • the regulatory notice may be specifically directed toward any party or entity, or a group of parties or entities.
  • a particular notice or communication may be advisable or required to be provided to a borrower, such as on circumstances of a borrower's failure to provide scheduled payments on a loan resulting in a default.
  • a regulatory foreclosure requirement is directed to a particular user, such as a lender or borrower, and may be a result of a regulatory foreclosure requirement that is jurisdiction-specific or policy-based, or otherwise.
  • the foreclosure requirement may be related to a specific entity involved with a transaction (e.g., the current borrower has been a customer for 30 years, so s/he receives unique treatment), or to a class of entities (e.g., "preferred" borrowers, or "first time default” borrowers).
  • a smart contract circuit may process or trigger an obligation or action that must be taken pursuant to a foreclosure, where the action is directed or from a specific party such as a lender or a borrower, which may or may not necessarily be required by a law, rule, or code, but may otherwise be provided out of prudence, courtesy, or custom.
  • the obligation or condition that is to be performed with regard to the specific user may form a part of the terms and conditions or otherwise be known to the specific user to which it applies (e.g., an insurance company or bank that advertises a specific practice with regard to a specific class of customers, such as first-time default customers, firsttime accident customers, etc.), and in certain embodiments the obligation or condition that is to be performed with regard to the specific user may be unknown to the specific user to which it applies (e.g., a bank has a policy relating to a class of users to which the specific user belongs, but the specific user is not aware of the classification).
  • a valuation model may be used in conjunction with: collateral (e.g. a secured property), artificial intelligence services (e.g. to improve a valuation model), data collection and monitoring services (e.g. to set a valuation amount), valuation services (e.g. the process of informing, using, and/or improving a valuation model), and/or outcomes relating to transactions in collateral (e.g. as a basis of improving the valuation model).
  • collateral e.g. a secured property
  • artificial intelligence services e.g. to improve a valuation model
  • data collection and monitoring services e.g. to set a valuation amount
  • valuation services e.g. the process of informing, using, and/or improving a valuation model
  • outcomes relating to transactions in collateral e.g. as a basis of improving the valuation model.
  • Jurisdiction-specific valuation model is also used as a valuation model used in a specific geographic/jurisdictional area or region; wherein, the jurisdiction can be specific to jurisdiction of the lender, the borrower, the delivery of funds, the payment of the loan or the collateral of the loan, or combinations thereof.
  • a jurisdiction-specific valuation model considers jurisdictional effects on a valuation of collateral, including at least: rights and obligations for borrowers and lenders in the relevant jurisdiction(s); jurisdictional effects on the ability to move, import, export, substitute, and/or liquidate the collateral; jurisdictional effects on the timing between default and foreclosure or collection of collateral; and/or jurisdictional effects on the volatility and/or sensitivity of collateral value determinations.
  • a geolocation-specific valuation model considers geolocation effects on a valuation of the collateral, which may include a similar list of considerations relative jurisdictional effects (although the jurisdictional location(s) may be distinct from the geolocation(s)), but may also include additional effects, such as: weather-related effects; distance of the collateral from monitoring, maintenance, or seizure services; and/or proximity of risk phenomenon (e.g., fault lines, industrial locations, a nuclear plant, etc.).
  • a valuation model may utilize a valuation of offset collateral (e.g., a similar item of collateral, a generic value such as a market value of similar or fungible collateral, and/or a value of an item that correlates with a value of the collateral) as a part of the valuation of the collateral.
  • an artificial intelligence circuit includes one or more machine learning and/or artificial intelligence algorithms, to improve a valuation model, including, for example, utilizing information over time between multiple transactions involving similar or offset collateral, and/or utilizing outcome information (e.g., where loan transactions are completed successfully or unsuccessfully, and/or in response to collateral seizure or liquidation events that demonstrate real- world collateral valuation determinations) from the same or other transactions to iteratively improve the valuation model.
  • an artificial intelligence circuit is trained on a collateral valuation data set, for example previously determined valuations and/or through interactions with a trainer (e.g., a human, accounting valuations, and/or other valuation data).
  • the valuation model and/or parameters of the valuation model may be determined and/or negotiated as a part of the terms and conditions of the transaction (e.g., a loan, a set of loans, and/or a subset of the set of loans).
  • a loan e.g., a loan, a set of loans, and/or a subset of the set of loans.
  • One of skill in the art having the benefit of the disclosure herein and knowledge ordinarily available about a contemplated system, can readily determine which aspects of the present disclosure will benefit a particular application for a valuation model, and how to choose or combine valuation models to implement an embodiment of a valuation model.
  • market value data, or marketplace information may be understood broadly to describe data or information relating to the valuation of a property, asset, collateral, or other valuable items which may be used as the subject of a loan, collateral, or transaction.
  • Market value data or marketplace information may change from time to time, and may be estimated, calculated, or objectively or subjectively determined from various sources of information.
  • Market value data or marketplace information may be related directly to an item of collateral or to an off-set item of collateral.
  • Market value data or marketplace information may include financial data, market ratings, product ratings, customer data, market research to understand customer needs or preferences, competitive intelligence re.
  • Market value data or marketplace information may be used as a noun to identify a single figure or a plurality of figures or data.
  • market value data or marketplace information may be utilized by a lender to determine if a property or asset will serve as collateral for a secured loan, or may alternatively be utilized in the determination of foreclosure if a loan is in default, without limitation to these circumstances in use of the term.
  • Marketplace value data or marketplace information may also be used to determine loan-to-value figures or calculations.
  • a collection service, smart contract circuit, and/or robotic process automation system may estimate or calculate market value data or marketplace information from one or more sources of data or information.
  • market data value or marketplace information depending upon the data/information contained therein, may enable automated action, or trigger other conditions or terms.
  • One of skill in the art having the benefit of the disclosure herein and knowledge ordinarily available about a contemplated system and available relevant marketplace information, can readily determine the purposes and use of this term in various forms, embodiments and contexts disclosed herein.
  • collateral similar to collateral, off-set collateral, and other forms or variations as utilized herein may be understood broadly to describe a property, asset or valuable item that may be like in nature to a collateral (e.g. an article of value held in security) regarding a loan or other transaction.
  • collateral e.g. an article of value held in security
  • Similar collateral may refer to a property, asset, collateral or other valuable item which may be aggregated, substituted, or otherwise referred to in conjunction with other collateral, whether the similarity comes in the form of a common attribute such as type of item of collateral, category of the item of collateral, an age of the item of collateral, a condition of the item of collateral, a history of the item of collateral, an ownership of the item of collateral, a caretaker of the item of collateral, a security of the item of collateral, a condition of an owner of the item of collateral, a lien on the item of collateral, a storage condition of the item of collateral, a geolocation of the item of collateral, and a jurisdictional location of the item of collateral, and the like.
  • a common attribute such as type of item of collateral, category of the item of collateral, an age of the item of collateral, a condition of the item of collateral, a history of the item of collateral, an ownership of the item of collateral, a caretaker of the item of collateral, a security of the item of collateral, a condition of
  • an offset collateral references an item that has a value correlation with an item of collateral - for example, an offset collateral may exhibit similar price movements, volatility, storage requirements, or the like for an item of collateral.
  • similar collateral may be aggregated to form a larger security interest or collateral for an additional loan or distribution, or transaction.
  • offset collateral may be utilized to inform a valuation of the collateral.
  • a smart contract circuit or robotic process automation system may estimate or calculate figures, data or information relating to similar collateral, or may perform a function with respect to aggregating similar collateral.
  • restructure and other forms such as restructuring
  • Restructuring may be understood broadly to describe a modification of terms or conditions, properties, collateral, or other considerations affecting a loan or transaction. Restructuring may result in a successful outcome where amended terms or conditions are adopted between parties, or an unsuccessful outcome where no modification or restructure occurs, without limitation. Restructuring can occur in many contexts of contracts or loans, such as application, lending, refinancing, collection, consolidation, factoring, brokering, foreclosure, and combinations thereof, without limitation.
  • Debt may also be restructured, which may indicate that debts owed to a party are modified as to timing, amounts, collateral, or other terms.
  • a borrower may restructure debt of a loan to accommodate a change of financial conditions, or a lender may offer to a borrower the restructuring of a debt for its own needs or prudence.
  • a smart contract circuit or robotic process automation system may automatically or manually restructure debt based on a monitored condition, or create options for restructuring a debt, administrate the process of negotiating or effecting the restructuring of a debt, or other actions in connection with restructuring or modifying terms of a loan or transaction.
  • social network data collection, social network monitoring services, and social network data collection and monitoring services may be understood broadly to describe services relating to the acquisition, organizing, observing, or otherwise acting upon data or information derived from one or more social networks.
  • the social network data collection and monitoring services may be a part of a related system of services or a standalone set of services.
  • Social network data collection and monitoring services may be provided by a platform or system, without limitation.
  • Social network data collection and monitoring services may be used in a variety of contexts such as lending, refinancing, negotiation, collection, consolidation, factoring, brokering, foreclosure, and combinations thereof, without limitation.
  • Requests of social network data collection and monitoring, with configuration parameters, may be requested by other services, automatically initiated, or triggered to occur based on conditions or circumstances that occur.
  • An interface may be provided to configure, initiate, display, or otherwise interact with social network data collection and monitoring services.
  • Social networks as utilized herein, reference any mass platform where data and communications occur between individuals and/or entities, where the data and communications are at least partially accessible to an embodiment system.
  • the social network data includes publicly available (e.g., accessible without any authorization) information.
  • the social network data includes information that is properly accessible to an embodiment system, but may include subscription access or other access to information that is not freely available to the public, but may be accessible (e.g., consistent with a privacy policy of the social network with its users).
  • a social network may be primarily social in nature, but may additionally or alternatively include professional networks, alumni networks, industry related networks, academically oriented networks, or the like.
  • a social network may be a crowdsourcing platform, such as a platform configured to accept queries or requests directed to users (and/or a subset of users, potentially meeting specified criteria), where users may be aware that certain communications will be shared and accessible to requestors, at least a portion of users of the platform, and/or publicly available.
  • social network data collection and monitoring services may be performed by a smart contract circuit or a robotic process automation system.
  • crowdsource and social network information may further be understood broadly to describe information acquired or provided in conjunction with a crowdsourcing model or transaction, or information acquired or provided on or in conjunction with a social network.
  • Crowdsource and social network information may be provided by a platform or system, without limitation.
  • Crowdsource and social network information may be acquired, provided, or communicated to or from a group of information suppliers and by which responses to the request may be collected and processed.
  • Crowdsource and social network information may provide information, conditions or factors relating to a loan or agreement.
  • Crowdsource and social network information may be private or published, or combinations thereof, without limitation.
  • crowdsource and social network information may be acquired, provided, organized, or processed, without limitation, by a smart contract circuit, wherein the crowdsource and social network information may be managed by a smart contract circuit that processes the information to satisfy a set of configured parameters.
  • a smart contract circuit that processes the information to satisfy a set of configured parameters.
  • the term negotiate (and other forms such as negotiating or negotiation) as utilized herein may be understood broadly to describe discussions or communications to bring about or obtain a compromise, outcome, or agreement between parties or entities.
  • Negotiation may result in a successful outcome where terms are agreed between parties, or an unsuccessful outcome where the parties do not agree to specific terms, or combinations thereof, without limitation.
  • a negotiation may be successful in one aspect or for a particular purpose, and unsuccessful in another aspect or for another purpose.
  • Negotiation can occur in many contexts of contracts or loans, such as lending, refinancing, collection, consolidation, factoring, brokering, foreclosure, and combinations thereof, without limitation.
  • a borrower may negotiate an interest rate or loan terms with a lender.
  • a borrower in default may negotiate an alternative resolution to avoid foreclosure with a lender.
  • a smart contract circuit or robotic process automation system may negotiate for one or more of the parties, and process appropriate tasks for completing or attempting to complete a negotiation of terms. In some cases negotiation by the smart contract or robotic process automation system may not complete or be successful. Successful negotiation may enable automated action or trigger other conditions or terms to be implemented by the smart contract circuit or robotic process automation system.
  • One of skill in the art having the benefit of the disclosure herein and knowledge ordinarily available about a contemplated system, can readily determine the purposes and use of negotiation in various embodiments and contexts disclosed herein.
  • the term negotiate in various forms may more specifically be utilized herein in verb form (e.g. to negotiate) or in noun forms (e.g. a negotiation), or other forms to describe a context of mutual discussion leading to an outcome.
  • a robotic process automation system may negotiate terms and conditions on behalf of a party, which would be a use as a verb clause.
  • a robotic process automation system may be negotiating terms and conditions for modification of a loan, or negotiating a consolidation offer, or other terms.
  • a negotiation e.g. an event
  • a smart contract circuit or robotic process automation system may negotiate (e.g.
  • the term negotiate in various forms may also specifically be utilized to describe an outcome, such as a mutual compromise or completion of negotiation leading to an outcome.
  • a loan may, by robotic process automation system or otherwise, be considered negotiated as a successful outcome that has resulted in an agreement between parties, where the negotiation has reached completion.
  • a smart contract circuit or robotic process automation system may have negotiated to completion a set of terms and conditions, or a negotiated loan.
  • One of skill in the art having the benefit of the disclosure herein and knowledge ordinarily available for a contemplated system, can readily determine the purposes and use of this term as it relates to a mutually agreed outcome through completion of negotiation in various embodiments and contexts disclosed herein.
  • the term negotiate in various forms may also specifically be utilized to characterize an event such as a negotiating event, or an event negotiation, including reaching a set of agreeable terms between parties.
  • An event requiring mutual agreement or compromise between parties may be considered a negotiating event, without limitation.
  • the process of reaching a mutually acceptable set of terms and conditions between parties could be considered a negotiating event.
  • a smart contract circuit or robotic process automation system may accommodate the communications, actions, or behaviors of the parties for a negotiated event.
  • collection and other forms such as collect or collecting
  • collection may be understood broadly to describe the acquisition of a tangible (e.g. physical item), intangible (e.g. data, a license, or a right), or monetary (e.g. payment) item, or other obligation or asset from a source.
  • the term generally may relate to the entire prospective acquisition of such an item from related tasks in early stages to related tasks in late stages or full completion of the acquisition of the item. Collection may result in a successful outcome where the item is tendered to a party, or may or an unsuccessful outcome where the item is not tendered or acquired to a party, or combinations thereof (e.g., a late or otherwise deficient tender of the item), without limitation.
  • Collection may occur in many different contexts of contracts or loans, such as lending, refinancing, consolidation, factoring, brokering, foreclosure, and data processing (e.g. data collection), or combinations thereof, without limitation.
  • Collection may be used in the form of a noun (e.g. data collection or the collection of an overdue payment where it refers to an event or characterizes an event), may refer as a noun to an assortment of items (e.g. a collection of collateral for a loan where it refers to a number of items in a transaction), or may be used in the form of a verb (e.g. collecting a payment from the borrower).
  • a noun e.g. data collection or the collection of an overdue payment where it refers to an event or characterizes an event
  • Collection may refer as a noun to an assortment of items (e.g. a collection of collateral for a loan where it refers to a number of items in a transaction), or may be used in the form of a verb (e.g
  • a lender may collect an overdue payment from a borrower through an online payment, or may have a successful collection of overdue payments acquired through a customer service telephone call.
  • a smart contract circuit or robotic process automation system may perform collection for one or more of the parties, and process appropriate tasks for completing or attempting collection for one or more items (e.g., an overdue payment).
  • negotiation by the smart contract or robotic process automation system may not complete or be successful, and depending upon such outcomes this may enable automated action or trigger other conditions or terms.
  • collection in various forms may also more specifically be utilized herein in noun form to describe a context for an event or thing, such as a collection event, or a collection payment.
  • a collection event may refer to a communication to a party or other activity that relates to acquisition of an item in such an activity, without limitation.
  • a collection payment for example, may relate to a payment made by a borrower that has been acquired through the process of collection, or through a collection department with a lender.
  • collection may characterize an event, payment or department, or other noun associated with a transaction or loan, as being a remedy for something that has become overdue.
  • a smart contract circuit or robotic process automation system may collect a payment or installment from a borrower, and the activity of doing so may be considered a collection event, without limitation.
  • the term collection in various forms may also more specifically be utilized herein as an adjective or other forms to describe a context relating to litigation, such as the outcome of a collection litigation (e.g. litigation regarding overdue or default payments on a loan).
  • a collection litigation e.g. litigation regarding overdue or default payments on a loan
  • the outcome of a collection litigation may be related to delinquent payments which are owed by a borrower or other party, and collection efforts relating to those delinquent payments may be litigated by parties.
  • a smart contract circuit or robotic process automation system may receive, determine, or otherwise administrate the outcome of collection litigation.
  • collection in various forms may also more specifically be utilized herein as an adjective or other forms to describe a context relating to an action of acquisition, such as a collection action (e.g. actions to induce tendering or acquisition of overdue or default payments on a loan or other obligation).
  • collection yield, financial yield of collection, and/or collection financial yield may be used.
  • the result of such a collection action may or may not have a financial yield.
  • a collection action may result in the payment of one or more outstanding payments on a loan, which may render a financial yield to another party such as the lender.
  • a smart contract circuit or robotic process automation system may render a financial yield from a collection action, or otherwise administrate or in some manner assist in a financial yield of a collection action.
  • a collection action may include the need for collection litigation.
  • collection ROI may also more specifically be utilized herein to describe a context relating to an action of receiving value, such as a collection action (e.g. actions to induce tendering or acquisition of overdue or default payments on a loan or other obligation), wherein there is a return on investment (ROI).
  • a collection action e.g. actions to induce tendering or acquisition of overdue or default payments on a loan or other obligation
  • ROI return on investment
  • the result of such a collection action may or may not have an ROI, either with respect to the collection action itself (as an ROI on the collection action) or as an ROI on the broader loan or transaction that is the subject of the collection action.
  • an ROI on a collection action may be prudent or not with respect to a default loan, without limitation, depending upon whether the ROI will be provided to a party such as the lender.
  • a projected ROI on collection may be estimated, or may also be calculated given real events that transpire.
  • a smart contract circuit or robotic process automation system may render an estimated ROI for a collection action or collection event, or may calculate an ROI for actual events transpiring in a collection action or collection event, without limitation.
  • such a ROI may be a positive or negative figure, whether estimated or actual.
  • reputation measure of reputation, lender reputation, borrower reputation, entity reputation, and the like may include general, widely held beliefs, opinions, and/or perceptions that are generally held about an individual, entity, collateral, and the like.
  • a measure for reputation may be determined based on social data including likes/dislikes, review of entity or products and services provided by the entity, rankings of the company or product, current and historic market and financial data include price, forecast, buy/sell recommendations, financial news regarding entity, competitors, and partners.
  • Reputations may be cumulative in that a product reputation and the reputation of a company leader or lead scientist may influence the overall reputation of the entity. Reputation of an institute associated with an entity (e.g. a school being attended by a student) may influence the reputation of the entity.
  • a smart contract circuit or robotic process automation system may collect, or initiate collection of data related to the above and determine a measure or ranking of reputation.
  • a measure or ranking of an entity's reputation may be used by a smart contract circuit or robotic process automation system in determining whether to enter into an agreement with the entity, determination of terms and conditions of a loan, interest rates, and the like.
  • indicia of a reputation determination may be related to outcomes of one or more transactions (e.g., a comparison of "likes" on a particular social media data set to an outcome index, such as successful payments, successful negotiation outcomes, ability to liquidate a particular type of collateral, etc.) to determine the measure or ranking of an entity's reputation.
  • the term collection in various forms may also more specifically be utilized herein to describe a party or entity that induces, administrates, or facilitates a collection action, collection event, or other collection related context.
  • the measure of reputation of a party involved may be estimated or calculated using objective, subjective, or historical metrics or data.
  • a collector may be involved in a collection action, and the reputation of that collector may be used to determine decisions, actions, or conditions.
  • a collection may be also used to describe objective, subjective or historical metrics or data to measure the reputation of a party involved, such as a lender, borrower, or debtor.
  • a smart contract circuit or robotic process automation system may render a collection or measures, or implement a collector, within the context of a transaction or loan.
  • the term collection and data collection in various forms, including data collection systems, may also more specifically be utilized herein to describe a context relating to the acquisition, organization, or processing of data, or combinations thereof, without limitation.
  • the result of such a data collection may be related or wholly unrelated to a collection of items (e.g., grouping of the items, either physically or logically), or actions taken for delinquent payments (e.g., collection of collateral, a debt, or the like), without limitation.
  • a data collection may be performed by a data collection system, wherein data is acquired, organized, or processed for decision-making, monitoring, or other purposes of prospective or actual transaction or loan.
  • a smart contract or robotic process automation system may incorporate data collection or a data collection system, to perform portions or entire tasks of data collection, without limitation.
  • data collection or a data collection system may incorporate data collection or a data collection system, to perform portions or entire tasks of data collection, without limitation.
  • refinance, refinancing activity(ies), refinancing interactions, refinancing outcomes, and similar terms, as utilized herein should be understood broadly. Without limitation to any other aspect or description of the present disclosure refinance and refinancing activities include replacing an existing mortgage, loan, bond, debt transaction, or the like with a new mortgage, loan, bond, or debt transaction that pays off or ends the previous financial arrangement. In certain embodiments, any change to terms and conditions of a loan, and/or any material change to terms and conditions of a loan, may be considered a refinancing activity. In certain embodiments, a refinancing activity is considered only those changes to a loan agreement that result in a different financial outcome for the loan agreement.
  • the new loan should be advantageous to the borrower or issuer, and/or mutually agreeable (e.g., improving a raw financial outcome of one, and a security or other outcome for the other).
  • Refinancing may be done to reduce interest rates, lower regular payments, change the loan term, change the collateral associated with the loan, consolidate debt into a single loan, restructure debt, change a type of loan (e.g. variable rate to fixed rate), pay off a loan that is due, in response to an improved credit score, to enlarge the loan, and/or in response to a change in market conditions (e.g. interest rates, value of collateral, and the like).
  • Refinancing activity may include initiating an offer to refinance, initiating a request to refinance, configuring a refinancing interest rate, configuring a refinancing payment schedule, configuring a refinancing balance in a response to the amount or terms of the refinanced loan, configuring collateral for a refinancing including changes in collateral used, changes in terms and conditions for the collateral, a change in the amount of collateral and the like, managing use of proceeds of a refinancing, removing or placing a lien on different items of collateral as appropriate given changes in terms and conditions as part of a refinancing, verifying title for a new or existing item of collateral to be used to secure the refinanced loan, managing an inspection process title for a new or existing item of collateral to be used to secure the refinanced loan, populating an application to refinance a loan, negotiating terms and conditions for a refinanced loan and closing a refinancing.
  • Refinance and refinancing activities may be disclosed in the context of data collection and monitoring services that collect a training set of interactions between entities for a set of loan refinancing activities.
  • Refinance and refinancing activities may be disclosed in the context of an artificial intelligence system that is trained using the collected training set of interactions that includes both refinancing activities and outcomes. The trained artificial intelligence may then be used to recommend a refinance activity, evaluate a refinance activity, make a prediction around an expected outcome of refinancing activity, and the like.
  • Refinance and refinancing activities may be disclosed in the context of smart contract systems which may automate a subset of the interactions and activities of refinancing.
  • a smart contract system may automatically adjust an interest rate for a loan based on information collected via at least one of an Internet of Things system, a crowdsourcing system, a set of social network analytic 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 recommend, an interest rate for refinancing a loan based on interest rates available to the lender from secondary lenders, risk factors of the borrower (including predicted risk based on one or more predictive models using artificial intelligence), marketing factors (such as competing interest rates offered by other lenders), and the like.
  • Outcomes and events of a refinancing activity may be recorded in a distributed ledger.
  • a smart contract for the refinance loan may be automatically reconfigured to define the terms and conditions for the new loan such as a principal amount of debt, a balance of debt, a fixed interest rate, a variable interest rate, a payment amount, a payment schedule, a balloon payment schedule, a specification of collateral, a specification of substitutability of collateral, a party, a guarantee, a guarantor, a security, a personal guarantee, a lien, a duration, a covenant, a foreclose condition, a default condition, and a consequence of default.
  • consolidate, consolidation activity(ies), loan consolidation, debt consolidation, consolidation plan, and similar terms, as utilized herein should be understood broadly. Without limitation to any other aspect or description of the present disclosure consolidate, consolidation activity(ies), loan consolidation, debt consolidation, or consolidation plan are related to the use of a single large loan to pay off several smaller loans, and/or the use of one or more of a set of loans to pay off at least a portion of one or more of a second set of loans. In embodiments, loan consolidation may be secured (i.e., backed by collateral) or unsecured.
  • Loans may be consolidated to obtain a lower interest rate than one or more of the current loans, to reduce total monthly loan payments, and/or to bring a debtor into compliance on the consolidated loans or other debt obligations of the debtor.
  • Loans that may be classified as candidates for consolidation may be determined based on a model that processes attributes of entities involved in the set of loans including identity of a party, interest rate, payment balance, payment terms, payment schedule, type of loan, type of collateral, financial condition of party, payment status, condition of collateral, and value of collateral.
  • Consolidation activities may include managing at least one of identification of loans from a set of candidate loans, preparation of a consolidation offer, preparation of a consolidation plan, preparation of content communicating a consolidation offer, scheduling a consolidation offer, communicating a consolidation offer, negotiating a modification of a consolidation offer, preparing a consolidation agreement, executing a consolidation agreement, modifying collateral for a set of loans, handling an application workflow for consolidation, managing an inspection, managing an assessment, setting an interest rate, deferring a payment requirement, setting a payment schedule, and closing a consolidation agreement.
  • a consolidation plan may be based on various factors, such as the status of payments, interest rates of the set of loans, prevailing interest rates in a platform marketplace or external marketplace, the status of the borrowers of a set of loans, the status of collateral or assets, risk factors of the borrower, the lender, one or more guarantors, market risk factors and the like.
  • Consolidation and consolidation activities may be disclosed in the context of data collection and monitoring services that collect a training set of interactions between entities for a set of loan consolidation activities, consolidation and consolidation activities may be disclosed in the context of an artificial intelligence system that is trained using the collected training set of interactions that includes both consolidation activities and outcomes associated with those activities.
  • the trained artificial intelligence may then be used to recommend a consolidation activity, evaluate a consolidation activity, make a prediction around an expected outcome of consolidation activity, and the like based models including status of debt, condition of collateral or assets used to secure or back a set of loans, the state of a business or business operation (e.g., receivables, payables, or the like), conditions of parties (such as net worth, wealth, debt, location, and other conditions), behaviors of parties (such as behaviors indicating preferences, behaviors indicating debt preferences), and others.
  • Debt consolidation, loan consolidation and associated consolidation activities may be disclosed in the context of smart contract systems which may automate a subset of the interactions and activities of consolidation.
  • consolidation may include consolidation with respect to terms and conditions of sets of loans, selection of appropriate loans, configuration of payment terms for consolidated loans, configuration of payoff plans for pre-existing loans, communications to encourage consolidation, and the like.
  • the artificial intelligence of a smart contract may automatically recommend or set rules, thresholds, actions, parameters and the like (optionally by learning to do so based on a training set of outcomes over time), resulting in a recommended consolidation plan, which may specify a series of actions required to accomplish a recommended or desired outcome of consolidation (such as within a range of acceptable outcomes), which may be automated and may involve conditional execution of steps based on monitored conditions and/or smart contract terms, which may be created, configured, and/or accounted for by the consolidation plan.
  • Consolidation plans may be determined and executed based at least one part on market factors (such as competing interest rates offered by other lenders, values of collateral, and the like) as well as regulatory and/or compliance factors. Consolidation plans may be generated and/or executed for creation of new consolidated loans, for secondary loans related to consolidated loans, for modifications of existing loans related to consolidation, for refinancing terms of a consolidated loan, for foreclosure situations (e.g., changing from secured loan rates to unsecured loan rates), for bankruptcy or insolvency situations, for situations involving market changes (e.g., changes in prevailing interest rates) and others, consolidation.
  • market factors such as competing interest rates offered by other lenders, values of collateral, and the like
  • Consolidation plans may be generated and/or executed for creation of new consolidated loans, for secondary loans related to consolidated loans, for modifications of existing loans related to consolidation, for refinancing terms of a consolidated loan, for foreclosure situations (e.g., changing from secured loan rates to unsecured loan rates), for bankruptcy or insolvency situations, for situations involving
  • Certain of the activities related to loans, collateral, entities, and the like may apply to a wide variety of loans and may not apply explicitly to consolidation activities.
  • the categorization of the activities as consolidation activities may be based on the context of the loan for which the activities are taking place.
  • one of skill in the art having the benefit of the disclosure herein and knowledge ordinarily available about a contemplated system can readily determine which aspects of the present disclosure will benefit from a particular application of a consolidation activity, how to choose or combine consolidation activities, how to implement selected services, circuits, and/or systems described herein to perform certain loan consolidation operations, and the like.
  • WIP Inventory and Work in Progress
  • References to accounts receivable should be understood to encompass these terms and not be limiting.
  • Factoring may include a sale of accounts receivable at a discounted rate for value in the present (often cash). Factoring may also include the use of accounts receivable as collateral for a short term loan. In both cases the value of the accounts receivable or invoices may be discounted for multiple reasons including the future value of money, a term of the accounts receivable (e.g., 30 day net payment vs.
  • a degree of default risk on the accounts receivable a status of receivables, a status of work-in-progress (WIP), a status of inventory, a status of delivery and/or shipment, financial condition(s) of parties owing against the accounts receivable, a status of shipped and/or billed, a status of payments, a status of the borrower, a status of inventory, a risk factor of a borrower, a lender, one or more guarantors, market risk factors, a status of debt (are there other liens present on the accounts receivable or payment owed on the inventory, a condition of collateral assets (e.g.
  • the condition of the inventory- is it current or out of date, are invoices in arrears), a state of a business or business operation, a condition of a party to the transaction (such as net worth, wealth, debt, location, and other conditions), a behavior of a party to the transaction (such as behaviors indicating preferences, behaviors indicating negotiation styles, and the like), current interest rates, any current regulatory and compliance issues associated with the inventory or accounts receivable (e.g. if inventory is being factored, has the intended product received appropriate approvals), and there legal actions against the borrower, and many others, including predicted risk based on one or more predictive models using artificial intelligence).
  • a state of a business or business operation such as net worth, wealth, debt, location, and other conditions
  • a behavior of a party to the transaction such as behaviors indicating preferences, behaviors indicating negotiation styles, and the like
  • current interest rates e.g. if inventory is being factored, has the intended product received appropriate approvals
  • a factor is an individual, business, entity, or groups thereof which agree to provide value in exchange for either the outright acquisition of the invoices in a sale or the use of the invoices as collateral for a loan for the value.
  • Factoring a loan may include the identification of candidates (both lenders and borrowers) for factoring, a plan for factoring specifying the proposed receivables (e.g. all, some, only those meeting certain criteria), and a proposed discount factor, communication of the plan to potential parties, proffering an offer and receiving an offer, verification of quality of receivables, conditions regarding treatment of the receivables for the term of the loan.
  • a mortgage is an interaction where a borrower provides the title or a lien on the title of an item of value, typically property, to a lender as security in exchange for money or another item of value, to be repaid, typically with interest, to the lender.
  • the exchange includes the condition that, upon repayment of the loan, the title reverts to the borrower and/or the lien on the property is removed.
  • the brokering of a mortgage may include the identification of potential properties, lenders, and other parties to the loan, and arranging or negotiating the terms of the mortgage.
  • Certain components or activities may not be considered mortgage related individually, but may be considered mortgage related when used in conjunction with a mortgage, act upon a mortgage, are related to an entity or party to a mortgage, and the like.
  • brokering may apply to the offering of a variety of loans including unsecured loans, outright sale of property and the like.
  • Mortgage activities and mortgage interactions may include mortgage marketing activity, identification of a set of prospective borrowers, identification of property to mortgage, identification of collateral property to mortgage, qualification of borrower, title search and/or title verification for prospective mortgage property, property assessment, property inspection, or property valuation for prospective mortgage property, income verification, borrower demographic analysis, identification of capital providers, determination of available interest rates, determination of available payment terms and conditions, analysis of existing mortgage(s), comparative analysis of existing and new mortgage terms, completion of application workflow (e.g.
  • debt management includes something of monetary value that is owed to another.
  • a loan typically results in the borrower holding the debt (e.g. the money that must be paid back according to the terms of the loan, which may include interest).
  • Consolidation of debt includes the use of a new, single loan to pay back multiple loans (or various other configurations of debt structuring as described herein, and as understood to one of skill in the art). Often the new loan may have better terms or lower interest rates.
  • Debt portfolios include a number of pieces or groups of debt, often having different characteristics including term, risk, and the like. Debt portfolio management may involve decisions regarding the quantity and quality of the debt being held and how best to balance the various debts to achieve a desired risk/reward position based on: investment policy, return on risk determinations for individual pieces of debt, or groups of debt. Debt may be syndicated where multiple lenders fund a single loan (or set of loans) to a borrower. Debt portfolios may be sold to a third party (e.g., at a discounted rate). Debt compliance includes the various measures taken to ensure that debt is repaid. Demonstrating compliance may include documentation of the actions taken to repay the debt.
  • Transactions related to a debt may include offering a debt transaction, underwriting a debt transaction, setting an interest rate, deferring a payment requirement, modifying an interest rate, validating title, managing inspection, recording a change in title, assessing the value of an asset, calling a loan, closing a transaction, setting terms and conditions for a transaction, providing notices required to be provided, foreclosing on a set of assets, modifying terms and conditions, setting a rating for an entity, syndicating debt, and/or consolidating debt.
  • Debt terms and conditions may include a balance of debt, a principal amount of debt, a fixed interest rate, a variable interest rate, a payment amount, a payment schedule, a balloon payment schedule, a specification of assets that back the bond, a specification of substitutability of assets, a party, an issuer, a purchaser, a guarantee, a guarantor, a security, a personal guarantee, a lien, a duration, a covenant, a foreclose condition, a default condition, and a consequence of default.
  • condition, condition classification, classification models, condition management, and similar terms, as utilized herein should be understood broadly. Without limitation to any other aspect or description of the present disclosure condition, condition classification, classification models, condition management, include classifying or determining a condition of an asset, issuer, borrower, loan, debt, bond, regulatory status, term or condition for a bond, loan or debt transaction that is specified and monitored in the contract, and the like. Based on a classified condition of an asset, condition management may include actions to maintain or improve a condition of the asset or the use of that asset as collateral. Based on a classified condition of an issuer, borrower, party regulatory status, and the like, condition management may include actions to alter the terms or conditions of a loan or bond.
  • Condition classification may include various rules, thresholds, conditional procedures, workflows, model parameters, and the like to classify a condition of an asset, issuer, borrower, loan, debt, bond, regulatory status, term or condition for a bond, loan or debt transaction, and the like based on data from Internet of Things devices, data from a set of environmental condition sensors, data from a set of social network analytic services and a set of algorithms for querying network domains, social media data, crowdsourced data, and the like.
  • Condition classification may include grouping or labeling entities, or clustering the entities, as similarly positioned with regard to some aspect of the classified condition (e.g., a risk, quality, ROI, likelihood for recovery, likelihood to default, or some other aspect of the related debt).
  • classification and classification models are disclosed where the classification and classification model may be tied to a geographic location relating to the collateral, the issuer, the borrower, the distribution of the funds or other geographic locations.
  • Classification and classification models are disclosed where artificial intelligence is used to improve a classification model (e.g. refine a model by making refinements using artificial intelligence data). Thus artificial intelligence may be considered, in some instances, as a part of a classification model and vice versa.
  • Classification and classification models are disclosed where social media data, crowdsourced data, or loT data is used as input for refining a model, or as input to a classification model. Examples of loT data may include images, sensor data, location data, and the like.
  • Examples of social media data or crowdsourced data may include behavior of parties to the loan, financial condition of parties, adherence to a parties to a term or condition of the loan, or bond, or the like.
  • Parties to the loan may include issuers of a bond, related entities, lender, borrower, 3rd parties with an interest in the debt.
  • Condition management may be discussed in connection with smart contract services which may include condition classification, data collection and monitoring, and bond, loan, and debt transaction management.
  • Data collection and monitoring services are also discussed in conjunction with classification and classification models which are related when classifying an issuer of a bond issuer, an asset or collateral asset related to the bond, collateral assets backing the bond, parties to the bond, and sets of the same.
  • a classification model may be included when discussing bond types.
  • classification model may change both in an embodiment, or in the same embodiment which is tied to a specific jurisdiction.
  • Different classification models may use different data sets (e.g. based on the issuer, the borrower, the collateral assets, the bond type, the loan type, and the like) and multiple classification models may be used in a single classification.
  • one type of bond such as a municipal bond
  • another classification model may emphasize data from loT sensors associated with a collateral asset. Accordingly, different classification models will offer benefits or risks over other classification models, depending upon the embodiment and the specifics of the bond, loan, or debt transaction.
  • a classification model includes an approach or concept for classification.
  • Conditions classified for a bond, loan, or debt transaction may include a principal amount of debt, a balance of debt, a fixed interest rate, a variable interest rate, a payment amount, a payment schedule, a balloon payment schedule, a specification of assets that back the bond, loan or debt transaction, a specification of substitutability of assets, a party, an issuer, a purchaser, a guarantee, a guarantor, a security, a personal guarantee, a lien, a duration, a covenant, a foreclose condition, a default condition, and/or a consequence of default.
  • Conditions classified may include type of bond issuer such as a municipality, a corporation, a contractor, a government entity, a non-governmental entity, and a non-profit entity. Entities may include a set of issuers, a set of bonds, a set of parties, and/or a set of assets. Conditions classified may include an entity condition such as net worth, wealth, debt, location, and other conditions), behaviors of parties (such as behaviors indicating preferences, behaviors indicating debt preferences), and the like.
  • Conditions classified may include an asset or type of collateral such as a municipal asset, a vehicle, a ship, a plane, a building, a home, real estate property, undeveloped land, a farm, a crop, a municipal facility, a warehouse, a set of inventory, a commodity, a security, a currency, a token of value, a ticket, a cryptocurrency, a consumable item, an edible item, a beverage, a precious metal, an item of jewelry, a gemstone, an item of intellectual property, an intellectual property right, a contractual right, an antique, a fixture, an item of furniture, an item of equipment, a tool, an item of machinery, and an item of personal property.
  • an asset or type of collateral such as a municipal asset, a vehicle, a ship, a plane, a building, a home, real estate property, undeveloped land, a farm, a crop, a municipal facility, a warehouse, a set of inventory, a commodity, a security, a currency, a token of value
  • Conditions classified may include a bond type where bond type may include a municipal bond, a government bond, a treasury bond, an asset-backed bond, and a corporate bond.
  • Conditions classified may include a default condition, a foreclosure condition, a condition indicating violation of a covenant, 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.
  • Conditions classified may include an environment where environment may include an environment selected from among a municipal environment, a corporate environment, a securities trading environment, a real property environment, a commercial facility, a warehousing facility, a transportation environment, a manufacturing environment, a storage environment, a home, and a vehicle.
  • Actions based on the condition of an asset, issuer, borrower, loan, debt, bond, regulatory status and the like may include managing, reporting on, syndicating, consolidating, or otherwise handling a set of bonds (such as municipal bonds, corporate bonds, performance bonds, and others), a set of loans (subsidized and unsubsidized, debt transactions and the like, monitoring, classifying, predicting, or otherwise handling the reliability, quality, status, health condition, financial condition, physical condition or other information about a guarantee, a guarantor, a set of collateral supporting a guarantee, a set of assets backing a guarantee, or the like.
  • bonds such as municipal bonds, corporate bonds, performance bonds, and others
  • a set of loans subsidized and unsubsidized, debt transactions and the like
  • monitoring classifying, predicting, or otherwise handling the reliability, quality, status, health condition, financial condition, physical condition or other information about a guarantee, a guarantor, a set of collateral supporting a guarantee, a set of assets backing a guarantee, or the like.
  • Bond transaction activities in response to a condition of the bond may include offering a debt transaction, underwriting a debt transaction, setting an interest rate, deferring a payment requirement, modifying an interest rate, validating title, managing inspection, recording a change in title, assessing the value of an asset, calling a loan, closing a transaction, setting terms and conditions for a transaction, providing notices required to be provided, foreclosing on a set of assets, modifying terms and conditions, setting a rating for an entity, syndicating debt, and/or consolidating debt.
  • Certain considerations for the person of skill in the art, or embodiments of the present disclosure in choosing an appropriate condition to manage include, without limitation: the legality of the condition given the jurisdiction of the transaction, the data available for a given collateral, the anticipated transaction type (loan, bond or debt), the specific type of collateral, the ratio of the loan to value, the ratio of the collateral to the loan, the gross trans action/loan amount, the credit scores of the borrower and the lender, and other considerations. While specific examples of conditions, condition classification, classification models, and condition management are described herein for purposes of illustration, any embodiment benefitting from the disclosures herein, and any considerations understood to one of skill in the art having the benefit of the disclosures herein, are specifically contemplated within the scope of the present disclosure.
  • classifying a condition or item may include actions to sort the condition or item into a group or category based on some aspect, attribute, or characteristic of the condition or item where the condition or item is common or similar for all the items placed in that classification, despite divergent classifications or categories based on other aspects or conditions at the time.
  • Classification may include recognition of one or more parameters, features, characteristics, or phenomena associated with a condition or parameter of an item, entity, person, process, item, financial construct, or the like.
  • Conditions classified by a condition classifying system may include a default condition, a foreclosure condition, a condition indicating violation of a covenant, a financial risk condition, a behavioral risk condition, a contractual performance condition, a policy risk condition, a financial health condition, a physical defect condition, a physical health condition, an entity risk condition, and/or an entity health condition.
  • a classification model may automatically classify or categorize items, entities, process, items, financial constructs or the like based on data received from a variety of sources.
  • the classification model may classify items based on a single attribute or a combination of attributes, and/or may utilize data regarding the items to be classified and a model.
  • the classification model may classify individual items, entities, financial constructs, or groups of the same.
  • a bond may be classified based on the type of bond ((e.g. municipal bonds, corporate bonds, performance bonds, and the like), rate of return, bond rating (3rd party indicator of bond quality with respect to bond issuer's financial strength, and/or ability to bap bond's principal and interest, and the like.
  • Lenders or bond issuers may be classified based on the type of lender or issuer, permitted attributes (e.g. based on income, wealth, location (domestic or foreign), various risk factors, status of issuers, and the like.
  • Borrowers may be classified based on permitted attributes (e.g. income, wealth, total assets, location, credit history), risk factors, current status (e.g.
  • a condition classifying system may classify a student recipient of a loan based on progress of the student toward a degree, the student’s grades or standing in their classes, students’ status at the school (matriculated, on probation and the like), the participation of a student in a non-profit activity, a deferment status of the student, and the participation of the student in a public interest activity.
  • Conditions classified by a condition classifying system may include a state of a set of collateral for a loan or a state of an entity relevant to a guarantee for a loan.
  • Conditions classified by a condition classifying system may include a medical condition of a borrower, guarantor, subsidizer, or the like. Conditions classified by a condition classifying system may include compliance with at least one of a law, a regulation, or a policy related to a lending transaction or lending institute. Conditions classified by a condition classifying system may include a condition of an issuer for a bond, a condition of a bond, a rating of a loan-related entity, and the like. Conditions classified by a condition classifying system may include an identify of a machine, a component, or an operational mode.
  • Conditions classified by a condition classifying system may include a state or context (such as a state of a machine, a process, a workflow, a marketplace, a storage system, a network, a data collector, or the like).
  • a condition classifying system may classify a process involving a state or context (e.g., a data storage process, a network coding process, a network selection process, a data marketplace process, a power generation process, a manufacturing process, a refining process, a digging process, a boring process, and/or other process described herein.
  • a condition classifying system may classify a set of loan refinancing actions based on a predicted outcome of the set of loan refinancing actions.
  • a condition classifying system may classify a set of loans as candidates for consolidation based on attributes such as identity of a party, an interest rate, a payment balance, payment terms, payment schedule, a type of loan, a type of collateral, a financial condition of party, a payment status, a condition of collateral, a value of collateral, and the like.
  • a condition classifying system may classify the entities involved in a set of factoring loans, bond issuance activities, mortgage loans, and the like.
  • a condition classifying system may classify a set of entities based on projected outcomes from various loan management activities.
  • a condition classifying system may classify a condition of a set of issuers based on information from Internet of Things data collection and monitoring services, a set of parameters associated with an issuer, a set of social network monitoring and analytic services, and the like.
  • a condition classifying system may classify a set of loan collection actions, loan consolidation actions, loan negotiation actions, loan refinancing actions and the like based on a set of projected outcomes for those activities and entities.
  • a subsidized loan is the loan of money or an item of value wherein payment of interest on the value of the loan may be deferred, postponed, or delayed, with or without accrual, such as while the borrower is in school, is unemployed, is ill, and the like.
  • a loan may be subsidized when the payment of interest on a portion or subset of the loan is borne or guaranteed by someone other than the borrower.
  • Examples of subsidized loans may include a municipal subsidized loan, a government subsidized loan, a student loan, an asset-backed subsidized loan, and a corporate subsidized loan.
  • An example of a subsidized student loan may include student loans which may be subsidized by the government and on which interest may be deferred or not accrue based on progress of the student toward a degree, the participation of a student in a non-profit activity, a deferment status of the student, and the participation of the student in a public interest activity.
  • An example of a government subsidized housing loan may include governmental subsidies which may exempt the borrower from paying closing costs, first mortgage payment and the like.
  • Conditions for such subsidized loans may include location of the property (rural or urban), income of the borrower, military status of the borrower, ability of the purchased home to meet health and safety standards, a limit on the profits you can earn on the sale of your home, and the like. Certain usages of the word loan may not apply to a subsidized loan but rather to a regular loan.
  • a contemplated system ordinarily available to that person can readily determine which aspects of the present disclosure will benefit from consideration of a subsidized loan (e.g., in determining the value of the loan, negotiations related to the loan, terms and conditions related to the loan, etc.) wherein the borrower may be relieved of some of the loan obligations common for non-subsidized loans, where the subsidy may include forgiveness, delay or deferment of interest on a loan, or the payment of the interest by a third party.
  • the subsidy may include the payment of closing costs including points, first payment and the like by a person or entity other than the borrower, and/or how to combine processes and systems from the present disclosure to enhance or benefit from title validation.
  • subsidized loan management may include a plurality of activities and solutions for managing or responding to one or more events related to a subsidized loan wherein such events may include requests for a subsidized loan, offering a subsidized loan, accepting a subsidized loan, providing underwriting information for a subsidized loan, providing a credit report on a borrower seeking a subsidized loan, deferring a required payment as part of the loan subsidy, setting an interest rate for a subsidized loan where a lower interest rate may be part of the subsidy, deferring a payment requirement as part of the loan subsidy, identifying collateral for a loan, validating title for collateral or security for a loan, recording a change in title of property, assessing the value of collateral or security for a loan, inspecting property that is involved in a loan, identifying a change in condition of an entity relevant to a loan
  • a system for handling a subsidized loan may include classifying a set of parameters of a set of subsidized loans on the basis of data relating to those parameters obtained from an Internet of Things data collection and monitoring service. Classifying the set of parameters of the set of subsidized loans may also be on the bases of data obtained from one or more configurable data collection and monitoring services that leverage social network analytic services, crowd sourcing services, and the like for obtaining parameter data (e.g., determination that a person or entity is qualified for the subsidized loan, determining a social value of providing the subsidized loan or removing a subsidization from a loan, determining that a subsidizing entity is legitimate, determining appropriate subsidization terms based on characteristics of the buyer and/or subsidizer, etc.).
  • parameter data e.g., determination that a person or entity is qualified for the subsidized loan, determining a social value of providing the subsidized loan or removing a subsidization from a loan, determining that a subsidizing entity is legitimate, determining appropriate subsid
  • foreclose, foreclosure, foreclose or foreclosure condition, default foreclosure collateral, default collateral, (and similar terms) as utilized herein should be understood broadly. Without limitation to any other aspect or description of the present disclosure, foreclose condition, default and the like describe the failure of a borrower to meet the terms of a loan. Without limitation to any other aspect or description of the present disclosure foreclose and foreclosure include processes by which a lender attempts to recover, from a borrower in a foreclose or default condition, the balance of a loan or take away in lieu, the right of a borrower to redeem a mortgage held in security for the loan.
  • Failure to meet the terms of the loan may include failure to make specified payments, failure to adhere to a payment schedule, failure to make a balloon payment, failure to appropriately secure the collateral, failure to sustain collateral in a specified condition (e.g. in good repair), acquisition of a second loan, and the like.
  • Foreclosure may include a notification to the borrower, the public, jurisdictional authorities of the forced sale of an item collateral such as through a foreclosure auction.
  • an item of collateral may be placed on a public auction site (such as eBay, Nc or an auction site appropriate for a particular type of property.
  • the minimum opening bid for the item of collateral may be set by the lender and may cover the balance of the loan, interest on the loan, fees associated with the foreclosure and the like.
  • Attempts to recover the balance of the loan may include the transfer of the deed for an item of collateral in lieu of foreclosure (e.g. a real-estate mortgage where the borrower holds the deed for a property which acts as collateral for the mortgage loan).
  • Foreclosure may include taking possession of or repossessing the collateral (e.g. a car, a sports vehicle such as a boat, ATV, ski-mobile, jewelry).
  • Foreclosure may include securing an item of collateral associated with the loan (such as by locking a connected device, such as a smart lock, smart container, or the like that contains or secures collateral).
  • Foreclosure may include arranging for the shipping of an item of collateral by a carrier, freight forwarder of the like.
  • Foreclosure may include arranging for the transport of an item of collateral by a drone, a robot, or the like for transporting collateral.
  • a loan may allow for the substitution of collateral or the shifting of the lien from an item of collateral initially used to secure the loan to a substitute collateral where the substitute collateral is of higher value (to the lender) than the initial collateral or is an item in which the borrower has a greater equity.
  • the result of the substitution of collateral is that when the loan goes into foreclosure, it is the substitute collateral that may be the subject of a forced sale or seizure. Certain usages of the word default may not apply to such as to foreclose but rather to a regular or default condition of an item.
  • validation of tile, title validation, validating title, and similar terms should be understood broadly. Without limitation to any other aspect or description of the present disclosure validation of title and title validation include any efforts to verify or confirm the ownership or interest by an individual or entity in an item of property such as a vehicle, a ship, a plane, a building, a home, real estate property, undeveloped land, a farm, a crop, a municipal facility, a warehouse, a set of inventory, a commodity, a security, a currency, a token of value, a ticket, a cryptocurrency, a consumable item, an edible item, a beverage, a precious metal, an item of jewelry, a gemstone, an item of intellectual property, an intellectual property right, a contractual right, an antique, a fixture, an item of furniture, an item of equipment, a tool, an item of machinery, and an item of personal property.
  • an item of property such as a vehicle, a ship, a plane, a building, a home, real estate property, undeveloped land, a farm,
  • Efforts to verify ownership may include reference to bills of sale, government documentation of transfer of ownership, a legal will transferring ownership, documentation of retirement of liens on the item of property, verification of assignment of Intellectual Property to the proposed borrower in the appropriate jurisdiction, and the like.
  • For real-estate property validation may include a review of deeds and records at a courthouse of a country, a state, a county, or a district in which a building, a home, real estate property, undeveloped land, a farm, a crop, a municipal facility, a vehicle, a ship, a plane, or a warehouse is located or registered.
  • Certain usages of the word validation may not apply to validation of a title or title validation but rather to confirmation that a process is operating correctly, that an individual has been correctly identified using biometric data, that intellectual property rights are in effect, that data is correct and meaningful, and the like.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit from title validation, and/or how to combine processes and systems from the present disclosure to enhance or benefit from title validation.
  • Certain considerations for the person of skill in the art, in determining whether the term validation is referring to title validation are specifically contemplated within the scope of the present disclosure.
  • validation includes any validating system including, without limitation, validating title for collateral or security for a loan, validating conditions of collateral for security or a loan, validating conditions of a guarantee for a loan, and the like.
  • a validation service may provide lenders a mechanism to deliver loans with more certainty, such as through validating loan or security information components (e.g., income, employment, title, conditions for a loan, conditions of collateral, and conditions of an asset).
  • a validation service circuit may be structured to validate a plurality of loan information components with respect to a financial entity configured to determine a loan condition for an asset.
  • Certain components may not be considered a validating system individually, but may be considered validating in an aggregated system - for example, an Internet of Things component may not be considered a validating component on its own, however an Internet of Things component utilized for asset data collection and monitoring may be considered a validating component when applied to validating a reliability parameter of a personal guarantee for a load when the Internet of Things component is associated with a collateralized asset.
  • otherwise similar looking systems may be differentiated in determining whether such systems are for validation. For example, a blockchain-based ledger may be used to validate identities in one instance and to maintain confidential information in another instance.
  • the benefits of the present disclosure may be applied in a wide variety of systems, and any such systems may be considered a system for validation herein, while in certain embodiments a given system may not be considered a validating system herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system, and/or how to combine processes and systems from the present disclosure to enhance operations of the contemplated system.
  • a contemplated system is a validating system and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation: a lending platform having a social network monitoring system for validating the reliability of a guarantee for a loan; a lending platform having an Internet of Things data collection and monitoring system for validating reliability of a guarantee for a loan; a lending platform having a crowdsourcing and automated classification system for validating conditions of an issuer for a bond; a crowdsourcing system for validating quality, title, or other conditions of collateral for a loan; a biometric identify validation application such as utilizing DNA or fingerprints; loT devices utilized to collectively validate location and identity of a fixed asset that is tagged by a virtual asset tag; validation systems utilizing voting or consensus protocols; artificial intelligence systems trained to recognize and validate events; validating information such as title records, video footage, photographs, or witnessed statements; validation representations related to behavior, such as to validate occurrence of conditions of compliance, to validate occurrence of conditions of
  • underwriting includes any underwriting, including, without limitation, relating to underwriters, providing underwriting information for a loan, underwriting a debt transaction, underwriting a bond transaction, underwriting a subsidized loan transaction, underwriting a securities transaction, and the like.
  • Underwriting services may be provided by financial entities, such as banks, insurance or investment houses, and the like, whereby the financial entity guarantees payment in case of a determination of a loss condition (e.g., damage or financial loss) and accept the financial risk for liability arising from the guarantee.
  • a loss condition e.g., damage or financial loss
  • a bank may underwrite a loan through a mechanism to perform a credit analysis that may lead to a determination of a loan to be granted, such as through analysis of personal information components related to an individual borrower requesting a consumer loan (e.g., employment history, salary and financial statements publicly available information such as the borrower's credit history), analysis of business financial information components from a company requesting a commercial load (e.g., tangible net worth, ratio of debt to worth (leverage), and available liquidity (current ratio)), and the like.
  • an underwriting services circuit may be structured to underwrite a financial transaction including a plurality of financial information components with respect to a financial entity configured to determine a financial condition for an asset.
  • underwriting components may be considered underwriting for some purposes but not for other purposes - for example, an artificial intelligence system to collect and analyze transaction data may be utilized in conjunction with a smart contract platform to monitor loan transactions, but alternately used to collect and analyze underwriting data, such as utilizing a model trained by human expert underwriters. Accordingly, the benefits of the present disclosure may be applied in a wide variety of systems, and any such systems may be considered underwriting herein, while in certain embodiments a given system may not be considered underwriting herein.
  • a lending platform having an underwriting system for a loan with a set of data-integrated microservices such as including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting lending entities and transactions; underwriting processes, operations, and services; underwriting data, such as data relating to identities of prospective and actual parties involved insurance and other transactions, actuarial data, data relating to probability of occurrence and/or extent of risk associated with activities, data relating to observed activities and other data used to underwrite or estimate risk; an underwriting application, such as, without limitation, for underwriting any insurance offering, any loan, or any other transaction, including any application for detecting, characterizing or predicting the likelihood and/or scope of a risk, an underwriting or inspection flow about an entity serving a lending solution, an analytics solution, or an asset management solution; underwriting of insurance policies, loans,
  • insuring includes any insuring, including, without limitation, providing insurance for a loan, providing evidence of insurance for an asset related to a loan, a first entity accepting a risk or liability for another entity, and the like.
  • Insuring, or insurance may be a mechanism through which a holder of the insurance is provided protection from a financial loss, such as in a form of risk management against the risk of a contingent or uncertain loss.
  • the insuring mechanism may provide for an insurance, determine the need for an insurance, determine evidence of insurance, and the like, such as related to an asset, transaction for an asset, loan for an asset, security, and the like.
  • An entity which provides insurance may be known as an insurer, insurance company, insurance carrier, underwriter, and the like.
  • a mechanism for insuring may provide a financial entity with a mechanism to determine evidence of insurance for an asset related to a loan.
  • an insurance service circuit may be structured to determine an evidence condition of insurance for an asset based on a plurality of insurance information components with respect to a financial entity configured to determine a loan condition for an asset.
  • components may be considered insuring for some purposes but not for other purposes - for example, a blockchain and smart contract platform may be utilized to manage aspects of a loan transaction such as for identity and confidentiality, but may alternately be utilized to aggregate identity and behavior information for insurance underwriting.
  • the benefits of the present disclosure may be applied in a wide variety of systems, and any such systems may be considered insuring herein, while in certain embodiments a given system may not be considered insuring herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system, and/or how to combine processes and systems from the present disclosure to enhance operations of the contemplated system.
  • a contemplated system insuring and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation: insurance facilities such as branches, offices, storage facilities, data centers, underwriting operations and others; insurance claims, such as for business interruption insurance, product liability insurance, insurance on goods, facilities, or equipment, flood insurance, insurance for contract-related risks, and many others, as well as claims data relating to product liability, general liability, workers compensation, injury and other liability claims and claims data relating to contracts, such as supply contract performance claims, product delivery requirements, contract claims, claims for damages, claims to redeem points or rewards, claims of access rights, warranty claims, indemnification claims, energy production requirements, delivery requirements, timing requirements, milestones, key performance indicators and others; insurance-related lending; an insurance service, an insurance brokerage service, a life insurance service, a health insurance service, a retirement insurance service, a property insurance service, a casualty insurance service, a finance and insurance service, a reinsurance service; a
  • an aggregation or to aggregate includes any aggregation including, without limitation, aggregating items together, such as aggregating or linking similar items together (e.g., collateral to provide collateral for a set of loans, collateral items for a set of loans is aggregated in real time based on a similarity in status of the set of items, and the like), collecting data together (e.g., for storage, for communication, for analysis, as training data for a model, and the like), summarizing aggregated items or data into a simpler description, or any other method for creating a whole formed by combining several (e.g., disparate) elements.
  • an aggregator may be any system or platform for aggregating, such as described. Certain components may not be considered aggregation individually but may be considered aggregation in an aggregated system - for example, a collection of loans may not be considered an aggregation of loans of itself but may be an aggregation if collected as such.
  • an aggregation circuit may be structured to provide lenders a mechanism to aggregate loans together from a plurality of loans, such as based on a loan attribute, parameter, term or condition, financial entity, and the like, to become an aggregation of loans.
  • an aggregation may be considered an aggregation for some purposes but not for other purposes - for example for example, an aggregation of asset collateral conditions may be collected for the purpose of aggregating loans together in one instance and for the purpose of determining a default action in another instance.
  • otherwise similar looking systems may be differentiated in determining whether such systems are aggregators, and/or which type of aggregating systems. For example, a first and second aggregator may both aggregate financial entity data, where the first aggregator aggregates for the sake of building a training set for an analysis model circuit and where the second aggregator aggregates financial entity data for storage in a blockchain-based distributed ledger.
  • the benefits of the present disclosure may be applied in a wide variety of systems, and any such systems may be considered as aggregation herein, while in certain embodiments a given system may not be considered aggregation herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system, and/or how to combine processes and systems from the present disclosure to enhance operations of the contemplated system.
  • forward market demand aggregation e.g., blockchain and smart contract platform for forward market demand aggregation, interest expressed or committed in a demand aggregation interface, blockchain used to aggregate future demand in a forward market with respect to a variety of products and services, process a set of potential configurations having different parameters for a subset of configurations that are consistent with each other and the subset of configurations used to aggregate committed future demand for the offering that satisfies a sufficiently large subset at a profitable price, and the like); correlated aggregated data (including trend information) on worker ages, credentials, experience (including by process type) with data on the processes in which those workers are involved; demand for accommodations aggregated in advance and conveniently fulfilled by automatic recognition of conditions that satisfy pre-configured commitments represented on a blockchain (e.g., distributed ledger); transportation offerings aggregated and fulfilled
  • linking includes any linking, including, without limitation, linking as a relationship between two things or situations (e.g., where one thing affects the other). For instance, linking a subset of similar items such as collateral to provide collateral for a set of loans. Certain components may not be considered linked individually, but may be considered in a process of linking in an aggregated system - for example, a smart contracts circuit may be structured to operate in conjunction with a blockchain circuit as part of a loan processing platform but where the smart contracts circuit processes contracts without storing information through the blockchain circuit, however the two circuits could be linked through the smart contracts circuit linking financial entity information through a distributed ledger on the blockchain circuit.
  • linking may be considered linking for some purposes but not for other purposes - for example, linking goods and services for users and radio frequency linking between access points are different forms of linking, where the linking of goods and services for users links thinks together while an RF link is a communications link between transceivers.
  • otherwise similar looking systems may be differentiated in determining whether such system are linking, and/or which type of linking. For example, linking similar data together for analysis is different from linking similar data together for graphing. Accordingly, the benefits of the present disclosure may be applied in a wide variety of systems, and any such systems may be considered linking herein, while in certain embodiments a given system may not be considered a linking herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system, and/or how to combine processes and systems from the present disclosure to enhance operations of the contemplated system.
  • Certain considerations for the person of skill in the art, in determining whether a contemplated system is linking and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation linking marketplaces or external marketplaces with a system or platform; linking data (e.g., data cluster including links and nodes); storage and retrieval of data linked to local processes; links (e.g.
  • an indicator of interest includes any indicator of interest including, without limitation, an indicator of interest from a user or plurality of users or parties related to a transaction and the like (e.g., parties interested in participating in a loan transaction), the recording or storing of such an interest (e.g., a circuit for recording an interest input from a user, entity, circuit, system, and the like), a circuit analyzing interest related data and setting an indicator of interest (e.g., a circuit setting or communicating an indicator based on inputs to the circuit, such as from users, parties, entities, systems, circuits, and the like), a model trained to determine an indicator of interest from input data related to an interest by one of a plurality of inputs from users, parties, or financial entities, and the like.
  • an indicator of interest includes any indicator of interest including, without limitation, an indicator of interest from a user or plurality of users or parties related to a transaction and the like (e.g., parties interested in participating in a loan transaction), the recording or storing of such an interest (e.g
  • Certain components may not be considered indicators of interest individually, but may be considered an indicator of interest in an aggregated system - for example, a party may seek information relating to a transaction such as though a translation marketplace where the party is interested in seeking information, but that may not be considered an indicator of interest in a transaction.
  • the party asserts a specific interest (e.g., through a user interface with control inputs for indicating interest) the party's interest may be recorded (e.g., in a storage circuit, in a blockchain circuit), analyzed (e.g., through an analysis circuit, a data collection circuit), monitored (e.g., through a monitoring circuit), and the like.
  • indicators of interest may be recorded (e.g., in a blockchain through a distributed ledger) from a set of parties with respect to the product, service, or the like, such as ones that define parameters under which a party is willing to commit to purchase a product or service.
  • an indicator of interest may be considered an indicator of interest for some purposes but not for other purposes - for example, a user may indicate an interest for a loan transaction but that does not necessarily mean the user is indicating an interest in providing a type of collateral related to the loan transaction.
  • a data collection circuit may record an indicator of interest for the transaction but may have a separate circuit structure for determining an indication of interest for collateral.
  • otherwise similar looking systems may be differentiated in determining whether such system are determining an indication of interest, and/or which type of indicator of interest exists.
  • one circuit or system may collect data from a plurality of parties to determine an indicator of interest in securing a loan and a second circuit or system may collect data from a plurality of parties to determine an indicator of interest in determining ownership rights related to a loan.
  • the benefits of the present disclosure may be applied in a wide variety of systems, and any such systems may be considered an indicator of interest herein, while in certain embodiments a given system may not be considered an indicator of interest herein.
  • a contemplated system is an indicator of interest and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation parties indicating an interest in participating in a transaction (e.g., a loan transaction), parties indicating an interest in securing in a product or service, recording or storing an indication of interest (e.g., through a storage circuit or blockchain circuit), analyzing an indication of interest (e.g., through a data collection and/or monitoring circuit), and the like.
  • parties indicating an interest in participating in a transaction e.g., a loan transaction
  • parties indicating an interest in securing in a product or service e.g., a storage circuit or blockchain circuit
  • recording or storing an indication of interest e.g., through a storage circuit or blockchain circuit
  • analyzing an indication of interest e.g., through a data collection and/or monitoring circuit
  • an accommodation includes any service, activity, event, and the like such as including, without limitation, a room, group of rooms, table, seating, building, event, shared spaces offered by individuals (e.g., Airbnb spaces), bed-and-breakfasts, workspaces, conference rooms, convention spaces, fitness accommodations, health and wellness accommodations, dining accommodations, and the like, in which someone may live, stay, sit, reside, participate, and the like.
  • a room, group of rooms, table, seating, building, event shared spaces offered by individuals (e.g., Airbnb spaces), bed-and-breakfasts, workspaces, conference rooms, convention spaces, fitness accommodations, health and wellness accommodations, dining accommodations, and the like, in which someone may live, stay, sit, reside, participate, and the like.
  • an accommodation may be purchased (e.g., a ticket through a sports ticketing application), reserved or booked (e.g., a reservation through a hotel reservation application), provided as a reward or gift, traded or exchanged (e.g., through a marketplace), provided as an access right (e.g., offering by way of an aggregation demand), provided based on a contingency (e.g., a reservation for a room being contingent on the availability of a nearby event), and the like.
  • Certain components may not be considered an accommodation individually but may be considered an accommodation in an aggregated system - for example, a resource such as a room in a hotel may not in itself be considered an accommodation but a reservation for the room may be.
  • a blockchain and smart contract platform for forward market rights for accommodations may provide a mechanism to provide access rights with respect to accommodations.
  • a blockchain circuit may be structured to store access rights in a forward demand market, where the access rights may be stored in a distributed ledger with related shared access to a plurality of actionable entities.
  • an accommodation may be considered an accommodation for some purposes but not for other purposes - for example, a reservation for a room may be an accommodation on its own, but may not be accommodation that is satisfied if a related contingency is not met as agreed upon at the time of the e.g. reservation.
  • otherwise similar looking systems may be differentiated in determining whether such systems are related to an accommodation, and/or which type of accommodation.
  • an accommodation offering may be made based on different systems, such as one where the accommodation offering is determined by a system collecting data related to forward demand and a second one where the accommodation offering is provided as a reward based on a system processing a performance parameter.
  • the benefits of the present disclosure may be applied in a wide variety of systems, and any such systems may be considered as related to an accommodation herein, while in certain embodiments a given system may not be considered related to an accommodation herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system, and/or how to combine processes and systems from the present disclosure to enhance operations of the contemplated system.
  • a contemplated system in determining whether a contemplated system is related to accommodation and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation accommodations provided as determined through a service circuit, trading or exchanging services (e.g., through an application and/or user interface), as an accommodation offering such as with respect to a combination of products, services, and access rights, processed (e.g., aggregation demand for the offering in a forward market), accommodation through booking in advance, accommodation through booking in advance upon meeting a certain condition (e.g., relating to a price within a given time window), and the like.
  • accommodations provided as determined through a service circuit trading or exchanging services (e.g., through an application and/or user interface)
  • an accommodation offering such as with respect to a combination of products, services, and access rights
  • processed e.g., aggregation demand for the offering in a forward market
  • accommodation through booking in advance accommodation through booking in advance upon meeting a certain condition (e.g., relating
  • contingencies includes any contingency including, without limitation, any action that is dependent upon a second action.
  • a service may be provided as contingent on a certain parameter value, such as collecting data as condition upon an asset tag indication from an Internet of Things circuit.
  • an accommodation such as a hotel reservation may be contingent upon a concert (local to the hotel and at the same time as the reservation) proceeding as scheduled.
  • Certain components may not be considered as relating to a contingency individually, but may be considered related to a contingency in an aggregated system - for example, a data input collected from a data collection service circuit may be stored, analyzed, processed, and the like, and not be considered with respect to a contingency, however a smart contracts service circuit may apply a contract term as being contingent upon the collected data.
  • the data may indicate a collateral status with respect to a loan transaction, and the smart contracts service circuit may apply that data to a term of contract that depends upon the collateral.
  • a contingency may be considered contingency for some purposes but not for other purposes - for example, a delivery of contingent access rights for a future event may be contingent upon a loan condition being satisfied, but the loan condition on its own may not be considered a contingency in the absence of the contingency linkage between the condition and the access rights.
  • otherwise similar looking systems may be differentiated in determining whether such systems are related to a contingency, and/or which type of contingency. For example, two algorithms may both create a forward market event access right token, but where the first algorithm creates the token free of contingencies and the second algorithm creates a token with a contingency for delivery of the token.
  • any such systems may be considered a contingency herein, while in certain embodiments a given system may not be considered a contingency herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system, and/or how to combine processes and systems from the present disclosure to enhance operations of the contemplated system.
  • a forward market operated within or by the platform may be a contingent forward market, such as one where a future right is vested, is triggered, or emerges based on the occurrence of an event, satisfaction of a condition, or the like; a blockchain used to make a contingent market in any form of event or access token by securely storing access rights on a distributed ledger; setting and monitoring pricing for contingent access rights, underlying access rights, tokens, fees and the like; optimizing offerings, timing, pricing, or the like, to recognize and predict patterns, to establish rules and contingencies; exchanging contingent access rights or underlying access rights or tokens access tokens and/or contingent access tokens; creating a contingent forward market event access right token where a token may be created and stored on a blockchain for contingent access right that could result in the ownership of a ticket; discovery and delivery of contingent access
  • level of service (and similar terms) as utilized herein should be understood broadly. Without limitation to any other aspect or description of the present disclosure, a level of service includes any level of service including, without limitation, any qualitative or quantitative measure of the extent to which a service is provided, such as, and without limitation, a first class vs. business class service (e.g., travel reservation or postal delivery), the degree to which a resource is available (e.g., service level A indicating that the resource is highly available vs.
  • a first class vs. business class service e.g., travel reservation or postal delivery
  • service level A indicating that the resource is highly available
  • level C indicating that the resource is constrained, such as in terms of traffic flow restrictions on a roadway), the degree to which an operational parameter is performing (e.g., a system is operating at a high state of service vs a low state of service, and the like.
  • level of service may be multi-modal such that the level of service is variable where a system or circuit provides a service rating (e.g., where the service rating is used as an input to an analytical circuit for determining an outcome based on the service rating).
  • Certain components may not be considered relative to a level of service individually, but may be considered relative to a level of service in an aggregated system - for example a system for monitoring a traffic flow rate may provide data on a current rate but not indicate a level of service, but when the determined traffic flow rate is provided to a monitoring circuit the monitoring circuit may compare the determined traffic flow rate to past traffic flow rates and determine a level of service based on the comparison.
  • a level of service may be considered a level of service for some purposes but not for other purposes - for example, the availability of first class travel accommodation may be considered a level of service for determining whether a ticket will be purchased but not to project a future demand for the flight.
  • otherwise similar looking systems may be differentiated in determining whether such system utilizes a level of service, and/or which type of level of service.
  • an artificial intelligence circuit may be trained on past level of service with respect to traffic flow patterns on a certain freeway and used to predict future traffic flow patterns based on current flow rates, but a similar artificial intelligence circuit may predict future traffic flow patterns based on the time of day. Accordingly, the benefits of the present disclosure may be applied in a wide variety of systems, and any such systems may be considered with respect to levels of service herein, while in certain embodiments a given system may not be considered with respect to levels of service herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system, and/or how to combine processes and systems from the present disclosure to enhance operations of the contemplated system.
  • Certain considerations for the person of skill in the art, in determining whether a contemplated system is a level of service and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation transportation or accommodation offerings with predefined contingencies and parameters such as with respect to price, mode of service, and level of service; warranty or guarantee application, transportation marketplace, and the like.
  • a payment includes any payment including, without limitation, an action or process of paying (e.g., a payment to a loan) or of being paid (e.g., a payment from insurance), an amount paid or payable (e.g., a payment of $1000 being made), a repayment (e.g., to pay back a loan), a mode of payment (e.g., use of loyalty programs, rewards points, or particular currencies, including cryptocurrencies) and the like.
  • an action or process of paying e.g., a payment to a loan
  • being paid e.g., a payment from insurance
  • an amount paid or payable e.g., a payment of $1000 being made
  • a repayment e.g., to pay back a loan
  • a mode of payment e.g., use of loyalty programs, rewards points, or particular currencies, including cryptocurrencies
  • Certain components may not be considered payments individually, but may be considered payments in an aggregated system - for example, submitting an amount of money may not be considered a payment as such, but when applied to a payment to satisfy the requirement of a loan may be considered a payment (or repayment).
  • a data collection circuit may provide lenders a mechanism to monitor repayments of a loan.
  • the data collection circuit may be structured to monitor the payments of a plurality of loan components with respect to a financial loan contract configured to determine a loan condition for an asset.
  • a payment may be considered a payment for some purposes but not for other purposes - for example, a payment to a financial entity may be for a repayment amount to pay back the loan, or it may be to satisfy a collateral obligation in a loan default condition.
  • otherwise similar looking systems may be differentiated in determining whether such system are related to a payment, and/or which type of payment. For example, funds may be applied to reserve an accommodation or to satisfy the delivery of services after the accommodation has been satisfied. Accordingly, the benefits of the present disclosure may be applied in a wide variety of systems, and any such systems may be considered a payment herein, while in certain embodiments a given system may not be considered a payment herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system, and/or how to combine processes and systems from the present disclosure to enhance operations of the contemplated system.
  • Certain considerations for the person of skill in the art, in determining whether a contemplated system is a payment and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation, deferring a required payment; deferring a payment requirement; payment of a loan; a payment amount; a payment schedule; a balloon payment schedule; payment performance and satisfaction; modes of payment; and the like.
  • a location includes any location including, without limitation, a particular place or position of a person, place, or item, or location information regarding the position of a person, place, or item, such as a geolocation (e.g., geolocation of a collateral), a storage location (e.g., the storage location of an asset), a location of a person (e.g., lender, borrower, worker), location information with respect to the same, and the like.
  • a geolocation e.g., geolocation of a collateral
  • a storage location e.g., the storage location of an asset
  • a location of a person e.g., lender, borrower, worker
  • a smart contract circuit may be structured to specify a requirement for a collateral to be stored at a fixed location but not specify the specific location for a specific collateral.
  • a location may be considered a location for some purposes but not for other purposes - for example, the address location of a borrower may be required for processing a loan in one instance, and a specific location for processing a default condition in another instance.
  • otherwise similar looking systems may be differentiated in determining whether such system are a location, and/or which type of location. For example, the location of a music concert may be required to be in a concert hall seating 10,000 people in one instance but specify the location of an actual concert hall in another.
  • the benefits of the present disclosure may be applied in a wide variety of systems, and any such systems may be considered with respect to a location herein, while in certain embodiments a given system may not be considered with respect to a location herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system, and/or how to combine processes and systems from the present disclosure to enhance operations of the contemplated system.
  • a contemplated system in determining whether a contemplated system is considered with respect to a location and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation a geolocation of an item or collateral; a storage location of item or asset; location information; location of a lender or a borrower; location-based product or service targeting application; a location-based fraud detection application; indoor location monitoring systems (e.g., cameras, IR systems, motiondetection systems); locations of workers (including routes taken through a location); location parameters; event location; specific location of an event; and the like.
  • a geolocation of an item or collateral e.g., a storage location of item or asset
  • location information e.g., location of a lender or a borrower
  • location-based product or service targeting application e.g., a location-based fraud detection application
  • indoor location monitoring systems e.g., cameras, IR systems, motiondetection systems
  • locations of workers including routes taken through a location
  • route includes any route including, without limitation, a way or course taken in getting from a starting point to a destination, to send or direct along a specified course, and the like. Certain components may not be considered with respect to a route individually, but may be considered a route in an aggregated system - for example, a mobile data collector may specify a requirement for a route for collecting data based on an input from a monitoring circuit, but only in receiving that input does the mobile data collector determine what route to take and begin traveling along the route.
  • a route may be considered a route for some purposes but not for other purposes - for example possible routes through a road system may be considered differently than specific routes taken through from one location to another location.
  • otherwise similar looking systems may be differentiated in determining whether such systems are specified with respect to a location, and/or which types of locations.
  • routes depicted on a map may indicate possible routes or actual routes taken by individuals. Accordingly, the benefits of the present disclosure may be applied in a wide variety of systems, and any such systems may be considered with respect to a route herein, while in certain embodiments a given system may not be considered with respect to a route herein.
  • a contemplated system in determining whether a contemplated system is utilizing a route and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation delivery routes; routes taken through a location; heat map showing routes traveled by customers or workers within an environment; determining what resources are deployed to what routes or types of travel; direct route or multi- stop route, such as from the destination of the consumer to a specific location or to wherever an event takes place; a route for a mobile data collector; and the like.
  • a future offing includes any offer of an item or service in the future including, without limitation, a future offer to provide an item or service, a future offer with respect to a proposed purchase, a future offering made through a forward market platform, a future offering determined by a smart contract circuit, and the like.
  • a future offering may be a contingent future offer, or an offer based on conditions resulting on the offer being a future offering, such as where the future offer is contingent upon or with the conditions imposed by a predetermined condition (e.g., a security may be purchased for $1000 at a set future date contingent upon a predetermined state of a market indicator).
  • Certain components may not be considered a future offering individually, but may be considered a future offering in an aggregated system - for example, an offer for a loan may not be considered a future offering if the offer is not authorized through a collective agreement amongst a plurality of parties related to the offer, but may be considered a future offer once a vote has been collected and stored through a distributed ledger, such as through a blockchain circuit.
  • a future offering may be considered a future offering for some purposes but not for other purposes - for example, a future offering may be contingent upon a condition being met in the future, and so the future offering may not be considered a future offer until the condition is met.
  • otherwise similar looking systems may be differentiated in determining whether such systems are future offerings, and/or which type of future offerings. For example, two security offerings may be determined to be offerings to be made at a future time, however, one may have immediate contingences to be met and thus may not be considered to be a future offering but rather an immediate offering with future declarations.
  • the benefits of the present disclosure may be applied in a wide variety of systems, and any such systems may be considered in association with a future offering herein, while in certain embodiments a given system may not be considered in association with a future offering herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system, and/or how to combine processes and systems from the present disclosure to enhance operations of the contemplated system.
  • a contemplated system in association with a future offering and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation a forward offering, a contingent forward offering, a forward offing in a forward market platform (e.g., for creating a future offering or contingent future offering associated with identifying offering data from a platform-operated marketplace or external marketplace); a future offering with respect to entering into a smart contract (e.g., by executing an indication of a commitment to purchase, attend, or otherwise consume a future offering), and the like.
  • access right and derivatives or variations as utilized herein may be understood broadly to describe an entitlement to acquire or possess a property, article, or other thing of value.
  • a contingent access right may be conditioned upon a trigger or condition being met before such an access right becomes entitled, vested or otherwise defensible.
  • An access right or contingent access right may also serve specific purposes or be configured for different applications or contexts, such as, without limitation, loan-related actions or any service or offering. Without limitation, notices may be required to be provided to the owner of a property, article, or item of value before such access rights or contingent access rights are exercised.
  • Access rights and contingent access rights in various forms may be included where discussing a legal action, a delinquent or defaulted loan or agreement, or other circumstances where a lender may be seeking remedy, without limitation.
  • One of skill in the art having the benefit of the disclosure herein and knowledge ordinarily available about a contemplated system, can readily determine the value of such rights implemented in an embodiment. While specific examples of access rights and contingent access rights are described herein for purposes of illustration, any embodiment benefitting from the disclosures herein, and any considerations understood to one of skill in the art having the benefit of the disclosures herein, are specifically contemplated within the scope of the present disclosure.
  • smart contract (and other forms or variations) as utilized herein may be understood broadly to describe a method, system, connected resource or wide area network offering one or more resources useful to assist or perform actions, tasks or things by embodiments disclosed herein.
  • a smart contract may be a set of steps or a process to negotiate, administrate, restructure, or implement an agreement or loan between parties.
  • a smart contract may also be implemented as an application, website, FTP site, server, appliance or other connected component or Internet related system that renders resources to negotiate, administrate, restructure, or implement an agreement or loan between parties.
  • a smart contract may be a self-contained system, or may be part of a larger system or component that may also be a smart contract.
  • a smart contract may refer to a loan or an agreement itself, conditions or terms, or may refer to a system to implement such a loan or agreement.
  • a smart contract circuit or robotic process automation system may incorporate or be incorporated into automatic robotic process automation system to perform one or more purposes or tasks, whether part of a loan or transaction process, or otherwise.
  • allocation of reward may be understood broadly to describe a thing or consideration allocated or provided as consideration, or provided for a purpose.
  • the allocation of rewards can be of a financial type, or non-financial type, without limitation.
  • a specific type of allocation of reward may also serve a number of different purposes or be configured for different applications or contexts, such as, without limitation: a reward event, claims for rewards, monetary rewards, rewards captured as a data set, rewards points, and other forms of rewards.
  • an allocation of rewards may be provided as a consideration within the context of a loan or agreement.
  • Systems may be utilized to allocate rewards.
  • the allocation of rewards in various forms may be included where discussing a particular behavior, or encouragement of a particular behavior, without limitation.
  • An allocation of a reward may include an actual dispensation of the award, and/or a recordation of the reward.
  • the allocation of rewards may be performed by a smart contract circuit or a robotic processing automation system.
  • One of skill in the art, having the benefit of the disclosure herein and knowledge ordinarily available about a contemplated system, can readily determine the value of the allocation of rewards in an embodiment. While specific examples of the allocation of rewards are described herein for purposes of illustration, any embodiment benefitting from the disclosures herein, and any considerations understood to one of skill in the art having the benefit of the disclosures herein, are specifically contemplated within the scope of the present disclosure.
  • satisfaction of parameters or conditions may be understood broadly to describe completion, presence or proof of parameters or conditions that have been met.
  • the term generally may relate to a process of determining such satisfaction of parameters or conditions, or may relate to the completion of such a process with a result, without limitation. Satisfaction may result in a successful outcome of other triggers or conditions or terms that may come into execution, without limitation. Satisfaction of parameters or conditions may occur in many different contexts of contracts or loans, such as lending, refinancing, consolidation, factoring, brokering, foreclosure, and data processing (e.g. data collection), or combinations thereof, without limitation.
  • Satisfaction of parameters or conditions may be used in the form of a noun (e.g. the satisfaction of the debt repayment), or may be used in a verb form to describe the process of determining a result to parameters or conditions.
  • a borrower may have satisfaction of parameters by making a certain number of payments on time, or may cause satisfaction of a condition allowing access rights to an owner if a loan defaults, without limitation.
  • a smart contract or robotic process automation system may perform or determine satisfaction of parameters or conditions for one or more of the parties and process appropriate tasks for satisfaction of parameters or conditions. In some cases satisfaction of parameters or conditions by the smart contract or robotic process automation system may not complete or be successful, and depending upon such outcomes, this may enable automated action or trigger other conditions or terms.
  • One of skill in the art having the benefit of the disclosure herein and knowledge ordinarily available about a contemplated system can readily determine the purposes and use of this term in various forms, embodiments and contexts disclosed herein.
  • information may be understood broadly in a variety of contexts with respect to an agreement or a loan.
  • the term generally may relate to a large context, such as information regarding an agreement or loan, or may specifically relate to a finite piece of information (e.g. a specific detail of an event that happened on a specific date).
  • information may occur in many different contexts of contracts or loans, and may be used in the contexts, without limitation of evidence, transactions, access, and the like.
  • information may be used in conjunction with stages of an agreement or transaction, such as lending, refinancing, consolidation, factoring, brokering, foreclosure, and information processing (e.g. data or information collection), or combinations thereof.
  • information as evidence, transaction, access, etc. may be used in the form of a noun (e.g. the information was acquired from the borrower), or may refer as a noun to an assortment of informational items (e.g. the information about the loan may be found in the smart contract), or may be used in the form of characterizing as an adjective (e.g. the borrower was providing an information submission).
  • a lender may collect an overdue payment from a borrower through an online payment, or may have a successful collection of overdue payments acquired through a customer service telephone call.
  • a smart contract circuit or robotic process automation system may perform collection, administration, calculating, providing, or other tasks for one or more of the parties and process appropriate tasks relating to information (e.g.
  • Information by the smart contract circuit or robotic process automation system may be incomplete, and depending upon such outcomes this may enable automated action or trigger other conditions or terms.
  • Information may be linked to external information (e.g. external sources). The term more specifically may relate to the acquisition, parsing, receiving, or other relation to an external origin or source, without limitation.
  • information linked to external information or sources may be used in conjunction with stages of an agreement or transaction, such as lending, refinancing, consolidation, factoring, brokering, foreclosure, and information processing (e.g. data or information collection), or combinations thereof.
  • information linked to external information may change as the external information changes, such as a borrower's credit score, which is based on an external source.
  • a smart contract circuit or robotic process automation system may perform acquisition, administration, calculating, receiving, updating, providing or other tasks for one or more of the parties and process appropriate tasks relating to information that is linked to external information.
  • information that is linked to external information by the smart contract or robotic process automation system may be incomplete, and depending upon such outcomes this may enable automated action or trigger other conditions or terms.
  • One of skill in the art having the benefit of the disclosure herein and knowledge ordinarily available about a contemplated system can readily determine the purposes and use of this term in various forms, embodiments and contexts disclosed herein.
  • Information that is a part of a loan or agreement may be separated from information presented in an access location.
  • the term more specifically may relate to the characterization that information can be apportioned, split, restricted, or otherwise separated from other information within the context of a loan or agreement.
  • information presented or received on an access location may not necessarily be the whole information available for a given context.
  • information provided to a borrower may be different information received by a lender from an external source, and may be different than information received or presented from an access location.
  • a smart contract circuit or robotic process automation system may perform separation of information or other tasks for one or more of the parties and process appropriate tasks.
  • encryption of information and control of access may be understood broadly to describe generally whether a party or parties may observe or possess certain information, actions, events, or activities relating to a transaction or loan. Encryption of information may be utilized to prevent a party from accessing, observing, or receiving information, or may alternatively be used to prevent parties outside the transaction or loan from being able to access, observe or receive confidential (or other) information. Control of access to information relates to the determination of whether a party is entitled to such access of information.
  • Encryption of information or control of access may occur in many different contexts of loans, such as lending, refinancing, consolidation, factoring, brokering, foreclosure, administration, negotiating, collecting, procuring, enforcing, and data processing (e.g., data collection), or combinations thereof, without limitation.
  • An encryption of information or control of access to information may refer to a single instance, or may characterize a larger amount of information, actions, events, or activities, without limitation. For example, a borrower or lender may have access to information about a loan, but other parties outside the loan or agreement may not be able to access the loan information due to encryption of the information, or a control of access to the loan details.
  • a smart contract circuit or robotic process automation system may perform encryption of information or control of access to information for one or more of the parties and process appropriate tasks for encryption or control of access of information.
  • potential access party list (and other related terms) as utilized herein may be understood broadly to describe generally whether a party or parties may observe or possess certain information, actions, events, or activities relating to a transaction or loan.
  • a potential access party list may be utilized to authorize one or more parties to access, observe or receive information, or may alternatively be used to prevent parties from being able to do so.
  • a potential access party list information relates to the determination of whether a party (either on the potential access party list or not on the list) is entitled to such access of information.
  • a potential access party list may occur in many different contexts of loans, such as lending, refinancing, consolidation, factoring, brokering, foreclosure, administration, negotiating, collecting, procuring, enforcing and data processing (e.g.
  • a potential access party list may refer to a single instance, or may characterize a larger amount of parties or information, actions, events, or activities, without limitation. For example, a potential access party list may grant (or deny) access to information about a loan, but other parties outside potential access party list may not be able to (or may be granted) access the loan information.
  • a smart contract circuit or robotic process automation system may perform administration or enforcement of a potential access party list for one or more of the parties and process appropriate tasks for encryption or control of access of information.
  • an offering includes any offer of an item or service including, without limitation, an insurance offering, a security offering, an offer to provide an item or service, an offer with respect to a proposed purchase, an offering made through a forward market platform, a future offering, a contingent offering, offers related to lending (e.g.
  • Offerings may include physical goods, virtual goods, software, physical services, access rights, entertainment content, accommodations, or many other items, services, solutions, or considerations.
  • a third party offer may be to schedule a band instead of just an offer of tickets for sale. Further, an offer may be based on pre-determined conditions or contingencies.
  • Certain components may not be considered an offering individually, but may be considered an offering in an aggregated system - for example, an offer for insurance may not be considered an offering if the offer is not approved by one or more parties related to the offer, however once approval has been granted, it may be considered an offer. Accordingly, the benefits of the present disclosure may be applied in a wide variety of systems, and any such systems may be considered in association with an offering herein, while in certain embodiments a given system may not be considered in association with an offering herein.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system, and/or how to combine processes and systems from the present disclosure to enhance operations of the contemplated system.
  • Certain considerations for the person of skill in the art, in determining whether a contemplated system is in association with an offering and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation the item or service being offered, a contingency related to the offer, a way of tracking if a contingency or condition has been met, an approval of the offering, an execution of an exchange of consideration for the offering, and the like.
  • an Al solution includes a coordinated group of Al related aspects to perform one or more tasks or operations as set forth throughout the present disclosure.
  • An example Al solution includes one or more Al components, including any Al components set forth herein, including at least a neural network, an expert system, and/or a machine learning component.
  • the example Al solution may include as an aspect the types of components of the solution, such as a heuristic Al component, a model based Al component, a neural network of a selected type (e.g., recursive, convolutional, perceptron, etc.), and/or an Al component of any type having a selected processing capability (e.g., signal processing, frequency component analysis, auditory processing, visual processing, speech processing, text recognition, etc.).
  • a heuristic Al component e.g., a model based Al component
  • a neural network of a selected type e.g., recursive, convolutional, perceptron, etc.
  • an Al component of any type having a selected processing capability e.g., signal processing, frequency component analysis, auditory processing, visual processing, speech processing, text recognition, etc.
  • a given Al solution may be formed from the number and type of Al components of the Al solution, the connectivity of the Al components (e.g., to each other, to inputs from a system including or interacting with the Al solution, and/or to outputs to the system including or interacting with the Al solution).
  • the given Al solution may additionally be formed from the connection of the Al components to each other within the Al solution, and to boundary elements (e.g., inputs, outputs, stored intermediary data, etc.) in communication with the Al solution.
  • the given Al solution may additionally be formed from a configuration of each of the Al components of the Al solution, where the configuration may include aspects such as: model calibrations for an Al component; connectivity and/or flow between Al components (e.g., serial and/or parallel coupling, feedback loops, logic junctions, etc.); the number, selected input data, and/or input data processing of inputs to an Al component; a depth and/or complexity of a neural network or other components; a training data description of an Al component (e.g., training data parameters such as content, amount of training data, statistical description of valid training data, etc.); and/or a selection and/or hybrid description of a type for an Al component.
  • An Al solution includes a selection of Al elements, flow connectivity of those Al elements, and/or configuration of those Al elements.
  • One of skill in the art can readily determine an Al solution for a given system, and/or configure operations to perform a selection and/or configuration operation for an Al solution for a given system.
  • Certain considerations to determining an Al solution, and/or configuring operations to perform a selection and/or configuration operation for an Al solution include, without limitation: an availability of Al components and/or component types for a given implementation; an availability of supporting infrastructure to implement given Al components (e.g., data input values available, including data quality, level of service, resolution, sampling rate, etc.; availability of suitable training data for a given Al solution; availability of expert inputs, such as for an expert system and/or to develop a model training data set; regulatory and/or policy based considerations including permitted action by the Al solution, requirements for acquisition and/or retention of sensitive data, difficult to obtain data, and/or expensive data); operational considerations for a system including or interacting with the Al solution, including response time specifications, safety considerations, liability considerations, etc.; available computing resources
  • a selected and/or configured Al solution may be utilized with any of the systems, procedures, and/or aspects of embodiments as set forth throughout the present disclosure.
  • a system utilizing an expert system may include the expert system as all or a part of a selected, configured Al solution.
  • a system utilizing a neural network, and/or a combination of neural networks may include the neural network(s) as all or a part of a selected, configured Al solution.
  • the described aspects of an Al solution, including the selection and configuration of the Al solution are non-limiting illustrations.
  • FIG. 1-2B a set of systems, methods, components, modules, machines, articles, blocks, circuits, services, programs, applications, hardware, software and other elements are provided, collectively referred to herein interchangeably as the system 100 or the platform 100,
  • the platform 100 enables a wide range of improvements of and for various machines, systems, and other components that enable transactions involving the exchange of value (such as using currency, cryptocurrency, tokens, rewards or the like, as well as a wide range of in-kind and other resources) in various markets, including current or spot markets 170, forward markets 130 and the like, for various goods, services, and resources.
  • value such as using currency, cryptocurrency, tokens, rewards or the like
  • currency should be understood to encompass fiat currency issued or regulated by governments, cryptocurrencies, tokens of value, tickets, loyalty points, rewards points, coupons, and other elements that represent or may be exchanged for value.
  • Resources such as ones that may be exchanged for value in a marketplace, should be understood to encompass goods, services, natural resources, energy resources, computing resources, energy storage resources, data storage resources, network bandwidth resources, processing resources and the like, including resources for which value is exchanged and resources that enable a transaction to occur (such as necessary computing and processing resources, storage resources, network resources, and energy resources that enable a transaction).
  • the platform 100 may include a set of forward purchase and sale machines 110, each of which may be configured as an expert system or automated intelligent agent for interaction with one or more of the set of spot markets 170 and forward markets 130.
  • Enabling the set of forward purchase and sale machines 110 are 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 the intelligent sale of allocated or coordinated resources, such as compute resources, energy resources, and other resources involved in or enabling a transaction; an intelligent sale engine 172 for intelligent coordination of a sale of allocated resources in spot and futures markets; and an automated spot market testing and arbitrage transaction execution engine 194 for performing spot testing of spot and forward markets, such as with microtransactions and, where conditions indicate favorable arbitrage conditions, automatically executing transactions in resources that take advantage of the favorable conditions.
  • Each of the engines may use model-based or rule-based expert systems, such as based on rules or heuristics, as well as deep learning systems by which rules or heuristics may be learned over trials involving a large set of inputs.
  • the engines may use any of the expert systems and artificial intelligence capabilities described throughout this disclosure.
  • Interactions within the platform 100 including of all platform components, and of interactions among them and with various markets, may be tracked and collected, such as by a data aggregation system 144, such as for aggregating data on purchases and sales in various marketplaces by the set of machines described herein.
  • Aggregated data may include tracking and outcome data that may be fed to artificial intelligence and machine learning systems, such as to train or supervise the same.
  • the various engines may operate on a range of data sources, including aggregated data from marketplace 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 (such as social networking sites like FacebookTM and TwitterTM), Internet of Things (loT) data sources (including from sensors, cameras, data collectors, and instrumented machines and systems), such as loT sources that provide information about machines and systems that enable transactions and machines and systems that are involved in production and consumption of resources.
  • social media data sources 180 such as social networking sites like FacebookTM and TwitterTM
  • LoT Internet of Things
  • loT sources that provide information about machines and systems that enable transactions and machines and systems that are involved in production and consumption of resources.
  • External data sources 182 may include behavioral data sources, such as automated agent behavioral data sources 188 (such as tracking and reporting on behavior of automated agents that are used for conversation and dialog management, agents used for control functions for machines and systems, agents used for purchasing and sales, agents used for data collection, agents used for advertising, and others), human behavioral data sources (such as data sources tracking online behavior, mobility behavior, energy consumption behavior, energy production behavior, network utilization behavior, compute and processing behavior, resource consumption behavior, resource production behavior, purchasing behavior, attention behavior, social behavior, and others), and entity behavioral data sources 190 (such as behavior of business organizations and other entities, such as purchasing behavior, consumption behavior, production behavior, market activity, merger and acquisition behavior, transaction behavior, location behavior, and others).
  • automated agent behavioral data sources 188 such as tracking and reporting on behavior of automated agents that are used for conversation and dialog management, agents used for control functions for machines and systems, agents used for purchasing and sales, agents used for data collection, agents used for advertising, and others
  • human behavioral data sources such as data sources tracking online behavior, mobility behavior, energy consumption behavior, energy production behavior, network
  • the loT, social and behavioral data from and about sensors, machines, humans, entities, and automated agents may collectively be used to populate expert systems, machine learning systems, and other intelligent systems and engines described throughout this disclosure, such as being provided as inputs to deep learning systems and being provided as feedback or outcomes for purposes of training, supervision, and iterative improvement of systems for prediction, forecasting, classification, automation and control.
  • the data may be organized as a stream of events.
  • the data may be stored in a distributed ledger or other distributed system.
  • the data may be stored in a knowledge graph where nodes represent entities and links represent relationships.
  • the external data sources may be queried via various database query functions.
  • the external data sources 182 may be accessed via APIs, brokers, connectors, protocols like REST and SOAP, and other data ingestion and extraction techniques.
  • Data may be enriched with metadata and may be subject to transformation and loading into suitable forms for consumption by the engines, such as by cleansing, normalization, de-duplication, and the like.
  • the platform 100 may include a set of intelligent forecasting engines 192 for forecasting events, activities, variables, and parameters of spot markets 170, forward markets 130, resources that are traded in such markets, resources that enable such markets, behaviors (such as any of those tracked in the external data sources 182), transactions, and the like.
  • the intelligent forecasting engines 192 may operate on data from the data aggregation systems 144 about elements of the platform 100 and on data from the external data sources 182.
  • the platform may include a set of intelligent transaction engines 136 for automatically executing transactions in spot markets 170 and forward markets 130. This may include executing intelligent cryptocurrency transactions with an intelligent cryptocurrency execution engine 183 as described in more detail below.
  • the platform 100 may make use of asset of improved distributed ledgers 113 and improved smart contracts 103, including ones that embed and operate on proprietary information, instruction sets and the like that enable complex transactions to occur among individuals with reduced (or without) reliance on intermediaries. These and other components are described in more detail throughout this disclosure.
  • the set of forward purchase and sale machines 110 may include a regeneration capacity allocation engine 102 (such as for allocating energy generation or regeneration capacity, such as within a hybrid vehicle or system that includes energy generation or regeneration capacity, a renewable energy system that has energy storage, or other energy storage system, where energy is allocated for one or more of sale on a forward market 130, sale in a spot market 170, use in completing a transaction (e.g., mining for cryptocurrency), or other purposes.
  • a regeneration capacity allocation engine 102 such as for allocating energy generation or regeneration capacity, such as within a hybrid vehicle or system that includes energy generation or regeneration capacity, a renewable energy system that has energy storage, or other energy storage system, where energy is allocated for one or more of sale on a forward market 130, sale in a spot market 170, use in completing a transaction (e.g., mining for cryptocurrency), or other purposes.
  • the regeneration capacity allocation engine 102 may explore available options for use of stored energy, such as sale in current and forward energy markets that accept energy from producers, keeping the energy in storage for future use, or using the energy for work (which may include processing work, such as processing activities of the platform like data collection or processing, or processing work for executing transactions, including mining activities for cryptocurrencies).
  • the set of forward purchase and sale machines 110 may include an energy purchase and sale machine 104 for purchasing or selling energy, such as in an energy spot market 148 or an energy forward market 122.
  • the energy purchase and sale machine 104 may use an expert system, neural network or other intelligence to determine timing of purchases, such as based on current and anticipated state information with respect to pricing and availability of energy and based on current and anticipated state information with respect to needs for energy, including needs for energy to perform computing tasks, cryptocurrency mining, data collection actions, and other work, such as work done by automated agents and systems and work required for humans or entities based on their behavior.
  • the energy purchase machine may recognize, by machine learning, that a business is likely to require a block of energy in order to perform an increased level of manufacturing based on an increase in orders or market demand and may purchase the energy at a favorable price on a futures market, based on a combination of energy market data and entity behavioral data.
  • market demand may be understood by machine learning, such as by processing human behavioral data sources 184, such as social media posts, e-commerce data and the like that indicate increasing demand.
  • the energy purchase and sale machine 104 may sell energy in the energy spot market 148 or the energy forward market 122. Sale may also be conducted by an expert system operating on the various data sources described herein, including with training on outcomes and human supervision.
  • the set of forward purchase and sale machines 110 may include a renewable energy credit (REC) purchase and sale machine 108, which may purchase renewable energy credits, pollution credits, and other environmental or regulatory credits in a spot market 150 or forward market 124 for such credits.
  • Purchasing may be configured and managed by an expert system operating on any of the external data sources 182 or on data aggregated by the set of data aggregation systems 144 for the platform.
  • Renewable energy credits and other credits may be purchased by an automated system using an expert system, including machine learning or other artificial intelligence, such as where credits are purchased with favorable timing based on an understanding of supply and demand that is determined by processing inputs from the data sources.
  • the expert system may be trained on a data set of outcomes from purchases under historical input conditions.
  • the expert system may be trained on a data set of human purchase decisions and/or may be supervised by one or more human operators.
  • the renewable energy credit (REC) purchase and sale machine 108 may also sell renewable energy credits, pollution credits, and other environmental or regulatory credits in a spot market 150 or forward market 124 for such credits. Sale may also be conducted by an expert system operating on the various data sources described herein, including with training on outcomes and human supervision.
  • the set of forward purchase and sale machines 110 may include an attention purchase and sale machine 112, which may purchase one or more attention-related resources, such as advertising space, search listing, keyword listing, banner advertisements, participation in a panel or survey activity, participation in a trial or pilot, or the like in a spot market for attention 152 or a forward market for attention 128.
  • Attention resources may include the attention of automated agents, such as bots, crawlers, dialog managers, and the like that are used for searching, shopping, and purchasing. Purchasing of attention resources may be configured and managed by an expert system operating on any of the external data sources 182 or on data aggregated by the set of data aggregation systems 144 for the platform. Attention resources may be purchased by an automated system using an expert system, including machine learning or other artificial intelligence, such as where resources are purchased with favorable timing, such as based on an understanding of supply and demand, that is determined by processing inputs from the various data sources. For example, the attention purchase and sale machine 112 may purchase advertising space in a forward market for advertising based on learning from a wide range of inputs about market conditions, behavior data, and data regarding activities of agent and systems within the platform 100.
  • automated agents such as bots, crawlers, dialog managers, and the like that are used for searching, shopping, and purchasing.
  • Purchasing of attention resources may be configured and managed by an expert system operating on any of the external data sources 182 or on data aggregated by the set
  • the expert system may be trained on a data set of outcomes from purchases under historical input conditions.
  • the expert system may be trained on a data set of human purchase decisions and/or may be supervised by one or more human operators.
  • the attention purchase and sale machine 112 may also sell one or more attention-related resources, such as advertising space, search listing, keyword listing, banner advertisements, participation in a panel or survey activity, participation in a trial or pilot, or the like in a spot market for attention 152 or a forward market for attention 128, which may include offering or selling access to, or attention or, one or more automated agents of the platform 100. Sale may also be conducted by an expert system operating on the various data sources described herein, including with training on outcomes and human supervision.
  • the set of forward purchase and sale machines 110 may include a compute purchase and sale machine 114, which may purchase one or more computation-related resources, such as processing resources, database resources, computation resources, server resources, disk resources, input/output resources, temporary storage resources, memory resources, virtual machine resources, container resources, and others in a spot market for compute 154 or a forward market for compute 132.
  • Purchasing of compute resources may be configured and managed by an expert system operating on any of the external data sources 182 or on data aggregated by the set of data aggregation systems 144 for the platform.
  • Compute resources may be purchased by an automated system using an expert system, including machine learning or other artificial intelligence, such as where resources are purchased with favorable timing, such as based on an understanding of supply and demand, that is determined by processing inputs from the various data sources.
  • the compute purchase and sale machine 114 may purchase or reserve compute resources on a cloud platform in a forward market for compute resources based on learning from a wide range of inputs about market conditions, behavior data, and data regarding activities of agent and systems within the platform 100, such as to obtain such resources at favorable prices during surge periods of demand for computing.
  • the expert system may be trained on a data set of outcomes from purchases under historical input conditions.
  • the expert system may be trained on a data set of human purchase decisions and/or may be supervised by one or more human operators.
  • the compute purchase and sale machine 114 may also sell one or more computation-related resources that are connected to, part of, or managed by the platform 100, such as processing resources, database resources, computation resources, server resources, disk resources, input/output resources, temporary storage resources, memory resources, virtual machine resources, container resources, and others in a spot market for compute 154 or a forward market for compute 132. Sale may also be conducted by an expert system operating on the various data sources described herein, including with training on outcomes and human supervision.
  • the set of forward purchase and sale machines 110 may include a data storage 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 others in a spot market for storage resources 158 or a forward market for data storage 134.
  • Purchasing of data storage resources may be configured and managed by an expert system operating on any of the external data sources 182 or on data aggregated by the set of data aggregation systems 144 for the platform.
  • Data storage resources may be purchased by an automated system using an expert system, including machine learning or other artificial intelligence, such as where resources are purchased with favorable timing, such as based on an understanding of supply and demand, that is determined by processing inputs from the various data sources.
  • the compute purchase and sale machine 114 may purchase or reserve compute resources on a cloud platform in a forward market for compute resources based on learning from a wide range of inputs about market conditions, behavior data, and data regarding activities of agent and systems within the platform 100, such as to obtain such resources at favorable prices during surge periods of demand for storage.
  • the expert system may be trained on a data set of outcomes from purchases under historical input conditions.
  • the expert system may be trained on a data set of human purchase decisions and/or may be supervised by one or more human operators.
  • the data storage purchase and sale machine 118 may also sell one or more data storage-related resources that are connected to, part of, or managed by the platform 100 in a spot market for storage resources 158 or a forward market for data storage 134. Sale may also be conducted by an expert system operating on the various data sources described herein, including with training on outcomes and human supervision.
  • the set of forward purchase and sale machines 110 may include a 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 a spot market for bandwidth resources 160 or a forward market for bandwidth 138.
  • 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 a spot market for bandwidth resources 160 or a forward market for bandwidth 138.
  • Purchasing of bandwidth resources may be configured and managed by an expert system operating on any of the external data sources 182 or on data aggregated by the set of data aggregation systems 144 for the platform.
  • Bandwidth resources may be purchased by an automated system using an expert system, including machine learning or other artificial intelligence, such as where resources are purchased with favorable timing, such as based on an understanding of supply and demand, that is determined by processing inputs from the various data sources.
  • the bandwidth purchase and sale machine 120 may purchase or reserve bandwidth on a network resource for a future networking activity managed by the platform based on learning from a wide range of inputs about market conditions, behavior data, and data regarding activities of agent and systems within the platform 100, such as to obtain such resources at favorable prices during surge periods of demand for bandwidth.
  • the expert system may be trained on a data set of outcomes from purchases under historical input conditions.
  • the expert system may be trained on a data set of human purchase decisions 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 that are connected to, part of, or managed by the platform 100 in a spot market for bandwidth resources 160 or a forward market for bandwidth 138. Sale may also be conducted by an expert system operating on the various data sources described herein, including with training on outcomes and human supervision.
  • the set of forward purchase and sale machines 110 may include a spectrum purchase and sale machine 142, which may purchase one or more spectrum-related resources, such as cellular spectrum, 3G spectrum, 4G spectrum, LTE spectrum, 5G spectrum, cognitive radio spectrum, peer-to-peer network spectrum, emergency responder spectrum and the like in a spot market for spectrum resources 162 or a forward market for spectrum/bandwidth 140.
  • spectrum-related resources such as cellular spectrum, 3G spectrum, 4G spectrum, LTE spectrum, 5G spectrum, cognitive radio spectrum, peer-to-peer network spectrum, emergency responder spectrum and the like in a spot market for spectrum resources 162 or a forward market for spectrum/bandwidth 140.
  • Purchasing of spectrum resources may be configured and managed by an expert system operating on any of the external data sources 182 or on data aggregated by the set of data aggregation systems 144 for the platform.
  • Spectrum resources may be purchased by an automated system using an expert system, including machine learning or other artificial intelligence, such as where resources are purchased with favorable timing, such as based on an understanding of supply and demand, that is determined by processing inputs from the various data sources.
  • the spectrum purchase and sale machine 142 may purchase or reserve spectrum on a network resource for a future networking activity managed by the platform based on learning from a wide range of inputs about market conditions, behavior data, and data regarding activities of agent and systems within the platform 100, such as to obtain such resources at favorable prices during surge periods of demand for spectrum.
  • the expert system may be trained on a data set of outcomes from purchases under historical input conditions.
  • the expert system may be trained on a data set of human purchase decisions 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 that are connected to, part of, or managed by the platform 100 in a spot market for spectrum resources 162 or a forward market for spectrum/bandwidth 140. Sale may also be conducted by an expert system operating on the various data sources described herein, including with training on outcomes and human supervision.
  • the intelligent resource allocation and coordination system 168 may provide coordinated and automated allocation of resources and coordinated execution of transactions across the various forward markets 130 and spot markets 170 by coordinating the various purchase and sale machines, such as by an expert system, such as a machine learning system (which may model-based or a deep learning system, and which may be trained on outcomes and/or supervised by humans).
  • an expert system such as a machine learning system (which may model-based or a deep learning system, and which may be trained on outcomes and/or supervised by humans).
  • the intelligent resource allocation and coordination system 168 may coordinate purchasing of resources for a set of assets and coordinated sale of resources available from a set of assets, such as a fleet of vehicles, a data center of processing and data storage resources, an information technology network (on premises, cloud, or hybrids), a fleet of energy production systems (renewable or non-renewable), a smart home or building (including appliances, machines, infrastructure components and systems, and the like thereof that consume or produce resources), and the like.
  • the platform 100 may optimize allocation of resource purchasing, sale and utilization based on data aggregated in the platform, such as by tracking activities of various engines and agents, as well as by taking inputs from external data sources 182.
  • outcomes may be provided as feedback for training the intelligent resource allocation and coordination system 168, such as outcomes based on yield, profitability, optimization of resources, optimization of business objectives, satisfaction of goals, satisfaction of users or operators, or the like.
  • the platform 100 may learn to optimize how a set of machines that have energy storage capacity allocate that capacity among computing tasks (such as for cryptocurrency mining, application of neural networks, computation on data and the like), other useful tasks (that may yield profits or other benefits), storage for future use, or sale to the provider of an energy grid.
  • the platform 100 may be used by fleet operators, enterprises, governments, municipalities, military units, first responder units, manufacturers, energy producers, cloud platform providers, and other enterprises and operators that own or operate resources that consume or provide energy, computation, data storage, bandwidth, or spectrum.
  • the platform 100 may also be used in connection with markets for attention, such as to use available capacity of resources to support attention-based exchanges of value, such as in advertising markets, micro-transaction markets, and others.
  • the platform 100 may include a set of intelligent forecasting engines 192 that forecast one or more attributes, parameters, variables, or other factors, such as for use as inputs by the set of forward purchase and sale machines, the intelligent transaction engines 126 (such as for intelligent cryptocurrency execution) or for other purposes.
  • Each of the set of intelligent forecasting engines 192 may use data that is tracked, aggregated, processed, or handled within the platform 100, such as by the data aggregation system 144, as well as input data from external data sources 182, such as social media data sources 180, automated agent behavioral data sources 188, human behavioral data sources 184, entity behavioral data sources 190 and loT data sources 198.
  • the set of intelligent forecasting engines 192 may include one or more specialized engines that forecast market attributes, such as capacity, demand, supply, and prices, using particular data sources for particular markets.
  • These may include an energy price forecasting engine 215 that bases its forecast on behavior of an automated agent, a network spectrum price forecasting engine 217 that bases its forecast on behavior of an automated agent, a REC price forecasting engine 219 that bases its forecast on behavior of an automated agent, a compute price forecasting engine 221 that bases its forecast on behavior of an automated agent, a network spectrum price forecasting engine 223 that bases its forecast on behavior of an automated agent.
  • observations regarding the behavior of automated agents such as ones used for conversation, for dialog management, for managing electronic commerce, for managing advertising and others may be provided as inputs for forecasting to the engines.
  • the intelligent forecasting engines 192 may also include a range of engines that provide forecasts at least in part based on entity behavior, such as behavior of business and other organizations, such as marketing behavior, sales behavior, product offering behavior, advertising behavior, purchasing behavior, transactional behavior, merger and acquisition behavior, and other entity behavior. These may include an energy price forecasting engine 225 using entity behavior, a network spectrum price forecasting engine 227 using entity behavior, a REC price forecasting engine 229 using entity behavior, a compute price forecasting engine 231 using entity behavior, and a network spectrum price forecasting engine 233 using entity behavior.
  • the intelligent forecasting engines 192 may also include a range of engines that provide forecasts at least in part based on human behavior, such as behavior of consumers and users, such as purchasing behavior, shopping behavior, sales behavior, product interaction behavior, energy utilization behavior, mobility behavior, activity level behavior, activity type behavior, transactional behavior, and other human behavior.
  • human behavior such as behavior of consumers and users, such as purchasing behavior, shopping behavior, sales behavior, product interaction behavior, energy utilization behavior, mobility behavior, activity level behavior, activity type behavior, transactional behavior, and other human behavior.
  • These may include an energy price forecasting engine 235 using human behavior, a network spectrum price forecasting engine 237 using human behavior, a REC price forecasting engine 239 using human behavior, a compute price forecasting engine 241 using human behavior, and a network spectrum price forecasting engine 243 using human behavior.
  • the platform 100 may include a set of intelligent transaction engines 136 that automate execution of transactions in forward markets 130 and/or spot markets 170 based on determination that favorable conditions exist, such as by the intelligent resource allocation and coordination system 168 and/or with use of forecasts form the intelligent forecasting engines 192.
  • the intelligent transaction engines 136 may be configured to automatically execute transactions, using available market interfaces, such as APIs, connectors, ports, network interfaces, and the like, in each of the markets noted above.
  • the intelligent transaction engines may execute transactions based on event streams that come from external data sources, such as loT data sources 198 and social media data sources 180.
  • the engines may include, for example, an loT forward energy transaction engine 195 and/or an loT compute market transaction engine 106, either or both of which may use data from the Internet of Things to determine timing and other attributes for market transaction in a market for one or more of the resources described herein, such as an energy market transaction, a compute resource transaction or other resource transaction.
  • loT data may include instrumentation and controls data for one or more machines (optionally coordinated as a fleet) that use or produce energy or that use or have compute resources, weather data that influences energy prices or consumption (such as wind data influencing production of wind energy), sensor data from energy production environments, sensor data from points of use for energy or compute resources (such as vehicle traffic data, network traffic data, IT network utilization data, Internet utilization and traffic data, camera data from work sites, smart building data, smart home data, and the like), and other data collected by or transferred within the Internet of Things, including data stored in loT platforms and of cloud services providers like Amazon, IBM, and others.
  • the intelligent transaction engines 136 may include engines that use social data to determine timing of other attributes for a market transaction in one or more of the resources described herein, such as a social data forward energy transaction engine 199 and/or a social data compute market transaction engine 116.
  • Social data may include data from social networking sites (e.g., FacebookTM, YouTubeTM, TwitterTM, SnapchatTM, InstagramTM, and others, data from websites, data from e-commerce sites, and data from other sites that contain information that may be relevant to determining or forecasting behavior of users or entities, such as data indicating interest or attention to particular topics, goods or services, data indicating activity types and levels (such as may be observed by machine processing of image data showing individuals engaged in activities, including travel, work activities, leisure activities, and the like.
  • Social data may be supplied to machine learning, such as for learning user behavior or entity behavior, and/or as an input to an expert system, a model, or the like, such as one for determining, based on the social data, the parameters for a transaction.
  • machine learning such as for learning user behavior or entity behavior
  • an expert system such as one for determining, based on the social data, the parameters for a transaction.
  • an event or set of events in a social data stream may indicate the likelihood of a surge of interest in an online resource, a product, or a service, and compute resources, bandwidth, storage, or like may be purchased in advance (avoiding surge pricing) to accommodate the increased interest reflected by the social data stream.
  • the platform 100 may include capabilities for transaction execution that involve one or more distributed ledgers 113 and one or more smart contracts 103, where the distributed ledgers 113 and smart contracts 103 are configured to enable specialized transaction features for specific transaction domains.
  • One such domain is intellectual property, which transactions are highly complex, involving licensing terms and conditions that are somewhat difficult to manage, as compared to more straightforward sales of goods or services.
  • a smart contract wrapper 105 such as wrapper aggregating intellectual property, is provided, using a distributed ledger, wherein the smart contract embeds IP licensing terms for intellectual property that is embedded in the distributed ledger and wherein executing an operation on the distributed ledger provides access to the intellectual property and commits the executing party to the IP licensing terms.
  • Licensing terms for a wide range of goods and services including digital goods like video, audio, video game, video game element, music, electronic book, and other digital goods may be managed by tracking transactions involving them on a distributed ledger, whereby publishers may verify a chain of licensing and sublicensing.
  • the distributed ledger may be configured to add each licensee to the ledger, and the ledger may be retrieved at the point of use of a digital item, such as in a streaming platform, to validate that licensing has occurred.
  • an improved distributed ledger is provided with the smart contract wrapper 105, such as an IP wrapper, container, smart contract, or similar mechanism for aggregating intellectual property licensing terms, wherein a smart contract wrapper on the distributed ledger allows an operation on the ledger to add intellectual property to an aggregate stack of intellectual property.
  • intellectual property builds on other intellectual property, such as where software code is derived from other code, where trade secrets or knowhow for elements of a process are combined to enable a larger process, where patents covering sub-components of a system or steps in a process are pooled, where elements of a video game include sub-component assets from different creators, where a book contains contributions from multiple authors, and the like.
  • a smart IP wrapper aggregates licensing terms for different intellectual property items (including digital goods, including ones embodying different types of intellectual property rights, and transaction data involving the item, as well as optionally one or more portions of the item corresponding to the transaction data, are stored in a distributed ledger that is configured to enable validation of agreement to the licensing terms (such as at appoint of use) and/or access control to the item.
  • a royalty apportionment wrapper 115 may be provided in a system having a distributed ledger for aggregating intellectual property licensing terms, wherein a smart contract wrapper on the distributed ledger allows an operation on the ledger to add intellectual property and to agree to an apportionment of royalties among the parties in the ledger.
  • a ledger may accumulate contributions to the ledger along with evidence of agreement to the apportionment of any royalties among the contributors of the IP that is embedded in and/or controlled by the ledger.
  • the ledger may record licensing terms and automatically vary them as new contributions are made, such as by one or more rules. For example, contributors may be given a share of a royalty stack according to a rule, such as based on a fractional contribution, such as based on lines of code contributed, lines of authorship, contribution to components of a system, and the like.
  • a distributed ledger may be forked into versions that represent varying combinations of sub-components of IP, such as to allow users to select combinations that are of most use, thereby allowing contributors who have contributed the most value to be rewarded. Variation and outcome tracking may be iteratively improved, such as by machine learning.
  • a distributed ledger is provided for aggregating intellectual property licensing terms, wherein a smart contract wrapper on the distributed ledger allows an operation on the ledger to add intellectual property to an aggregate stack of intellectual property.
  • the platform 100 may have an improved distributed ledger for aggregating intellectual property licensing terms, wherein a smart contract wrapper on the distributed ledger allows an operation on the ledger to commit a party to a contract term via an IP transaction wrapper 119 of the ledger.
  • This may include operations involving cryptocurrencies, tokens, or other operations, as well as conventional payments and in-kind transfers, such as of various resources described herein.
  • the ledger may accumulate evidence of commitments to IP transactions by parties, such as entering into royalty terms, revenue sharing terms, IP ownership terms, warranty and liability terms, license permissions and restrictions, field of use terms, and many others.
  • improved distributed ledgers may include ones having a tokenized instruction set, such that operation on the distributed ledger provides provable access to the instruction set.
  • a party wishing to share permission to know how, a trade secret or other valuable instructions may thus share the instruction set via a distributed ledger that captures and stores evidence of an action on the ledger by a third party, thereby evidencing access and agreement to terms and conditions of access.
  • the platform 100 may have a distributed ledger that tokenizes executable algorithmic logic 121, such that operation on the distributed ledger provides provable access to the executable algorithmic logic.
  • a variety of instruction sets may be stored by a distributed ledger, such as to verify access and verify agreement to terms (such as smart contract terms).
  • instruction sets that embody trade secrets may be separated into sub-components, so that operations must occur on multiple ledgers to get (provable) access to a trade secret. This may permit parties wishing to share secrets, such as with multiple sub-contractors or vendors, to main provable access control, while separating components among different vendors to avoid sharing an entire set with a single party.
  • Various kinds of executable instruction sets may be stored on specialized distributed ledgers that may include smart wrappers for specific types of instruction sets, such that provable access control, validation of terms, and tracking of utilization may be performed by operations on the distributed ledger (which may include triggering access controls within a content management system or other systems upon validation of actions taken in a smart contract on the ledger.
  • the platform 100 may have a distributed ledger that tokenizes a 3D printer instruction set 123, such that operation on the distributed ledger provides provable access to the instruction set.
  • the platform 100 may have a distributed ledger that tokenizes an instruction set for a coating process 125, such that operation on the distributed ledger provides provable access to the instruction set.
  • the platform 100 may have a distributed ledger that tokenizes an instruction set for a semiconductor fabrication process 129, such that operation on the distributed ledger provides provable access to the fabrication process.
  • the platform 100 may have a distributed ledger that tokenizes a firmware program 131, such that operation on the distributed ledger provides provable access to the firmware program.
  • the platform 100 may have a distributed ledger that tokenizes an instruction set for an FPGA 133, such that operation on the distributed ledger provides provable access to the FPGA.
  • the platform 100 may have a distributed ledger that tokenizes serverless code logic 135, such that operation on the distributed ledger provides provable access to the serverless code logic.
  • the platform 100 may have a distributed ledger that tokenizes an instruction set for a crystal fabrication system 139, such that operation on the distributed ledger provides provable access to the instruction set.
  • the platform 100 may have a distributed ledger that tokenizes an instruction set for a food preparation process 141, such that operation on the distributed ledger provides provable access to the instruction set.
  • the platform 100 may have a distributed ledger that tokenizes an instruction set for a polymer production process 143, such that operation on the distributed ledger provides provable access to the instruction set.
  • the platform 100 may have a distributed ledger that tokenizes an instruction set for chemical synthesis process 145, such that operation on the distributed ledger provides provable access to the instruction set.
  • the platform 100 may have a distributed ledger that tokenizes an instruction set for a biological production process 149, such that operation on the distributed ledger provides provable access to the instruction set.
  • the platform 100 may have a distributed ledger that tokenizes a trade secret with an expert wrapper 151, such that operation on the distributed ledger provides provable access to the trade secret and the wrapper provides validation of the trade secret by the expert.
  • An interface may be provided by 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.
  • the platform 100 may have a distributed ledger that aggregates views of a trade secret into a chain that proves which and how many parties have viewed the trade secret. Views may be used to allocate value to creators of the trade secret, to operators of the platform 100, or the like.
  • the platform 100 may have a distributed ledger that tokenizes an instruction set 111, such that operation on the distributed ledger provides provable access 155 to the instruction set and execution of the instruction set on a system results in recording a transaction in the distributed ledger.
  • the platform 100 may have a distributed ledger that tokenizes an item of intellectual property and a reporting system that reports an analytic result based on the operations performed on the distributed ledger or the intellectual property.
  • the platform 100 may have a distributed ledger that aggregates a set of instructions, where an operation on the distributed ledger adds at least one instruction to a pre-existing set of instructions 161 to provide a modified set of instructions.
  • an intelligent cryptocurrency execution engine 183 may provide intelligence for the timing, location, and other attributes of a cryptocurrency transaction, such as a mining transaction, an exchange transaction, a storage transaction, a retrieval transaction, or the like.
  • Cryptocurrencies like BitcoinTM are increasingly widespread, with specialized coins having emerged for a wide variety of purposes, such as exchanging value in various specialized market domains.
  • Initial offerings of such coins, or ICOs are increasingly subject to regulations, such as securities regulations, and in some cases to taxation.
  • jurisdictional factors may be important in determining where, when, and how to execute a transaction, store a cryptocurrency, exchange it for value.
  • intelligent cryptocurrency execution engine 183 may use features embedded in or wrapped around the digital object representing a coin, such as features that cause the execution of transactions in the coin to be undertaken with awareness of various conditions, including geographic conditions, regulatory conditions, tax conditions, market conditions, and the like.
  • the platform 100 may include a tax aware coin 165 or smart wrapper for a cryptocurrency coin that directs execution of a transaction involving the coin to a geographic location based on tax treatment of at least one of the coin and the transaction in the geographic location.
  • the platform 100 may include a location-aware coin 169 or smart wrapper that enables a self-executing cryptocurrency coin that commits a transaction upon recognizing a location-based parameter that provides favorable tax treatment.
  • the platform 100 may include an expert system or Al agent 171 that uses machine learning to optimize the execution of cryptocurrency transactions based on tax status.
  • Machine learning may use one or more models or heuristics, such as populated with relevant jurisdictional tax data, may be trained on a training set of human trading operations, may be supervised by human supervisors, and/or may use a deep learning technique based on outcomes over time, such as when operating on a wide range of internal system data and external data sources 182 as described throughout this disclosure.
  • the platform 100 may include regulation aware coin 173 having a coin, a smart wrapper, and/or an expert system that aggregates regulatory information covering cryptocurrency transactions and automatically selects a jurisdiction for an operation based on the regulatory information.
  • Machine learning may use one or more models or heuristics, such as populated with relevant jurisdictional regulatory data, may be trained on a training set of human trading operations, may be supervised by human supervisors, and/or may use a deep learning technique based on outcomes over time, such as when operating on a wide range of internal system data and external data sources 182 as described throughout this disclosure.
  • the platform 100 may include an energy price- aware coin 175, wrapper, or expert system that uses machine learning to optimize the execution of a cryptocurrency transaction based on real time energy price information for an available energy source.
  • Cryptocurrency transactions such as coin mining and blockchain operations, may be highly energy intensive.
  • An energy price-aware coin may be configured to time such operations based on energy price forecasts, such as with one or more of the intelligent forecasting engines 192 described throughout this disclosure.
  • the platform 100 may include an energy source aware coin 179, wrapper, or expert system that uses machine learning to optimize the execution of a cryptocurrency transaction based on an understanding of available energy sources to power computing resources to execute the transaction. For example, coin mining may be performed only when renewable energy sources are available.
  • Machine learning for optimization of a transaction may use one or more models or heuristics, such as populated with relevant energy source data (such as may be captured in a knowledge graph, which may contain energy source information by type, location and operating parameters), may be trained on a training set of input-output data for human-initiated transactions, may be supervised by human supervisors, and/or may use a deep learning technique based on outcomes over time, such as when operating on a wide range of internal system data and external data sources 182 as described throughout this disclosure.
  • relevant energy source data such as may be captured in a knowledge graph, which may contain energy source information by type, location and operating parameters
  • the platform 100 may include a charging cycle aware coin 181, wrapper, or an expert system that uses machine learning to optimize charging and recharging cycle of a rechargeable battery system to provide energy for execution of a cryptocurrency transaction.
  • a battery may be discharged for a cryptocurrency transaction only if a minimum threshold of battery charge is maintained for other operational use, if re-charging resources are known to be readily available, or the like.
  • Machine learning for optimization of charging and recharging may use one or more models or heuristics, such as populated with relevant battery data (such as may be captured in a knowledge graph, which may contain energy source information by type, location and operating parameters), may be trained on a training set of human operations, may be supervised by human supervisors, and/or may use a deep learning technique based on outcomes over time, such as when operating on a wide range of internal system data and external data sources 182 as described throughout this disclosure.
  • models or heuristics such as populated with relevant battery data (such as may be captured in a knowledge graph, which may contain energy source information by type, location and operating parameters), may be trained on a training set of human operations, may be supervised by human supervisors, and/or may use a deep learning technique based on outcomes over time, such as when operating on a wide range of internal system data and external data sources 182 as described throughout this disclosure.
  • optimization of various intelligent coin operations may occur with machine learning that is trained on outcomes, such as financial profitability. Any of the machine learning systems described throughout this disclosure may be used for optimization of intelligent cryptocurrency transaction management.
  • compute resources such as those mentioned throughout this disclosure, may be allocated to perform a range of computing tasks, both for operations that occur within the platform 100, ones that are managed by the platform, and ones that involve the activities, workflows and processes of various assets that may be owned, operated or managed in conjunction with the platform, such as sets or fleets of assets that have or use computing resources.
  • compute tasks include, without limitation, cryptocurrency mining, distributed ledger calculations and storage, forecasting tasks, transaction execution tasks, spot market testing tasks, internal data collection tasks, external data collection, machine learning tasks, and others.
  • energy, compute resources, bandwidth, spectrum, and other resources may be coordinated, such as by machine learning, for these tasks.
  • Outcome and feedback information may be provided for the machine learning, such as outcomes for any of the individual tasks and overall outcomes, such as yield and profitability for business or other operations involving the tasks.
  • networking resources may be allocated to perform a range of networking tasks, both for operations that occur within the platform 100, ones that are managed by the platform, and ones that involve the activities, workflows and processes of various assets that may be owned, operated or managed in conjunction with the platform, such as sets or fleets of assets that have or use networking resources.
  • networking tasks include cognitive network coordination, network coding, peer bandwidth sharing (including, for example cost-based routing, value-based routing, outcome-based routing, and the like), distributed transaction execution, spot market testing, randomization (e.g., using genetic programming with outcome feedback to vary network configurations and transmission paths), internal data collection and external data collection.
  • energy, compute resources, bandwidth, spectrum, and other resources may be coordinated, such as by machine learning, for these networking tasks.
  • Outcome and feedback information may be provided for the machine learning, such as outcomes for any of the individual tasks and overall outcomes, such as yield and profitability for business or other operations involving the tasks.
  • data storage resources may be allocated to perform a range of data storage tasks, both for operations that occur within the platform 100, ones that are managed by the platform, and ones that involve the activities, workflows and processes of various assets that may be owned, operated or managed in conjunction with the platform, such as sets or fleets of assets that have or use networking resources.
  • data storage tasks include distributed ledger storage, storage of internal data (such as operational data with the platform), cryptocurrency storage, smart wrapper storage, storage of external data, storage of feedback and outcome data, and others.
  • data storage, energy, compute resources, bandwidth, spectrum, and other resources may be coordinated, such as by machine learning, for these data storage tasks. Outcome and feedback information may be provided for the machine learning, such as outcomes for any of the individual tasks and overall outcomes, such as yield and profitability for business or other operations involving the tasks.
  • smart contracts such as ones embodying terms relating to intellectual property, trade secrets, know how, instruction sets, algorithmic logic, and the like may embody or include contract terms, which may include terms and conditions for options, royalty stacking terms, field exclusivity, partial exclusivity, pooling of intellectual property, standards terms (such as relating to essential and non-essential patent usage), technology transfer terms, consulting service terms, update terms, support terms, maintenance terms, derivative works terms, copying terms, and performance-related rights or metrics, among many others.
  • contract terms may include terms and conditions for options, royalty stacking terms, field exclusivity, partial exclusivity, pooling of intellectual property, standards terms (such as relating to essential and non-essential patent usage), technology transfer terms, consulting service terms, update terms, support terms, maintenance terms, derivative works terms, copying terms, and performance-related rights or metrics, among many others.
  • an instruction set is embodied in digital form, such as contained in or managed by a distributed ledger transactions system
  • various systems may be configured with interfaces that allow them to access and use the instruction sets.
  • such systems may include access control features that validate proper licensing by inspection of a distributed ledger, a key, a token, or the like that indicates the presence of access rights to an instruction set.
  • Such systems that execute distributed instruction sets may include systems for 3D printing, crystal fabrication, semiconductor fabrication, coating items, producing polymers, chemical synthesis, and biological production, among others.
  • Networking capabilities and network resources should be understood to include a wide range of networking systems, components and capabilities, including infrastructure elements for 3G, 4G, LTE, 5G and other cellular network types, access points, routers, and other Wi-Fi elements, cognitive networking systems and components, mobile networking systems and components, physical layer, MAC layer and application layer systems and components, cognitive networking components and capabilities, peer-to-peer networking components and capabilities, optical networking components and capabilities, and others.
  • embodiments of the present disclosure may benefit from the use of a neural net, such as a neural net trained for pattern recognition, for classification of one or more parameters, characteristics, or phenomena, for support of autonomous control, and other purposes.
  • a neural net such as a neural net trained for pattern recognition, for classification of one or more parameters, characteristics, or phenomena, for support of autonomous control, and other purposes.
  • references to a neural net throughout this disclosure should be understood to encompass a wide range of 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-layered neural networks, convolutional neural networks, hybrids of neural networks with other expert systems (e.g., hybrid fuzzy logic - neural network systems), Autoencoder 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, fully recurrent neural networks, simple recurrent neural networks, echo state neural networks, long short-term memory neural networks, bi-directional neural networks, hierarchical neural networks, stochastic neural networks, genetic scale
  • Figures 5 through 31 depict exemplary neural networks and Figure 4 depicts a legend showing the various components of the neural networks depicted throughout Figures 5 to 31.
  • Figure 4 depicts various neural net components depicted in cells that are assigned functions and requirements.
  • the various neural net examples may include back fed data/sensor cells, data/sensor cells, noisy input cells, and hidden cells.
  • the neural net components also include probabilistic hidden cells, spiking hidden cells, output cells, match input/output cells, recurrent cells, memory cells, different memory cells, kernels, and convolution or pool cells.
  • Figure 5 depicts an exemplary perceptron neural network that may connect to, integrate with, or interface with the platform 100.
  • the platform may also be associated with further neural net systems such as a feed forward neural network (Figure 6), a radial basis neural network (Figure 7), a deep feed forward neural network (Figure 8), a recurrent neural network (Figure 9), a long/short term neural network ( Figure 10), and a gated recurrent neural network (Figure 11).
  • the platform may also be associated with further neural net systems such as an auto encoder neural network (Figure 12), a variational neural network ( Figure 13), a denoising neural network (Figure 14), a sparse neural network (Figure 15), a Markov chain neural network ( Figure 16), and a Hopfield network neural network (Figure 17).
  • the platform may further be associated with additional neural net systems such as a Boltzmann machine neural network (Figure 18), a restricted BM neural network ( Figure 19), a deep belief neural network ( Figure 20), a deep convolutional neural network (Figure 21), a decon volutional neural network ( Figure 22), and a deep convolutional inverse graphics neural network (Figure 23).
  • the platform may also be associated with further neural net systems such as a generative adversarial neural network (Figure 24), a liquid state machine neural network (Figure 25), an extreme learning machine neural network (Figure 26), an echo state neural network (Figure 27), a deep residual neural network (Figure 28), a Kohonen neural network (Figure 29), a support vector machine neural network (Figure 30), and a neural Turing machine neural network (Figure 31).
  • a generative adversarial neural network Figure 24
  • a liquid state machine neural network Figure 25
  • an extreme learning machine neural network Figure 26
  • an echo state neural network Figure 27
  • a deep residual neural network Figure 28
  • a Kohonen neural network Figure 29
  • Support vector machine neural network Figure 30
  • a neural Turing machine neural network Figure 31
  • the foregoing neural networks may have a variety of nodes or neurons, which may perform a variety of functions on inputs, such as inputs received from sensors or other data sources, including other nodes. Functions may involve weights, features, feature vectors, and the like. Neurons may include perceptrons, neurons that mimic biological functions (such as of the human senses of touch, vision, taste, hearing, and smell), and the like. Continuous neurons, such as with sigmoidal activation, may be used in the context of various forms of neural net, such as where back propagation is involved.
  • an expert system or neural network may be trained, such as by a human operator or supervisor, or based on a data set, model, or the like. Training may include presenting the neural network with one or more training data sets that represent values, such as sensor data, event data, parameter data, and other types of data (including the many types described throughout this disclosure), as well as one or more indicators of an outcome, such as an outcome of a process, an outcome of a calculation, an outcome of an event, an outcome of an activity, or the like.
  • Training may include training in optimization, such as training a neural network to optimize one or more systems based on one or more optimization approaches, such as Bayesian approaches, parametric Bayes classifier approaches, k-nearest-neighbor classifier approaches, iterative approaches, interpolation approaches, Pareto optimization approaches, algorithmic approaches, and the like.
  • Feedback may be provided in a process of variation and selection, such as with a genetic algorithm that evolves one or more solutions based on feedback through a series of rounds.
  • a plurality of neural networks may be deployed in a cloud platform that receives data streams and other inputs collected (such as by mobile data collectors) in one or more transactional environments and transmitted to the cloud platform over one or more networks, including using network coding to provide efficient transmission.
  • a plurality of different neural networks of various types may be used to undertake prediction, classification, control functions, and provide other outputs as described in connection with expert systems disclosed throughout this disclosure.
  • the different neural networks may be structured to compete with each other (optionally including use evolutionary algorithms, genetic algorithms, or the like), such that an appropriate type of neural network, with appropriate input sets, weights, node types and functions, and the like, may be selected, such as by an expert system, for a specific task involved in a given context, workflow, environment process, system, or the like.
  • feed forward neural network which moves information in one direction, such as from a data input, like a data source related to at least one resource or parameter related to a transactional environment, such as any of the data sources mentioned throughout this disclosure, through a series of neurons or nodes, to an output. Data may move from the input nodes to the output nodes, optionally passing through one or more hidden nodes, without loops.
  • feed forward neural networks may be constructed with various types of units, such as binary McCulloch-Pitts neurons, the simplest of which is a perceptron.
  • methods and systems described herein that involve an expert system or self-organization capability may use a capsule neural network, such as for prediction, classification, or control functions with respect to a transactional environment, such as relating to one or more of the machines and automated systems described throughout this disclosure.
  • methods and systems described herein that involve an expert system or self-organization capability may use a radial basis function (RBF) neural network, which may be preferred in some situations involving interpolation in a multi-dimensional space (such as where interpolation is helpful in optimizing a multi-dimensional function, such as for optimizing a data marketplace as described here, optimizing the efficiency or output of a power generation system, a factory system, or the like, or other situation involving multiple dimensions.
  • RBF radial basis function
  • each neuron in the RBF neural network stores an example from a training set as a “prototype.” Linearity involved in the functioning of this neural network offers RBF the advantage of not typically suffering from problems with local minima or maxima.
  • methods and systems described herein that involve an expert system or self-organization capability may use a radial basis function (RBF) neural network, such as one that employs a distance criterion with respect to a center (e.g., a Gaussian function).
  • a radial basis function may be applied as a replacement for a hidden layer, such as a sigmoidal hidden layer transfer, in a multi-layer perceptron.
  • An RBF network may have two layers, such as where an input is mapped onto each RBF in a hidden layer.
  • an output layer may comprise a linear combination of hidden layer values representing, for example, a mean predicted output.
  • the output layer value may provide an output that is the same as or similar to that of a regression model in statistics.
  • the output layer may be a sigmoid function of a linear combination of hidden layer values, representing a posterior probability. Performance in both cases is often improved by shrinkage techniques, such as ridge regression in classical statistics. This corresponds to a prior belief in small parameter values (and therefore smooth output functions) in a Bayesian framework.
  • RBF networks may avoid local minima, because the only parameters that are adjusted in the learning process are the linear mapping from hidden layer to output layer. Linearity ensures that the error surface is quadratic and therefore has a single minimum. In regression problems, this may be found in one matrix operation.
  • RBF networks may use kernel methods such as support vector machines (SVM) and Gaussian processes (where the RBF is the kernel function).
  • SVM support vector machines
  • Gaussian processes where the RBF is the kernel function.
  • a non-linear kernel function may be used to project the input data into a space where the learning problem may be solved using a linear model.
  • an RBF neural network may include an input layer, a hidden layer, and a summation layer.
  • the input layer one neuron appears in the input layer for each predictor variable.
  • N-l neurons are used, where N is the number of categories.
  • the input neurons may, in embodiments, standardize the value ranges by subtracting the median and dividing by the interquartile range.
  • the input neurons may then feed the values to each of the neurons in the hidden layer.
  • a variable number of neurons may be used (determined by the training process).
  • Each neuron may consist of a radial basis function that is centered on a point with as many dimensions as a number of predictor variables.
  • the spread (e.g., radius) of the RBF function may be different for each dimension.
  • the centers and spreads may be determined by training.
  • a hidden neuron When presented with the vector of input values from the input layer, a hidden neuron may compute a Euclidean distance of the test case from the neuron’ s center point and then apply the RBF kernel function to this distance, such as using the spread values.
  • the resulting value may then be passed to the summation layer.
  • the summation layer the value coming out of a neuron in the hidden layer may be multiplied by a weight associated with the neuron and may add to the weighted values of other neurons. This sum becomes the output.
  • one output is produced (with a separate set of weights and summation units) for each target category.
  • the value output for a category is the probability that the case being evaluated has that category.
  • various parameters may be determined, such as the number of neurons in a hidden layer, the coordinates of the center of each hidden-layer function, the spread of each function in each dimension, and the weights applied to outputs as they pass to the summation layer. Training may be used by clustering algorithms (such as k-means clustering), by evolutionary approaches, and the like.
  • a recurrent neural network may have a time-varying, real- valued (more than just zero or one) activation (output). Each connection may have a modifiable real- valued weight.
  • Some of the nodes are called labeled nodes, some output nodes, and others hidden nodes.
  • training sequences of real- valued input vectors may become sequences of activations of the input nodes, one input vector at a time.
  • each non-input unit may compute its current activation as a nonlinear function of the weighted sum of the activations of all units from which it receives connections.
  • the system may explicitly activate (independent of incoming signals) some output units at certain time steps.
  • methods and systems described herein that involve an expert system or self-organization capability may use a self-organizing neural network, such as a Kohonen self-organizing neural network, such as for visualization of views of data, such as lowdimensional views of high-dimensional data.
  • the self-organizing neural network may apply competitive learning to a set of input data, such as from one or more sensors or other data inputs from or associated with a transactional environment, including any machine or component that relates to the transactional environment.
  • the self-organizing neural network may be used to identify structures in data, such as unlabeled data, such as in data sensed from a range of data sources about or sensors in or about in a transactional environment, where sources of the data are unknown (such as where events may be coming from any of a range of unknown sources).
  • the self-organizing neural network may organize structures or patterns in the data, such that they may be recognized, analyzed, and labeled, such as identifying market behavior structures as corresponding to other events and signals.
  • methods and systems described herein that involve an expert system or self-organization capability may use a recurrent neural network, which may allow for a bidirectional flow of data, such as where connected units (e.g., neurons or nodes) form a directed cycle.
  • a network may be used to model or exhibit dynamic temporal behavior, such as involved in dynamic systems, such as a wide variety of the automation systems, machines and devices described throughout this disclosure, such as an automated agent interacting with a marketplace for purposes of collecting data, testing spot market transactions, execution transactions, and the like, where dynamic system behavior involves complex interactions that a user may desire to understand, predict, control and/or optimize.
  • the recurrent neural network may be used to anticipate the state of a market, such as one involving a dynamic process or action, such as a change in state of a resource that is traded in or that enables a marketplace of transactional environment.
  • the recurrent neural network may use internal memory to process a sequence of inputs, such as from other nodes and/or from sensors and other data inputs from or about the transactional environment, of the various types described herein.
  • the recurrent neural network may also be used for pattern recognition, such as for recognizing a machine, component, agent, or other item based on a behavioral signature, a profile, a set of feature vectors (such as in an audio file or image), or the like.
  • a recurrent neural network may recognize a shift in an operational mode of a marketplace or machine by learning to classify the shift from a training data set consisting of a stream of data from one or more data sources of sensors applied to or about one or more resources.
  • a modular neural network may comprise a series of independent neural networks (such as ones of various types described herein) that are moderated by an intermediary.
  • Each of the independent neural networks in the modular neural network may work with separate inputs, accomplishing sub tasks that make up the task the modular network as whole is intended to perform.
  • a modular neural network may comprise a recurrent neural network for pattern recognition, such as to recognize what type of machine or system is being sensed by one or more sensors that are provided as input channels to the modular network and an RBF neural network for optimizing the behavior of the machine or system once understood.
  • the intermediary may accept inputs of each of the individual neural networks, process them, and create output for the modular neural network, such an appropriate control parameter, a prediction of state, or the like.
  • Combinations among any of the pairs, triplets, or larger combinations, of the various neural network types described herein, are encompassed by the present disclosure. This may include combinations where an expert system uses one neural network for recognizing a pattern (e.g., a pattern indicating a problem or fault condition) and a different neural network for selforganizing an activity or workflow based on the recognized pattern (such as providing an output governing autonomous control of a system in response to the recognized condition or pattern).
  • a pattern e.g., a pattern indicating a problem or fault condition
  • a different neural network for selforganizing an activity or workflow based on the recognized pattern (such as providing an output governing autonomous control of a system in response to the recognized condition or pattern).
  • This may also include combinations where an expert system uses one neural network for classifying an item (e.g., identifying a machine, a component, or an operational mode) and a different neural network for predicting a state of the item (e.g., a fault state, an operational state, an anticipated state, a maintenance state, or the like).
  • an expert system uses one neural network for classifying an item (e.g., identifying a machine, a component, or an operational mode) and a different neural network for predicting a state of the item (e.g., a fault state, an operational state, an anticipated state, a maintenance state, or the like).
  • Modular neural networks may also include situations where an expert system uses one neural network for determining a state or context (such as a state of a machine, a process, a workflow, a marketplace, a storage system, a network, a data collector, or the like) and a different neural network for self-organizing a process involving the state or context (e.g., a data storage process, a network coding process, a network selection process, a data marketplace process, a power generation process, a manufacturing process, a refining process, a digging process, a boring process, or other process described herein).
  • a state or context such as a state of a machine, a process, a workflow, a marketplace, a storage system, a network, a data collector, or the like
  • a different neural network for self-organizing a process involving the state or context (e.g., a data storage process, a network coding process, a network selection process, a data marketplace process, a power generation process
  • methods and systems described herein that involve an expert system or self-organization capability may use a physical neural network where one or more hardware elements is used to perform or simulate neural behavior.
  • one or more hardware neurons may be configured to stream voltage values, current values, or the like that represent sensor data, such as to calculate information from analog sensor inputs representing energy consumption, energy production, or the like, such as by one or more machines providing energy or consuming energy for one or more transactions.
  • One or more hardware nodes may be configured to stream output data resulting from the activity of the neural net.
  • Hardware nodes which may comprise one or more chips, microprocessors, integrated circuits, programmable logic controllers, application-specific integrated circuits, field-programmable gate arrays, or the like, may be provided to optimize the machine that is producing or consuming energy, or to optimize another parameter of some part of a neural net of any of the types described herein.
  • Hardware nodes may include hardware for acceleration of calculations (such as dedicated processors for performing basic or more sophisticated calculations on input data to provide outputs, dedicated processors for filtering or compressing data, dedicated processors for decompressing data, dedicated processors for compression of specific file or data types (e.g., for handling image data, video streams, acoustic signals, thermal images, heat maps, or the like), and the like.
  • a physical neural network may be embodied in a data collector, including one that may be reconfigured by switching or routing inputs in varying configurations, such as to provide different neural net configurations within the data collector for handling different types of inputs (with the switching and configuration optionally under control of an expert system, which may include a software-based neural net located on the data collector or remotely).
  • a physical, or at least partially physical, neural network may include physical hardware nodes located in a storage system, such as for storing data within a machine, a data storage system, a distributed ledger, a mobile device, a server, a cloud resource, or in a transactional environment, such as for accelerating input/output functions to one or more storage elements that supply data to or take data from the neural net.
  • a physical, or at least partially physical, neural network may include physical hardware nodes located in a network, such as for transmitting data within, to or from an industrial environment, such as for accelerating input/output functions to one or more network nodes in the net, accelerating relay functions, or the like.
  • an electrically adjustable resistance material may be used for emulating the function of a neural synapse.
  • the physical hardware emulates the neurons, and software emulates the neural network between the neurons.
  • neural networks complement conventional algorithmic computers. They are versatile and may be trained to perform appropriate functions without the need for any instructions, such as classification functions, optimization functions, pattern recognition functions, control functions, selection functions, evolution functions, and others.
  • methods and systems described herein that involve an expert system or self-organization capability may use a multilayered feed forward neural network, such as for complex pattern classification of one or more items, phenomena, modes, states, or the like.
  • a multilayered feed forward neural network may be trained by an optimization technique, such as a genetic algorithm, such as to explore a large and complex space of options to find an optimum, or near-optimum, global solution.
  • one or more genetic algorithms may be used to train a multilayered feed forward neural network to classify complex phenomena, such as to recognize complex operational modes of machines, such as modes involving complex interactions among machines (including interference effects, resonance effects, and the like), modes involving non-linear phenomena, modes involving critical faults, such as where multiple, simultaneous faults occur, making root cause analysis difficult, and others.
  • a multilayered feed forward neural network may be used to classify results from monitoring of a marketplace, such as monitoring systems, such as automated agents, that operate within the marketplace, as well as monitoring resources that enable the marketplace, such as computing, networking, energy, data storage, energy storage, and other resources.
  • methods and systems described herein that involve an expert system or self-organization capability may use a feed- forward, back-propagation multi-layer perceptron (MLP) neural network, such as for handling one or more remote sensing applications, such as for taking inputs from sensors distributed throughout various transactional environments.
  • MLP multi-layer perceptron
  • the MLP neural network may be used for classification of transactional environments and resource environments, such as spot markets, forward markets, energy markets, renewable energy credit (REC) markets, networking markets, advertising markets, spectrum markets, ticketing markets, rewards markets, compute markets, and others mentioned throughout this disclosure, as well as physical resources and environments that produce them, such as energy resources (including renewable energy environments, mining environments, exploration environments, drilling environments, and the like, including classification of geological structures (including underground features and above ground features), classification of materials (including fluids, minerals, metals, and the like), and other problems. This may include fuzzy classification.
  • methods and systems described herein that involve an expert system or self-organization capability may use a structure- adaptive neural network, where the structure of a neural network is adapted, such as based on a rule, a sensed condition, a contextual parameter, or the like. For example, if a neural network does not converge on a solution, such as classifying an item or arriving at a prediction, when acting on a set of inputs after some amount of training, the neural network may be modified, such as from a feed forward neural network to a recurrent neural network, such as by switching data paths between some subset of nodes from unidirectional to bi-directional data paths.
  • the structure adaptation may occur under control of an expert system, such as to trigger adaptation upon occurrence of a trigger, rule, or event, such as recognizing occurrence of a threshold (such as an absence of a convergence to a solution within a given amount of time) or recognizing a phenomenon as requiring different or additional structure (such as recognizing that a system is varying dynamically or in a non-linear fashion).
  • an expert system may switch from a simple neural network structure like a feed forward neural network to a more complex neural network structure like a recurrent neural network, a convolutional neural network, or the like upon receiving an indication that a continuously variable transmission is being used to drive a generator, turbine, or the like in a system being analyzed.
  • methods and systems described herein that involve an expert system or self-organization capability may use an autoencoder, autoassociator or Diabolo neural network, which may be similar to a multilayer perceptron (MLP) neural network, such as where there may be an input layer, an output layer and one or more hidden layers connecting them.
  • MLP multilayer perceptron
  • the output layer in the auto-encoder may have the same number of units as the input layer, where the purpose of the MLP neural network is to reconstruct its own inputs (rather than just emitting a target value). Therefore, the auto encoders may operate as an unsupervised learning model.
  • An auto encoder may be used, for example, for unsupervised learning of efficient codings, such as for dimensionality reduction, for learning generative models of data, and the like.
  • an auto-encoding neural network may be used to self-learn an efficient network coding for transmission of analog sensor data from a machine over one or more networks or of digital data from one or more data sources.
  • an auto- encoding neural network may be used to self-learn an efficient storage approach for storage of streams of data.
  • a probabilistic neural network may comprise a multi-layer (e.g., four-layer) feed forward neural network, where layers may include input layers, hidden layers, pattern/summation layers and an output layer.
  • a parent probability distribution function (PDF) of each class may be approximated, such as by a Parzen window and/or a non-parametric function. Then, using the PDF of each class, the class probability of a new input is estimated, and Bayes’ rule may be employed, such as to allocate it to the class with the highest posterior probability.
  • PDF parent probability distribution function
  • a PNN may embody a Bayesian network and may use a statistical algorithm or analytic technique, such as Kernel Fisher discriminant analysis technique.
  • the PNN may be used for classification and pattern recognition in any of a wide range of embodiments disclosed herein.
  • a probabilistic neural network may be used to predict a fault condition of an engine based on collection of data inputs from sensors and instruments for the engine.
  • TDNN time delay neural network
  • a time delay neural network may form part of a larger pattern recognition system, such as using a perceptron network.
  • a TDNN may be trained with supervised learning, such as where connection weights are trained with back propagation or under feedback.
  • a TDNN may be used to process sensor data from distinct streams, such as a stream of velocity data, a stream of acceleration data, a stream of temperature data, a stream of pressure data, and the like, where time delays are used to align the data streams in time, such as to help understand patterns that involve understanding of the various streams (e.g., changes in price patterns in spot or forward markets).
  • methods and systems described herein that involve an expert system or self-organization capability may use a convolutional neural network (referred to in some cases as a CNN, a ConvNet, a shift invariant neural network, or a space invariant neural network), wherein the units are connected in a pattern similar to the visual cortex of the human brain.
  • Neurons may respond to stimuli in a restricted region of space, referred to as a receptive field.
  • Receptive fields may partially overlap, such that they collectively cover the entire (e.g., visual) field.
  • Node responses may be calculated mathematically, such as by a convolution operation, such as using multilayer perceptrons that use minimal preprocessing.
  • a convolutional neural network may be used for recognition within images and video streams, such as for recognizing a type of machine in a large environment using a camera system disposed on a mobile data collector, such as on a drone or mobile robot.
  • a convolutional neural network may be used to provide a recommendation based on data inputs, including sensor inputs and other contextual information, such as recommending a route for a mobile data collector.
  • a convolutional neural network may be used for processing inputs, such as for natural language processing of instructions provided by one or more parties involved in a workflow in an environment.
  • a convolutional neural network may be deployed with a large number of neurons (e.g., 100,000, 500,000 or more), with multiple (e.g., 4, 5, 6 or more) layers, and with many (e.g., millions) of parameters.
  • a convolutional neural net may use one or more convolutional nets.
  • methods and systems described herein that involve an expert system or self-organization capability may use a regulatory feedback network, such as for recognizing emergent phenomena (such as new types of behavior not previously understood in a transactional environment).
  • methods and systems described herein that involve an expert system or self-organization capability may use a self-organizing map (SOM), involving unsupervised learning.
  • SOM self-organizing map
  • a set of neurons may learn to map points in an input space to coordinates in an output space.
  • the input space may have different dimensions and topology from the output space, and the SOM may preserve these while mapping phenomena into groups.
  • methods and systems described herein that involve an expert system or self-organization capability may use a learning vector quantization neural net (LVQ).
  • LVQ learning vector quantization neural net
  • Prototypical representatives of the classes may parameterize, together with an appropriate distance measure, in a distance-based classification scheme.
  • an ESN may comprise a recurrent neural network with a sparsely connected, random hidden layer.
  • the weights of output neurons may be changed (e.g., the weights may be trained based on feedback).
  • an ESN may be used to handle time series patterns, such as, in an example, recognizing a pattern of events associated with a market, such as the pattern of price changes in response to stimuli.
  • a Bi-directional, recurrent neural network such as using a finite sequence of values (e.g., voltage values from a sensor) to predict or label each element of the sequence based on both the past and the future context of the element. This may be done by adding the outputs of two RNNs, such as one processing the sequence from left to right, the other one from right to left. The combined outputs are the predictions of target signals, such as ones provided by a teacher or supervisor.
  • a bi-directional RNN may be combined with a long short-term memory RNN.
  • methods and systems described herein that involve an expert system or self-organization capability may use a hierarchical RNN that connects elements in various ways to decompose hierarchical behavior, such as into useful subprograms.
  • a hierarchical RNN may be used to manage one or more hierarchical templates for data collection in a transactional environment.
  • methods and systems described herein that involve an expert system or self-organization capability may use a stochastic neural network, which may introduce random variations into the network. Such random variations may be viewed as a form of statistical sampling, such as Monte Carlo sampling.
  • methods and systems described herein that involve an expert system or self-organization capability may use a genetic scale recurrent neural network.
  • an RNN (often an LSTM) is used where a series is decomposed into a number of scales where every scale informs the primary length between two consecutive points.
  • a first order scale consists of a normal RNN, a second order consists of all points separated by two indices and so on.
  • the Nth order RNN connects the first and last node.
  • the outputs from all the various scales may be treated as a committee of members, and the associated scores may be used genetically for the next iteration.
  • methods and systems described herein that involve an expert system or self-organization capability may use a committee of machines (CoM), comprising a collection of different neural networks that together "vote" on a given example.
  • CoM committee of machines
  • neural networks may suffer from local minima, starting with the same architecture and training, but using randomly different initial weights often gives different results.
  • a CoM tends to stabilize the result.
  • ASNN associative neural network
  • An associative neural network may have a memory that may coincide with a training set. If new data become available, the network instantly improves its predictive ability and provides data approximation (self-leams) without retraining.
  • Another important feature of ASNN is the possibility to interpret neural network results by analysis of correlations between data cases in the space of models.
  • methods and systems described herein that involve an expert system or self-organization capability may use an instantaneously trained neural network (ITNN), where the weights of the hidden and the output layers are mapped directly from training vector data.
  • ITNN instantaneously trained neural network
  • methods and systems described herein that involve an expert system or self-organization capability may use a spiking neural network, which may explicitly consider the timing of inputs.
  • the network input and output may be represented as a series of spikes (such as a delta function or more complex shapes).
  • SNNs may process information in the time domain (e.g., signals that vary over time, such as signals involving dynamic behavior of markets or transactional environments). They are often implemented as recurrent networks.
  • methods and systems described herein that involve an expert system or self-organization capability may use a dynamic neural network that addresses nonlinear multivariate behavior and includes learning of time-dependent behavior, such as transient phenomena and delay effects.
  • Transients may include behavior of shifting market variables, such as prices, available quantities, available counterparties, and the like.
  • cascade correlation may be used as an architecture and supervised learning algorithm, supplementing adjustment of the weights in a network of fixed topology.
  • Cascade-correlation may begin with a minimal network, then automatically trains, and adds new hidden units one by one, creating a multi-layer structure. Once a new hidden unit has been added to the network, its input- side weights may be frozen. This unit then becomes a permanent feature-detector in the network, available for producing outputs or for creating other, more complex feature detectors.
  • the cascade-correlation architecture may learn quickly, determine its own size and topology, and retain the structures it has built even if the training set changes and requires no back-propagation.
  • methods and systems described herein that involve an expert system or self-organization capability may use a neuro-fuzzy network, such as involving a fuzzy inference system in the body of an artificial neural network.
  • a neuro-fuzzy network such as involving a fuzzy inference system in the body of an artificial neural network.
  • several layers may simulate the processes involved in a fuzzy inference, such as fuzzification, inference, aggregation and defuzzification.
  • Embedding a fuzzy system in a general structure of a neural net as the benefit of using available training methods to find the parameters of a fuzzy system.
  • compositional pattern-producing network such as a variation of an associative neural network (ANN) that differs the set of activation functions and how they are applied. While typical ANNs often contain only sigmoid functions (and sometimes Gaussian functions), CPPNs may include both types of functions and many others. Furthermore, CPPNs may be applied across the entire space of possible inputs, so that they may represent a complete image. Since they are compositions of functions, CPPNs in effect encode images at infinite resolution and may be sampled for a particular display at whatever resolution is optimal.
  • CPPN compositional pattern-producing network
  • ANN associative neural network
  • This type of network may add new patterns without re-training.
  • methods and systems described herein that involve an expert system or self-organization capability may use a one-shot associative memory network, such as by creating a specific memory structure, which assigns each new pattern to an orthogonal plane using adjacently connected hierarchical arrays.
  • HTM hierarchical temporal memory
  • HTM may use a biomimetic model based on memory -prediction theory. HTM may be used to discover and infer the high-level causes of observed input patterns and sequences.
  • HAM holographic associative memory
  • Information may be mapped onto the phase orientation of complex numbers.
  • the memory is effective for associative memory tasks, generalization and pattern recognition with changeable attention.
  • various embodiments involving network coding may be used to code transmission data among network nodes in a neural net, such as where nodes are located in one or more data collectors or machines in a transactional environment.
  • one or more of the controllers, circuits, systems, data collectors, storage systems, network elements, or the like as described throughout this disclosure may be embodied in or on an integrated circuit, such as an analog, digital, or mixed signal circuit, such as a microprocessor, a programmable logic controller, an application-specific integrated circuit, a field programmable gate array, or other circuits, such as embodied on one or more chips disposed on one or more circuit boards, such as to provide in hardware (with potentially accelerated speed, energy performance, input-output performance, or the like) one or more of the functions described herein.
  • an integrated circuit such as an analog, digital, or mixed signal circuit, such as a microprocessor, a programmable logic controller, an application-specific integrated circuit, a field programmable gate array, or other circuits, such as embodied on one or more chips disposed on one or more circuit boards, such as to provide in hardware (with potentially accelerated speed, energy performance, input-output performance, or the like) one or more of the functions described here
  • a digital IC typically a microprocessor, digital signal processor, microcontroller, or the like may use Boolean algebra to process digital signals to embody complex logic, such as involved in the circuits, controllers, and other systems described herein.
  • a data collector, an expert system, a storage system, or the like may be embodied as a digital integrated circuit, such as a logic IC, memory chip, interface IC (e.g., a level shifter, a serializer, a deserializer, and the like), a power management IC and/or a programmable device; an analog integrated circuit, such as a linear IC, RF IC, or the like, or a mixed signal IC, such as a data acquisition IC (including A/D converters, D/A converter, digital potentiometers) and/or a clock/timing IC.
  • a digital integrated circuit such as a logic IC, memory chip, interface IC (e.g., a level shifter, a serializer, a deserializer, and the like), a power management IC and/or a programmable device; an analog integrated circuit, such as a linear IC, RF IC, or the like, or a mixed signal IC, such as
  • the environment includes an intelligent energy and compute facility (such as a large scale facility hosting many compute resources and having access to a large energy source, such as a hydropower source), as well as a host intelligent energy and compute facility resource management platform, referred to in some cases for convenience as the energy and information technology platform (with networking, data storage, data processing and other resources as described herein), a set of data sources, a set of expert systems, interfaces to a set of market platforms and external resources, and a set of user (or client) systems and devices.
  • an intelligent energy and compute facility such as a large scale facility hosting many compute resources and having access to a large energy source, such as a hydropower source
  • a host intelligent energy and compute facility resource management platform referred to in some cases for convenience as the energy and information technology platform (with networking, data storage, data processing and other resources as described herein)
  • a set of data sources with networking, data storage, data processing and other resources as described herein
  • a set of expert systems interfaces to a set of market platforms and external resources
  • a facility may be configured to access an inexpensive (at least during some time periods) power source (such as a hydropower dam, a wind farm, a solar array, a nuclear power plant, or a grid), to contain a large set of networked information technology resources, including processing units, servers, and the like that are capable of flexible utilization (such as by switching inputs, switching configurations, switching programming and the like), and to provide a range of outputs that can also be flexibly configured (such as passing through power to a smart grid, providing computational results (such as for cryptocurrency mining, artificial intelligence, or analytics).
  • a facility may include a power storage system, such as for large scale storage of available power.
  • a user can access the energy and information technology platform to initiate and manage a set of activities that involve optimizing energy and computing resources among a diverse set of available tasks.
  • Energy resources may include hydropower, nuclear power, wind power, solar power, grid power and the like, as well as energy storage resources, such as batteries, gravity power, and storage using thermal materials, such as molten salts.
  • Computing resources may include GPUs, FPGAs, servers, chips, Asics, processors, data storage media, networking resources, and many others.
  • Available tasks may include cryptocurrency hash processing, expert system processing, computer vision processing, NLP, path optimization, applications of models such as for analytics, etc.
  • the platform may include various subsystems that may be implemented as micro services, such that other subsystems of the system access the functionality of a subsystem providing a micro service via application programming interface API.
  • the various services that are provided by the subsystems may be deployed in bundles that are integrated, such as by a set of APIs. Each of the subsystems is described in greater detail with respect to Figure 130.
  • the External Data Sources can include any system or device that can provide data to the platform.
  • data sources can include market data sources (e.g., for financial markets, commercial markets (including e-commerce), advertising markets, energy markets, telecommunication markets, and many others).
  • the energy and computing resource platform accesses external data sources via a network (e.g., the Internet) in any suitable manner (e.g., crawlers, extract-transform-load (ETL) systems, gateways, brokers, application programming interfaces (APIs), spiders, distributed database queries, and the like).
  • a network e.g., the Internet
  • any suitable manner e.g., crawlers, extract-transform-load (ETL) systems, gateways, brokers, application programming interfaces (APIs), spiders, distributed database queries, and the like.
  • a facility is a facility that has an energy resource (e.g., a hydro power resource) and a set of compute resource (e.g., a set of flexible computing resources that can be provisioned and managed to perform computing tasks, such as GPUs, FPGAs and many others, a set of flexible networking resources that can similarly be provisioned and managed, such as by adjusting network coding protocols and parameters), and the like.
  • an energy resource e.g., a hydro power resource
  • a set of compute resource e.g., a set of flexible computing resources that can be provisioned and managed to perform computing tasks, such as GPUs, FPGAs and many others, a set of flexible networking resources that can similarly be provisioned and managed, such as by adjusting network coding protocols and parameters
  • User and client systems and devices can include any system or device that may consume one or more computing or energy resource made available by the energy and computing resource platform.
  • cryptocurrency systems e.g., for Bitcoin and other cryptocurrency mining operations
  • expert and artificial intelligence systems such as neural networks and other systems, such as for computer vision, natural language processing, path determination and optimization, pattern recognition, deep learning, supervised learning, decision support, and many others
  • energy management systems such as smart grid systems
  • User and client systems may include user devices, such as smartphones, tablet computer devices, laptop computing devices, personal computing devices, smart televisions, gaming consoles, and the like.
  • Figure 130 illustrates an example energy and computing resource platform according to some embodiments of the present disclosure.
  • the energy and computing resource platform may include a processing system 13002, a storage system 13004, and a communication system 13006.
  • the processing system 13002 may include one or more processors and memory.
  • the processors may operate in an individual or distributed manner.
  • the processors may be in the same physical device or in separate 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.
  • the processing system 13002 may execute the facility management system 13008, the data acquisition system 13010, the cognitive processes system 13012, the lead generation system 13014, the content generation system 13016, and the workflow system 13018.
  • the storage system 13004 may include one or more computer-readable storage mediums.
  • the computer-readable storage mediums may be located in the same physical device or in separate devices, which may or may not be located in the same facility, which may or may not be located in the same facility.
  • the computer-readable storage mediums may include flash devices, solid-state memory devices, hard disk drives, and the like.
  • the storage system 13004 stores one or more of a facility data store 13020, a person data store 13022, and an external data store 13024.
  • the communication system 13006 may include one or more transceivers that are configured to effectuate wireless or wired communication with one or more external devices, including user devices and/or servers, via a network (e.g., the Internet and/or a cellular network).
  • the communication system 13006 may implement any suitable communication protocol.
  • the communication system xxx may implement an IEEE 801.11 wireless communication protocol and/or any suitable cellular communication protocol to effectuate wireless communication with external devices and external data stores 13024 via a wireless network.
  • the facility data records may include a facility identifier (e.g., a unique identifier that corresponds to the facility), a facility type (e.g., energy system and capabilities, compute systems and capabilities, networking systems and capabilities), facility attributes (e.g., name of the facility, name of the facility initiator, description of the facility, keywords of the facility, goals of the facility, timing elements, schedules, and the like), participants/potential participants in the facility (e.g., identifiers of owners, operators, hosts, service providers, consumers, clients, users, workers, and others), and any suitable metadata (e.g., creation date, launch date, scheduled requirements and the like).
  • a facility identifier e.g., a unique identifier that corresponds to the facility
  • a facility type e.g., energy system and capabilities, compute systems and capabilities, networking systems and capabilities
  • facility attributes e.g., name of the facility, name of the facility initiator, description of the facility, keywords of the facility, goals of the facility, timing elements,
  • content such as a document, message, alert, report, webpage and/or application page based on the contents of the data record.
  • there can be management of existing facilities updates the data record of a facility, determinations of outcomes (e.g., energy produced, compute tasks completed, processing outcomes achieved, financial outcomes achieved, service levels met and many others), and sending of information (e.g., updates, alerts, requests, instructions, and the like) to individuals and systems.
  • outcomes e.g., energy produced, compute tasks completed, processing outcomes achieved, financial outcomes achieved, service levels met and many others
  • information e.g., updates, alerts, requests, instructions, and the like
  • Data Acquisition Systems can acquire various types of data from different data sources and organizes that data into one or more data structures.
  • the data acquisition system receives data from users via a user interface (e.g., user types in profile information).
  • the data acquisition system can retrieve data from passive electronic sources.
  • the data acquisition system can implement crawlers to crawl different websites or applications.
  • the data acquisition system can implement an API to retrieve data from external data sources or user devices (e.g., various contact lists from user’s phone or email account).
  • the data acquisition system can structure the obtained data into appropriate data structures.
  • the data acquisition system generates and maintains person records based on data collected regarding individuals.
  • a person datastore stores person records.
  • the person datastore may include one or more databases, indexes, tables, and the like. Each person record may correspond to a respective individual and may be organized according to any suitable schema.
  • Figure 132 illustrates an example schema of a person record.
  • each person record may include a unique person identifier (e.g., username or value), and may define all data relating to a person, including a person’s name, facilities they are a part of or associated with (e.g., a list of facility identifiers), attributes of the person (age, location, job, company, role, skills, competencies, capabilities, education history, job history, and the like), a list of contacts or relationships of the person (e.g., in a role hierarchy or graph), and any suitable metadata (e.g., date joined, dates actions were taken, dates input was received, and the like).
  • a unique person identifier e.g., username or value
  • data relating to a person including a person’s name, facilities they are a part of or associated with (e.g., a list of facility identifiers), attributes of the person (age, location, job, company, role, skills, competencies, capabilities, education history, job history,
  • the data acquisition system generates and maintains one or more graphs based on the retrieved data.
  • a graph datastore may store the one or more graphs.
  • the graph may be specific to a facility or may be a global graph.
  • the graph may be used in many different applications (e.g., identifying a set of roles, such as for authentication, for approvals, and the like for persons, or identifying system configurations, capabilities, or the like, such as hierarchies of energy producing, computing, networking, or other systems, subsystems and/or resources).
  • a graph may be stored in a graph database, where data is stored in a collection of nodes and edges.
  • a 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.
  • a person node may include a person ID that identifies the individual represented by the node and a company node may include a company identifier that identifies a company.
  • a “works for” edge that is directed from a person node to a company node may denote that the person represented by the edge node works for the company represented by the company node.
  • a person node may include a person ID that identifies the individual represented by the node and a facility node may include a facility identifier that identifies a facility.
  • a “manages” edge that is directed from a person node to a facility node may denote that the person represented by the person node is a manager of the facility represented by the facility node.
  • an edge or node may contain or reference additional data.
  • a “manages” edge may include a function that indicates a specific function within a facility that is managed by a person.
  • the graph(s) can be used in a number of different applications, which are discussed with respect to the cognitive processing system.
  • validated Identity information may be imported from one or more identity information providers, as well as data from LinkedlnTM and other social network sources regarding data acquisition and structuring data.
  • the data acquisition system may include an identity management system (not shown in Figs) of the platform may manage identity stitching, identity resolution, identity normalization, and the like, such as determining where an individual represented across different social networking sites and email contacts is in fact the same person.
  • the data acquisition system may include a profile aggregation system (not shown in Figs) that finds and aggregates disparate pieces of information to generate a comprehensive profile for a person. The profile aggregation system may also deduplicate individuals.
  • the cognitive processing system 13312 may implement one or more of machine learning processes, artificial intelligence processes, analytics processes, natural language processing processes, and natural language generation processes.
  • FIG. 133 illustrates an example cognitive processing system according to some embodiments of the present disclosure.
  • the cognitive processing system may include a machine learning system 13302, an artificial intelligence (Al) system 13304, an analytics system 13306, a natural language processing system 13308, and a natural language generation system 13310.
  • the machine learning system may train models, such as predictive models (e.g., various types of neural networks, regression based models, and other machine- learned models).
  • training can be supervised, semi-supervised, or unsupervised.
  • training can be done using training data, which may be collected or generated for training purposes.
  • a facility output model may be a model that receive facility attributes and outputs one or more predictions regarding the production or other output of a facility. Examples of predictions may be the amount of energy a facility will produce, the amount of processing the facility will undertake, the amount of data a network will be able to transfer, the amount of data that can be stored, the price of a component, service or the like (such as supplied to or provided by a facility), a profit generated by accomplishing a given tasks, the cost entailed in performing an action, and the like.
  • the machine learning system optionally trains a model based on training data.
  • the machine learning system may receive vectors containing facility attributes (e.g., facility type, facility capability, objectives sought, constraints or rules that apply to utilization of resources or the facility, or the like), person attributes (e.g., role, components managed, and the like), and outcomes (e.g., energy produced, computing tasks completed, and financial results, among many others).
  • facility attributes e.g., facility type, facility capability, objectives sought, constraints or rules that apply to utilization of resources or the facility, or the like
  • person attributes e.g., role, components managed, and the like
  • outcomes e.g., energy produced, computing tasks completed, and financial results, among many others.
  • Each vector corresponds to a respective outcome and the attributes of the respective facility and respective actions that led to the outcome.
  • the machine learning system takes in the vectors and generates predictive model based thereon.
  • the machine learning system may store the predictive models in the model datastore.
  • training can also be done based on feedback received by the system, which is also referred to as “reinforcement learning.”
  • the machine learning system may receive a set of circumstances that led to a prediction (e.g., attributes of facility, attributes of a model, and the like) and an outcome related to the facility and may update the model according to the feedback.
  • training may be provided from a training data set that is created by observing actions of a set of humans, such as facility managers managing facilities that have various capabilities and that are involved in various contexts and situations.
  • This may include use of robotic process automation to learn on a training data set of interactions of humans with interfaces, such as graphical user interfaces, of one or more computer programs, such as dashboards, control systems, and other systems that are used to manage an energy and compute management facility.
  • the Al system leverages the predictive models to make predictions regarding facilities. Examples of predictions include ones related to inputs to a facility (e.g., available energy, cost of energy, cost of compute resources, networking capacity and the like, as well as various market information, such as pricing information for end use markets), ones related to components or systems of a facility (including performance predictions, maintenance predictions, uptime/downtime predictions, capacity predictions and the like), ones related to functions or workflows of the facility (such as ones that involved conditions or states that may result in following one or more distinct possible paths within a workflow, a process, or the like), ones related to outputs of the facility, and others.
  • the Al system receives a facility identifier.
  • the Al system may retrieve attributes corresponding to the facility.
  • the Al system may obtain the facility attributes from a graph. Additionally or alternatively, the Al system may obtain the facility attributes from a facility record corresponding to the facility identifier, and the person attributes from a person record corresponding to the person identifier.
  • the Al system may output scores for each possible prediction, where each prediction corresponds to a possible outcome. For example, in using a prediction model used to determine a likelihood that a hydroelectric source for a facility will produce 5 MW of power, the prediction model can output a score for a “will produce” outcome and a score for a “will not produce” outcome. The Al system may then select the outcome with the highest score as the prediction. Alternatively, the Al system may output the respective scores to a requesting system.
  • a clustering system clusters records or entities based on attributes contained herein. For example, similar facilities, resources, people, clients, or the like may be clustered.
  • the clustering system may implement any suitable clustering algorithm. For example, when clustering people records to identify a list of customer leads corresponding to resources that can be sold by a facility, the clustering system may implement k-nearest neighbors clustering, whereby the clustering system identifies k people records that most closely relate 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 people records, whereby the clustering system or another system selects items from the cluster. [000485] Analytics System
  • an analytics system may perform analytics relating to various aspects of the energy and computing resource platform.
  • the analytics system may analyze certain communications to determine which configurations of a facility produce the greatest yield, what conditions tend to indicate potential faults or problems, and the like.
  • Figure 134 shows the manner by which the lead generation system generates a lead list.
  • Lead generation system receives a list of potential leads 13402 (such as for consumers of available products or resources).
  • the lead generation system may provide the list of leads to the clustering system 13404.
  • the clustering system clusters the profile of the lead with the clusters of facility attributes 13406 to identify one or more clusters.
  • the clustering system returns a list of leads 13410.
  • the clustering system returns the clusters 13408, and the lead generation system selects the list of leads 13410 from the cluster to which a prospect belongs.
  • Figure 135 illustrates the manner by which the lead generation system determines facility outputs for leads identified in the list of leads.
  • the lead generation system provides a lead identifier of a respective lead to the Al system (step 13502).
  • the Al system may then obtain the lead attributes of the lead and facility attributes of the facility and may feed the respective attributes into a prediction model (step 13504).
  • the prediction model outputs a prediction, which may be scores associated with each possible outcome, or a single predicted outcome that was selected based on its respective score (e.g., the outcome having the highest score) (step 13506).
  • the lead generation system may iterate in this manner for each lead in the lead list.
  • the lead generation system may generate leads that are consumers of compute capabilities, energy capabilities, predictions and forecasts, optimization results, and others.
  • the lead generation system categorizes the lead (step 13508) and generates a lead list (step 13512) which it provides to the facility operator or host of the systems, including an indicator of the reason why a lead may be willing to engage the facility, such as, for example, that the lead is an intensive user of computing resources, such as to forecast behavior of a complex, multi-variable market, or to mine for cryptocurrency.
  • the lead generation system continues checking the lead list (step 13510).
  • a content generation system of the platform generates content for a contact event, such as an email, text message, or a post to a network, or a machine-to-machine message, such as communicating via an API or a peer-to-peer system.
  • the content is customized using artificial intelligence based on the attributes of the facility, attributes of a recipient (e.g., based on the profile of a person, the role of a person, or the like), and/or relating to the project or activity to which the facility relates.
  • the content generation system may be seeded with a set of templates, which may be customized, such as by training the content generation system on a training set of data created by human writers, and which may be further trained by feedback based on outcomes tracked by the platform, such as outcomes indicating success of particular forms of communication in generating donations to a facility, as well as other indicators as noted throughout this disclosure.
  • the content generation system may customize content based on attributes of the facility, a project, and/or one or more people, and the like. For example, a facility manager may receive short messages about events related to facility operations, including codes, acronyms, and jargon, while an outside consumer of outputs from the facility may receive a more formal report relating to the same event.
  • Figure 136 illustrates a manner by which the content generation system may generate personalized content.
  • the content generation system receives a recipient id, a sender id (which may be a person or a system, among others), and a facility id (step 13602).
  • the content generation system may determine the appropriate template (step 13604) to use based on the relationships among the recipient, sender, and facility and/or based on other considerations (e.g., a recipient who is a busy manager is more likely to respond to less formal messages or more formal messages).
  • the content generation system may provide the template (or an identifier thereof) to the natural language generation system, along with the recipient id, the sender id, and the facility id.
  • the natural language generation system may obtain facility attributes based on the facility id, and person attributes corresponding to the recipient or sender based on their identities (step 13606). The natural language generation system may then generate the personalized or customized content (step 13608) based on the selected template, the facility parameters, and/or other attributes of the various types described herein. The natural language generation system may output the generated content (step 13610) to the content generation system.
  • a person such as a facility manager, may approve the generated content provided by the content generation system and/or make edits to the generated content, then send the content, such as via email and/or other channels.
  • the platform tracks the contact event.
  • an 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 may define a machine learning model 13702 for performing analytics, simulation, decision making, and prediction making 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 algorithm and/or statistical model that performs specific tasks without using explicit instructions, relying instead on patterns and inference.
  • the machine learning model 13702 builds one or more mathematical models based on training data to make predictions and/or decisions without being explicitly programmed to perform the specific tasks.
  • the machine learning model 13702 may receive inputs of sensor data as training data, including event data 13724 and state data 13772 related to one or more of the transaction entities through data collection systems 13718 and monitoring systems 13706 and connectivity facilities 13716.
  • the event data 13724 and state data 13772 may be stored in a data storage system 13710
  • the sensor data input to the machine learning model 13702 may be used to train the machine learning model 13702 to perform the analytics, simulation, decision making, and prediction making relating to the data processing, data analysis, simulation creation, and simulation analysis of the one or more of the transaction entities.
  • the machine learning model 13702 may also use input data from a user or 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 configured to learn through supervised learning, unsupervised learning, reinforcement learning, self-learning, feature learning, sparse dictionary learning, anomaly detection, association rules, a combination thereof, or any other suitable algorithm for learning.
  • the artificial intelligence system 13748 may also define the digital twin system 13720 to create a digital replica of one or more of the transaction entities.
  • the digital replica of the one or more of the transaction entities may use substantially real-time sensor data to provide for substantially real-time virtual representation of the transaction entity and provides for simulation of one or more possible future states of the one or more transaction entities.
  • the digital replica exists simultaneously with the one or more transaction entities being replicated.
  • the digital replica provides one or more simulations of both physical elements and properties of the one or more transaction entities being replicated and the dynamics thereof, in embodiments, throughout the lifestyle of the one or more transaction entities being replicated.
  • the digital replica may provide a hypothetical simulation of the one or more transaction entities, for example during a design phase before the one or more transaction entities are constructed or fabricated, or during or after construction or fabrication of the one or more transaction entities by allowing for hypothetical extrapolation of sensor data to simulate a state of the one or more transaction entities, such as during high stress, after a period of time has passed during which component wear may be an issue, during maximum throughput operation, after one or more hypothetical or planned improvements have been made to the one or more transaction entities, or any other suitable hypothetical situation.
  • the machine learning model 13702 may automatically predict hypothetical situations for simulation with the digital replica, such as by predicting possible improvements to the one or more transaction entities, predicting when one or more components of the one or more transaction entities may fail, and/or suggesting possible improvements to the one or more transaction entities, such as changes to timing settings, arrangement, components, or any other suitable change to the transaction entities.
  • the digital replica allows for simulation of the one or more transaction entities during both design and operation phases of the one or more transaction entities, as well as simulation of hypothetical operation conditions and configurations of the one or more transaction entities.
  • the digital replica allows for invaluable analysis and simulation of the one or more transaction entities, by facilitating observation and measurement of nearly any type of metric, including temperature, wear, light, vibration, etc.
  • the machine learning model 13702 may process the sensor data including the event data 13724 and the state data 13772 to define simulation data for use by the digital twin system 13720.
  • the machine learning model 13702 may, for example, receive state 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 state data 13772 and the event data 13724 to format the state data 13772 and the event data 13724 into a format suitable for use by the digital twin system 13720 in creation of a digital replica of the transaction entity.
  • one or more transaction entities may include a robot configured to augment products on an adjacent assembly line.
  • the machine learning model 13702 may collect data from one or more sensors positioned 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 simulation data and output the simulation data to the digital twin system 13720.
  • the digital twin system 13720 simulation may use the simulation data to create one or more digital replicas of the robot, the simulation including for example metrics including temperature, wear, speed, rotation, and vibration of the robot and components thereof.
  • the simulation may be a substantially real-time simulation, allowing for a human user of the information technology to view the simulation of the robot, metrics related thereto, and metrics related to components thereof, in substantially real time.
  • the simulation may be a predictive or hypothetical situation, allowing for a human user of the information technology to view a predictive or hypothetical simulation of the robot, metrics related thereto, and metrics related to components thereof.
  • the machine learning model 13702 and the digital twin system 13720 may process sensor data and create a digital replica 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 related group of transaction entities.
  • the digital replica of the set of transaction entities may use substantially real-time sensor data to provide for substantially real-time virtual representation of the set of transaction entities and provide for simulation of one or more possible future states of the set of transaction entities.
  • the digital replica exists simultaneously with the set of transaction entities being replicated.
  • the digital replica provides one or more simulations of both physical elements and properties of the set of transaction entities being replicated and the dynamics thereof, in embodiments throughout the lifestyle of the set of transaction entities being replicated.
  • the one or more simulations may include a visual simulation, such as a wire-frame virtual representation of the one or more transaction entities that may be viewable on a monitor, using an augmented reality (AR) apparatus, or using a virtual reality (VR) apparatus.
  • the visual simulation may be able to be manipulated by a human user of the information technology system, such as zooming or highlighting components of the simulation and/or providing an exploded view of the one or more transaction entities.
  • the digital replica may provide a hypothetical simulation of the set of transaction entities, for example during a design phase before the one or more transaction entities are constructed or fabricated, or during or after construction or fabrication of the one or more transaction entities by allowing for hypothetical extrapolation of sensor data to simulate a state of the set of transaction entities, such as during high stress, after a period of time has passed during which component wear may be an issue, during maximum throughput operation, after one or more hypothetical or planned improvements have been made to the set of transaction entities, or any other suitable hypothetical situation.
  • the machine learning model 13702 may automatically predict hypothetical situations for simulation with the digital replica, 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 timing settings, arrangement, components, or any other suitable change to the transaction entities.
  • the digital replica allows for simulation of the set of transaction entities during both design and operation phases of the set of transaction entities, as well as simulation of hypothetical operation conditions and configurations of the set of transaction entities.
  • the digital replica allows for invaluable analysis and simulation of the one or more transaction entities, by facilitating observation and measurement of nearly any type of metric, including temperature, wear, light, vibration, etc.
  • the machine learning model 13702 may process the sensor data including the event data 13724 and the state data 13772 to define simulation data for use by the digital twin system 13720.
  • the machine learning model 13702 may, for example, receive state 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 state data 13772 and the event data 13724 to format the state data 13772 and the event data 13724 into a format suitable for use by the digital twin system 13720 in the creation of a digital replica of the set of transaction entities.
  • a set of transaction entities may include a die machine configured to place products on a conveyor belt, the conveyor belt on which the die machine is configured to place the products, and a plurality of robots configured to add parts to the products as they move along the assembly line.
  • the machine learning model 13702 may collect data from one or more sensors positioned on, near, in, and/or around each of the die machines, 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 simulation data and output the simulation data to the digital twin system 13720.
  • the digital twin system 13720 simulation may use the simulation data to create one or more digital replicas of the die machine, the conveyor belt, and the plurality of robots, the simulation including for example metrics including temperature, wear, speed, rotation, and vibration of the die machine, the conveyor belt, and the plurality of robots and components thereof.
  • the simulation may be a substantially real-time simulation, allowing for a human user of the information technology to view the simulation of the die machine, the conveyor belt, and the plurality of robots, metrics related thereto, and metrics related to components thereof, in substantially real time.
  • the simulation may be a predictive or hypothetical situation, allowing for a human user of the information technology to view a predictive or hypothetical simulation of the die machine, the conveyor belt, and the plurality of robots, metrics related thereto, and metrics related to components thereof.
  • the machine learning model 13702 may prioritize collection of sensor data for use in digital replica simulations of one or more of the transaction entities.
  • the machine learning model 13702 may use sensor data and user inputs to train, thereby learning which types of sensor data are most effective for creation of digital replicate simulations 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 affected by temperature, humidity, and load.
  • the machine learning model 13702 may, through machine learning, prioritize collection of sensor data related to temperature, humidity, and load, and may prioritize processing sensor data of the prioritized type into simulation data for output to the digital twin system 13720.
  • the machine learning model 13702 may suggest to a user of the information technology system that more and/or different sensors of the prioritized type be implemented in the information technology near and around the transaction entity being simulation such that more and/or better data of the prioritized type may be used in simulation of the transaction entity via the digital replica thereof.
  • the machine learning model 13702 may be configured to learn to determine which types of sensor data are to be processed into simulation data for transmission to the digital twin system 13720 based on one or both of a modeling goal and a quality or type of sensor data.
  • a modeling goal may be an objective set by a user of the information technology system or may be predicted or learned by the machine learning model 13702.
  • Examples of modeling goals include creating a digital replica capable of showing dynamics of throughput on an assembly line, which may include collection, simulation, and modeling of, e.g., thermal, electrical power, component wear, and other metrics of a conveyor belt, an assembly machine, one or more products, and other components of the transaction ecosystem.
  • the machine learning model 137102 may be configured to learn to determine which types of sensor data are necessary to be processed into simulation data for transmission to the digital twin system 13720 to achieve such a model.
  • the machine learning model 13702 may analyze which types of sensor data are being collected, the quality and quantity of the sensor data being collected, and what the sensor data being collected represents, and may make decisions, predictions, analyses, and/or determinations related to which types of sensor data are and/or are not relevant to achieving the modeling goal and may make decisions, predictions, analyses, and/or determinations to prioritize, improve, and/or achieve the quality and quantity of sensor data being processed into simulation data for use by the digital twin system 13720 in achieving the modeling goal.
  • a user of the information technology system may input a modeling goal into the machine learning model 13702.
  • the machine learning model 13702 may learn to analyze training data to output suggestions to the user of the information technology system regarding which types of sensor data are most relevant to achieving the modeling goal, such as one or more types of sensors positioned in, on, or near a transaction entity or a plurality of transaction entities that is relevant to the achievement of the modeling goal is and/or are not sufficient for achieving the modeling goal, and how a different configuration of the types of sensors, such as by adding, removing, or repositioning sensors, may better facilitate achievement of the modeling goal by the machine learning model 13702 and the digital twin system 13720.
  • the machine learning model 13702 may automatically increase or decrease collection rates, processing, storage, sampling rates, bandwidth allocation, bitrates, and other attributes of sensor data collection to achieve or better achieve the modeling goal. In some embodiments, the machine learning model 13702 may make suggestions or predictions to a user of the information technology system related to increasing or decreasing collection rates, processing, storage, sampling rates, bandwidth allocation, bitrates, and other attributes of sensor data collection to achieve or better achieve the modeling goal. In some embodiments, the machine learning model 13702 may use sensor data, simulation data, previous, current, and/or future digital replica simulations of one or more transaction entities of the plurality of transaction entities to automatically create and/or propose modeling goals.
  • modeling goals automatically created by the machine learning model 13702 may be automatically implemented by the machine learning model 13702. In some embodiments, modeling goals automatically created by the machine learning model 13702 may be proposed to a user of the information technology system, and implemented only after acceptance and/or partial acceptance by the user, such as after modifications are made to the proposed modeling goal by the user.
  • the user may input the one or more modeling goals, for example, by inputting one or more modeling commands to the information technology system.
  • the one or more modeling commands may include, for example, a command for the machine learning model 13702 and the digital twin system 13720 to create a digital replica simulation of one transaction entity or a set of transaction entities, may include a command for the digital replica simulation to be one or more of a real-time simulation, and a hypothetical simulation.
  • the modeling command may also include, for example, parameters for what types of sensor data should be used, sampling rates for the sensor data, and other parameters for the sensor data used in the one or more digital replica simulations.
  • the machine learning model 13702 may be configured to predict modeling commands, such as by using previous modeling commands as training data.
  • the machine learning model 13702 may propose predicted modeling commands to a user of the information technology system, for example, to facilitate simulation of one or more of the transaction entities that may be useful for the management of the transaction entities and/or to allow the user to easily identify potential issues with or possible improvements to the transaction entities.
  • the system of Fig. 137 may include a transactions management platform and applications.
  • the machine learning model 13702 may be configured to evaluate a set of hypothetical 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 twin system 13720 as a result of one or more modeling commands, as a result of one or more modeling goals, one or more modeling commands, by prediction by the machine learning model 13702, or a combination thereof.
  • the machine learning model 13702 may evaluate the set of hypothetical simulations based on one or more metrics defined by the user, one or more metrics defined by the machine learning model 13702, or a combination thereof.
  • the machine learning model 13702 may evaluate each of the hypothetical simulations of the set of hypothetical simulations independently of one another.
  • the machine learning model 13702 may evaluate one or more of the hypothetical simulations of the set of hypothetical simulations in relation to one another, for example by ranking the hypothetical simulations or creating tiers of the hypothetical simulations based on one or more metrics.
  • the machine learning model 13702 may include one or more model interpretability systems to facilitate human understanding of outputs of the machine learning model 13702, as well as information and insight related to cognition and processes of the machine learning model 13702, i.e., the one or more model interpretability systems allow for human understanding of not only “what” the machine learning model 13702 is outputting, but also “why” the machine learning model 13702 is outputting the outputs thereof, and what process led to the machine learning models 13702 formulating the outputs.
  • the one or more model interpretability systems may also be used by a human user to improve and guide training of the machine learning model 13702, to help debug the machine learning model 13702, to help recognize bias in the machine learning model 13702.
  • the one or more model interpretability systems may include one or more of linear regression, logistic regression, a generalized linear model (GLM), a generalized additive model (GAM), a decision tree, a decision rule, RuleFit, Naive Bayes Classifier, a K-nearest neighbors algorithm, a partial dependence plot, individual conditional expectation (ICE), an accumulated local effects (ALE) plot, feature interaction, permutation feature importance, a global surrogate model, a local surrogate (LIME) model, scoped rules, i.e., anchors, Shapley values, Shapley additive explanations (SHAP), feature visualization, network dissection, or any other suitable machine learning interpretability implementation.
  • LLM generalized linear model
  • GAM generalized additive model
  • a decision tree a decision rule, Rule
  • the one or more model interpretability systems may include a model dataset visualization system.
  • the model dataset visualization system is configured to automatically provide to a human user of the information technology system visual analysis related to distribution of values of the sensor data, the simulation data, and data nodes of the machine learning model 13702.
  • the machine learning model 13702 may include and/or implement an embedded model interpretability system, such as a Bayesian case model (BCM) or glass box.
  • BCM Bayesian case model
  • the Bayesian case model uses Bayesian case-based reasoning, prototype classification, and clustering to facilitate human understanding of data such as the sensor data, the simulation data, and data nodes of the machine learning model 13702.
  • the model interpretability system may include and/or implement a glass box interpretability method, such as a Gaussian process, to facilitate human understanding of data such as the sensor data, the simulation data, and data nodes of the machine learning model 13702.
  • the machine learning model 13702 may include and/or implement testing with concept activation vectors (TCAV).
  • TCAV allows the machine learning model 13702 to learn human-interpretable concepts, such as “running,” “not running,” “powered,” “not powered,” “robot,” “human,” “truck,” or “ship” from examples by a process including defining the concept, determining concept activation vectors, and calculating directional derivatives.
  • human-interpretable concepts, objects, states, etc. may allow the machine learning model 13702 to output useful information related to the transaction entities and data collected therefrom in a format that is readily understood by a human user of the information technology system.
  • the machine learning model 13702 may be and/or include an artificial neural network, e.g. a connectionist system configured to “learn” to perform tasks by considering examples and 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 that may act like artificial neurons that may in some ways emulate neurons in a biological brain.
  • the units and/or nodes may each have one or more connections to other units and/or nodes.
  • the units and/or nodes may be configured 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 the units and/or nodes and connections therebetween may have one or more numerical “weights” assigned.
  • the assigned weights may be configured to facilitate learning, i.e., training, of the machine learning model 13702.
  • the weights 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 a signal is only sent between one or more units and/or nodes if a signal and/or aggregate signal crosses the threshold.
  • the units and/or nodes may be assigned to a plurality of layers, each of the layers having one or both of inputs and outputs.
  • a first layer may be configured to receive training data, transform at least a portion of the training data, and transmit signals related to the training data and transformation thereof to a second layer.
  • a final layer may be configured to output an estimate, conclusion, product, or other consequence of processing of one or more inputs by the machine learning model 13702.
  • Each of the layers may perform one or more types of transformations, and one or more signals may pass through one or more of the layers one or more times.
  • the machine learning model 13702 may employ deep learning and being at least partially modeled and/or configured as a deep neural network, a deep belief network, a recurrent neural network, and/or a convolutional neural network, such as by being configured to include one or more hidden layers.
  • the machine learning model 13702 may be and/or include a decision tree, e.g. a tree-based predictive model configured to identify one or more observations and determine one or more conclusions based on an input.
  • the observations may be modeled as one or more “branches” of the decision tree, and the conclusions may be modeled as one or more “leaves” of the decision tree.
  • the decision tree may be a classification tree, the classification tree may include one or more leaves representing one or more class labels, and one or more branches representing one or more conjunctions of features configured to lead to the class labels.
  • the decision tree may be a regression tree. The regression tree may be configured such that one or more target variables may take continuous values.
  • 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 use in one or both of classification and regression-based modeling of data.
  • the support vector machine may be configured to predict whether a new example falls into one or more categories, the one or more categories being configured during training of the support vector machine.
  • the machine learning model 13702 may be configured 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.
  • Regression analysis may include linear regression, wherein the machine learning model 13702 may calculate a single line to best fit input data according to one or more mathematical criteria.
  • inputs to the machine learning model 13702 (such as a regression model, Bayesian network, supervised model, or other type of model) may be tested, such as by using a set of testing data that is independent from the data set used for the creation and/or training of the machine learning model, such as to test the impact of various inputs to the accuracy of the model 13702.
  • inputs to the regression model may be removed, including single inputs, pairs of inputs, triplets, and the like, to determine whether the absence of inputs creates a material degradation of the success of the model 13702.
  • This may assist with recognition of inputs that are in fact correlated (e.g., are linear combinations of the same underlying data), that are overlapping, or the like.
  • Comparison of model success may help select among alternative input data sets that provide similar information, such as to identify the inputs (among several similar ones) that generate the least “noise” in the model, that provide the most impact on model effectiveness for the lowest cost, or the like.
  • input variation and testing of the impact of input variation on model effectiveness may be used to prune or enhance model performance for any of the machine learning systems described throughout this disclosure.
  • the machine learning model 13702 may be and/or include a Bayesian network.
  • the Bayesian network may be a probabilistic graphical model configured to represent a set of random variables and conditional independence of the set of random variables.
  • the Bayesian network may be configured to represent the random variables and conditional independence via a directed acyclic graph.
  • the Bayesian network may include one or both of a dynamic Bayesian network and an influence diagram.
  • the machine learning model 13702 may be defined via supervised learning, i.e., one or more algorithms configured to build a mathematical model of a set of training data containing one or more inputs and desired outputs.
  • the training data may consist of a set of training examples, each of the training examples having one or more inputs and desired outputs, i.e., a supervisory signal.
  • Each of the training examples may be represented in the machine learning model 13702 by an array and/or a vector, i.e., a feature vector.
  • the training data may be represented in the machine learning model 13702 by a matrix.
  • the machine learning model 13702 may learn one or more functions via iterative optimization of an objective function, thereby learning to predict an output associated with new inputs. Once optimized, the objective function may provide the machine learning model 13702 with the ability to accurately determine an output for inputs other than inputs included in the training data.
  • the machine learning model 13702 may be defined via one or more supervised learning algorithms such as active learning, statistical classification, regression analysis, and similarity learning. Active learning may include interactively querying, by the machine learning model 13702, a user and/or an information source to label new data points with desired outputs.
  • Statistical classification may include identifying, by the machine learning model 13702, to which a set of subcategories, i.e., subpopulations, a new observation belongs based on a training set of data containing observations having known categories.
  • Regression analysis may include estimating, by the machine learning model 13702 relationships between a dependent variable, i.e., an outcome variable, and one or more independent variables, i.e., predictors, covariates, and/or features.
  • Similarity learning may include learning, by the machine learning model 13702, from examples using a similarity function, the similarity function being designed to measure how similar or related two objects are.
  • the machine learning model 13702 may be defined via unsupervised learning, i.e., one or more algorithms configured to build a mathematical model of a set of data containing only inputs by finding structure in the data such as grouping or clustering of data points.
  • the machine learning model 13702 may learn from test data, i.e., training data, that has not been labeled, classified, or categorized.
  • the unsupervised learning algorithm may include identifying, by the machine learning model 13702, commonalities in the training data and learning by reacting based on the presence or absence of the identified commonalities in new pieces of data.
  • the machine learning model 13702 may generate one or more probability density functions.
  • the machine learning model 13702 may learn by performing cluster analysis, such as by assigning a set of observations into subsets, i.e., clusters, according to one or more predesignated criteria, such as according to a similarity metric of which internal compactness, separation, estimated density, and/or graph connectivity are factors.
  • the machine learning model 13702 may be defined via semisupervised learning, i.e., one or more algorithms using training data wherein some training examples may be missing training labels.
  • the semi- supervised learning may be weakly supervised learning, wherein the training labels may be noisy, limited, and/or imprecise.
  • the noisy, limited, and/or imprecise training labels may be cheaper and/or less labor intensive to produce, thus allowing the machine learning model 13702 to train on a larger set of training data for less cost and/or labor.
  • the machine learning model 13702 may be defined via reinforcement learning, such as one or more algorithms using dynamic programming techniques such that the machine learning model 13702 may train by taking actions in an environment in order to maximize a cumulative reward.
  • the training data is represented as a Markov Decision Process.
  • the machine learning model 13702 may be defined via selflearning, wherein the machine learning model 13702 is configured to train using training data with no external rewards and no external teaching, such as by employing a Crossbar Adaptive Array (CAA).
  • CAA Crossbar Adaptive Array
  • the CAA may compute decisions about actions and/or emotions about consequence situations in a crossbar fashion, thereby driving teaching of the machine learning model 13702 by interactions between cognition and emotion.
  • the machine learning model 13702 may be defined via feature learning, i.e., one or more algorithms designed to discover increasingly accurate and/or apt representations of one or more inputs provided during training, e.g. training data.
  • Feature learning may include training via principal component analysis and/or cluster analysis.
  • Feature learning algorithms may include attempting, by the machine learning model 13702, to preserve input training data while also transforming the input training data such that the transformed input training data is useful.
  • the machine learning model 13702 may be configured to transform the input training data prior to performing one or more classifications and/or predictions of the input training data.
  • the machine learning model 13702 may be configured to reconstruct input training data from one or more unknown data-generating distributions without necessarily conforming to implausible configurations of the input training data according to the distributions.
  • the feature learning algorithm may be performed by the machine learning model 13702 in a supervised, unsupervised, or semisupervised manner.
  • the machine learning model 13702 may be defined via anomaly detection, i.e., by identifying rare and/or outlier instances of one or more items, events and/or observations.
  • the rare and/or outlier instances may be identified by the instances differing significantly from patterns and/or properties of a majority of the training data.
  • Unsupervised anomaly detection may include detecting of anomalies, by the machine learning model 13702, in an unlabeled training data set under an assumption that a majority of the training data is “normal.”
  • Supervised anomaly detection may include training on a data set wherein at least a portion of the training data has been labeled as “normal” and/or “abnormal.”
  • the machine learning model 13702 may be defined via robot learning.
  • Robot learning may include generation, by the machine learning model 13702, of one or more curricula, the curricula being sequences of learning experiences, and cumulatively acquiring new skills via exploration guided by the machine learning model 13702 and social interaction with humans by the machine learning model 13702. Acquisition of new skills may be facilitated by one or more guidance mechanisms such as active learning, maturation, motor synergies, and/or imitation.
  • the machine learning model 13702 can be defined via association rule learning.
  • Association rule learning may include discovering relationships, by the machine learning model 13702, between variables in databases, in order to identify strong rules using some measure of “interestingness.”
  • 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 configured to learn by identifying and/or utilizing a set of relational rules, the relational rules collectively representing 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.
  • Learning classifier systems are algorithms that may combine a discovery component, such as one or more genetic algorithms, with a learning component, such as one or more algorithms for supervised learning, reinforcement learning, or unsupervised learning.
  • Inductive logic programming may include rule-learning, by the machine learning model 13702, using logic programming to represent one or more of input examples, background knowledge, and hypothesis determined by the machine learning model 13702 during training.
  • the machine learning model 13702 may be configured to derive a hypothesized logic program entailing all positive examples given an encoding of known background knowledge and a set of examples represented as a logical database of facts.
  • personality rights may refer to an entity's ability to control the use of his, her, or its identity for commercial purposes.
  • entity may refer to an individual or an organization (e.g., a university, a school, a team, a corporation, or the like) that agrees to license its personality rights, unless context suggests otherwise. This may include an entity's ability to control the use of its name, image, likeness, voice, or the like.
  • an individual exercising their personality rights for commercial purposes may include appearing in a commercial, television show, or movie, making a sponsored social media post (e.g., Instagram post, Facebook post, Twitter tweet, or the like), having their name appear on clothing (e.g., a jersey, t-shirts, sweatshirts, or the like) or other goods, appearing in a video game, or the like.
  • individuals may refer to student athletes or professional athletes, but may include other classes of individuals as well. While the current description makes reference to the NCAA, the system may be used to monitor and facilitate transactions relating to other individuals and organizations. For example, the system may be used in the context of professional sports, where organizations may use sponsorships and other licensing deals to circumvent salary caps or other league rules (e.g., FIFA fair play rules).
  • the compliance system 13800 maintains one or more digital ledgers that record transactions relating to the licensing of personality rights of entities.
  • a digital ledger may be a distributed ledger that is distributed amongst a set of computing devices 13870, 13880, 13890 (also referred to as nodes) and/or may be encrypted. Put another way, each participating node may store a copy of the distributed ledger.
  • An example of the digital ledger is a Blockchain ledger.
  • a distributed ledger is stored across a set of public nodes.
  • a distributed ledger is stored across a set of whitelisted participant nodes (e.g., on the servers of participating universities or teams).
  • the digital ledger is privately maintained by the compliance system 13800.
  • the latter configuration provides a more energy efficient means of maintaining a digital ledger; while the former configurations (e.g., distributed ledgers) provide a more secure/verifiable means of maintaining a digital ledger.
  • a distributed ledger may store tokens.
  • the tokens may be cryptocurrency tokens that are transferrable to licensors and licensees.
  • a distributed ledger may store the ownership data of each token.
  • a token (or a portion thereof) may be owned by the compliance system, the governing organization (e.g., the NCAA), a licensor, a licensee, a team, an institution, an individual or the like.
  • the distributed ledger may store event records. Event records may store information relating to events associated with the entities involved with the compliance system.
  • an event record may record an agreement entered into by two parties, the completion of an obligation by a licensor, the distribution of funds to a licensor from a license, the non-completion of an obligation by a licensor, the distribution of funds to entities associated with the licensee (e.g., teammates, institution, team, etc.), and the like.
  • the digital ledger may store smart contracts that govern agreements between licensors and licensees.
  • a licensee may be an organization or person that wishes to enter an agreement to license a licensor's personality rights.
  • licensees may include, but are not limited to, a car dealership that wants a star student athlete to appear in a print ad, a company that wants the likeness of a licensor (e.g., an athlete and/or a team) to appear in a commercial, a video game maker that wants to use team names, team apparel, player names and/or numbers in a video game, a shoe maker that wants an athlete to endorse a sneaker, a television show producer that wants an athlete to appear in the television show, or the like.
  • a car dealership that wants a star student athlete to appear in a print ad
  • a company that wants the likeness of a licensor (e.g., an athlete and/or a team) to appear in a commercial
  • a video game maker that wants to use team names, team apparel, player names and/or numbers in a video game
  • a shoe maker that wants an athlete to endorse a sneaker
  • a television show producer that wants an athlete to appear in the television show,
  • the compliance system 13800 generates a smart contract that memorializes an agreement between the individual and a licensee and facilitates the transfer of consideration (e.g., money) when the parties agree that the individual has performed his or her requirements as put forth in the agreement. For example, an athlete may agree to appear in a commercial on behalf of a local car dealership.
  • consideration e.g., money
  • the smart contract in this example may include an identifier of the athlete (e.g., an individual ID and/or an individual account ID), an identifier of the organization (e.g., an organization ID and/or an organization account ID), the requirements of the individual (e.g., to appear in a commercial, to make a sponsored social media post, to appear at an autograph signing, or the like), and the consideration (e.g., a monetary amount).
  • the smart contract may include additional terms.
  • the additional terms may include an allocation rule that defines a manner by which the consideration is allocated to the athlete and one or more other parties (e.g., agent, manager, university, team, teammates, or the like).
  • a smart contract may define a split between the licensing athlete, the athletic department of the student athlete's university, and the student athlete's teammates.
  • a university may have a policy that requires a player appearing in any advertisement to split the funds according to a 60/20/20 split, whereby 60% of the funds are allocated to the student athlete appearing in the commercial, 20% of the funds are allocated to the athletic department, and 20% of the funds are allocated to the student athlete's teammates.
  • the compliance system 13800 utilizes cryptocurrency to facilitate the transfer of funds.
  • the cryptocurrency is mined by participant nodes and/or generated by the compliance system.
  • the cryptocurrency can be an established type of cryptocurrency (e.g., Bitcoin, Ethereum, Litecoin, or the like) or may be a proprietary cryptocurrency.
  • the cryptocurrency is a pegged cryptocurrency that is pegged to a particular fiat currency (e.g., pegged to the US dollar. British Pound, Euro, or the like).
  • a single unit of cryptocurrency also referred to as a "coin”
  • a licensee may exchange fiat currency for a corresponding amount of cryptocurrency.
  • the compliance system 13800 may keep a percentage of the real-world currency as a transaction fee (e.g., 5%).
  • the compliance system 13800 may distribute $9,500 dollars' worth of cryptocurrency to an account of the licensee and may keep the $5,000 dollars as a transaction fee. Once the cryptocurrency is deposited in an account of a licensee, the licensee may enter into transactions with individuals.
  • the compliance system 13800 may allow organizations to create smart contract templates that define one or more conditions/restrictions on the contract.
  • an organization may predefine the allocation between the licensee, the organization, and any other individuals (e.g., coaches, teammates, representatives). Additionally or alternatively, the organization may place minimum and/or maximum amounts of agreements. Additionally or alternatively, the organization may place restrictions on when an agreement can be entered into and/or performed. For example, players may be restricted from appearing in commercials or advertisements during the season and/or during exam periods. These details may be stored in an organization datastore 13856A Organizations may place other conditions/restrictions in a smart contract.
  • an individual and licensee wishing to enter to an agreement must use a smart contract template provided by the organization to which the individual belongs.
  • the compliance system 13800 may only allow an individual that has an active relationship with an organization (e.g., plays on a team of a university) to participate in a smart contract if the smart contract is defined by or otherwise approved by the organization.
  • the compliance system 13800 manages a clearinghouse process that approves potential licensees. Before a licensee can participate in agreements facilitated by the compliance system 13800, the licensee can provide information relating to the licensee. This may include a tax ID number, an entity name, incorporation information (e.g., state and type), a list of key personnel (e.g., directors, executives, board members, approved decision makers, and/or the like), and any other suitable information.
  • incorporation information e.g., state and type
  • key personnel e.g., directors, executives, board members, approved decision makers, and/or the like
  • the potential licensee may be required to sign (e.g., eSign or wet ink signature) a document indicating that the organization will not willingly use the compliance system 13800 to circumvent any rules, laws, or regulations (e.g., they will not circumvent NCAA regulations).
  • the compliance system 13800 or another entity e.g., the NCAA
  • the compliance system 13800 may create accounts for licensors once they have joined an organization (e.g., signed an athletic scholarship with a university). Once a licensor is verified as being affiliated with the organization, the compliance system 13800 may create an account for the licensor and may create a relationship between the individual and the organization, whereby the licensor may be required to use smart contracts that are approved or provided by the organization. Should the licensor join another organization (e.g., transfers to another school), the compliance system 13800 may sever the relationship with the previous organization and may create a new relationship with the other organization.
  • an organization e.g., signed an athletic scholarship with a university
  • the compliance system 13800 may create an account for the licensor and may create a relationship between the individual and the organization, whereby the licensor may be required to use smart contracts that are approved or provided by the organization. Should the licensor join another organization (e.g., transfers to another school), the compliance system 13800 may sever the relationship with the previous
  • the compliance system 13800 may prevent the licensor from participating in transactions on the compliance system 13800.
  • the compliance system 13800 may provide a graphical user interface that allows users to create smart contracts governing personality rights licenses.
  • the compliance system allows a user (e.g., a licensor) to select a smart contract template.
  • the compliance system 13800 may restrict the user to only select a smart contract template that is associated with an institution of the licensor.
  • the graphical user interface allows a user to define certain terms (e.g., the type or types of obligations placed on the licensor, an amount of funds to paid, a date by which the obligations of the licensor must be completed by, a location at which the obligation is completed, and/or other suitable terms).
  • the compliance system 13800 may generate a smart contract by parameterizing one or more variables in the smart contract with the provided input.
  • the compliance system 13800 may deploy the smart contract.
  • the compliance system 13800 may deploy the smart contract by broadcasting the parameterized smart contract to the participant nodes, which in turn may update each respective instance of the distributed ledger with the new smart contract.
  • an institution of the licensor must approve the parameterized smart contract before the parameterized smart contract may be deployed to the distributed ledger.
  • the compliance system 13800 may provide a graphical user interface to verify performance of an obligation by a licensor.
  • the compliance system 13800 may include an application that is accessed by licensors, that allows a licensor to prove that he or she performed an obligation.
  • the application may allow a user to record locations that the licensor went to (e.g., locations of film or photo shoots), to upload records (e.g., screen shots of social media posts) or to provide other corroborating evidence that the licensor has performed his or her obligations with respect to a licensing transaction. In this way, the licensor can prove that he or she performed the tasks required by the licensing deal.
  • the application may interact with a wearable device or may capture other digital exhaust, such as social media posts of the user (e.g., licensor) to collect evidence that supports or disproves a licensor's claim that he or she performed the obligations under the transaction agreement.
  • the corroborating evidence collected by the application may be recorded by the application and stored on the distributed ledger as a licensor datastore 13856C.
  • the compliance system 13800 may complete transactions pertaining to a smart contract governing the licensing of the personality rights of a licensor upon verification that licensor has performed his or her obligations defined in the agreement.
  • the licensor may use an application to provide evidence of satisfaction of the obligations of the agreement.
  • the licensee may provide verification that the licensor has performed his or her obligations (e.g., using an application).
  • the smart contract governing the agreement may receive verification that the licensor has performed his or her obligations defined by the agreement.
  • the smart contract may release (or initiate the release of) the cryptocurrency amount defined in the smart contract.
  • the cryptocurrency amount may be distributed to the accounts of the licensor and any other parties defined in the agreement (e.g., teammates of the licensor, the program of the licensor, the regulating body, or the like).
  • the compliance system 13800 is configured to perform analytics and provide reports to a regulatory body and/or other entities (e.g., the other organizations).
  • the analytics may be used to identify individuals that are potentially circumventing the rules and regulations of the regulatory body.
  • transaction records may be maintained on a distributed ledger, whereby different organizations may be able to view agreements entered into by individuals affiliated with other organizations such that added levels of transparency and oversight may disincentivize individuals, organizations, and/or licensees from circumventing rules and regulations.
  • the compliance system 13800 may train and/or leverage machine- learned models to identify potential instances of circumvention of rules or regulations.
  • the compliance system 13800 may train machine-learned models using outcome data. Examples of outcome data may include data relating to a set of transactions where an organization (e.g., a team or university), licensee (e.g., a company), and/or licensor (e.g., an athlete) were determined to be circumventing rules or regulations and data relating to a set of transactions where an organization, licensee, and/or licensor were found to be in compliance with the rules and regulations.
  • an organization e.g., a team or university
  • licensee e.g., a company
  • licensor e.g., an athlete
  • the compliance system 13800 may leverage a machine- learned model by obtaining a set of records relating to transactions a licensee, a licensor, and/or an organization (e.g., a team or university) from the distributed ledger.
  • the compliance system may extract relevant features, such as the amount paid to a particular licensor by a licensee, amounts paid to other licensors on other teams, affiliations of the licensor, amounts paid to a licensor by other licensees, and the like, and may feed the features to the machine-learned model.
  • FIG. 139 illustrates an example system 13900 configured for electronically facilitating licensing of one or more personality rights of a licensor, in accordance with some embodiments of the present disclosure.
  • the system 13900 may include one or more computing platforms 13902.
  • Computing platform(s) 13902 may be configured to communicate with one or more remote platforms 13904 according to a client/server architecture, a peer-to- peer architecture, and/or other architectures.
  • Remote platform(s) 13904 may be configured to communicate with other remote platforms via computing platform(s) 13902 and/or according to a client/server architecture, a peer-to-peer architecture, and/or other architectures. Users may access system 13900 via remote platform(s) 13904.
  • computing platform(s) 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 fund management module 13112, a ledger management module 13116, a verification module 13118, an analytics module 13120, and/or other instruction modules.
  • the access module 13108 may be configured to receive an access request from a licensee to obtain approval to license personality rights from a set of available licensors. In embodiments, the access module 13108 may be configured to selectively grant access to the licensee based on the access request. For example, the access module 13108 may receive a name of a potential licensee (e.g., corporate name), a list of principals (e.g., executives and/or owners) of the potential licensee, a location of the licensee, affiliations of the licensee and the principals thereof, and the like. In embodiments, the access module 13108 may provide this information to a human that grants access and/or may feed this information into an artificial intelligence system that vets potential licensees.
  • a potential licensee e.g., corporate name
  • principals e.g., executives and/or owners
  • the access module 13108 is configured to selectively grant access to a licensor by verifying that the licensee is permitted to engage with a set of licensors including the licensor based on the set of affiliations.
  • Selectively granting access to the licensor may include, in response to verifying that the licensee is permitted to engage with the set of licensors, granting the licensee approval to engage with the set of licensees.
  • the set of affiliations of the licensee may include organizations to which the licensee or a principal associated with the licensee donates to or owns.
  • the fund management module 13112 may be configured to receive confirmation of a deposit of an amount of funds from the licensee. In some embodiments, the fund management module 13112 may be configured to issue an amount of cryptocurrency corresponding to the amount of funds deposited by the licensee to an account of the licensee. In embodiments, the fund management module 13112 may be configured to escrow the consideration amount of cryptocurrency from the account of the licensee until the funds are released by a smart contract.
  • the ledger management module 13116 may be configured to receive a smart contract request to create a smart contract governing the licensing of the one or more personality rights of the licensor by the licensee. In embodiments, the ledger management module 13116 may be configured to generate the smart contract based on the smart contract request.
  • the smart contract may be generated using a smart contract template provided by an interested third party (e.g., a university, a governing body, or the like) and by one or more parameters provided by a user (e.g., the licensor, the team of the licensor, an institution, and/or licensee)
  • the interested third party may be one of a university, a sports team, or a collegiate athletics governance organization.
  • the smart contract request may indicate one or more terms including a consideration amount of cryptocurrency to be paid to the licensor in exchange for one or more obligations on the licensor.
  • the ledger management module 13116 may be configured to deploy the smart contract to a distributed ledger.
  • the distributed ledger may be auditable by a set of third parties, including the interested third party.
  • the distributed ledger may be a public ledger.
  • the distributed ledger may be a private ledger that is only hosted on computing devices associated with interested third parties.
  • the distributed ledger may be a blockchain.
  • the verification module 13118 may be configured to verify that the licensor has performed the one or more obligation.
  • verifying that a licensor has performed the one or more obligations may include receiving location data from a wearable device associated with the licensor and verifying that the licensor has performed the one or more obligations based on the location data, whereby the location may be used to show that the licensor was at a particular location at a particular time (e.g., a photoshoot or a filming).
  • verifying that the licensor may have performed the one or more obligations includes receiving social media data from a social media website and verifying that the licensor has performed the one or more obligations based on the social media data, whereby the social media data may be used to show that the licensor has made a required social media posting.
  • verifying that the licensor may have performed the one or more obligations includes receiving media content from an external data source and verifying that the licensor has performed the one or more obligations based on the media content, whereby a licensor and/or licensee may upload the media content to prove that the licensor has appeared in the media content.
  • the media content may be one of a video, a photograph, or an audio recording.
  • the verification module 13118 may generate and output an event record to the participating nodes upon verifying that a licensor has performed its obligations.
  • the verification module 13118 may generate and output an event record to the participating nodes that indicates that the compliance system 13100 has received corroborating evidence (e.g., social media data, location data, and/or media contents) that show that the licensor has performed his or her obligations.
  • the verification module 13118 may be configured to output an event record indicating completion of a licensing transaction defined by the smart contract to the distributed ledger.
  • the verification module 13118 may be configured to verify, by the smart contract, that the licensor has performed the one or more obligations.
  • the verification module 13118 and/or a smart contract may be configured to, in response to receiving verification that the licensor has performed the one or more obligations, release at least a portion of the consideration amount of cryptocurrency into a licensor account of the licensor.
  • Releasing the at least a portion of the consideration amount of cryptocurrency into a licensee account of the licensee may include identifying an allocation smart contract associated with the licensee and distributing the consideration amount of the cryptocurrency in accordance with the allocation rules.
  • the additional entities may include one or more of teammates of the licensor, coaches of the licensor, a team of the licensor, a university of the licensee, and a governing body (e.g., the NBA).
  • an analytics module 13120 may be configured to obtain a set of records indicating completion of a set of respective transactions from the distributed ledger.
  • the set of records may include the record indicating the completion of the transaction defined by the smart contract.
  • the analytics module 13120 may be configured to determine whether an organization associated with the licensor is likely in violation of one or more regulations based on the set of records and a fraud detection model.
  • the fraud detection model may be trained using training data that indicates permissible transactions and fraudulent transactions.
  • the allocation smart contract may define allocation rules governing a manner by which funds resulting from licensing the one or more personality rights are to be distributed amongst the licensor and one or more additional entities.
  • the regulations may be provided by one of Georgia, FIFA, NBA, MLB, NFL, MLS, NHL, and the like.
  • computing platform(s) 13902, remote platform(s) 13904, and/or external resources 13934 may be operatively linked via one or more electronic communication links.
  • electronic communication links may be established, at least in part, via a network such as the Internet and/or other networks. It will be appreciated that this is not intended to be limiting, and that the scope of this disclosure includes implementations in which computing platform(s) 13902, remote platform(s) 13904, and/or external resources 13934 may be operatively linked via some other communication media.
  • a given remote platform 13904 may include one or more processors configured to execute computer program modules.
  • the computer program modules may be configured to enable an expert or user associated with the given remote platform 13904 to interface with compliance system 13100 and/or external resources 13934, and/or provide other functionality attributed herein to remote platform(s).
  • a given remote platform 13904 and/or a given computing platform 13902 may include one or more of a server, a desktop computer, a laptop computer, a handheld computer, a tablet computing platform, a Netbook, a Smartphone, a gaming console, and/or other computing platforms.
  • External resources 13934 may include sources of information outside of compliance system 13100, external entities participating with compliance system 13100, and/or other resources. In some implementations, some or all of the functionality attributed herein to external resources 13934 may be provided by resources included in compliance system 13100.
  • Computing platform(s) 202 may include electronic storage 13936, one or more processors 13938, and/or other components.
  • Computing platform(s) 1202 may include communication lines, or ports to enable the exchange of information with a network and/or other computing platforms. Illustration of computing platform(s) 13902 in FIG. 139 is not intended to be limiting.
  • Computing platform(s) 13902 may include a plurality of hardware, software, and/or firmware components operating together to provide the functionality attributed herein to computing platform(s) 13902.
  • computing platform(s) 13902 may be implemented by a cloud of computing platforms operating together as computing platform(s) 13902.
  • Electronic storage 13936 may comprise non-transitory storage media that electronically stores information.
  • the electronic storage media of electronic storage 13936 may include one or both of system storage that is provided integrally (i.e., substantially nonremovable) with computing platform(s) 13902 and/or removable storage that is removably connectable to computing platform(s) 13902 via, for example, a port (e.g., a USB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.).
  • a port e.g., a USB port, a firewire port, etc.
  • a drive e.g., a disk drive, etc.
  • Electronic storage 13936 may include one or more of optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical chargebased storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), and/or other electronically readable storage media.
  • Electronic storage 13936 may include one or more virtual storage resources (e.g., cloud storage, a virtual private network, and/or other virtual storage resources).
  • Electronic storage 13936 may store software algorithms, information determined by processor(s) 13938, information received from computing platform(s) 13902, information received from remote platform(s) 13904, and/or other information that enables computing platform(s) 13902 to function as described herein.
  • Processor(s) 13938 may be configured to provide information processing capabilities in computing platform(s) 13902.
  • processor(s) 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.
  • processor(s) 13938 is shown in FIG. 139 as a single entity, this is for illustrative purposes only.
  • processor(s) 13938 may include a plurality of processing units. These processing units may be physically located within the same device, or processor(s) 13938 may represent processing functionality of a plurality of devices operating in coordination.
  • Processor(s) 13938 may be configured to execute modules 13108, 13112, 13116, 13118, 13120, and/or other modules.
  • Processor(s) 13938 may be configured to execute modules 13108, 13112, 13116, 13118, 13120, and/or other modules by software; hardware; firmware; some combination of software, hardware, and/or firmware; and/or other mechanisms for configuring processing capabilities on processor(s) 13938.
  • the term "module” may refer to any component or set of components that perform the functionality attributed to the module. This may include one or more physical processors during execution of processor readable instructions, the processor readable instructions, circuitry, hardware, storage media, or any other components.
  • modules 13108, 13112, 13116, 13118, and 13120 are illustrated in FIG. 139 as being implemented within a single processing unit, in implementations in which processor(s) 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.
  • 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.
  • processor(s) 13938 may be configured to execute one or more additional modules that may perform some or all of the functionality attributed below to one of modules 13108, 13112, 13116, 13118, and/or 13120.
  • FIGS. 140 and/or 141 illustrates an example method 14000 for electronically facilitating licensing of one or more personality rights of a licensor, in accordance with some embodiments of the present disclosure.
  • the operations of method 14000 presented below are intended to be illustrative. In some embodiments, method 14000 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of method 14000 are illustrated in FIGS. 140 and/or 141 and described below is not intended to be limiting.
  • method 14000 may be implemented in one or more processing devices (e.g., 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).
  • the one or more processing devices may include one or more devices executing 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 through hardware, firmware, and/or software to be specifically designed for execution of one or more of the operations of method 14000.
  • FIG. 140 illustrates method 14000, in accordance with one or more implementations of the present disclosure.
  • the method includes receiving an access request from a licensee to obtain approval to license personality rights from a set of available licensors.
  • Operation 14002 may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to access module 13108, in accordance with one or more implementations.
  • the method includes selectively granting access to the licensee based on the access request.
  • Operation 14004 may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to access module 13108, in accordance with one or more implementations.
  • the method includes receiving confirmation of a deposit of an amount of funds from the licensee. Operation 14006 may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to fund management module 13112, in accordance with one or more implementations. [000557] At 14008, the method includes issuing an amount of cryptocurrency corresponding to the amount of funds deposited by the licensee to an account of the licensee. Operation 14008 may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to fund management module 13112, in accordance with one or more implementations.
  • FIG. 141 illustrates method 14100, in accordance with one or more implementations of the present disclosure.
  • the method includes receiving a smart contract request to create a smart contract governing the licensing of the one or more personality rights of the licensor by the licensee.
  • the smart contract request may indicate one or more terms including a consideration amount of cryptocurrency to be paid to the licensor in exchange for one or more obligations on the licensor.
  • Operation 14122 may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to the ledger management module 13116, in accordance with one or more implementations.
  • the method includes generating the smart contract based on the smart contract request.
  • Operation 14124 may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to ledger management module 13116, in accordance with one or more implementations.
  • the method includes escrowing the consideration amount of cryptocurrency from the account of the licensee. Operation 14126 may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to fund management module 13112, in accordance with one or more implementations. [000562] At 14128, the method includes deploying the smart contract to a distributed ledger. Operation 14128 may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to ledger management module 13116, in accordance with one or more implementations.
  • the method includes verifying, by the smart contract, that the licensor has performed the one or more obligations.
  • Operation 14130 may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to verification module 13118, in accordance with one or more implementations .
  • the method includes in response to receiving verification that the licensor has performed the one or more obligations, releasing at least a portion of the consideration amount of cryptocurrency into a licensor account of the licensor. Operation 14132 may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to the verification module 13118, in accordance with one or more implementations. [000565] At 14134, the method includes outputting a record indicating a completion of a licensing transaction defined by the smart contract to the distributed ledger.
  • Operation 14134 may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to the verification module 13118 and/or the ledger management module 13116, in accordance with one or more implementations.
  • FIG. 142 illustrates method 14200, in accordance with one or more implementations.
  • the method includes obtaining a set of records indicating completion of a set of respective transactions from the distributed ledger.
  • the set of records may include the record indicating the completion of the transaction defined by the smart contract.
  • Operation 14202 may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to the analytics module 13120, in accordance with one or more implementations.
  • the method includes determining whether an organization associated with the licensor is likely in violation of one or more regulations based on the set of records and a fraud detection model. Operation 14204 may be performed by one or more hardware processors configured by machine-readable instructions including a module that is the same as or similar to the analytics module 13120, in accordance with one or more implementations.
  • the computer-implemented method may include receiving one or more functional media 14302.
  • the functional media may include information indicative of brain activity of a worker engaged in a task to be automated.
  • the functional media may be functional imaging, such an MRI, an FMRI, and the like from which an area of neocortex activity may be identified.
  • the functional media may be an image, a video stream, an audio stream, and the like, from which a type of brain activity may be inferred.
  • the functional media may be acquired while the worker is performing the work or while performing a simulation of the work, for example in an augmented reality, a virtual reality environment, or on a model of the equipment and/or environment.
  • the functional media(s) are analyzed 14304 to identify an activity level in at least one brain region 14306. Based on the activity level, a brain region parameter and/or an activity parameter are identified 14308.
  • the brain region parameter may represent a specific region of the neocortex such as frontal, parietal, occipital, and temporal lobes of the neocortex, including primary visual cortex and the primary auditory cortex, or subdivisions of the neocortex, including ventrolateral prefrontal cortex (Broca's area), and orbitofrontal cortex.
  • the activity parameter may represent functional areas of the brain, such as visual processing, inductive reasoning, audio processing, olfactory processing, muscle control, and the like.
  • An activity parameter may be representative of a type of activity in which the worker is engaged such as visual processing (looking) audio processing (listening), olfactory processing (smelling), motion activity, listening to the sound of the equipment, watching another negotiator, and the like.
  • An activity level may be representative of a strength or level of activity, such as an extent of the brain region involved, a signal strength, whether a brain region is engaged or unengaged, and the like.
  • an action parameter may be identified 14310.
  • An action parameter may provide additional information regarding the activity parameter.
  • activity parameter is indicative of motion
  • an action parameter may describe a range of motion, a speed of motion, a repetition of motion, a use of muscle memory, a smoothness of motion, a flow of motion, a timing of motion, and the like.
  • a component to be incorporated in the final Al solution may be selected 14312.
  • the component may include one or more of a model, an expert system, a neural network, and the like. After the component for the Al solution has been selected, configuration parameters may be determined 14314.
  • the configuration parameters may be based, in part, on the type of component selected, the brain region parameter, the activity parameter, the activity level, or the action parameter.
  • Configuring and configuration parameters may include selecting an input for a machine learning process, identifying an output to be provided by the machine learning process, identifying an input for an operational solution process 14316, identifying an output an operational solution process, tuning a learning parameter, identifying a change rates, identifying a weighting factor, identifying a parameter for inclusion, identifying a parameter for exclusion of a parameter, setting a threshold for input data, setting an output threshold for the operational robotic process, or setting a parameter threshold.
  • analysis of the functional media 14304 may include identifying a second brain region parameter or a second activity parameter 14318.
  • the component of the Al solution may be revised 14320 based on the second brain region parameter or the second activity parameter.
  • a second component of the Al solution may be selected 14322 based on the second brain region parameter or the second activity parameter.
  • the final Al solution may be assembled from the component 14324 or the second component 14326. In embodiments, the final Al solution may be assembled from the component and the second components, optionally along with any standard or mandatory components that enable operation.
  • a computer-implemented method 14400 for selecting an Al solution for use in a robotic or automated process may include receiving a user-related input 14402 comprising a timestamp and analyzing the user-related input 14404.
  • the user- related input may include an audio feed, a motion sensor, a video feed, a heartbeat monitor, an eye tracker, a biosensor (e.g., galvanic skin response), and the like.
  • the analysis may enable the identification of a series of user actions and associated activity parameters 14406.
  • a component for an Al solution may be selected based on a user action of the series of user actions 14408.
  • the analysis may enable the identification of a second user action of the series of user actions 14410.
  • the selected component for the Al solution may be revised 14412.
  • a second component for the Al solution may be selected 14414 based on the second user action.
  • An action parameter may be identified 14416 based on the user action and/or the associated activity parameters. For example, if the user action is motion, an action parameter may include a range of motion, a speed of motion, a repetition of motion, a use of muscle memory, a smoothness of motion, a flow of motion, a timing of motion, and the like.
  • the selected component of the Al solution may be configured 14418 based on the action parameter. In embodiments, at least one device input performed by the user may be received (14420).
  • the device input may be synchronized with the user actions based on the timestamp and a correlation between the device input and the user action determined 14419.
  • the component may be revised 14423 based on the correlation.
  • the selection of the component of the Al solution may be partially based on the correlation between the device input and the user- related input 14421.
  • the Al solution may be assembled 14422 from the component.
  • the Al solution may be assembled from the second component 14424.
  • the Al may be assembled from both the component and the second component, optionally along with any standard or mandatory components that enable operation.
  • the assembled Al solution 14502 may include the selected component 14504 and a second selected component 14506, as well as other components 14508.
  • Configuration data 14514 for the first selected component and configuration data 14512 for the second selected component may be provided.
  • Runtime input data 14510 may be specified as part of the component configuration process.
  • Components may be structured to run serially (such as the selected component 14504 and the second selected component 14506 which received input from the selected component 14504) or in parallel (such as the second component 14506 and the other component(s) 14508).
  • Some of the components may provide input for other components (such as the selected component 14504 providing input to the second selected component 14506). Multiple components may provide various portions of the overall Al solution output 14518 (such as the second selected component 14506 and the other components 14508).
  • This depiction is not meant to be limiting and the final solution may include a varying number of components, configuration data and input, as well as other components (e.g., sensors, voice modulators, and the like) and may be interconnected in a variety of configurations.
  • Figs. 146-147 a computer-implemented method for selecting an Al solution for use in a robotic or automated process is depicted.
  • the method may include receiving temporal biometric measurement data 14602 of a worker performing a task and receiving spatial-temporal environmental data 14604 experienced by the worker performing the task. Using the received data, a spatial-temporal activity pattern may be identified 14606. Based on the spatial-temporal activity pattern, an active area of the worker’ s neocortex may be identified 14608. A type of reasoning used when performing the task may be identified 14610 based on the active area of the neocortex and/or the biometric measurement data, or the spatial- temporal environmental data. A component may be selected 14612 for use in the Al solution to replicate the type of reasoning. The component of the Al solution may be configured 14614 based on the spatial-temporal environmental input.
  • a set of configuration inputs to the component may be identified 14618 and an ordered set of inputs to the component of the Al solution may be identified 14620.
  • Training the machine may include providing various subsets of the spatial- temporal environmental input to determine appropriate input weightings and identify efficiencies from combinations of spatial-temporal environmental input 14622. Desirable or undesirable combinations of the spatial-temporal environmental data may also be identified 14624. Based on the identified required input, input environmental data may be processed to reduce input noise 14626 (e.g. improve signal to noise for a signal of interest), filtered to provide the appropriate input signals to the component, and the like.
  • input noise 14626 e.g. improve signal to noise for a signal of interest
  • a second temporal biometric measurement data of the same worker performing the task may be received 14702 and a plurality of performed tasks identified from the biometric measurements 14704.
  • a performance parameter may be extracted from the biometric measurements 14706 (e.g. worker heartrate, galvanic skin response, and the like).
  • the component may be configured based on the performance parameter 14707.
  • the second temporal biometric measurements may be provided to the configuration module as a training set 14709. Results data related to the task may be received 14708 and the second temporal biometric measurement data may be correlated with the received results data 14710.
  • the component may be selected based, at least in part, on the correlation 14711.
  • a series of time intervals between each of the plurality of performed tasks may be identified 14712 and the component of the Al solution configured based on at least one of the time intervals 14714. For example, if the worker inspects an object for a long period of time before moving on to the next action, this may indicate complex visual processing as well as mental processing and may indicate that the corresponding component for the task be configured for in-depth, fine detail processing and the like.
  • An example selection and configuration system 14802 may include a media input module 14804 structured to receive user-related functional media 14814.
  • the user-related functional media 14814 may include images of a person engaged in a task to be automated, audio recordings, video feeds, biometric data (e.g., heartbeat data, galvanic skin response data, and the like), motion data, and the like.
  • a media analysis module 14806 may analyze the received media and identify an action parameter.
  • the action parameter may be representative of a type of activity in which the person appears to be engaged such as watching, listening, moving, thinking, and the like.
  • the functional media is indicative of a type of brain activity of a human engaged in the task to be automated and the media analysis module 148206 identifies an activity level in at least one brain region and provide a brain region parameter corresponding with the activity level in the identified brain region.
  • the media analysis module may also identify an activity parameter indicative of a level of engagement such as engaged, unengaged, level of activity, type of activity, and the like.
  • a solution selection module 14808 may be structured to select at least one component of the Al solution for use in the automated process based, at least in part, on the action parameter, the brain region parameter, or the activity parameter.
  • the brain region parameter or the action parameter may suggest a type of component to select and the activity parameter may suggest a level of processing required for that component.
  • an action parameter of watching would suggest selecting a component suited to visual processing.
  • the input specification module may identify at least one chemical sensor as an input. If the activity parameter is representative of visual processing the input specification module 13116 may identify at least one visual sensor as a robotic input. In some embodiments, the visual sensor may be selected to be sensitive to a portion of the visible spectrum with wavelengths between about 380 to 700 nanometers. If the activity parameter is representative of auditory processing, the input specification module 13116 may identify at least one microphone as a robotic input. If the activity parameter was representative of a very high level of concentration, the solution selection module 14808 may suggest a level of processing that will be required, where the processing might occur, and the like.
  • a component configuration module 14810 may configure the component 14812. Configuring the component may include: selecting an input for a machine learning process for the selected component, identifying an output to be provided by the machine learning process, identifying an input for an operational solution process, identifying an output an operational solution process, tuning a learning parameter, identifying a change rates, identifying a weighting factor, identifying a parameter for inclusion, identifying a parameter for exclusion of a parameter, setting a threshold for input data, setting an output threshold for the operational robotic process, setting a parameter threshold, and the like.
  • a solution assembly module 14818 may assemble the final Al solution based on one or more selected components, configuration components, and required runtime.
  • An input specification module 14816 may suggest input sources based on the selected component, the action parameter, brain region parameter, activity parameter, or the like.
  • An example selection system 14902 may include an image input module 14904 structured to receive functional images 14914 of the brain such as, such as functional MRI or other magnetic imaging, electroencephalogram (EEG), or other imaging, such as by identifying broad brain activity (e.g., wave bands of activity, such as delta, theta, alpha and gamma waves), by identifying a set of brain regions that are activated and/or inactive while the worker is performing one of the tasks to be automated.
  • functional images 14914 of the brain such as, such as functional MRI or other magnetic imaging, electroencephalogram (EEG), or other imaging, such as by identifying broad brain activity (e.g., wave bands of activity, such as delta, theta, alpha and gamma waves), by identifying a set of brain regions that are activated and/or inactive while the worker is performing one of the tasks to be
  • the image input module 14904 may provide a subset of the functional images 14914 to the image analysis module 14906.
  • the image input module 14904 may perform some preprocessing for the subset of functional images 14914, such as noise reduction, histogram adjustment, filtering, and the like, prior to providing the subset of functional images 14914 to the image analysis module 14906.
  • the image analysis module 14906 may identify an activity level in at least one brain region and provide a brain region parameter based on the subset of functional images.
  • the brain region parameter may represent a specific region of the neocortex such as frontal, parietal, occipital, and temporal lobes of the neocortex, including primary visual cortex and the primary auditory cortex, or subdivisions of the neocortex, including ventrolateral prefrontal cortex (Broca's area), and orbitofrontal cortex.
  • the brain region parameter may represent functional areas of the brain, such as visual processing, inductive reasoning, audio processing, olfactory processing, muscle control, and the like.

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Abstract

La présente invention concerne des systèmes et des procédés de configuration et de lancement d'une place de marché. Un procédé peut consister à identifier une opportunité pour une nouvelle place de marché, à recevoir des données relatives à une opportunité de place de marché, à déterminer des paramètres de configuration, et à déterminer une faisabilité de mise en œuvre de la nouvelle configuration de place de marché. Une architecture de la nouvelle place de marché peut être déterminée, et des objets de place de marché peuvent être configurés. Des ressources de données et leur configuration dans un modèle peuvent être déterminées et les ressources de données peuvent être reliées aux objets de place de marché. La nouvelle place de marché peut ensuite être lancée.
EP21907893.8A 2020-12-18 2021-12-17 Système d'orchestration de marché pour faciliter les transactions de places de marché électroniques Pending EP4264533A2 (fr)

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AU2021401069A1 (en) 2023-06-22
JP2024500137A (ja) 2024-01-04
CA3177410A1 (fr) 2022-06-23
WO2022133210A3 (fr) 2022-08-04
WO2022133210A2 (fr) 2022-06-23

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