US20230351371A1 - Transaction platforms where systems include sets of other systems - Google Patents

Transaction platforms where systems include sets of other systems Download PDF

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Publication number
US20230351371A1
US20230351371A1 US18/117,860 US202318117860A US2023351371A1 US 20230351371 A1 US20230351371 A1 US 20230351371A1 US 202318117860 A US202318117860 A US 202318117860A US 2023351371 A1 US2023351371 A1 US 2023351371A1
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United States
Prior art keywords
asset
data
assets
network
digital
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US18/117,860
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Charles Howard Cella
Brad Kell
Mehul Desai
Teymour El-Tahry
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Strong Force Tx Portfolio 2018 LLC
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Strong Force Tx Portfolio 2018 LLC
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Priority to US18/117,860 priority Critical patent/US20230351371A1/en
Assigned to Strong Force TX Portfolio 2018, LLC reassignment Strong Force TX Portfolio 2018, LLC ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: CELLA, Charles Howard, DESAI, MEHUL, EL-TAHRY, TEYMOUR, KELL, BRAD
Publication of US20230351371A1 publication Critical patent/US20230351371A1/en
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Definitions

  • the present disclosure relates to transaction platforms, and more particularly relates to transaction platforms that include systems that include sets of other systems interoperating within the transaction platforms to define the larger systems.
  • a method for configuring and launching a marketplace includes: identifying, by a processing system having one or more processors, an opportunity to facilitate configuration of a new marketplace; receiving, by a processing system, marketplace opportunity data, where the marketplace opportunity data includes information related to a set of assets of one or more types; determining, by the processing system, configuration parameters to be implemented in the new marketplace; determining, by the processing system, the feasibility of implementing the configuration parameters in the new marketplace; determining, by the processing system, data resources to support the new marketplace; determining, by the processing system, an architecture of the new marketplace; determining, by the processing system, the configuration of the data resources in a data model for the marketplace; configuring, by the processing system, a marketplace object; connecting, by the processing system, selected data resources to populate the marketplace object; and launching, by the processing system, the new marketplace.
  • a method for generating a fairness score for a transaction includes: receiving, by a fairness engine, transaction data from a set of transactions from an execution engine; and calculating, by the fairness engine, a fairness score representing the fairness of a transaction.
  • the fairness engine includes 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 includes determining the ping, the upload speed, or the download speed.
  • the set of transactions are executed based upon the fairness score exceeding a predetermined threshold.
  • a computer-implemented method includes: receiving, at an access layer controlled by an enterprise, a data set characterizing one or more attributes associated with a group of assets or resources controlled by the enterprise, where the access layer corresponds to an intelligence system that hosts exchangeable enterprise assets; determining, by a permissions system of the access layer, whether the data set satisfies a set of permission criteria indicating a set of governing rules for assets or resources controlled by the enterprise; in response to the data set satisfying the permission criteria, generating, by a data services system associated with the access layer, an encoded data set that satisfies the set of governing rules; and converting the encoded data set to an exchangeable digital asset by: publishing a representation of the encoded data set to a digital wallet system of the access layer; and configuring an interface system of the access layer with access to the encoded data set represented in the digital wallet system, where the interface system is accessible by a third party.
  • Some embodiments further include assigning a monetary value to the encoded data set that is viewable via the interface system.
  • assigning the monetary value to the encoded data set includes generating an estimated monetary value from valuation data compiled from a set of target consumers.
  • assigning the monetary value to the encoded data set includes: generating an invite to a set of target consumers for the data set; requesting the set of target consumers assign a proposed value to a set of secondary data sets that share one or more characteristics with the data set; and determining the monetary value for the encoded data set by statistical inference from the proposed values returned from the set of target consumers.
  • Some embodiments further include adjusting the monetary value based on feedback from the enterprise.
  • adjusting the monetary value includes: generating a feedback request to the enterprise to authorize the monetary value assigned to the encoded data set; and in response to the feedback request, receiving a message from the enterprise to modify the monetary value of the encoded data set.
  • generating the encoded data set includes partially encoding a portion of the data set that includes information failing to satisfy the set of governing rules.
  • publishing the representation of the encoded data set to the digital wallet system includes publishing the representation of the encoded data set to a hot wallet of the wallet system.
  • publishing the representation of the encoded data set to the digital wallet system includes publishing the representation of the encoded data set to a cold wallet of the wallet system.
  • publishing the encoded data set to the digital wallet system includes publishing the encoded data set to a custodial wallet of the wallet system.
  • the group of resources is enterprise-owned devices. In embodiments, the group of resources is production equipment of the enterprise.
  • the data set includes logistics information. In embodiments, the data set includes inventory information. In embodiments, the data set includes procurement information. In embodiments, the data set includes enterprise marketing information. In embodiments, the data set includes client-purchasing information.
  • the access layer is a network access layer.
  • the enterprise assets are digital assets.
  • the governing rules are privacy rules. In embodiments, re the governing rules are prioritization rules.
  • a system includes: an access layer including a processor and storage hardware in communication with the processor, where the storage hardware includes instructions that when executed by the processor perform operations, and where the operations include: receiving, at the access layer controlled by an enterprise, a data set characterizing one or more attributes associated with a group of assets or resources controlled by the enterprise, where the access layer corresponds to an intelligence system that hosts exchangeable enterprise assets; determining, by a permissions system of the access layer, whether the data set satisfies a set of permission criteria indicating a set of governing rules for resources controlled by the enterprise; in response to the data set satisfying the permission criteria, generating, by a data services system associated with the access layer, an encoded data set that satisfies the set of governing rules; and converting the encoded data set to an exchangeable digital asset by: publishing a representation of the encoded data set to a digital wallet system of the access layer; and configuring an interface system of the access layer with access to the encoded data set represented in the digital wallet system, where the interface system is accessible
  • a computer-implemented method includes: receiving, at a network access layer, an asset request from a requesting entity, where the asset request indicates an asset available in a digital wallet system associated with the network access layer, and where the network access layer includes a data plane configured to exchange assets privately-generated by an enterprise entity operating a control plane associated with the network access layer; identifying an asset control associated with the asset indicated by the asset request, where the asset control is configured by a permissions system of the network access layer and indicates a control parameter determined by an intelligence system of the network access layer, and where the control parameter is configured using data derived from the enterprise entity that privately generated the asset; determining whether the asset control is satisfied by at least one of the asset request or the requesting entity; and in response to the asset control being satisfied, facilitating fulfillment of the asset request.
  • the asset is available in a hot wallet of the digital wallet system. In embodiments, the asset is available in a cold wallet of the digital wallet system. In embodiments, the asset is available in a custodial wallet of the digital wallet system. In embodiments, facilitating fulfillment of the asset request includes transferring a set of keys for the cold wallet to a hot wallet of the digital wallet system. In embodiments, facilitating fulfillment of the asset request includes: signing a transaction involving the asset on the cold wallet; and relaying the signed transaction using a hot wallet of the digital wallet system that is associated with the cold wallet. In embodiments, facilitating fulfillment of the asset request includes connecting the cold wallet to the requesting entity. In embodiments, the asset control matches an access control for an enterprise entity that submitted the asset to the digital wallet system. In embodiments, the asset control indicates a security clearance level. In embodiments, the asset control includes transactional detail requirements for the asset.
  • a system includes: a network access layer including a processor and storage hardware in communication with the processor, where the storage hardware includes instructions that when executed by the processor perform operations, and where the operations include: receiving, at the network access layer, an asset request from a requesting entity, where the asset request indicates an asset available in a digital wallet system associated with the network access layer, and where the network access layer includes a data plane configured to exchange assets privately-generated by an enterprise entity operating a control plane associated with the network access layer; identifying an asset control associated with the asset indicated by the asset request, where the asset control is configured by a permissions system of the network access layer and indicates a control parameter determined by an intelligence system of the network access layer, and where the control parameter is configured using data derived from the enterprise entity that privately generated the asset; determining whether the asset control is satisfied by at least one of the asset request or the requesting entity; and in response to the asset control being satisfied, facilitating fulfillment of the asset request.
  • the asset is available in a hot wallet of the digital wallet system. In embodiments, the asset is available in a cold wallet of the digital wallet system. In embodiments, the asset is available in a custodial wallet of the digital wallet system. In embodiments, facilitating fulfillment of the asset request includes transferring a set of keys for the cold wallet to a hot wallet of the digital wallet system. In embodiments, facilitating fulfillment of the asset request includes: signing a transaction involving the asset on the cold wallet; and relaying the signed transaction using a hot wallet of the digital wallet system that is associated with the cold wallet. In embodiments, facilitating fulfillment of the asset request includes connecting the cold wallet to the requesting entity. In embodiments, the asset control matches an access control for an enterprise entity that submitted the asset to the digital wallet system. In embodiments, the asset control indicates a security clearance level. In embodiments, the asset control includes transactional detail requirements for the asset.
  • a computer-implemented method includes: receiving, at a network access layer, an asset request from a requesting entity, where the asset request indicates an asset available in a digital wallet system associated with the network access layer, where the network access layer corresponds to a client-facing intelligence system that hosts exchangeable digital assets, and where the exchangeable digital assets correspond to one or more assets stored in a private append-only data structure associated with an owner of the exchangeable digital assets; identifying an asset control associated with the asset indicated by the asset request, where the asset control is configured by a permissions system of the network access layer and indicates a control parameter determined by an intelligence system of the network access layer; determining whether the asset control is satisfied by at least one of the asset request or the requesting entity; and in response to the asset control being satisfied by the at least one of the asset request or the requesting entity, facilitating fulfillment of the asset request, where fulfillment includes storing the asset in a public append-only data structure to represent an exchange of the asset with the requesting entity.
  • the asset is available in a hot wallet of the digital wallet system. In embodiments, the asset is available in a cold wallet of the digital wallet system. In embodiments, the asset is available in a custodial wallet of the digital wallet system. In embodiments, facilitating fulfillment of the asset request includes transferring a set of keys for the cold wallet to a hot wallet of the digital wallet system. In embodiments, facilitating fulfillment of the asset request includes: signing a transaction involving the asset on the cold wallet; and relaying the signed transaction using a hot wallet of the digital wallet system that is associated with the cold wallet. In embodiments, facilitating fulfillment of the asset request includes connecting the cold wallet to the requesting entity. In embodiments, the asset control matches an access control for an enterprise entity that submitted the asset to the digital wallet system. In embodiments, the asset control indicates a security clearance level. In embodiments, the asset control includes transactional detail requirements for the asset.
  • a system includes: a network access layer including a processor and storage hardware in communication with the processor, where the storage hardware includes instructions that when executed by the processor perform operations, and where the operations include: receiving, at a network access layer, an asset request from a requesting entity, where the asset request indicates an asset available in a digital wallet system associated with the network access layer, where the network access layer corresponds to a client-facing intelligence system that hosts exchangeable digital assets, and where the exchangeable digital assets correspond to one or more assets stored in a private append-only data structure associated with an owner of the exchangeable digital assets; identifying an asset control associated with the asset indicated by the asset request, where the asset control is configured by a permissions system of the network access layer and indicates a control parameter determined by an intelligence system of the network access layer; determining whether the asset control is satisfied by at least one of the asset request or the requesting entity; and in response to the asset control being satisfied by the at least one of the asset request or the requesting entity, facilitating fulfillment of the asset request, where fulfillment includes storing the asset
  • the asset is available in a hot wallet of the digital wallet system. In embodiments, the asset is available in a cold wallet of the digital wallet system. In embodiments, the asset is available in a custodial wallet of the digital wallet system. In embodiments, facilitating fulfillment of the asset request includes transferring a set of keys for the cold wallet to a hot wallet of the digital wallet system. In embodiments, facilitating fulfillment of the asset request includes: signing a transaction involving the asset on the cold wallet; and relaying the signed transaction using a hot wallet of the digital wallet system that is associated with the cold wallet. In embodiments, facilitating fulfillment of the asset request includes connecting the cold wallet to the requesting entity. In embodiments, the asset control matches an access control for an enterprise entity that submitted the asset to the digital wallet system. In embodiments, the asset control indicates a security clearance level. In embodiments, the asset control includes transactional detail requirements for the asset.
  • a computer-implemented method includes: receiving, at a network access layer controlled by an enterprise, a set of assets privately generated by the enterprise, where the network access layer corresponds to a client-facing intelligence system that hosts exchangeable enterprise digital assets; for each asset of the set of assets: classifying, by an artificial-intelligence system of the network access layer, the respective asset into an access control category, where each asset control category is associated with a set of asset controls that dictate one or more transaction parameters for the exchange of the respective asset with a third party; and assigning, by a permissions system of the network access layer, the set of asset controls for the access control category classified by the AI system for the respective asset; and
  • publishing the set of assets to the digital wallet system includes publishing at least a portion of assets in the set to a hot wallet of the digital wallet system.
  • publishing the set of assets to the digital wallet system includes publishing at least a portion of assets in the set to a cold wallet of the digital wallet system.
  • publishing the set of assets to the digital wallet system includes publishing at least a portion of assets in the set to a custodial wallet of the digital wallet system.
  • publishing the set of assets to the digital wallet system includes publishing a first portion of assets in the set to a hot wallet of the digital wallet system and a second portion of the assets in the set to a cold wallet of the digital wallet system.
  • the first portion has a first access control category that indicates that a first set of asset controls of the first access control category is less restrictive than a second set of asset controls for a second access control category classified for the second portion.
  • the first portion has a first access control category that indicates a greater frequency of access than a second access control category classified for the second portion.
  • the set of asset controls includes an asset control that matches an access control for an enterprise entity that communicated at least one of the assets from the set of assets to the network asset layer.
  • the set of asset controls includes an asset control that indicates a security clearance level.
  • the one or more transaction parameters include a minimum pricing requirement.
  • a system including: a network access layer including a processor and storage hardware in communication with the processor, where the storage hardware includes instructions that when executed by the processor perform operations, and where the operations include: receiving, at a network access layer controlled by an enterprise, a set of assets privately generated by the enterprise, where the network access layer corresponds to a client-facing intelligence system that hosts exchangeable enterprise digital assets; for each asset of the set of assets: classifying, by an artificial-intelligence system of the network access layer, the respective asset into an access control category, where each asset control category is associated with a set of asset controls that dictate one or more transaction parameters for the exchange of the respective asset with a third party; and assigning, by a permissions system of the network access layer, the set of asset controls for the access control category classified by the AI system for the respective asset; and converting the set of assets to exchangeable digital assets by: publishing the set of assets
  • publishing the set of assets to the digital wallet system includes publishing at least a portion of assets in the set to a hot wallet of the digital wallet system. In embodiments, publishing the set of assets to the digital wallet system includes publishing at least a portion of assets in the set to a cold wallet of the digital wallet system. In embodiments, publishing the set of assets to the digital wallet system includes publishing at least a portion of assets in the set to a custodial wallet of the digital wallet system. In embodiments, publishing the set of assets to the digital wallet system includes publishing a first portion of assets in the set to a hot wallet of the digital wallet system and a second portion of the assets in the set to a cold wallet of the digital wallet system.
  • the first portion has a first access control category that indicates that a first set of asset controls of the first access control category is less restrictive than a second set of asset controls for a second access control category classified for the second portion. In embodiments, the first portion has a first access control category that indicates a greater frequency of access than a second access control category classified for the second portion.
  • the set of asset controls includes an asset control that matches an access control for an enterprise entity that communicated at least one of the assets from the set of assets to the network asset layer. In embodiments, the set of asset controls includes an asset control that indicates a security clearance level.
  • the one or more transaction parameters include a minimum pricing requirement.
  • a computer-implemented method includes: monitoring a plurality of public market participants via an interface system of a network access layer, where the network access layer is controlled by an enterprise and corresponds to an intelligence system that hosts exchangeable enterprise digital assets; receiving, at the network access layer via the interface system, an indication that a monitored public market participant requests a digital asset candidate; determining, by the intelligence system of the network access layer, whether the digital asset candidate matches an asset available in a digital wallet system associated with the network access layer; and in response to the digital asset candidate matching the asset available in the digital wallet system: identifying a set of asset controls managed by a permission system of the network asset layer, where the permission system is configured to assign the set of asset controls to exchangeable enterprise digital assets in the digital wallet system; determining whether a transaction with the monitored public market participant that involves the asset available in the digital wallet system satisfies an asset control criteria corresponding to the asset available, where the asset control criteria indicates that a threshold number of the set of asset controls have been violated; and in response to determining that the transaction with
  • facilitating fulfillment of the asset request includes: signing the actual transaction involving the asset on a cold wallet; and relaying the signed transaction using a hot wallet of the digital wallet system that is associated with the cold wallet.
  • storing the digital form of the asset to a public append-only data structure facilitating uses at least one key from a hot wallet of the digital wallet system.
  • storing the digital form of the asset to a public append-only data structure facilitating uses at least one key from a cold wallet of the digital wallet system.
  • the set of asset controls includes an asset control that matches an access control for an enterprise entity that submitted the asset to the digital wallet system.
  • the set of asset controls includes an asset control that indicates a security clearance level for the asset.
  • the set of asset controls transactional detail requirements for the asset.
  • a system includes: a network access layer including a processor and storage hardware in communication with the processor, where the storage hardware includes instructions that when executed by the processor perform operations, and where the operations include: monitoring a plurality of public market participants via an interface system of a network access layer, where the network access layer is controlled by an enterprise and corresponds to an intelligence system that hosts exchangeable enterprise digital assets; receiving, at the network access layer via the interface system, an indication that a monitored public market participant requests a digital asset candidate; determining, by the intelligence system of the network access layer, whether the digital asset candidate matches an asset available in a digital wallet system associated with the network access layer; and in response to the digital asset candidate matching the asset available in the digital wallet system: identifying a set of asset controls managed by a permission system of the network asset layer, where the permission system is configured to assign the set of asset controls to exchangeable enterprise digital assets in the digital wallet system; determining whether a transaction with the monitored public market participant that involves the asset available in the digital wallet system satisfies an asset control criteria corresponding to the asset
  • the asset is available in a hot wallet of the digital wallet system. In embodiments, the asset is available in a cold wallet of the digital wallet system. In embodiments, the asset is available in a custodial wallet of the digital wallet system. In embodiments, the operations further comprise: receiving a response message from the monitored public market participant; and determining that the response message indicates an acknowledgement to fulfill the request for the actual transaction; and facilitating fulfillment of the actual transaction. In embodiments, facilitating fulfillment of the actual transaction includes storing a digital form of the asset in a public append-only data structure to represent execution of the actual transaction.
  • facilitating fulfillment of the asset request includes: signing the actual transaction involving the asset on a cold wallet; and relaying the signed transaction using a hot wallet of the digital wallet system that is associated with the cold wallet.
  • storing the digital form of the asset to a public append-only data structure facilitating uses at least one key from a hot wallet of the digital wallet system.
  • storing the digital form of the asset to a public append-only data structure facilitating uses at least one key from a cold wallet of the digital wallet system.
  • the set of asset controls includes an asset control that matches an access control for an enterprise entity that submitted the asset to the digital wallet system.
  • the set of asset controls includes an asset control that indicates a security clearance level for the asset.
  • the set of asset controls transactional detail requirements for the asset.
  • a computer-implemented method includes: monitoring, using an access layer accessible to a plurality of tenant enterprises, a set of assets associated with a set of digital wallets of a digital wallet system for the access layer, where the access layer corresponds to a tenant-facing intelligence system that hosts exchangeable enterprise assets; receiving, at the access layer, an indication that a requesting tenant enterprise of the plurality of tenant enterprises requests a transaction involving an asset of the set of assets; determining, by the access layer, whether the requesting tenant enterprise has a set of access rights that satisfy an access criteria for the asset of the requested transaction; in response to the requesting tenant having the set of access rights that satisfy the access criteria, deploying, for the requesting tenant enterprise, a set of resources associated with the access layer and shared among the plurality of tenant enterprises to facilitate the transaction involving the asset on behalf of the tenant enterprise.
  • the requesting tenant enterprise includes a first tenant enterprise and a second tenant enterprise; the method further includes determining a transaction priority for each of the first tenant enterprise and the second tenant enterprise; and deploying the set of resources occurs for the first tenant enterprise having a first transaction priority greater than a second transaction priority of the second tenant enterprise.
  • each tenant enterprise is associated with (i) a set of private resources inaccessible to each other tenant and (ii) a set of shared resources associated with the access layer and shared among the plurality of tenant enterprises.
  • the digital wallet system includes: a first subset of digital wallets accessible to one of the tenant enterprises and inaccessible to other tenant enterprises; and a second subset of digital wallets accessible to and shared among a set of the plurality of tenant enterprises.
  • the access layer is a network access layer.
  • the exchangeable enterprise assets are digital assets.
  • the set of digital wallets includes a cold wallet.
  • the set of digital wallets includes a hot wallet and a cold wallet.
  • the set of digital wallets includes a custodial wallet.
  • the set of digital wallets includes a custodial wallet and a cold wallet.
  • the set of digital wallets includes at least two of a hot wallet, a cold wallet, or a custodial wallet.
  • a computer-implemented method includes: receiving, at an access layer, an asset request from a requesting entity, where the asset request indicates a transaction involving an asset available in a digital wallet system associated with the access layer, and where the access layer corresponds to an intelligence system that hosts exchangeable enterprise assets; identifying an asset control associated with the asset indicated by the asset request, where the asset control is configured by a permissions system of the access layer and indicates a control parameter determined by an intelligence system of the access layer; determining whether the asset control is satisfied by at least one of the asset request or the requesting entity; and in response to the asset control being satisfied, establishing a peer-to-peer access layer between the requesting entity and another transacting entity associated with the transaction indicated by the asset request, where the peer-to-peer access layer provides the other transacting entity with access to a limited set of digital assets and resources of the requesting entity.
  • the transacting entity includes a plurality of entities forming a multilateral connection between the requesting entity and the plurality of entities.
  • the peer-to-peer connection is a secure connection. Some embodiments further include generating, using a data processing system of the access layer, an encrypted message packet for communication using the peer-to-peer connection.
  • the access layer is a network access layer.
  • the exchangeable enterprise assets are digital assets.
  • the asset is available in a digital wallet of the digital wallet system.
  • the digital wallet is a cold wallet.
  • the digital wallet is a hot wallet.
  • the digital wallet is a custodial wallet.
  • the peer-to-peer access layer provides an interface that is accessible by a wallet system of the other transacting party, whereby the wallet system of the other transacting party accesses the limited set of digital assets and resources of the requesting enterprise via the interface.
  • Some embodiments further include: receiving a set of access rules from a user device associated with the requesting entity, where the set of access rules define the set of digital assets and resources that are accessible to the other transacting enterprise; and configuring the peer-to-peer access layer based on the set of access rules.
  • a system for normalizing an item value for a plurality of exchanges includes: a plurality of electronic exchanges configured for conducting transactions for at least one item in a set of items; an item value normalization system configured to identify a reference item in the set of items, and state a value for at least one other item in the set of items as a normalized value relative to a value of the reference item; and a robotic process automation system executing a set of computer-readable instructions on at least one processor, the instructions causing the robotic process automation system to automate item value normalization through automated operation of the item value normalization system.
  • the item value normalization system is configured to identify the reference item based on a transaction history for one or more candidate reference items in the set of items.
  • the item value normalization system is configured to identify the reference item based on a transaction history for one or more items that are similar to a candidate reference item. In embodiments, the item value normalization system is configured to identify the reference item based on a degree of commonality of a candidate reference item to other items in the set of items. In embodiments, an item identified as a reference item from the set of items for a first exchange is distinct from an item identified as a reference item from the set of items for a second exchange. In embodiments, to automate item value normalization includes stating the normalized item value based on a native currency of a target electronic exchange of the plurality exchanges.
  • to state a value for at least one other item in the set of items as a normalized value includes at least one exchange-specific fee associated with conducting a transaction for the item.
  • the item value normalization system is further configured to identify a reference set of items, and state a value for at least one other item in a different set of items as a normalized value relative to a value of at least one item in the set of reference items.
  • Some embodiments further include a set of robotic process automation services that are configured to generate a token that represents an item in the second exchange based on characteristics of the item determined from data from the first exchange.
  • Some embodiments further include a set of robotic process automation services that are configured to generate a digital representation of a set of rights relating to an item that is consistent with governing rules of the second exchange based on processing at least one of a set of smart contracts and a set of terms and conditions relating to the item. Some embodiments further include a set of robotic process automation services that are configured to orchestrate a set of transaction workflows in each of a plurality of exchanges, such that initiation of a set of actions in one exchange of the plurality of exchanges automatically results in the triggering of a set of actions in at least one other exchange.
  • Some embodiments further include a digital twin that represents a set of entities, workflows, and transaction parameters of a plurality of exchanges, such that interaction with an interface of the digital twin can orchestrate an interaction in each of the plurality of exchanges.
  • Some embodiments further include a data and network infrastructure pipeline that is configured to deliver data from a set of assets to set of smart contracts that include terms, conditions and parameters for a set of transaction workflows involving the assets, where the pipeline is automatically configured to adjust a network path based on the characteristics of the data and at least one performance parameter of the network path.
  • Some embodiments further include a data and network infrastructure pipeline that is configured to deliver data from a set of assets to an interface by which an operator orchestrates a set of parameters for a set of transaction workflows involving the assets, where the pipeline is automatically configured to adjust timing of data delivery based on at least one of a transaction parameter and a network performance parameter.
  • Some embodiments further include a set of application programming interfaces to a marketplace that are configured to be integrated into an electronic wallet system, such that interactions with a set of interfaces of the wallet system automatically trigger a set of transaction workflows within the marketplace. Some embodiments further include a set of application programming interfaces to a marketplace that are configured to be integrated into a digital twin platform, such that interactions with a set of interfaces of the digital twin platform automatically trigger a set of transaction workflows within the marketplace. Some embodiments further include a set of application programming interfaces to a marketplace that are configured to be integrated into an enterprise database platform, such that interactions with a set of interfaces of the enterprise database platform automatically trigger a set of transaction workflows within the marketplace.
  • Some embodiments further include a set of application programming interfaces to a marketplace that are configured to be integrated into a platform-as-a-service platform, such that interactions with a set of interfaces of the platform-as-a-service platform automatically trigger a set of transaction workflows within the marketplace. Some embodiments further include a set of application programming interfaces to a marketplace that are configured to be integrated into a computer-aided design platform, such that interactions with a set of interfaces of the computer-aided design platform automatically trigger a set of transaction workflows within the marketplace. Some embodiments further include a set of application programming interfaces to a marketplace that are configured to be integrated into a video game, such that interactions with a set of interfaces of the video game automatically trigger a set of transaction workflows within the marketplace.
  • a system for normalizing an item value for a plurality of exchanges includes: a plurality of electronic exchanges configured for conducting transactions for at least one item in a set of items in an exchange-native currency for each of the plurality of electronic exchanges; an item value normalization system configured to identify a reference currency of a plurality of exchange-native currencies for the plurality of electronic exchanges, and to state a value for the at least one item in the set of items as a normalized value relative to a reference currency value of the at least one item; and a robotic process automation system executing a set of computer-readable instructions on at least one processor, the instructions causing the robotic process automation system to automate item value normalization through automated operation of the item value normalization system.
  • the item value normalization system is further configure to identify a reference currency based on a candidate currency exchange rate history, a futures value of a candidate currency, a volatility score of a candidate currency, or a relative valuation of a candidate currency.
  • the item value normalization system is configured to identify the reference currency based on an exchange rate for a portion of the plurality of exchange-native currencies.
  • to state a value for the at least one item in the set of items as a normalized value includes at least one exchange-specific fee associated with conducting a transaction for the item.
  • Some embodiments further include a set of robotic process automation services that are configured to generate a token that represents an item in the second exchange based on characteristics of the item determined from data from the first exchange.
  • Some embodiments further include a set of robotic process automation services that are configured to generate a digital representation of a set of rights relating to an item that is consistent with governing rules of the second exchange based on processing at least one of a set of smart contracts and a set of terms and conditions relating to the item.
  • Some embodiments further include a set of robotic process automation services that are configured to orchestrate a set of transaction workflows in each of a plurality of exchanges, such that initiation of a set of actions in one exchange of the plurality of exchanges automatically results in the triggering of a set of actions in at least one other exchange.
  • Some embodiments further include a digital twin that represents a set of entities, workflows, and transaction parameters of a plurality of exchanges, such that interaction with an interface of the digital twin can orchestrate an interaction in each of the plurality of exchanges.
  • Some embodiments further include a data and network infrastructure pipeline that is configured to deliver data from a set of assets to set of smart contracts that include terms, conditions and parameters for a set of transaction workflows involving the assets, where the pipeline is automatically configured to adjust a network path based on the characteristics of the data and at least one performance parameter of the network path.
  • Some embodiments further include a data and network infrastructure pipeline that is configured to deliver data from a set of assets to an interface by which an operator orchestrates a set of parameters for a set of transaction workflows involving the assets, where the pipeline is automatically configured to adjust timing of data delivery based on at least one of a transaction parameter and a network performance parameter.
  • Some embodiments further include a set of application programming interfaces to a marketplace that are configured to be integrated into an electronic wallet system, such that interactions with a set of interfaces of the wallet system automatically trigger a set of transaction workflows within the marketplace. Some embodiments further include a set of application programming interfaces to a marketplace that are configured to be integrated into a digital twin platform, such that interactions with a set of interfaces of the digital twin platform automatically trigger a set of transaction workflows within the marketplace.
  • Some embodiments further include a set of application programming interfaces to a marketplace that are configured to be integrated into an enterprise database platform, such that interactions with a set of interfaces of the enterprise database platform automatically trigger a set of transaction workflows within the marketplace. Some embodiments further include a set of application programming interfaces to a marketplace that are configured to be integrated into a platform-as-a-service platform, such that interactions with a set of interfaces of the platform-as-a-service platform automatically trigger a set of transaction workflows within the marketplace. Some embodiments further include a set of application programming interfaces to a marketplace that are configured to be integrated into a computer-aided design platform, such that interactions with a set of interfaces of the computer-aided design platform automatically trigger a set of transaction workflows within the marketplace.
  • a system for item token generation including: a smart contact for an item, the smart contract for control of at least a portion of aspects of conducting a transaction for the item in a first exchange; a set of item characteristics that facilitate tokenization of the item; a set of target exchange characteristics rules; a smart contract parsing system configured to parse the smart contract for the item into a set of contract terms for the item; a token generation system configured to receive the set of contract terms for the item, to receive the set of item characteristics, to receive the set of target exchange characteristics rules and to generate through cooperative operation of a smart contract engine, a token for the item for use in the target exchange; and a smart contract engine interfacing with the token generation system and configured to perform validation of at least one of the contract terms through emulation of a smart contract generated for
  • the smart contract engine is further configured to perform validation of at least one of a set of contract terms for a smart contract configured for the target exchange.
  • Some embodiments further include a set of characteristics harvesting functions configured to facilitate harvesting the set of item characteristics from a digital representation of the item in the first exchange.
  • Some embodiments further include a set of robotic process automation services executing a set of computer-readable instructions on at least one processor, the instructions causing the robotic process automation system to automate item token generation through automated operation of the token generation system.
  • a system for item token generation including: a first token representing characteristics of an item in a first electronic exchange; a set of target exchange characteristics rules; a set of item characteristics harvesting services configured to extract one or more item characteristics from the first token; a token generation system configured to receive the first token, to receive the set of target exchange characteristics rules and to generate a token for the item for use in the target exchange by applying at least one of the set item characteristic harvesting services to harvest a set of characteristics of an item represented by the first token; and a robotic process automation system executing a set of computer-readable instructions on at least one processor, the instructions causing the robotic process automation system to automate item token generation through automated operation of the token generation system.
  • an intelligent data layer system includes: a computer-readable storage system that stores a layer configuration data store that maintains: ingestion parameters including one or more data structures that represent aspects of one or more of a plurality of data sources including a source location, an interface protocol, a source data ontology, and an ingestion cost; parsing rules that facilitate determining one or more of structure, content, relationships among data elements, intended meaning of the data elements, or relationships of data, structure, and intended meaning; and one or more analysis algorithms; and a set of one or more processors that execute a set of computer-readable instructions, where the set of one or more processors collectively: receive an intelligence request from an intelligence consumer portal; determine at least one data source for deriving intelligence for the consumer portal based on the received request; configure an ingestion system based on the ingestion parameters and parsing rules in the layer configuration data store for the at least one data source; configure an analysis system based on the analysis algorithms in the layer configuration data store for the at least one data source; configure an intelligence deriving system based on information
  • the computer-readable storage system stores an intelligent data layer store that maintains results of operations of one or more systems of the intelligent data layer system.
  • the one or more systems includes the ingestion system, the analysis system, and the intelligence deriving system.
  • the result of operations includes intermediate results of at least one of the one or more systems and at least one role-adapted final result variant of the intermediate results.
  • to configure the analysis system is further based on consumer intelligence objectives of the request. In embodiments, to configure the analysis system is further based on aspects of the request.
  • the set of one or more processors is configured in an intelligent data layer control tower that configures and operates the intelligent data layer system by communicating control sequences with the ingestion system, the analysis system, and the intelligence deriving system. Some embodiments further include an algorithm portal of an intelligent data layer control tower of the system through which at least one of the analysis algorithms is received.
  • the ingestion system parses content of data sources to determine structure of the content and relationships among elements in the data.
  • the ingestion system parsing a content of data sources results in generating characterization data that includes an intended meaning of elements of the data and relationships among the data, structures of the data, and meaning of the data parsed from the content.
  • the ingestion system assigns a relationship attribute to a pair of data values that are configured as parent/child in a hierarchy of the data source.
  • the ingestion system is configured to maintain a schedule of collection activity for one or more data sources.
  • the ingestion system is configured to parse source data according to at least one of a specification of the source or a context of a supply chain for an ingestion instance of the source data.
  • the ingestion system communicates ingested data, results of ingestion, and results of parsing, to an intelligent data layer control tower of the system.
  • the location of the data source is a source address selected from a list of source addresses consisting of a universal record locator, port number, stream identifier, publication and/or broad channel, sensor output address.
  • the analysis system compares data from the data source against a target use of intelligence derived from a data source to determine a degree of fitness for use of the data source by the intelligence deriving system.
  • one or more systems of the intelligent data layer is configured as a micro-service architecture for isolated and independent operation of instances of the one or more systems for a plurality of distinct consumer portals.
  • the one or more systems of the intelligent data layer system is initiated as a virtualized container to perform system-specific intelligent data layer system functions.
  • the virtualized container is executed on a cloud-processing architecture.
  • the virtualized container is configured with a consumer portal-specific instance of at least one of the ingestion system, the analysis system, or the intelligence deriving system.
  • the intelligent data layer system ingests data from a plurality of types of data sources including data channels, on-demand data sources, and published data sources.
  • the system-focused probes include probes that monitor source data for source data impacting activity and that signal to an intelligent data layer control tower for taking action within the system based on a projected impact of the source data impacting activity.
  • the system-focused probes monitor for time-related triggers for data sources, including early release of an update of source data, delayed release of an update of source data, and an announcement of new sources of data.
  • the ingestion system monitors a port on a data network for an indication of data availability at a data source.
  • the system develops a multi-dimensional understanding of source data value by applying a value determination cross matrix that facilitates mapping a data source-relevant value of the source data to a consumer portal-relevant value of the source data.
  • a method of operating an intelligent data layer includes: receiving an intelligence request from an intelligence consumer portal; determining at least one data source for deriving intelligence for the consumer portal based on the received request; configuring an ingestion system based on ingestion parameters and parsing rules in a layer configuration data store for the at least one data source; configuring an analysis system based on one or more analysis algorithms in the layer configuration data store for the at least one data source; configuring an intelligence deriving system based on information in the request and available intelligence services in an intelligence service system; and operating the system to ingest data from the at least one data source using the ingestion system, analyze the ingested data from the at least one data source using the analysis system, derive a set of intelligence data from at least one of the ingested data form the at least one data source and an outcome of using the analysis system, and communicating the set of intelligence data to at least one of the consumer portal or an intelligent data layer store.
  • Some embodiments further include receiving, through an algorithm portal of an intelligent data layer control tower, at least one analysis algorithm used by the analysis system.
  • the ingestion system applies the parsing rules to content of data sources to determine structure of the content and relationships among elements in the data.
  • the ingestion system applies the parsing rules to content of data sources thereby generating characterization data that includes an intended meaning of elements of the data and relationships among the data, structures of the data, and meaning of the data parsed from the content.
  • the ingestion system assigns a relationship attribute to a pair of data values that are configured as parent/child in a hierarchy of the data source.
  • the ingestion system is configured to maintain a schedule of collection activity for one or more data sources.
  • the ingestion system is configured to parse source data according to at least one of a specification of the source or a context of a supply chain for an ingestion instance of the source data.
  • the ingestion system communicates ingested data, results of ingestion, and results of parsing, to an intelligent data layer control tower of the system.
  • a location of the data source is a source address selected from a list of source address consisting of a universal record locator, port number, stream identifier, publication and/or broad channel, sensor output address.
  • the analysis system compares data from the data source against a target use of intelligence derived from a data source to determine a degree of fitness for use of the data source by the intelligence deriving system.
  • the analysis system analyzes ingestion system results for meeting at least one consumption target requirement of the consumer portal request.
  • the consumption target requirement includes one or more of a validity time constraint, an accuracy constraint, a frequency of update constraint, or relevance to a consumption subject matter focus.
  • an intelligent data layer control tower adapts a configuration of the ingestion system based on a type of data source for a data source selected by the intelligent data layer control tower for each of a plurality of instances of ingestion. In embodiments, an intelligent data layer control tower adapts a configuration of the ingestion system, the analysis system, and the intelligence deriving system. In embodiments, the intelligent data layer ingests data differently from a single data source based on ingestion requirements accessible through the request. In embodiments, the intelligent data layer further includes a plurality of system-focused probes that provide near real-time context of a range of aspects of system services.
  • the system-focused probes include probes that monitor source data for source data impacting activity and that signal to an intelligent data layer control tower for taking action within the system based on a projected impact of the source data impacting activity.
  • the system-focused probes monitor for time-related triggers for data sources, including early release of an update of source data, delayed release of an update of source data, and an announcement of new sources of data.
  • the ingestion system monitors a port on a data network for an indication of data availability at a data source.
  • the intelligent data layer develops a multi-dimensional understanding of source data value by applying a value determination cross matrix that facilitates mapping a data source-relevant value of the source data to a consumer portal-relevant value of the source data.
  • a system of intelligent data layer network elements includes: a first intelligent data layer element deriving a first degree of source data intelligence from a first source of data; a second intelligent data layer element deriving a second degree of source data intelligence from a second source of data, the second source of data representing a context of the first source of data; a third intelligent data layer element forming an intelligent data layer network through interconnections with the first intelligent data layer element and the second intelligent data layer element and deriving composite intelligence by processing data from a local source of data with the first degree of source data intelligence received through the interconnections and the second degree of source data intelligence received through the interconnections; and a fourth intelligent data layer element extending the intelligent data layer network through interconnection with the third intelligent data layer element and deriving a set of intelligence data structures by processing data from a second local source with one or more of the first degree of source data intelligence, the second degree of source data intelligence, or the composite intelligence, where deriving a set of intelligence data structures is based on intelligent data structures requirements of an intelligent data layer
  • the first intelligent data layer derives marketplace bidding activity intelligence
  • the second intelligent data layer derives marketplace settlement activity intelligence
  • the third intelligent data layer derives the composite intelligence including relative impacts of changes in bidding activity on settlement terms.
  • the data from the second local source is monitored marketplace regulatory compliance and the set of intelligence data structures includes analysis of regulatory compliance of at least one of the bidding activity intelligence, the settlement activity intelligence, and the composite intelligence.
  • the data from the second local source includes one or more of, raw transaction data, analyzed transaction data, marketplace data, or financial data for a plurality of transactions in the monitored marketplace.
  • the ingestion system assigns a relationship attribute to a pair of data values that are configured as parent/child in a hierarchy of a corresponding source of data.
  • the ingestion system is configured to maintain a schedule of collection activity for one or more sources of data.
  • the ingestion system is configured to parse source data according to at least one of a specification of the source or a context of a supply chain for an ingestion instance of the source data.
  • the analysis system compares data from the source data against a target use of corresponding sourced data intelligence to determine a degree of fitness for use of the source of data by the intelligence deriving system.
  • one or more intelligent data layer network elements in the system of intelligent data layer network elements is initiated as a virtualized container of system-specific intelligent data layer element functions.
  • the virtualized container is executed on a cloud-processing architecture.
  • at least one of the intelligent data layer elements ingests data from a plurality of types of sources of data including data channels, on-demand data sources, and published data sources.
  • the ingestion system monitors a port on a data network for an indication of data availability at a source of data.
  • the set of intelligence data structures includes a multi-dimensional representation of source data value by applying a value determination cross matrix that facilitates mapping a data source-relevant value of the source data to a consumer portal-relevant value of the source data.
  • a method of networking intelligent data layer elements including: deriving a first degree of source data intelligence from a first source of data with a first intelligent data layer element; deriving a second degree of source data intelligence with a second intelligent data layer element from a second source of data, the second source of data representing a context of the first source of data; forming an intelligent data layer network through interconnections of a third intelligent data layer element with the first intelligent data layer element and the second intelligent data layer element and deriving composite intelligence by processing data from a local source of data with the first degree of source data intelligence received through the interconnections and the second degree of source data intelligence received through the interconnections; and extending the intelligent data layer network through interconnection of a fourth intelligent data layer element with the third intelligent data layer element and deriving a set of intelligence data structures by processing data from a second local source with one or more of the first degree of source data intelligence, the second degree of source data intelligence, or the composite intelligence, where deriving a set of intelligence data structures is based on intelligent data structures requirements of
  • the first intelligent data layer derives marketplace bidding activity intelligence
  • the second intelligent data layer derives marketplace settlement activity intelligence
  • the third intelligent data layer derives the composite intelligence including relative impacts of changes in bidding activity on settlement terms.
  • the data from the second local source is monitored marketplace regulatory compliance data and the set of intelligence data structures includes analysis of regulatory compliance of at least one of the bidding activity intelligence, the settlement activity intelligence, and the composite intelligence.
  • the data from the second local source includes one or more of, raw transaction data, analyzed transaction data, marketplace data, or financial data for a plurality of transactions in the monitored marketplace.
  • At least one intelligent data layer element includes an intelligent data layer control tower that configures and operates the at least one intelligent data layer element by communicating control sequences with an ingestion system that receives source data, an analysis system that evaluates a data output of the ingestion system, and an intelligence deriving system that produces a corresponding one of source data intelligence, composite intelligence, and the set of intelligence data structures.
  • the ingestion system parses content of source data to determine structure of source data content and relationships among elements in the source data.
  • the ingestion system parses a content of data sources resulting in generating characterization data that includes an intended meaning of data elements and relationships among the data elements, structures of the data, and the intended meaning of the data parsed from the content.
  • the ingestion system assigns a relationship attribute to a pair of data values that are configured as parent/child in a hierarchy of a corresponding source of data.
  • the ingestion system is configured to maintain a schedule of collection activity for one or more sources of data.
  • the ingestion system is configured to parse source data according to at least one of a specification of the source or a context of a supply chain for an ingestion instance of the source data.
  • the analysis system compares data from the source data against a target use of corresponding sourced data intelligence to determine a degree of fitness for use of the source of data by the intelligence deriving system.
  • one or more intelligent data layer network elements is initiated as a virtualized container of system-specific intelligent data layer element functions.
  • the virtualized container is executed on a cloud-processing architecture.
  • at least one of the intelligent data layer elements ingests data from a plurality of types of sources of data including data channels, on-demand data sources, and published data sources.
  • the ingestion system monitors a port on a data network for an indication of data availability at a source of data.
  • the set of intelligence data structures includes a multi-dimensional representation of source data value by applying a value determination cross matrix that facilitates mapping a data source-relevant value of the source data to a consumer portal-relevant value of the source data.
  • a system for discovering data sources for an intelligent data layer includes: a computer-readable storage system that stores a source discovery data store that maintains a data store storing information about existing data sources; an ingestion system for capturing data from candidate data sources; an analysis system for evaluating content ingested from the candidate data sources for meeting one or more aspects of a target source discovery criteria; a similarity engine that produces a degree of similarity signal indicative of a degree of similarity of the candidate data source to at least one of the existing data sources; a relevance engine that produces a degree of usefulness signal indicative of a utility of the candidate source for producing at least one intelligence outcome; and an intelligent data layer control tower that applies artificial intelligence techniques for determining at least one of ingestion actions for the ingestion system and analysis actions for the analysis engine, and for making a determination of use of the candidate data source.
  • the intelligent data layer control tower applies machine learning to train the artificial intelligence techniques.
  • the intelligent data layer is integrated into a marketplace system of systems.
  • the marketplace system of systems is an automated market orchestration system of systems.
  • the intelligent data layer control tower determines that at least one integration action includes capturing information from and about candidate sources.
  • the intelligent data layer control tower determines that at least one integration action includes advertising for candidate sources.
  • the intelligent data layer control tower determines that at least one integration action includes contacting a plurality of known content sources with sets of criteria that are descriptive of a type of content.
  • the ingestion system adapts at least a portion of a set of criteria for seeking a source of data by performing at least one of adjusting a range of a value that is descriptive of target source data, or broadening the set of criteria by abstracting at least one data requirement.
  • the intelligent data layer control tower suggests source content discovery criteria based on analysis of existing sources, based on requests for variation of intelligence from consumers of the intelligent data layer, and feedback relating to usefulness of existing sources.
  • the ingestion system adapts an original ingestion profile for one or more existing data sources thereby causing ingestion of content from the one or more existing data sources that is excluded from ingestion under the original ingestion profile.
  • the ingestion system filters data from the candidate data sources based on compliance with at least a portion of a target content ingestion criteria and forwards data from the candidate data source that is accepted through the filter to the analysis engine.
  • the target content ingestion criteria includes requirements of a data format, a language, and a minimum precision.
  • the ingestion system provides source discovery status information to the intelligent data layer control tower for candidate sources of data.
  • the analysis system processes data forwarded by the ingestion system to determine compliance with source discovery criteria.
  • the source discovery criteria includes consistency of source terminology.
  • the source discovery criteria includes consistency of terminology in the candidate data source to terminology of at least one of the existing data sources.
  • the analysis system applies a data stabilization algorithm to a portion of the data from the candidate source, a result of which is compared to a data stability criteria of the source discovery criteria.
  • the similarity engine determines a degree of similarity of a portion of the candidate source data and at least one existing source of data by comparing data values of the portion to data values of a portion of an existing source of data.
  • a degree of usefulness signal includes a predicted impact on intelligence derivable from the candidate source used by one or more intelligence derivation algorithms.
  • the degree of usefulness signal includes an indication that a corresponding candidate source is to be added to a list of approved sources.
  • the intelligent data layer control tower applies machine learning to train the artificial intelligence techniques.
  • the intelligent data layer is integrated into a marketplace system of systems.
  • the marketplace system of systems is an automated market orchestration system of systems.
  • the intelligent data layer control tower determines that at least one integration action includes capturing information from and about candidate sources.
  • the intelligent data layer control tower determines that at least one integration action includes advertising for candidate sources.
  • the intelligent data layer control tower determines that at least one integration action includes contacting a plurality of known content sources with sets of criteria that are descriptive of a type of content.
  • the intelligent data layer control tower suggests source content discovery criteria based on analysis of existing sources, based on requests for variation of intelligence from consumers of the intelligent data layer, and feedback relating to usefulness of existing sources.
  • the ingestion system adapts an original ingestion profile for one or more existing data sources thereby causing ingestion of content from the one or more existing data sources that is excluded from ingestion under the original ingestion profile.
  • the ingestion system filters data from the candidate data sources based on compliance with at least a portion of a target content ingestion criteria and forwards data from the candidate data source that is accepted through the filter to the analysis engine.
  • the target content ingestion criteria includes requirements of a data format, a language, and a minimum precision.
  • the ingestion system provides source discovery status information to the intelligent data layer control tower for candidate sources of data.
  • the analysis system processes data forwarded by the ingestion system to determine compliance with source discovery criteria.
  • the source discovery criteria includes consistency of source terminology.
  • the source discovery criteria includes consistency of terminology in the candidate data source to terminology of at least one of the existing data sources.
  • the analysis system applies a data stabilization algorithm to a portion of the data from the candidate source, a result of which is compared to a data stability criteria of the source discovery criteria.
  • the similarity engine determines a degree of similarity of a portion of the candidate source data and at least one existing source of data by comparing data values of the portion to data values of a portion of an existing source of data.
  • a degree of usefulness signal includes a predicted impact on intelligence derivable from the candidate source used by one or more intelligence derivation algorithms.
  • the degree of usefulness signal includes an indication that a corresponding candidate source is to be added to a list of approved sources.
  • a method of adapting a route for delivery of asset data to a marketplace orchestration interface through a network pipeline is embodied as a set of computer-readable instructions that is executed by a set of one or more processors and including: identifying a set of asset-centric network resources in the network pipeline, a portion of the set of asset-centric network resources providing an interface to an asset in a set of assets for which transactions are conducted in a marketplace, the interface to the asset further configured to facilitate delivery of the asset data from the asset through the network pipeline; identifying a set of marketplace-centric network resources in the network pipeline, a portion of the set of marketplace-centric network resources providing access to a transaction orchestration system interface, the transaction orchestration system interface configured for an operator to orchestrate a set of parameters for a set of transaction workflows of the marketplace involving the set of assets; adapting a network path within the network pipeline that enables delivery of the asset data from the asset in the set of assets through the portion of the set of asset-centric network resources and through the portion of the set of marketplace-
  • the interface to an asset in a set of assets communicates with a native network interface of the asset.
  • the interface to the asset in the set of assets communicates with an asset management resource.
  • the asset data is provided by the asset management resource.
  • the interface to an asset in a set of assets communicates with a digital twin of the asset.
  • the asset data is provided by the digital twin.
  • the set of assets include one or more assets selected from a list of assets consisting of electronic devices, non-electronic devices, digital rights, services, humans, robots, and on-demand built items.
  • the asset resource controller configures the portion of the set of asset-centric network resources based on a result of analysis of the asset data by the asset-centric handling service. In embodiments, the asset resource controller retrieves the result of analysis from the asset-localized network data store.
  • the set of marketplace-centric network resources includes at least one resource providing a service selected from a list of services consisting of electronic wallet services, digital twin services, enterprise database services, platform as a service platform services, computer aided design services, and video game services.
  • adapting the network path is based on one or more security characteristics of the asset data. In embodiments, adapting the network path based on the one or more security characteristics of the data includes configuring a path through the network pipeline that avoids poor reputation network resources.
  • adapting the network path is based on one or more jurisdiction characteristics of the asset data. In embodiments, adapting the network path based on the one or more jurisdiction characteristics of the data includes configuring a path through the network pipeline that avoids network resources based on a jurisdiction of the network resources.
  • the set of marketplace-centric network resources includes smart contract-centric network resources that provide an interface to a set of smart contracts.
  • the set of marketplace-centric network resources includes workflow centric resources that provide an interface to a set workflow resources.
  • the interface to an asset in a set of assets communicates with a native network interface of the asset.
  • the interface to the asset in the set of assets communicates with an asset management resource.
  • the asset data is provided by the asset management resource.
  • the interface to an asset in a set of assets communicates with a digital twin of the asset.
  • the asset data is provided by the digital twin.
  • the set of assets includes one or more assets selected from a list of assets consisting of electronic devices, non-electronic devices, digital rights, services, humans, robots, and on-demand built items.
  • the set of assets includes at least one interface for a plurality of assets in the set of assets, where the asset data for the plurality of assets is provided to the network pipeline through the at least one interface.
  • the set of asset-centric network resources in the network pipeline includes asset interfacing resources, an asset-centric network resource controller and an asset-localized network data store.
  • the set of asset-centric network resources perform asset-centric data handling.
  • a portion of the set of asset-centric network resources is configured by an asset resource controller to work cooperatively with an asset-centric data handling service for processing and storing the asset data in an asset-localized network data store.
  • the asset resource controller configures the portion of the set of asset-centric network resources based on a result of analysis of the asset data by the asset-centric handling service. In embodiments, the asset resource controller retrieves the result of analysis from the asset-localized network data store.
  • the set of marketplace-centric network resources includes at least one resource providing a service selected from a list of services consisting of electronic wallet services, digital twin services, enterprise database services, platform as a service platform services, computer aided design services, and video game services.
  • to adapt the network path is based on one or more security characteristics of the asset data.
  • to adapt the network path based on the one or more security characteristics of the data includes configuring a path through the network pipeline that avoids poor reputation network resources.
  • to adapt the network path is based on one or more jurisdiction characteristics of the asset data.
  • to adapt the network path based on the one or more jurisdiction characteristics of the data includes configuring a path through the network pipeline that avoids network resources based on a jurisdiction of the network resources.
  • the set of marketplace-centric network resources includes smart contract-centric network resources that provide an interface to a set of smart contracts.
  • the set of marketplace-centric network resources includes workflow centric resources that provide an interface to a set workflow resources.
  • a system includes: a network adaptation system that automatically constructs a network infrastructure path in a network pipeline to deliver data from an asset to a market orchestration recipient, the constructed network infrastructure path is automatically adapted based on one or more characteristics of the data from the asset and at least one performance parameter for the network infrastructure path; a network timing adaptation system that automatically adapts network infrastructure resources in a network pipeline that delivers data from the asset to the market orchestration recipient for orchestration of a transaction of the asset, where the network infrastructure resources are adapted based on at least one of a parameter of the transaction of the asset and a performance parameter of the network pipeline; a set of asset-centric network resources that facilitate ingestion of the data from the asset into the network pipeline; and a set of marketplace-centric network resources that facilitate delivery of the asset data from the adapted network pipeline to the market orchestration recipient.
  • the network pipeline delivers the data from the asset to the market orchestration recipient for orchestration of a transaction of the asset.
  • the network timing adaptation system adapts the network infrastructure resources in the network pipeline to satisfy a data delivery timing requirement associated with a transaction workflow for the asset.
  • the market orchestration recipient is a smart contract that includes terms, conditions, and parameters for a set of transaction workflows involving the asset.
  • adapting the network infrastructure path is based on one or more security characteristics of the asset data. In embodiments, adapting the network path based on the one or more security characteristics of the data includes configuring a path through the network pipeline that avoids poor reputation network resources. In embodiments, constructing a network infrastructure path in a network pipeline includes adjusting a communication protocol that avoids exposing data from the asset in a context that gives meaning to the data. In embodiments, adjusting the communication protocol includes delivering a first portion of the asset data through a first network path and a second portion of the asset data through a second network path.
  • constructing a network infrastructure path in a network pipeline includes adapting the network path for delivering the data from the asset so that the network path changes over time. In embodiments, constructing a network infrastructure path in a network pipeline includes adapting the network path to include at least one infrastructure node that is different than infrastructure nodes used previously to deliver the data from the asset. In embodiments, constructing a network infrastructure path in a network pipeline includes adapting the network infrastructure path so that it is different than prior network infrastructure paths used to deliver the data from the asset that are recorded in a historical record of network paths for the asset data. In embodiments, adapting the network infrastructure path based on one or more characteristics of the data from the asset includes configuring a plurality of recipients for one or more portions of the data from the asset, where the plurality of recipients is determined from a transaction workflow for the asset.
  • a method embodied as a set of computer-readable instructions that is executed by a set of one or more processors and includes: constructing a network infrastructure path in a network pipeline with a network adaptation system to deliver data from an asset to a market orchestration recipient, the network infrastructure path automatically adapted based on one or more characteristics of the data from the asset and at least one performance parameter for the network infrastructure path; adapting network infrastructure resources in the network pipeline, with a network timing adaptation system, that delivers data from the asset to the market orchestration recipient, where the network infrastructure resources are adapted based on at least one of a parameter of a transaction of the asset and a performance parameter of the network pipeline; ingesting the data from the asset into the network pipeline with a set of asset-centric network resources; and delivering the asset data from the adapted network pipeline to the market orchestration recipient with a set of marketplace-centric network resources.
  • the adapted network pipeline delivers the data from the asset to the market orchestration recipient for orchestration of a transaction of the asset.
  • the network timing adaptation system adapts the network infrastructure resources in the network pipeline to satisfy a data delivery timing requirement associated with a transaction workflow for the asset.
  • the market orchestration recipient is a smart contract that includes terms, conditions, and parameters for a set of transaction workflows involving the asset.
  • adapting the network path is based on one or more security characteristics of the asset data. In embodiments, adapting the network path based on the one or more security characteristics of the data includes configuring a path through the network pipeline that avoids poor reputation network resources. In embodiments, constructing a network infrastructure path in a network pipeline includes adjusting a communication protocol that avoids exposing data from the asset in a context that gives meaning to the data. In embodiments, adjusting the communication protocol includes delivering a first portion of the asset data through a first network path and a second portion of the asset data through a second network path. In embodiments, constructing a network infrastructure path in a network pipeline includes adapting the network path for delivering the data from the asset so that the network path changes over time.
  • constructing a network infrastructure path in a network pipeline includes adapting the network path to include at least one infrastructure node that is different than infrastructure nodes used previously to deliver the data from the asset.
  • constructing a network infrastructure path in a network pipeline includes adapting the network infrastructure path so that it is different than prior network infrastructure paths used to deliver the data from the asset that are recorded in a historical record of network paths for the asset data.
  • adapting the network infrastructure path based on one or more characteristics of the data from the asset includes configuring a plurality of recipients for one or more portions of the data from the asset, where the plurality of recipients is determined from a transaction workflow for the asset.
  • a system of network infrastructure resources includes: a first network interface connecting a set of the network infrastructure resources to an asset network resource; a second network interface connecting the set of network infrastructure resources to a second set of network infrastructure resources forming a portion of a network path for delivering data from the asset to a marketplace orchestration system interface, the network path automatically adapted to deliver the data from the asset to the marketplace orchestration system interface based on one or more characteristics of the data from the asset and at least one performance parameter for the network path; an asset-centric controller communicating with the asset through the first network interface and controlling delivery of the data from the asset over the adapted network path; an asset-centric data handling system communicating with the asset through the first network interface and processing the data from the asset in support of delivery of the data from the asset over the adapted network path; and an asset-centric data storage facility controlled by the asset-centric controller to receive data processed by the asset-centric data handling system, where data stored in the asset-centric data storage facility is accessible through the second interface by a portion of the second set of network infrastructure resources for delivering data
  • the network path is further automatically adapted to adjust timing of delivery of data from the asset to the marketplace orchestration system interface based on at least one of a transaction parameter and a network performance parameter.
  • the first network interface communicates with a native network interface of the asset.
  • the first network interface communicates with an asset management resource.
  • the asset data is provided by the asset management resource.
  • the first network interface communicates with a digital twin of the asset.
  • the data from the asset is provided by the digital twin.
  • the asset-centric controller configures the first network interface based on a result of analysis of the data from the asset by the asset-centric data handling system.
  • the asset-centric controller retrieves the result of analysis from the asset-centric data storage facility.
  • the network path is further automatically adapted based on one or more security characteristics of the data from the asset.
  • further automatically adapting the network path based on the one or more security characteristics of the data includes configuring the network path to avoid poor reputation network resources.
  • the automatically adapted network path includes an adapted communication protocol that avoids exposing data from the asset in a context that gives meaning to the data.
  • adjusting the adapted communication protocol delivers a first portion of the data from the asset through a first network path and a second portion of the data from the asset through a second network path.
  • the automatically adapted network path for delivering the data from the asset changes over time.
  • the automatically adapted network path is different than prior network infrastructure paths used to deliver the data from the asset that are recorded in a historical record of network paths for the data from the asset.
  • the asset-centric controller configures the first network interface and the asset-centric data handling system so delivery of the data from the asset over the adapted network path is independent of how data from the asset is received from the asset by the first network communication interface.
  • the marketplace orchestration system interface is a set of smart contracts that includes terms, conditions, and parameters for a set of transaction workflows involving the asset.
  • the marketplace orchestration system interface is an interface through which an operator orchestrates parameters of a set of transaction workflows associated with transactions for the assets.
  • the operator orchestrates parameters of a set of transaction workflows based on the data from the asset.
  • the marketplace orchestration system interface is adapted to facilitate orchestrating parameters of a set of transaction workflows involving the assets.
  • a method includes: connecting via a first network interface a set of network infrastructure resources to an asset; connecting via a second network interface the set of network infrastructure resources to a second set of network infrastructure resources forming a portion of a network path for delivering data from the asset to a marketplace orchestration system interface, the network path automatically adapted to deliver the data from the asset to the marketplace orchestration system interface based on one or more characteristics of the data from the asset and at least one performance parameter for the network path; controlling delivery of the data from the asset over the adapted network path with an asset-centric controller disposed to communicate with the asset through the first network interface; processing the data from the asset in support of delivery of the data from the asset over the adapted network path with an asset-centric data handling system disposed to communicate with the asset through the first network interface; and storing data processed by the asset-centric data handling system in an asset-centric data storage facility controlled by the asset-centric controller, where data stored in the asset-centric data storage facility is accessible through the second interface by a portion of the second set of network infrastructure resources for
  • the network path is further automatically adapted to adjust timing of delivery of data from the asset to the marketplace orchestration system interface based on at least one of a transaction parameter and a network performance parameter.
  • the first network interface communicates with a native network interface of the asset.
  • the first network interface communicates with an asset management resource.
  • the asset data is provided by the asset management resource.
  • the first network interface communicates with a digital twin of the asset.
  • the data from the asset is provided by the digital twin.
  • the asset-centric controller configures the first network interface based on a result of analysis of the data from the asset by the asset-centric data handling system.
  • the asset-centric controller retrieves the result of analysis from the asset-centric data storage facility.
  • the network path is further automatically adapted based on one or more security characteristics of the data from the asset.
  • further automatically adapting the network path based on the one or more security characteristics of the data includes configuring the network path to avoid poor reputation network resources.
  • the automatically adapted network path includes an adapted communication protocol that avoids exposing data from the asset in a context that gives meaning to the data.
  • adjusting the adapted communication protocol delivers a first portion of the data from the asset through a first network path and a second portion of the data from the asset through a second network path.
  • the automatically adapted network path for delivering the data from the asset changes over time.
  • the automatically adapted network path is different than prior network infrastructure paths used to deliver the data from the asset that are recorded in a historical record of network paths for the data from the asset.
  • the asset-centric controller configures the first network interface and the asset-centric data handling system so delivery of the data from the asset over the adapted network path is independent of how data from the asset is received from the asset by the first network communication interface.
  • the marketplace orchestration system interface is a set of smart contracts that includes terms, conditions, and parameters for a set of transaction workflows involving the asset.
  • the marketplace orchestration system interface is an interface through which an operator orchestrates parameters of a set of transaction workflows associated with transactions for the assets.
  • the operator orchestrates parameters of a set of transaction workflows based on the data from the asset.
  • the marketplace orchestration system interface is adapted to facilitate orchestrating parameters of a set of transaction workflows involving the assets.
  • a system includes: a network adaptation system that automatically adapts a network infrastructure path from an asset to a market orchestration recipient in a network pipeline that delivers asset data from the asset to the recipient, the network infrastructure path automatically adapted based on one or more characteristics of the asset data and at least one performance parameter for the network path; a set of asset-centric network resources that facilitate ingestion of the data from the asset into the adapted network pipeline; a set of marketplace-centric network resources that facilitate delivery of the asset data of the adapted network pipeline to the market orchestration recipient; and a set of application programming interfaces for a marketplace that executes transaction workflows for conducting a transaction for the asset based on workflow parameters determined by the market orchestration recipient, the set of application programming interfaces integrated into an auxiliary system that includes a set of interfaces for activating a function of the auxiliary system that when activated sends a transaction workflow activation signal to the marketplace through the set of integrated application programming interfaces, where a portion of the transaction workflows is activated.
  • the auxiliary system is an electronic wallet platform and the function is a transaction settlement function that, when activated causes activation of the portion of the transaction workflows in the marketplace.
  • the transaction settlement function signals to the marketplace to activate the portion of the transaction workflows through the integrated set of application programming interfaces.
  • the auxiliary system is a digital twin platform and the function is a digital twin of a function of the asset that, when activated causes activation of the portion of the transaction workflows in the marketplace.
  • the digital twin of the function of the asset signals to the marketplace to activate the portion of the transaction workflows through the integrated set of application programming interfaces.
  • the auxiliary system is an enterprise database platform and the function is a database update detection function that, when activated causes activation of the portion of the transaction workflows in the marketplace.
  • the database update detection function signals to the marketplace to activate the portion of the transaction workflows through the integrated set of application programming interfaces.
  • the auxiliary system is a platform as-a-service platform and the function monitors a status of a service that, when activated causes activation of the portion of the transaction workflows in the marketplace.
  • the function that monitors a status of a service signals to the marketplace to activate the portion of the transaction workflows through the integrated set of application programming interfaces.
  • the auxiliary system is a computer aided design platform and the function is an automated design function that, when activated causes activation of the portion of the transaction workflows in the marketplace.
  • the automated design function signals to the marketplace to activate the portion of the transaction workflows through the integrated set of application programming interfaces.
  • the auxiliary system is a video game platform and the function reflects an action by a user of the video game that, when activated causes activation of the portion of the transaction workflows in the marketplace.
  • the function reflects an action by a user of the video game signals to the marketplace to activate the portion of the transaction workflows through the integrated set of application programming interfaces.
  • a method includes: adapting a network infrastructure path from an asset to a smart contract in a network pipeline with a network adaptation system, the adapted network pipeline delivering asset data from the asset to the smart contract, the network infrastructure path automatically adapted based on one or more characteristics of the asset data and at least one performance parameter for the network path; ingesting the data from the asset into the adapted network pipeline with a set of asset-centric network resources; delivering the ingested asset data of the adapted network pipeline to the smart contract with a set of marketplace-centric network resources; and activating a portion of a set of transaction workflows with a set of application programming interfaces for a marketplace that executes transaction workflows for conducting a transaction for the asset based on workflow parameters determined by the smart contract, the set of application programming interfaces integrated into an auxiliary system that includes a set of interfaces for activating a function of the auxiliary system that when activated sends a transaction workflow activation signal to the marketplace through the set of integrated application programming interfaces, where the portion of the transaction workflow
  • the auxiliary system is an electronic wallet platform and the function is a transaction settlement function that, when activated causes activation of the portion of the transaction workflows in the marketplace.
  • the transaction settlement function signals to the marketplace to activate the portion of the transaction workflows through the integrated set of application programming interfaces.
  • the auxiliary system is a digital twin platform and the function is a digital twin of a function of the asset that, when activated causes activation of the portion of the transaction workflows in the marketplace.
  • the digital twin of the function of the asset signals to the marketplace to activate the portion of the transaction workflows through the integrated set of application programming interfaces.
  • the auxiliary system is an enterprise database platform and the function is a database update detection function that, when activated causes activation of the portion of the transaction workflows in the marketplace.
  • the database update detection function signals to the marketplace to activate the portion of the transaction workflows through the integrated set of application programming interfaces.
  • the auxiliary system is a platform as-a-service platform and the function monitors a status of a service that, when activated causes activation of the portion of the transaction workflows in the marketplace.
  • the function that monitors a status of a service signals to the marketplace to activate the portion of the transaction workflows through the integrated set of application programming interfaces.
  • the auxiliary system is a computer aided design platform and the function is an automated design function that, when activated causes activation of the portion of the transaction workflows in the marketplace.
  • the automated design function signals to the marketplace to activate the portion of the transaction workflows through the integrated set of application programming interfaces.
  • the auxiliary system is a video game platform and the function reflects an action by a user of the video game that, when activated causes activation of the portion of the transaction workflows in the marketplace.
  • the function reflects an action by a user of the video game signals to the marketplace to activate the portion of the transaction workflows through the integrated set of application programming interfaces.
  • a system includes: a network timing adaptation system that automatically adapts timing of data transfer through a network pipeline that delivers data from an asset to a market orchestration recipient by adapting one or more network resources of the network pipeline to control data transfer within the network pipeline, the timing of data transfer associated with a time requirement for a transaction of the asset, where the one or more network resources are adapted based on at least one of a parameter of the transaction of the asset and a performance parameter of the network pipeline; a set of asset-centric network resources that facilitate ingestion of the data from the asset into the network pipeline; a set of marketplace-centric network resources that facilitate delivery of the ingested asset data of the adapted network pipeline to the market orchestration recipient; a set of application programming interfaces for a marketplace that executes transaction workflows for conducting a transaction for the asset based on workflow parameters determined by the market orchestration recipient according to data from the asset that is delivered through the adapted one or more network infrastructure resources, the set of application programming interfaces integrated into an auxiliary system that includes a set of interfaces for activ
  • the auxiliary system is an enterprise database platform and the function is a database update detection function that, when activated causes activation of the portion of the transaction workflows in the marketplace.
  • the database update detection function signals to the marketplace to activate the portion of the transaction workflows through the integrated set of application programming interfaces.
  • the auxiliary system is a platform as-a-service platform and the function monitors a status of a service that, when activated causes activation of the portion of the transaction workflows in the marketplace.
  • the function that monitors a status of a service signals to the marketplace to activate the portion of the transaction workflows through the integrated set of application programming interfaces.
  • the auxiliary system is a computer aided design platform and the function is an automated design function that, when activated causes activation of the portion of the transaction workflows in the marketplace.
  • the automated design function signals to the marketplace to activate the portion of the transaction workflows through the set of application programming interfaces.
  • the auxiliary system is a video game platform and the function reflects an action by a user of the video game that, when activated causes activation of the portion of the transaction workflows in the marketplace.
  • the function reflects an action by a user of the video game signals to the marketplace to activate the portion of the transaction workflows through the set of application programming interfaces.
  • a method includes: adapting timing of data transfer through a network pipeline that delivers data from an asset to a smart contract with a network timing adaptation system by adapting one or more network resources of the network pipeline to control data transfer within the network pipeline, the timing of data transfer associated with a time requirement for a transaction of the asset determined by the smart contract, where the one or more network resources are adapted based on at least one of a parameter of the transaction of the asset and a performance parameter of the network pipeline; ingesting the data from the asset into the network pipeline with a set of asset-centric network resources; delivering the ingested asset data of the adapted network pipeline to the smart contract with a set of marketplace-centric network resources; activating a portion of a set of transaction workflows of the transaction of the asset determined by the smart contract with a set of application programming interfaces for a marketplace that executes the set of transaction workflows based on workflow parameters determined by the smart contract according to data from the asset that is delivered through the adapted one or more network infrastructure resources, the set of application programming interface
  • the auxiliary system is an electronic wallet platform and the function is a transaction settlement function that, when activated causes activation of the portion of the transaction workflows in the marketplace.
  • the transaction settlement function signals to the marketplace to activate the portion of the transaction workflows through the integrated set of application programming interfaces.
  • the auxiliary system is a digital twin platform and the function is a digital twin of a function of the asset that, when activated causes activation of the portion of the transaction workflows in the marketplace.
  • the digital twin of the function of the asset signals to the marketplace to activate the portion of the transaction workflows through the integrated set of application programming interfaces.
  • the auxiliary system is an enterprise database platform and the function is a database update detection function that, when activated causes activation of the portion of the transaction workflows in the marketplace.
  • the database update detection function signals to the marketplace to activate the portion of the transaction workflows through the integrated set of application programming interfaces.
  • the auxiliary system is a platform as-a-service platform and the function monitors a status of a service that, when activated causes activation of the portion of the transaction workflows in the marketplace.
  • the function that monitors a status of a service signals to the marketplace to activate the portion of the transaction workflows through the integrated set of application programming interfaces.
  • the auxiliary system is a computer aided design platform and the function is an automated design function that, when activated causes activation of the portion of the transaction workflows in the marketplace.
  • the automated design function signals to the marketplace to activate the portion of the transaction workflows through the integrated set of application programming interfaces.
  • the auxiliary system is a video game platform and the function reflects an action by a user of the video game that, when activated causes activation of the portion of the transaction workflows in the marketplace.
  • the function reflects an action by a user of the video game signals to the marketplace to activate the portion of the transaction workflows through the integrated set of application programming interfaces.
  • a market prediction system includes: a machine learning system that trains a set of machine-learned models to generate a market prediction using training data including demand features and outcomes; an artificial intelligence system that receives a request to generate a market prediction and outputs a market prediction based on the machine-learned models and the request.
  • the market prediction is a prediction for a parameter of demand in a forward market for an asset.
  • the market prediction is a prediction for a parameter of supply in a forward market for an asset.
  • the market prediction is a prediction of a set of terms and/or conditions for a smart contract.
  • the market prediction is based at least in part on crowd sourced data.
  • the market prediction is based at least in part on behavioral data collected from a set of IoT systems monitoring a set of entities in a set of environments.
  • the artificial intelligence system includes a recurrent neural network.
  • the artificial intelligence system includes a convolutional neural network.
  • the artificial intelligence system includes a combination of a recurrent neural network and a convolutional neural network.
  • the set of Internet of Things systems includes a set of smart home Internet of Things devices.
  • the set of Internet of Things systems includes a set of workplace Internet of Things devices.
  • the set of Internet of Things systems includes a set of Internet of Things device to monitor a set of consumer goods stores.
  • the set of entities comprises one or more of: products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, autonomous vehicles, hauling facilities, drones/robots/AVs, waterways, and port infrastructure facilities.
  • the set of environments comprises one or more of: home of a consumer, retail facilities, manufacturing facilities, supply chain facilities, ship containers, ship, boat, barge, maritime port, crane, container, container handling facilities, shipyard, maritime dock, warehouse, distribution facilities, fulfillment facilities, fueling facilities, refueling facilities, nuclear refueling facilities, waste removal facilities, food supply facilities, beverage supply facilities, drone facilities, robot facilities, autonomous vehicle, aircraft, automotive, truck, train, lift, forklift, hauling facilities, conveyor, loading dock, waterway, bridge, tunnel, airport, depot, vehicle station, train station, weigh station, inspection station or point, roadway, railway, highway, customs house, and border control facilities.
  • the set of entities comprises one or more of: products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, autonomous vehicles, hauling facilities, drones/robots/AVs, waterways, and port infrastructure facilities.
  • the set of environments comprises one or more of: home of a consumer, retail facilities, manufacturing facilities, supply chain facilities, ship containers, ship, boat, barge, maritime port, crane, container, container handling facilities, shipyard, maritime dock, warehouse, distribution facilities, fulfillment facilities, fueling facilities, refueling facilities, nuclear refueling facilities, waste removal facilities, food supply facilities, beverage supply facilities, drone facilities, robot facilities, autonomous vehicle, aircraft, automotive, truck, train, lift, forklift, hauling facilities, conveyor, loading dock, waterway, bridge, tunnel, airport, depot, vehicle station, train station, weigh station, inspection station or point, roadway, railway, highway, customs house, and border control facilities.
  • Some embodiments further include a security monitoring system for monitoring assets and/or collateral based on the data collected by the Internet of Things data collection platform.
  • the security monitoring system uses machine-learned models to determine the condition or value of items based on data collected by the Internet of Things data collection platform.
  • the data collected by the Internet of Things data collection platform is image data, sensor data, or location data.
  • Some embodiments further include a loan management system that enables a loan manager to access information from the Internet of Things data collection platform and the security monitoring system.
  • the artificial intelligence system includes a combination of a recurrent neural network and a convolutional neural network.
  • the set of Internet of Things systems includes a set of smart home Internet of Things devices.
  • the set of Internet of Things systems includes a set of workplace Internet of Things devices.
  • the set of Internet of Things systems includes a set of Internet of Things device to monitor a set of consumer goods stores.
  • the set of environments comprises one or more of: home of a consumer, retail facilities, manufacturing facilities, supply chain facilities, ship containers, ship, boat, barge, maritime port, crane, container, container handling facilities, shipyard, maritime dock, warehouse, distribution facilities, fulfillment facilities, fueling facilities, refueling facilities, nuclear refueling facilities, waste removal facilities, food supply facilities, beverage supply facilities, drone facilities, robot facilities, autonomous vehicle, aircraft, automotive, truck, train, lift, forklift, hauling facilities, conveyor, loading dock, waterway, bridge, tunnel, airport, depot, vehicle station, train station, weigh station, inspection station or point, roadway, railway, highway, customs house, and border control facilities.
  • Some embodiments further include a security monitoring system for monitoring assets and/or collateral based on the data collected by the Internet of Things data collection platform.
  • the security monitoring system uses machine-learned models to determine the condition or value of items based on data collected by the Internet of Things data collection platform.
  • the data collected by the Internet of Things data collection platform is image data, sensor data, or location data.
  • Some embodiments further include a loan management system that enables a loan manager to access information from the Internet of Things data collection platform and the security monitoring system.
  • 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
  • a system includes: a machine learning system that trains a set of machine-learned models to generate a market prediction using training data including market features and outcomes; an artificial intelligence system that receives a request to generate a market prediction and outputs a market prediction based on the machine-learned models and the request; a network access layer including a processor and storage hardware in communication with the processor, where the storage hardware includes instructions that when executed by the processor perform operations, and where the operations include: monitoring a plurality of public market participants via an interface system of a network access layer, where the network access layer is controlled by an enterprise and corresponds to an intelligence system that hosts exchangeable enterprise digital assets; receiving, at the network access layer via the interface system, an indication that a monitored public market participant requests a digital asset candidate; determining, by the intelligence system of the network access layer, whether the digital asset candidate matches an asset available in a digital wallet system associated with the network access layer; and in response to the digital asset candidate matching the asset available in the digital wallet system: identifying a set of asset controls managed by a permission system
  • the asset is available in a hot wallet of the digital wallet system. In embodiments, the asset is available in a cold wallet of the digital wallet system. In embodiments, the asset is available in a custodial wallet of the digital wallet system. In embodiments, the operations further comprise: receiving a response message from the monitored public market participant; and determining that the response message indicates an acknowledgement to fulfill the request for the actual transaction; and
  • facilitating fulfillment of the actual transaction includes storing a digital form of the asset in a public append-only data structure to represent execution of the actual transaction.
  • facilitating fulfillment of the asset request includes: signing the actual transaction involving the asset on a cold wallet; and relaying the signed transaction using a hot wallet of the digital wallet system that is associated with the cold wallet.
  • storing the digital form of the asset to a public append-only data structure facilitating uses at least one key from a hot wallet of the digital wallet system.
  • storing the digital form of the asset to a public append-only data structure facilitating uses at least one key from a cold wallet of the digital wallet system.
  • the market prediction is a prediction for a parameter of demand in a forward market for an asset. In embodiments, the market prediction is a prediction for a parameter of supply in a forward market for an asset. In embodiments, the market prediction is a prediction of a set of terms and/or conditions for a smart contract. In embodiments, the market prediction is based at least in part on crowdsourced data. In embodiments, the market prediction is based at least in part on behavioral data collected from a set of IoT systems monitoring a set of entities in a set of environments. In embodiments, the artificial intelligence system includes a recurrent neural network. In embodiments, the artificial intelligence system includes a convolutional neural network.
  • the artificial intelligence system includes a combination of a recurrent neural network and a convolutional neural network.
  • the set of Internet of Things systems includes a set of smart home Internet of Things devices.
  • the set of Internet of Things systems includes a set of workplace Internet of Things devices.
  • the set of Internet of Things systems includes a set of Internet of Things device to monitor a set of consumer goods stores.
  • the set of entities comprises one or more of: products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, autonomous vehicles, hauling facilities, drones/robots/AVs, waterways, and port infrastructure facilities.
  • the set of environments comprises one or more of: home of a consumer, retail facilities, manufacturing facilities, supply chain facilities, ship containers, ship, boat, barge, maritime port, crane, container, container handling facilities, shipyard, maritime dock, warehouse, distribution facilities, fulfillment facilities, fueling facilities, refueling facilities, nuclear refueling facilities, waste removal facilities, food supply facilities, beverage supply facilities, drone facilities, robot facilities, autonomous vehicle, aircraft, automotive, truck, train, lift, forklift, hauling facilities, conveyor, loading dock, waterway, bridge, tunnel, airport, depot, vehicle station, train station, weigh station, inspection station or point, roadway, railway, highway, customs house, and border control facilities.
  • a system includes: a machine learning system that trains a set of machine-learned models to cluster a set of smart contracts by attribute similarity using training data including smart contract features and outcomes; an artificial intelligence system that receives a request to cluster a set of smart contracts by attribute similarity and outputs a clustering of a set of smart contracts by attribute similarity based on the machine-learned models and the request; a network access layer including a processor and storage hardware in communication with the processor, where the storage hardware includes instructions that when executed by the processor perform operations, and where the operations include: monitoring a plurality of public market participants via an interface system of a network access layer, where the network access layer is controlled by an enterprise and corresponds to an intelligence system that hosts exchangeable enterprise digital assets; receiving, at the network access layer via the interface system, an indication that a monitored public market participant requests a digital asset candidate; determining, by the intelligence system of the network access layer, whether the digital asset candidate matches an asset available in a digital wallet system associated with the network access layer; and in response to the digital asset candidate
  • the asset is available in a hot wallet of the digital wallet system. In embodiments, the asset is available in a cold wallet of the digital wallet system. In embodiments, the asset is available in a custodial wallet of the digital wallet system. In embodiments, the operations further comprise: receiving a response message from the monitored public market participant; and determining that the response message indicates an acknowledgement to fulfill the request for the actual transaction; and
  • facilitating fulfillment of the actual transaction includes storing a digital form of the asset in a public append-only data structure to represent execution of the actual transaction.
  • facilitating fulfillment of the asset request includes: signing the actual transaction involving the asset on a cold wallet; and relaying the signed transaction using a hot wallet of the digital wallet system that is associated with the cold wallet.
  • storing the digital form of the asset to a public append-only data structure facilitating uses at least one key from a hot wallet of the digital wallet system.
  • storing the digital form of the asset to a public append-only data structure facilitating uses at least one key from a cold wallet of the digital wallet system.
  • Some embodiments further include a security monitoring system for monitoring assets and/or collateral based on the data collected by the Internet of Things data collection platform.
  • the security monitoring system uses machine-learned models to determine the condition or value of items based on data collected by the Internet of Things data collection platform.
  • the data collected by the Internet of Things data collection platform is image data, sensor data, or location data.
  • Some embodiments further include a loan management system that enables a loan manager to access information from the Internet of Things data collection platform and the security monitoring system.
  • 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.
  • LTSM long-term/short-term memory
  • a system includes: a machine learning system that trains a set of machine-learned models to generate a market prediction using training data including market features and outcomes; an artificial intelligence system that receives a request to generate a market prediction and outputs a market prediction based on the machine-learned models and the request; a machine learning system that trains a set of machine-learned models to cluster a set of smart contracts by attribute similarity using training data including smart contract features and outcomes; and an artificial intelligence system that receives a request to cluster a set of smart contracts by attribute similarity and outputs a clustering of a set of smart contracts by attribute similarity based on the machine-learned models and the request.
  • the market prediction is a prediction for a parameter of demand in a forward market for an asset. In embodiments, the market prediction is a prediction for a parameter of supply in a forward market for an asset. In embodiments, the market prediction is a prediction of a set of terms and/or conditions for a smart contract. In embodiments, the market prediction is based at least in part on crowdsourced data. In embodiments, the market prediction is based at least in part on behavioral data collected from a set of IoT systems monitoring a set of entities in a set of environments. In embodiments, the artificial intelligence system includes a recurrent neural network. In embodiments, the artificial intelligence system includes a convolutional neural network.
  • the artificial intelligence system includes a combination of a recurrent neural network and a convolutional neural network.
  • the set of Internet of Things systems includes a set of smart home Internet of Things devices.
  • the set of Internet of Things systems includes a set of workplace Internet of Things devices.
  • the set of Internet of Things systems includes a set of Internet of Things device to monitor a set of consumer goods stores.
  • the set of entities comprises one or more of: products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, autonomous vehicles, hauling facilities, drones/robots/AVs, waterways, and port infrastructure facilities.
  • the set of environments comprises one or more of: home of a consumer, retail facilities, manufacturing facilities, supply chain facilities, ship containers, ship, boat, barge, maritime port, crane, container, container handling facilities, shipyard, maritime dock, warehouse, distribution facilities, fulfillment facilities, fueling facilities, refueling facilities, nuclear refueling facilities, waste removal facilities, food supply facilities, beverage supply facilities, drone facilities, robot facilities, autonomous vehicle, aircraft, automotive, truck, train, lift, forklift, hauling facilities, conveyor, loading dock, waterway, bridge, tunnel, airport, depot, vehicle station, train station, weigh station, inspection station or point, roadway, railway, highway, customs house, and border control facilities.
  • Some embodiments further include a security monitoring system for monitoring assets and/or collateral based on the data collected by the Internet of Things data collection platform.
  • the security monitoring system uses machine-learned models to determine the condition or value of items based on data collected by the Internet of Things data collection platform.
  • the data collected by the Internet of Things data collection platform is image data, sensor data, or location data.
  • Some embodiments further include a loan management system that enables a loan manager to access information from the Internet of Things data collection platform and the security monitoring system.
  • 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.
  • LTSM long-term/short-term memory
  • a system includes: a network adaptation system that automatically constructs a network infrastructure path in a network pipeline to deliver data from an asset to a market orchestration recipient, the constructed network infrastructure path is automatically adapted based on one or more characteristics of the data from the asset and at least one performance parameter for the network infrastructure path; a network timing adaptation system that automatically adapts network infrastructure resources in a network pipeline that delivers data from the asset to the market orchestration recipient for orchestration of a transaction of the asset, where the network infrastructure resources are adapted based on at least one of a parameter of the transaction of the asset and a performance parameter of the network pipeline; a set of asset-centric network resources that facilitate ingestion of the data from the asset into the network pipeline; a set of marketplace-centric network resources that facilitate delivery of the asset data from the adapted network pipeline to the market orchestration recipient; a machine learning system that trains a set of machine-learned models to cluster a set of smart contracts by attribute similarity using training data including smart contract features and outcomes; and
  • an artificial intelligence system that receives a request to cluster a set of smart contracts by attribute similarity and outputs a clustering of a set of smart contracts by attribute similarity based on the machine-learned models and the request.
  • the network pipeline delivers the data from the asset to the market orchestration recipient for orchestration of a transaction of the asset.
  • the network timing adaptation system adapts the network infrastructure resources in the network pipeline to satisfy a data delivery timing requirement associated with a transaction workflow for the asset.
  • the market orchestration recipient is a smart contract that includes terms, conditions, and parameters for a set of transaction workflows involving the asset.
  • adapting the network infrastructure path is based on one or more security characteristics of the asset data. In embodiments, adapting the network path based on the one or more security characteristics of the data includes configuring a path through the network pipeline that avoids poor reputation network resources. In embodiments, constructing a network infrastructure path in a network pipeline includes adjusting a communication protocol that avoids exposing data from the asset in a context that gives meaning to the data. In embodiments, adjusting the communication protocol includes delivering a first portion of the asset data through a first network path and a second portion of the asset data through a second network path. In embodiments, constructing a network infrastructure path in a network pipeline includes adapting the network path for delivering the data from the asset so that the network path changes over time.
  • constructing a network infrastructure path in a network pipeline includes adapting the network path to include at least one infrastructure node that is different than infrastructure nodes used previously to deliver the data from the asset.
  • constructing a network infrastructure path in a network pipeline includes adapting the network infrastructure path so that it is different than prior network infrastructure paths used to deliver the data from the asset that are recorded in a historical record of network paths for the asset data.
  • adapting the network infrastructure path based on one or more characteristics of the data from the asset includes configuring a plurality of recipients for one or more portions of the data from the asset, where the plurality of recipients is determined from a transaction workflow for the asset.
  • a system includes: a computer-readable medium that stores a set of executable instructions; a processing system that executes an enterprise access layer that executes transactions on behalf of an enterprise having a plurality of different users, where the enterprise access layer includes a wallet system, a workflow system, and a permissions system, where: the wallet system manages a plurality of digital wallets associated with the enterprise and is configured to: receive a transaction request initiated by a user device associated with a user of the plurality of users, the transaction request requesting a transaction to be executed by the wallet system and having a set of attributes corresponding to the transaction; select a wallet of the plurality of wallets to execute the transaction based on the set of attributes; and initiating a transaction workflow from a set of workflows based on the selected wallet; when the transaction is a trustless transaction performed on a distributed ledger, the workflow system is configured to: obtain a distributed ledger address of a counterparty to the trustless transaction; obtain a trust score based on the distributed ledger address of the counterparty
  • the workflow system provides a request to the permissions system to verify that the user is authorized to perform the transaction based on the role of the user and one or more transaction attributes.
  • the workflow system provides a trust score request to a trust system, where the request indicates the distributed ledger address of the counterparty and the trust system returns the trust score.
  • the trust system comprises a decentralized network of node computing devices, where each respective node computing device independently determines a local trust score for the distributed ledger address based on distributed ledger data available to the respective node computing device.
  • the trust score is a consensus trust score that is based on the local trust scores determined by the respective node computing devices.
  • the trust system comprises a centralized system that monitors a distributed network.
  • the transaction workflow determines to execute the transaction in response to verifying that the user is authorized to perform the transaction and the trust score exceeding a trust threshold.
  • the wallet system in response to performing the trustless transaction, the wallet system generates a transaction record and stores the transaction record on a second distributed ledger.
  • the second distributed ledger is a private distributed ledger maintained by the enterprise.
  • the wallet system manages a set of private and public keys on behalf of the entity.
  • a method for executing user-initiated transactions on behalf of an enterprise having a plurality of different users includes: receiving, by a wallet system, a transaction request initiated by a user device associated with a user of the plurality of users, the transaction request requesting a transaction to be executed by the wallet system and having a set of attributes corresponding to the transaction; selecting, by the wallet system, a wallet of a plurality of digital wallets associated with the enterprise to execute the transaction based on the set of attributes; and initiating a transaction workflow from a set of workflows based on the selected wallet, where when the transaction is a trustless transaction performed on a distributed ledger, the selected workflow executes: obtaining a distributed ledger address of a counterparty to the trustless transaction; obtaining a trust score based on the distributed ledger address of the counterparty; determining whether to execute the transaction based on the trust score corresponding to the counterparty and a role of the user within the enterprise; and in response to determining to allow the trustless transaction, instructing the wallet
  • the workflow system provides a request to the permissions system to verify that the user is authorized to perform the transaction based on the role of the user and one or more transaction attributes.
  • the workflow system provides a trust score request to a trust system, where the request indicates the distributed ledger address of the counterparty and the trust system returns the trust score.
  • the trust system comprises a decentralized network of node computing devices, where each respective node computing device independently determines a local trust score for the distributed ledger address based on distributed ledger data available to the respective node computing device.
  • the trust score is a consensus trust score that is based on the local trust scores determined by the respective node computing devices.
  • the trust system comprises a centralized system that monitors a distributed network.
  • the transaction workflow determines to execute the transaction in response to verifying that the user is authorized to perform the transaction and the trust score exceeds a trust threshold.
  • the wallet system in response to performing the trustless transaction, the wallet system generates a transaction record and stores the transaction record on a second distributed ledger.
  • the second distributed ledger is a private distributed ledger maintained by the enterprise.
  • the wallet system manages a set of private and public keys on behalf of the entity.
  • An intelligent data layer system includes: a computer-readable storage system that stores a layer configuration data store that maintains: ingestion parameters including one or more data structures that represent aspects of one or more of a plurality of data sources including a source location, an interface protocol, a source data ontology, and an ingestion cost; parsing rules that facilitate determining one or more of structure, content, relationships among data elements, intended meaning of the data elements, or relationships of data, structure, and intended meaning; and one or more analysis algorithms; and a set of one or more processors that execute a set of computer-readable instructions, where the set of one or more processors collectively: receive an intelligence request pertaining to an asset in a set of assets from an intelligence consumer portal a set of marketplace-centric network resources that provide access to a transaction orchestration system interface; determine at least one data source for deriving intelligence for use by the transaction orchestration system interface based on the received request, the at least one data source being accessible on a computing network via a set of data source-centric network resources; configure an ingestion system based on
  • the set of asset-data source-centric network resources is an asset management resource.
  • the data pertaining to the asset is ingested from the asset management resource.
  • the at least one data source pertaining to the asset is a digital twin of the asset.
  • the data pertaining to the asset is provided by the digital twin.
  • the set of asset-data source-centric network resources includes asset interfacing resources, an asset-centric network resource controller and an asset-localized network data store.
  • the asset resource controller configures a portion of the set of asset-data source-centric network resources based on a result of analysis of the data pertaining to the asset data by the asset resource controller.
  • the at least one data source pertaining to the asset is the asset.
  • the network path is adapted based on one or more security characteristics of the data pertaining to the asset ingested from the at least one data source. In embodiments, the network path is adapted based on the one or more security characteristics of the data includes configuring a path through the network pipeline that avoids poor reputation network resources. In embodiments, the network path is adapted based on one or more jurisdiction characteristics of the asset data. In embodiments, the network path is adapted based on the one or more jurisdiction characteristics of the data includes configuring a path through the network pipeline that avoids network resources based on a jurisdiction of the network resources.
  • a computer-implemented method includes: monitoring a plurality of public market participants via an interface system of a network access layer, where the network access layer is controlled by an enterprise and corresponds to an intelligence system that hosts exchangeable enterprise digital assets; receiving, at the network access layer via the interface system, an indication that a monitored public market participant requests a digital asset candidate; identifying a digital wallet system associated with the network access layer, the digital wallet system making available a digital asset of a set of assets for which transactions are conducted in a marketplace, the digital wallet further configured to facilitate delivery of the digital asset through a network pipeline associated with the network access layer to an interface of the marketplace; determining, by the intelligence system of the network access layer, whether the digital asset candidate matches the digital asset available in the digital wallet system; and
  • Some embodiments further include: receiving a message from the monitored public market participant that indicates an acknowledgement to fulfill the request for the actual transaction; and facilitating fulfillment of the actual transaction in the marketplace.
  • facilitating fulfillment of the actual transaction includes storing a digital form of the digital asset in a public append-only data structure to represent execution of the actual transaction.
  • facilitating fulfillment of the actual transaction includes: signing the actual transaction involving the asset on a cold wallet; and
  • the set of asset controls includes an asset control that matches an access control for an enterprise entity that submitted the asset to the digital wallet system.
  • the set of asset controls includes an asset control that indicates a security clearance level for the asset.
  • the set of asset controls transactional detail requirements for the asset.
  • An intelligent data enterprise network access layer system includes: a computer-readable storage system that stores a network access layer configuration data store that maintains: ingestion parameters including one or more data structures that represent aspects of one or more of a plurality of data sources including a source location, an interface protocol, a source data ontology, and an ingestion cost; parsing rules that facilitate determining one or more of structure, content, relationships among data elements, intended meaning of the data elements, or relationships of data, structure, and intended meaning; and one or more analysis algorithms; and a set of one or more processors of the network access layer that is controlled by an enterprise, the set of one or more processors that execute a set of computer-readable instructions, where the set of one or more processors collectively: monitor a plurality of public market participants via an interface system of the network access layer; receive an indication that a monitored public market participant requests a digital asset candidate; determine at least one digital wallet system associated with the network access layer with available assets based on the received request; configure an ingestion system based on the ingestion parameters and parsing rules
  • the set of one or more processors is configured in an intelligent data enterprise network access layer control tower that configures and operates the intelligent data enterprise network access layer system by communicating control sequences with the ingestion system, the analysis system, the permission system, the interface system, and the intelligence deriving system.
  • Some embodiments include an algorithm portal of an intelligent data enterprise network access layer control tower of the system through which at least one of the analysis algorithms is received.
  • the ingestion system parses content of digital wallets to determine structure of the content and relationships among elements in the data.
  • the matching asset is available in a hot wallet of the digital wallet system.
  • the matching asset is available in a cold wallet of the digital wallet system.
  • matching asset is available in a custodial wallet of the digital wallet system.
  • storing the digital form of the asset to a public append-only data structure facilitates use of at least one key from a cold wallet of the digital wallet system.
  • the set of asset controls includes an asset control that matches an access control for an enterprise entity that submitted the asset to the digital wallet system.
  • the set of asset controls includes an asset control that indicates a security clearance level for the asset.
  • the set of asset controls transactional detail requirements for the asset.
  • a computer-implemented method includes: monitoring a plurality of public market participants in a plurality of markets via an interface system of a network access layer, where the network access layer is controlled by an enterprise and corresponds to an intelligence system that hosts exchangeable enterprise digital assets; receiving, at the network access layer via the interface system, market data from a plurality of data source feeds, each of the data source feeds corresponding to one or more of the plurality of markets, the market data including an indication that a monitored public market participant requests a digital asset candidate; processing the market data by performing one or more of filtering, normalizing, deduplicating, organizing, summarizing, and compressing, the market data; creating a distributed ledger, the distributed ledger being based on a blockchain; storing the processed data via the distributed ledger, the processed data being stored via one or more blocks of the distributed ledger; determining, by the intelligence system of the network access layer, whether the digital asset candidate matches an asset available in a digital wallet system associated with the network access layer; and in response to the digital asset candidate matching the asset available
  • a computer-implemented method includes: monitoring a plurality of public market participants in a plurality of markets via an interface system of a network access layer, where the network access layer is controlled by an enterprise and corresponds to an intelligence system that hosts exchangeable enterprise digital assets; receiving, at the network access layer via the interface system, market data from the plurality of markets, the market data including an indication that a monitored public market participant requests a digital asset candidate; determining, by the intelligence system of the network access layer, whether the digital asset candidate matches an asset available in a digital wallet system associated with the network access layer; and in response to the digital asset candidate matching the asset available in the digital wallet system: identifying a set of asset controls managed by a permission system of the network access layer, where the permission system is configured to assign the set of asset controls to exchangeable enterprise digital assets in the digital wallet system; determining whether a prospective transaction with the monitored public market participant that involves the asset available in the digital wallet system satisfies an asset control criteria corresponding to the asset available, the prospective transaction determined via a machine learned model, where the asset control
  • the asset is available in a hot wallet of the digital wallet system. In embodiments, the asset is available in a cold wallet of the digital wallet system. In embodiments, the asset is available in a custodial wallet of the digital wallet system. Some embodiments include: receiving a response message from the monitored public market participant; determining that the response message indicates an acknowledgement to fulfill the request for the actual transaction; and facilitating fulfillment of the actual transaction according to the set of asset control criteria of the prospective transaction configured into the smart contract.
  • facilitating fulfillment of the actual transaction includes storing a digital form of the asset in a public append-only data structure to represent execution of the actual transaction, where the public append-only data structure is the distributed ledger, and storing a digital form of the asset includes being stored via one or more blocks of the distributed ledger.
  • facilitating fulfillment of the asset transaction request includes: signing the actual transaction involving the asset on a cold wallet; and relaying the signed transaction using a hot wallet of the digital wallet system that is associated with the cold wallet.
  • storing the digital form of the asset to a public append-only data structure facilitates using at least one key from a hot wallet of the digital wallet system.
  • storing the digital form of the asset to a public append-only data structure facilitates using at least one key from a cold wallet of the digital wallet system.
  • the set of asset controls includes an asset control that matches an access control for an enterprise entity that submitted the asset to the digital wallet system.
  • the set of asset controls includes an asset control that indicates a security clearance level for the asset.
  • the set of asset controls define transactional detail requirements for the asset that are configured into the smart contract.
  • a system includes: a network access layer including a processor and storage hardware in communication with the processor, where the storage hardware includes instructions that when executed by the processor perform operations, and where the operations include: monitoring a plurality of public market participants in a plurality of markets via an interface system of a network access layer, where the network access layer is controlled by an enterprise and corresponds to an intelligence system that hosts exchangeable enterprise digital assets; receiving, at the network access layer via the interface system, market data from the plurality of markets, the market data including an indication that a monitored public market participant requests a digital asset candidate; and determining, by the intelligence system of the network access layer, whether the digital asset candidate matches an asset available in a digital wallet system associated with the network access layer; and in response to the digital asset candidate matching the asset available in the digital wallet system: identifying a set of asset controls managed by a permission system of the network access layer, where the permission system is configured to assign the set of asset controls to exchangeable enterprise digital assets in the digital wallet system; determining whether a prospective transaction with the monitored public market participant that involves
  • the asset is available in a hot wallet of the digital wallet system. In embodiments, the asset is available in a cold wallet of the digital wallet system. In embodiments, the asset is available in a custodial wallet of the digital wallet system. In embodiments, the operations further comprise: receiving a response message from the monitored public market participant; determining that the response message indicates an acknowledgement to fulfill the request for the actual transaction; and facilitating fulfillment of the actual transaction according to the set of asset control criteria of the prospective transaction configured into the smart contract.
  • facilitating fulfillment of the actual transaction includes storing a digital form of the asset in a public append-only data structure to represent execution of the actual transaction, where the public append-only data structure is the distributed ledger, and storing a digital form of the asset includes being stored via one or more blocks of the distributed ledger.
  • facilitating fulfillment of the asset request includes: signing the actual transaction involving the asset on a cold wallet; and relaying the signed transaction using a hot wallet of the digital wallet system that is associated with the cold wallet.
  • storing the digital form of the asset to a public append-only data structure facilitates using at least one key from a hot wallet of the digital wallet system.
  • the set of asset controls includes an asset control that matches an access control for an enterprise entity that submitted the asset to the digital wallet system.
  • the set of asset controls includes an asset control that indicates a security clearance level for the asset.
  • the set of asset controls defines transactional detail requirements for the asset that are configured into the smart contract.
  • a method includes identifying, by a device, an opportunity to engage in a transaction associated with a blockchain address of a blockchain ledger; receiving, by the device and from another device that is a member of a consensus trust network, a consensus trust score that is associated with the blockchain address; determining, by the device, whether to engage in the transaction with the blockchain address, wherein the determining is based on the consensus trust score received from the consensus trust network; and performing, by the device, an action to initiate an engagement in the transaction in response to determining to engage in the transaction with the blockchain address based on the consensus trust score.
  • a method includes receiving, by a device, information that associates a blockchain address with fraudulent activity, wherein the blockchain address is associated with a blockchain ledger; generating, by the device, a first trust score for the blockchain address, wherein the local node trust score is based on the information; receiving, by the device and from at least two other devices, at least two additional trust scores for the blockchain address; determining, by the device, a consensus trust score based on the first trust score and the at least two additional trust scores; and generating, by the device, a blockchain entry on the blockchain ledger, a blockchain entry that associates the consensus trust score with the blockchain address.
  • the device and the at least two other devices are members of a trust network of devices that determine consensus trust scores for blockchain addresses of the blockchain ledger.
  • the device is configured to generate the blockchain entry on the blockchain ledger in response to a request on the blockchain ledger for a trust evaluation of the blockchain address.
  • the device is configured to generate the blockchain entry on the blockchain ledger in response to an activity of the blockchain address on the blockchain ledger.
  • the device is configured to generate the blockchain entry on the blockchain ledger in response to a transaction on the blockchain ledger, wherein the transaction is associated with the blockchain address.
  • the device is further configured to monitor the blockchain ledger to detect transaction with the blockchain address.
  • the blockchain entry includes a blockchain report that provides a basis for the consensus trust rating. Some embodiments further include generating, on the blockchain ledger, a fraud entry associated with the blockchain address, wherein the fraud entry is based on a change of a consensus trust rating associated with the blockchain address. In some embodiments, the consensus trust score is based on a first reputation score associated with the device and additional reputation scores associated with each of the at least two other devices. In some embodiments, determining the consensus trust score further includes weighting the trust score generated by and/or received from a respective device, wherein the weighting is based on the reputation score associated with the respective device.
  • the reputation score associated with a respective device is based on an amount of work performed by the respective device in generating trust scores for blockchain addresses of the blockchain ledger.
  • determining the consensus trust score further includes excluding, from the determining of the consensus trust score, an outlier trust score that is statistically inconsistent with other trust scores included in the determining of the consensus trust score.
  • determining the consensus trust score includes determining that an average variance of the trust scores included in the determining of the consensus trust score is within a threshold variance.
  • a method includes receiving, by a device, a request to generate a trust score for a blockchain address associated with a blockchain ledger; determining, by the device, information that associates the blockchain address with fraudulent activity; generating, by the device, a first trust score for the blockchain address, wherein the local node trust score is based on the information; transmitting, by the device, the first trust score associated with the blockchain address to another device; receiving, by the device and from another device, a consensus trust score for the blockchain address, wherein the consensus trust score is based on the first trust score and at least two additional trust scores associated with the blockchain address; and generating, by the device, a blockchain entry on the blockchain ledger, a blockchain entry that associates the consensus trust score with the blockchain address.
  • the device and the other device are members of a trust network of devices that determine consensus trust scores for blockchain addresses of the blockchain ledger.
  • the device is configured to generate the blockchain entry on the blockchain ledger in response to a request on the blockchain ledger for a trust evaluation of the blockchain address.
  • the device is configured to generate the blockchain entry on the blockchain ledger in response to an activity of the blockchain address on the blockchain ledger.
  • the device is configured to generate the blockchain entry on the blockchain ledger in response to a transaction on the blockchain ledger, wherein the transaction is associated with the blockchain address.
  • the device is further configured to monitor the blockchain ledger to detect transaction with the blockchain address.
  • the blockchain entry includes a blockchain report that provides a basis for the consensus trust rating. Some embodiments further include generating, on the blockchain ledger, a fraud entry associated with the blockchain address, wherein the fraud entry is based on a change of a consensus trust rating associated with the blockchain address. In some embodiments, the consensus trust score is based on a first reputation score associated with the device and an additional reputation score associated with the other device. In some embodiments, determining the consensus trust score includes weighting the trust score generated by and/or received from a respective device, wherein the weighting is based on the reputation score associated with the respective device.
  • the device and the at least two other devices are members of a trust network of devices that determine consensus trust scores for blockchain addresses of the blockchain ledger.
  • the device is configured to generate the blockchain entry on the blockchain ledger in response to a request on the blockchain ledger for a trust evaluation of the blockchain address.
  • the device is configured to generate the blockchain entry on the blockchain ledger in response to an activity of the blockchain address on the blockchain ledger.
  • the device is configured to generate the blockchain entry on the blockchain ledger in response to a transaction on the blockchain ledger, wherein the transaction is associated with the blockchain address.
  • the device is further configured to monitor the blockchain ledger to detect transaction with the blockchain address.
  • the blockchain entry includes a blockchain report that provides a basis for the consensus trust rating. Some embodiments further include generating, on the blockchain ledger, a fraud entry associated with the blockchain address, wherein the fraud entry is based on a change of a consensus trust rating associated with the blockchain address. In some embodiments, the consensus trust score is based on a first reputation score associated with the device and additional reputation scores associated with each of the at least two other devices. In some embodiments, determining the consensus trust score includes weighting the trust score generated by and/or received from a respective device, wherein the weighting is based on the reputation score associated with the respective device.
  • the reputation score associated with a respective device is based on an amount of work performed by the respective device in generating trust scores for blockchain addresses of the blockchain ledger.
  • determining the consensus trust score includes excluding, from the determining of the consensus trust score, an outlier trust score that is statistically inconsistent with other trust scores included in the determining of the consensus trust score.
  • determining the consensus trust score includes determining that an average variance of the trust scores included in the determining of the consensus trust score is within a threshold variance.
  • a method includes receiving, by a device, information that associates a blockchain address with fraudulent activity, wherein the blockchain address is associated with a blockchain ledger; processing, by the device, the information with a quantum computing system, wherein the quantum computing system is configured to generate trust scores based on information that associates blockchain addresses with fraudulent activity; receiving, by the device, an output of the quantum computing system, wherein the output includes a first trust score for the blockchain address; receiving, by the device and from at least two other devices, at least two additional trust scores for the blockchain address; determining, by the device, a consensus trust score based on the first trust score and the at least two additional trust scores; and generating, by the device, a blockchain entry on the blockchain ledger, a blockchain entry that associates the consensus trust score with the blockchain address.
  • the device and the at least two other devices are members of a trust network of devices that determine consensus trust scores for blockchain addresses of the blockchain ledger.
  • the device is configured to generate the blockchain entry on the blockchain ledger in response to a request on the blockchain ledger for a trust evaluation of the blockchain address.
  • the device is configured to generate the blockchain entry on the blockchain ledger in response to an activity of the blockchain address on the blockchain ledger.
  • the device is configured to generate the blockchain entry on the blockchain ledger in response to a transaction on the blockchain ledger, wherein the transaction is associated with the blockchain address.
  • the device is further configured to monitor the blockchain ledger to detect transaction with the blockchain address.
  • the blockchain entry includes a blockchain report that provides a basis for the consensus trust rating. Some embodiments further include generating, on the blockchain ledger, a fraud entry associated with the blockchain address, wherein the fraud entry is based on a change of a consensus trust rating associated with the blockchain address. In some embodiments, the consensus trust score is based on a first reputation score associated with the device and additional reputation scores associated with each of the at least two other devices. In some embodiments, determining the consensus trust score includes weighting the trust score generated by and/or received from a respective device, wherein the weighting is based on the reputation score associated with the respective device.
  • the reputation score associated with a respective device is based on an amount of work performed by the respective device in generating trust scores for blockchain addresses of the blockchain ledger.
  • determining the consensus trust score includes excluding, from the determining of the consensus trust score, an outlier trust score that is statistically inconsistent with other trust scores included in the determining of the consensus trust score.
  • determining the consensus trust score includes determining that an average variance of the trust scores included in the determining of the consensus trust score is within a threshold variance.
  • the quantum computing system is configured to generate trust scores based on a graph clustering analysis of activities associated with the blockchain ledger, wherein the graph clustering analysis includes the blockchain address.
  • the quantum computing system is further configured to detect a market trend associated with an asset, and the quantum prediction algorithm is configured to generate trust scores for respective blockchain addresses based on an activity of the respective blockchain address that is associated with the asset. In some embodiments, the quantum computing system is further configured to generate trust scores based on a quantum principal component analysis of the information associated with the blockchain addresses.
  • the device and the at least two other devices are members of a trust network of devices that determine consensus trust scores for blockchain addresses of the blockchain ledger.
  • the device is configured to generate the blockchain entry on the blockchain ledger in response to a request on the blockchain ledger for a trust evaluation of the blockchain address.
  • the request is associated with a transaction that includes the blockchain address, and the device is configured to determine the first trust score based on a simulation of the transaction including the digital twin and a behavior of the digital twin during the simulation.
  • the device is configured to generate the blockchain entry on the blockchain ledger in response to an activity of the blockchain address on the blockchain ledger.
  • determining the consensus trust score includes weighting the trust score generated by and/or received from a respective device, wherein the weighting is based on the reputation score associated with the respective device.
  • the reputation score associated with a respective device is based on an amount of work performed by the respective device in generating trust scores for blockchain addresses of the blockchain ledger.
  • determining the consensus trust score includes excluding, from the determining of the consensus trust score, an outlier trust score that is statistically inconsistent with other trust scores included in the determining of the consensus trust score.
  • determining the consensus trust score includes determining that an average variance of the trust scores included in the determining of the consensus trust score is within a threshold variance.
  • a method includes receiving, by a device, information that associates a blockchain address with fraudulent activity, wherein the blockchain address is associated with a blockchain ledger; processing, by the device, the information with a dual purpose artificial neural network, wherein the dual purpose artificial neural network is configured to generate trust scores based on information that associates blockchain addresses with fraudulent activity; receiving, by the device, an output of the dual purpose artificial neural network, wherein the output includes a first trust score for the blockchain address; receiving, by the device and from at least two other devices, at least two additional trust scores for the blockchain address; determining, by the device, a consensus trust score based on the first trust score and the at least two additional trust scores; and generating, by the device, a blockchain entry on the blockchain ledger, a blockchain entry that associates the consensus trust score with the blockchain address.
  • the device and the at least two other devices are members of a trust network of devices that determine consensus trust scores for blockchain addresses of the blockchain ledger.
  • the device is configured to generate the blockchain entry on the blockchain ledger in response to a request on the blockchain ledger for a trust evaluation of the blockchain address.
  • the device is configured to generate the blockchain entry on the blockchain ledger in response to an activity of the blockchain address on the blockchain ledger.
  • the device is configured to generate the blockchain entry on the blockchain ledger in response to a transaction on the blockchain ledger, wherein the transaction is associated with the blockchain address.
  • the device is further configured to monitor the blockchain ledger to detect transaction with the blockchain address.
  • the blockchain entry includes a blockchain report that provides a basis for the consensus trust rating. Some embodiments further include generating, on the blockchain ledger, a fraud entry associated with the blockchain address, wherein the fraud entry is based on a change of a consensus trust rating associated with the blockchain address. In some embodiments, the consensus trust score is based on a first reputation score associated with the device and additional reputation scores associated with each of the at least two other devices. In some embodiments, determining the consensus trust score includes weighting the trust score generated by and/or received from a respective device, wherein the weighting is based on the reputation score associated with the respective device.
  • the reputation score associated with a respective device is based on an amount of work performed by the respective device in generating trust scores for blockchain addresses of the blockchain ledger.
  • determining the consensus trust score includes excluding, from the determining of the consensus trust score, an outlier trust score that is statistically inconsistent with other trust scores included in the determining of the consensus trust score.
  • determining the consensus trust score includes determining that an average variance of the trust scores included in the determining of the consensus trust score is within a threshold variance.
  • Some embodiments further include updating, by the device, a data set that is associated with a training of the dual purpose artificial neural network; and retraining, by the device, the dual purpose artificial neural network based on the updated data set.
  • updating the data set includes adding, to the data set, one or more data samples based on a subsequent activity associated with the blockchain address.
  • a method includes receiving, by a device, information that associates a blockchain address with fraudulent activity, wherein the blockchain address is associated with a blockchain ledger; generating, by the device, a first trust score for the blockchain address, wherein the local node trust score is based on the information; receiving, by the device and from at least two other devices, at least two additional trust scores for the blockchain address; determining, by the device, a consensus trust score based on the first trust score and the at least two additional trust scores; and processing, by the device, one or more transactions associated with the blockchain address by a robotic process automation module, wherein the robotic process automation module is configured to engage in transactions with respective blockchain addresses associated with the blockchain ledger based on respective consensus trust scores associated with the respective blockchain addresses.
  • the device and the at least two other devices are members of a trust network of devices that determine consensus trust scores for blockchain addresses of the blockchain ledger.
  • the device is further configured to monitor the blockchain ledger to detect transaction with the blockchain address.
  • the blockchain entry includes a blockchain report that provides a basis for the consensus trust rating.
  • Some embodiments further include generating, on the blockchain ledger, a fraud entry associated with the blockchain address, wherein the fraud entry is based on a change of a consensus trust rating associated with the blockchain address.
  • the consensus trust score is based on a first reputation score associated with the device and additional reputation scores associated with each of the at least two other devices.
  • determining the consensus trust score includes weighting the trust score generated by and/or received from a respective device, wherein the weighting is based on the reputation score associated with the respective device.
  • the reputation score associated with a respective device is based on an amount of work performed by the respective device in generating trust scores for blockchain addresses of the blockchain ledger.
  • determining the consensus trust score includes excluding, from the determining of the consensus trust score, an outlier trust score that is statistically inconsistent with other trust scores included in the determining of the consensus trust score.
  • determining the consensus trust score includes determining that an average variance of the trust scores included in the determining of the consensus trust score is within a threshold variance.
  • the robotic process automation module is further configured to determine whether or not to engage in transactions with respective blockchain addresses, and the determining is based on the respective consensus trust scores associated with the respective blockchain addresses.
  • the robotic process automation module is configured to engage in transactions with respective blockchain addresses associated with the blockchain ledger based on a training of the robotic process automation module, and the training is based on a training data set including data samples that correspond to actions taken by one or more users while engaging in transactions with blockchain addresses associated with the blockchain ledger.
  • FIGS. 2 A and 2 B 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.
  • FIG. 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. 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. 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 IoT, 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. 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. 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. 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. 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. 111 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 AI solution.
  • FIG. 144 depicts a method for selecting an AI solution.
  • FIG. 145 depicts an example of an assembled AI solution.
  • FIG. 146 depicts a method for selecting an AI solution.
  • FIG. 148 depicts an AI solution selection and configuration system.
  • FIG. 149 depicts an AI solution selection and configuration system.
  • FIG. 150 depicts an AI solution selection and configuration system.
  • FIG. 151 depicts a component configuration circuit.
  • FIG. 152 depicts an AI 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 bi-directional 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. 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. 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. 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. 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.
  • 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. 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. 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. 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. 210 is a block diagram depicting a gaming engine system of a gaming engine smart contract executing platform in an exemplary deployment environment.
  • FIG. 211 is a block diagram depicting an intelligence layer of a gaming engine smart contract executing platform in an exemplary deployment environment.
  • FIG. 212 is a block diagram depicting a cloud-based deployment of the gaming engine smart contract platform of FIGS. 209 A-C .
  • FIG. 213 is a block diagram depicting an exemplary embodiment of a gaming engine system of the gaming engine smart contract platform.
  • FIG. 215 is a flowchart depicting another exemplary execution flow of the gaming engine smart contract platform.
  • FIG. 220 is a block diagram depicting another exemplary embodiment of a distributed ledger network of the gaming engine smart contract platform.
  • FIG. 223 is a schematic illustrating an example implementation of an autonomous additive manufacturing platform for automating and optimizing the digital production workflow for metal additive manufacturing according to some embodiments of the present disclosure.
  • FIG. 225 A is a schematic illustrating an example artificial neural network used to provide real-time, adaptive control of an additive manufacturing process according to some embodiments of the present disclosure.
  • FIG. 226 is a schematic view illustrating a system for learning on data from an autonomous additive manufacturing platform to train an artificial learning system to use digital twins for classification, predictions and decision making according to some embodiments of the present disclosure.
  • FIG. 227 A , FIG. 227 B , and FIG. 227 C are schematics illustrating an example implementation of an autonomous additive manufacturing platform including various components along with other entities of a distributed manufacturing network according to some embodiments of the present disclosure.
  • FIG. 229 is a diagrammatic view of a distributed manufacturing network enabled by an autonomous additive manufacturing platform and built on a distributed ledger system according to some embodiments of the present disclosure.
  • FIG. 230 is a schematic illustrating an example implementation of a distributed manufacturing network where the digital thread data is tokenized and stored in a distributed ledger so as to ensure traceability of parts printed at one or more manufacturing nodes in the distributed manufacturing network according to some embodiments of the present disclosure.
  • FIG. 231 is a schematic view of an example of an enterprise ecosystem having an enterprise access layer.
  • FIG. 232 is a schematic view of another example of an enterprise ecosystem having an enterprise access layer.
  • FIG. 233 is a schematic view of examples as to how the enterprise access layer of FIG. 232 may be integrated with portions of an enterprise ecosystem.
  • FIG. 234 is a schematic view of an example market orchestration system that includes an enterprise access layer.
  • FIG. 235 is a schematic view of an example of an intelligence services system according to some embodiments.
  • FIG. 236 is a schematic view of an example of a neural network according to some embodiments.
  • FIG. 237 is a schematic view of an example of a convolutional neural network according to some embodiments.
  • FIG. 238 is a schematic view of an example of a neural network according to some embodiments.
  • FIG. 242 depicts an example of normalizing item values across sets of items for exchange-specific currencies.
  • FIG. 243 depicts an example of normalizing a value of an item across a plurality of exchange-specific currencies.
  • FIG. 246 depicts an example of item-representative token generation for use in a target exchange based on characteristics of the item from a source exchange.
  • FIG. 248 depicts an example of the item-representative token generation of FIG. 246 through processing of smart contracts associated with the item in a source exchange.
  • FIG. 251 depicts an example of generating a rights token for an item based on at least one of a smart contract and terms and conditions for the item and further based on conformance of detected rights with exchange governing rules.
  • FIG. 254 depicts an example of automatically cascading workflow initiation actions across exchanges in which the workflows are automated through robotic process automation.
  • FIG. 255 depicts an example of automatically cascading actions of workflows across exchanges in which the workflows are automated through robotic process automation.
  • FIG. 256 depicts an example of applying robotic process automation to generate a cross-exchange smart contract from discrete exchange-specific smart contracts.
  • FIG. 257 depicts an example of a self-adapting asset data delivery network infrastructure pipeline that includes one or more of the normalization, value translation, item tokenization, or rights tokenization methods or systems described herein.
  • FIG. 258 depicts a block diagram of exemplary features, capabilities, and interfaces of an intelligent data layer platform.
  • FIG. 259 depicts a block diagram of an exemplary intelligent data layer architecture.
  • FIG. 260 depicts a block diagram of an independently operated intelligent data layer for producing data for a plurality of data consumers.
  • FIG. 261 depicts a block diagram of an intelligent data layer platform deployment for data-strategic approach of an enterprise.
  • FIG. 262 depicts a block diagram of a remote intelligent data layer with actively deployed elements for dynamic on-demand IDL operation.
  • FIG. 263 depicts a diagram of mapping parameters of a data producer (e.g., source) with a data consumer.
  • a data producer e.g., source
  • FIG. 265 depicts a block diagram of a network constructed of intelligent data layers.
  • FIG. 266 depicts a block diagram of an exemplary cloud-based deployment for an intelligent data layer architecture.
  • FIG. 268 depicts a block diagram of a marketplace / transaction environment deployment of intelligent data layers.
  • FIG. 269 depicts a block diagram of use of intelligent data layers for source discovery.
  • FIGS. 270 - 287 illustrate various features associated with data network and infrastructure pipelines.
  • FIG. 288 illustrates an exemplary environment of a cross-market transaction engine according to some embodiments of the present disclosure.
  • FIG. 289 illustrates another exemplary environment of a cross-market transaction engine according to some embodiments of the present disclosure.
  • FIG. 290 is a diagrammatic view that illustrates embodiments of the market prediction system platform in accordance with the present disclosure.
  • FIG. 291 is a schematic view of an exemplary embodiment of the quantum computing service according to some embodiments of the present disclosure.
  • FIG. 292 illustrates quantum computing service request handling according to some embodiments of the present disclosure.
  • FIGS. 293 - 297 illustrate an example trust network in communication with cryptocurrency transactor computing devices, intermediate transaction systems, and automated transaction systems.
  • FIG. 298 is a method that describes operation of an example trust network.
  • FIG. 299 is a functional block diagram of an example node that calculates local trust scores and consensus trust scores.
  • FIG. 300 is a functional block diagram of an example node that calculates consensus trust scores.
  • FIG. 301 is a flow diagram that illustrates an example method for calculating a consensus trust score.
  • FIG. 302 is a functional block diagram of an example node that calculates reputation values.
  • FIG. 303 is a functional block diagram of an example node that implements a token economy for a trust network.
  • FIG. 304 illustrates an example method that describes operation of a reward protocol.
  • FIGS. 305 - 306 illustrate graphical user interfaces (GUIs) for requesting and reviewing trust reports.
  • FIG. 307 is a functional block diagram of a trust network being used in a payment insurance implementation.
  • FIG. 308 illustrates an example relationship of staked token and consensus trust score cost.
  • FIG. 309 illustrates example services associated with different levels of nodes.
  • FIG. 311 illustrates sample token staking amounts and number of nodes.
  • FIG. 312 is a functional block diagram of an example trust score determination module and local trust data store.
  • FIG. 313 is a method that describes operation of an example trust score determination module.
  • FIG. 314 is a functional block diagram of a data acquisition and processing module.
  • FIG. 315 is a functional block diagram of a blockchain data acquisition and processing module.
  • FIG. 319 is a functional block diagram that illustrates operation of a score generation module.
  • FIG. 320 illustrates an environment that includes a cryptocurrency blockchain network that executes smart contracts.
  • FIG. 321 illustrates a method that describes operation of the environment of FIG. 320 .
  • FIG. 322 is a functional block diagram that illustrates interactions between a sender user device, an intermediate transaction system, a blockchain network, and a trust network/system.
  • FIGS. 323 - 324 illustrate an example trust system and an example trust node that can determine trust scores for blockchain addresses.
  • FIGS. 325 - 326 illustrate an example sender interface on a user device.
  • FIG. 327 illustrates an example method describing operation of an intermediate transaction system.
  • FIG. 328 illustrates an example method describing operation of a trust network/system.
  • FIG. 329 is a diagrammatic view of a dual process artificial neural network system in accordance with some embodiments.
  • FIG. 330 is a diagrammatic view that illustrates embodiments of the biology-based system in accordance with the present disclosure.
  • FIG. 331 is a diagrammatic view of a thalamus service in accordance with the present disclosure.
  • 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 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., IoT 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
  • 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, websites, user devices, etc.) that are external to the system.
  • 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 un-installs 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 un-installs an application, and/or when a monitoring function is stopped and/or passed to another device.
  • 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, how to combine processes and systems from the present disclosure to construct, provide performance characteristics (e.g., bandwidth, computing power, time response, etc.), and/or provide operational capabilities (e.g., time between checks, up-time requirements including longitudinal (e.g., continuous operating time) and/or sequential (e.g., time-of-day, calendar time, etc.), resolution and/or accuracy of sensing, data determinations (e.g., accuracy, timing, amount of data), and/or actuator confirmation capability) of components of the service that are sufficient to provide a given embodiment of a service, platform, and/or microservice as described herein.
  • performance characteristics e.g., bandwidth, computing power, time response, etc.
  • operational capabilities e.g., time between checks, up-time requirements including longitudinal (e.g., continuous operating time) and/or sequential (e
  • 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 monitor and report on
  • 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., IoT 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 structured 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 structured 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 a value for collateral
  • 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.
  • 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, and similar terms as utilized herein should be understood broadly. Without limitation to any other aspect or description of the present disclosure, 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.
  • 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
  • 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.
  • 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
  • Certain conditions or processes may not relate to apportioned value.
  • the total value of an item may provide its inherent worth, but not how much of the value is held by each identified entity.
  • One of skill in the art having the benefit of the disclosure herein and knowledge about apportioned value, can readily determine which aspects of the present disclosure will benefit a particular application for apportioned value.
  • 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.
  • 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.
  • 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.
  • 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 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 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.
  • 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. In some cases, 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.
  • 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.
  • 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).
  • 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.
  • 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.
  • 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.
  • loan collateral While specific examples of loan collateral 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 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 self-enforcing.
  • 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.
  • an IoT 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 IoT system individually, but may be considered an IoT system in an aggregated system, for example, a single networked.
  • the sensor, smart speaker, and/or medical device may be not an IoT 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 IoT system and/or a part of an IoT system.
  • a system may be considered an IoT system for some purposes but not for other purposes - for example, a smart speaker may be considered part of an IoT system for certain operations, such as for providing surround sound, or the like, but not part of an IoT 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 IoT systems, and/or which type of IoT 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 IoT 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 IoT system herein, while in certain embodiments a given system may not be considered an IoT system herein.
  • a contemplated system is an IoT 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.
  • 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. 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 integration service herein, while in certain embodiments a given service may not be considered a data integration service herein.
  • 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
  • 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 IoT 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 transaction/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 transaction/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).
  • 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.
  • 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.
  • 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,
  • party 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.
  • 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.
  • 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.
  • 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.
  • 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 preconfigured 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.
  • domain may be understood broadly to describe a scope or context of a transaction and/or communications related to a transaction.
  • 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.
  • one or more domains may be utilized relative to any applications, circuits, controllers, processes, modules, services, layers, devices, components, machines, products, sub-systems, 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.
  • reward may be understood broadly to describe a thing or consideration received or provided in response to an action or stimulus.
  • Rewards can be of a financial type, or non-financial type, 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.
  • 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.
  • 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.
  • 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.
  • loan-related actions for a 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.
  • 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.
  • 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 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.
  • 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.
  • 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).
  • 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. competitors, suppliers, and the like, entities sales, transactions, customer acquisition cost, customer lifetime value, brand awareness, churn rate, and the like.
  • 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.
  • 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
  • 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.
  • 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.
  • 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 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., as a verb clause) terms and conditions, or the description of doing so may be considered a negotiation (e.g., as a noun clause).
  • a negotiation e.g., as a noun clause.
  • 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.
  • 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 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 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.
  • 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 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.
  • 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.
  • collection in various forms (e.g., collector) 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, such as a collector, or during the context of a collection 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.
  • 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.
  • 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.
  • 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).
  • a type of loan e.g., variable rate to fixed rate
  • 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.
  • 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. While specific examples of consolidation and consolidation activities 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.
  • factoring a loan factoring a loan transaction, factors, factoring a loan interaction, factoring assets or sets of assets used for factoring and similar terms, as utilized herein should be understood broadly. Without limitation to any other aspect or description of the present disclosure factoring may be applied to factoring assets such as invoices, inventory, accounts receivable, and the like, where the realized value of the item is in the future. For example, the accounts receivable is worth more when it has been paid and there is less risk of default. Inventory and Work in Progress (WIP) may be worth more as final product rather than components. References to accounts receivable should be understood to encompass these terms and not be limiting.
  • WIP Inventory and Work in Progress
  • 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 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., keep the application moving forward by initiating next steps in the process as appropriate), population of fields of application, preparation of mortgage agreement, completion of schedule for 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 on mortgaged property and closing of mortgage agreement, and similar terms, as utilized herein should be understood broadly.
  • debt management debt transactions, debt actions, debt terms and conditions, syndicating debt, consolidating debt, and/or debt portfolios, as utilized herein should be understood broadly.
  • a debt 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 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 IoT data is used as input for refining a model, or as input to a classification model. Examples of IoT 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 IoT sensors associated with a collateral asset.
  • 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.
  • 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 transaction/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.
  • 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 change in
  • 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, ⁇ 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.
  • 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.
  • 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; IoT 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
  • 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.
  • 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.
  • 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, 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.
  • 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.
  • individuals e.g., Airbnb spaces
  • bed-and-breakfasts e.g., 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
  • 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. service 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.
  • 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
  • service level C indicating that the resource is constrained, such as in terms of traffic flow restrictions on a roadway
  • an operational parameter e.g., a system is operating at a
  • 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.
  • 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.
  • 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 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.
  • 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. 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 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 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 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.
  • 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.
  • 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.
  • 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.
  • Information may be linked to external information (e.g., external sources).
  • 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.
  • 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.
  • 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., lending, refinancing, collection, consolidation, factoring, brokering, foreclosure), an offering determined by a smart contract circuit, an offer directed to a customer/debtor, an offering directed to a provider/lender, a 3rd party offer (e.g., regulator, auditor, partial owner, tiered provider) and the like.
  • lending e.g., lending, refinancing, collection, consolidation, factoring, brokering, foreclosure
  • an offering determined by a smart contract circuit e.g., an offer directed to a customer/debtor, an offering directed to a provider/lender, a 3rd party offer (e.g., regulator, auditor, partial owner, tiered provider) and the like.
  • 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.
  • 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.
  • 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 AI solution should be understood broadly. Without limitation to any other aspect of the present disclosure, an AI solution includes a coordinated group of AI related aspects to perform one or more tasks or operations as set forth throughout the present disclosure.
  • An example AI solution includes one or more AI components, including any AI components set forth herein, including at least a neural network, an expert system, and/or a machine learning component.
  • the example AI solution may include as an aspect the types of components of the solution, such as a heuristic AI component, a model based AI component, a neural network of a selected type (e.g., recursive, convolutional, perceptron, etc.), and/or an AI 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 AI component e.g., a model based AI component
  • a neural network of a selected type e.g., recursive, convolutional, perceptron, etc.
  • an AI 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 AI solution may be formed from the number and type of AI components of the AI solution, the connectivity of the AI components (e.g., to each other, to inputs from a system including or interacting with the AI solution, and/or to outputs to the system including or interacting with the AI solution).
  • the given AI solution may additionally be formed from the connection of the AI components to each other within the AI solution, and to boundary elements (e.g., inputs, outputs, stored intermediary data, etc.) in communication with the AI solution.
  • the given AI solution may additionally be formed from a configuration of each of the AI components of the AI solution, where the configuration may include aspects such as: model calibrations for an AI component; connectivity and/or flow between AI 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 AI component; a depth and/or complexity of a neural network or other components; a training data description of an AI 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 AI component.
  • An AI solution includes a selection of AI elements, flow connectivity of those AI elements, and/or configuration of those AI elements.
  • 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.
  • 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 micro-transactions 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 (IoT) data sources (including from sensors, cameras, data collectors, and instrumented machines and systems), such as IoT 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
  • IoT Internet of Things
  • the IoT, 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).
  • 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 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.
  • 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.
  • 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.
  • 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.
  • 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 IoT 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.

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230141966A1 (en) * 2021-11-10 2023-05-11 International Business Machines Corporation Using Device-Bound Credentials for Enhanced Security of Authentication in Native Applications
US20240380481A1 (en) * 2023-05-13 2024-11-14 Cisco Technology, Inc. Backscatter device onboarding
TWI905573B (zh) * 2023-11-24 2025-11-21 合作金庫商業銀行股份有限公司 婚後共享信託管理系統
US12609816B2 (en) 2024-08-22 2026-04-21 Bank Of America Corporation Dynamic creation of contextual protective shield to protect cards using neuromorphic tiny AI module and dynamic short code hashing

Families Citing this family (221)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10867298B1 (en) 2008-10-31 2020-12-15 Wells Fargo Bank, N.A. Payment vehicle with on and off function
US20100114768A1 (en) 2008-10-31 2010-05-06 Wachovia Corporation Payment vehicle with on and off function
US20120109821A1 (en) * 2010-10-29 2012-05-03 Jesse Barbour System, method and computer program product for real-time online transaction risk and fraud analytics and management
US20240354749A1 (en) * 2011-10-12 2024-10-24 Boost Payment Solutions, Inc. Electronic payment system having straight through dynamic variable processing
US11429975B1 (en) 2015-03-27 2022-08-30 Wells Fargo Bank, N.A. Token management system
US11170364B1 (en) 2015-07-31 2021-11-09 Wells Fargo Bank, N.A. Connected payment card systems and methods
US10817593B1 (en) * 2015-12-29 2020-10-27 Wells Fargo Bank, N.A. User information gathering and distribution system
US10021051B2 (en) * 2016-01-01 2018-07-10 Google Llc Methods and apparatus for determining non-textual reply content for inclusion in a reply to an electronic communication
US12276420B2 (en) 2016-02-03 2025-04-15 Strong Force Iot Portfolio 2016, Llc Industrial internet of things smart heating systems and methods that produce and use hydrogen fuel
US11615402B1 (en) 2016-07-01 2023-03-28 Wells Fargo Bank, N.A. Access control tower
US11935020B1 (en) 2016-07-01 2024-03-19 Wells Fargo Bank, N.A. Control tower for prospective transactions
US10992679B1 (en) 2016-07-01 2021-04-27 Wells Fargo Bank, N.A. Access control tower
US11886611B1 (en) 2016-07-01 2024-01-30 Wells Fargo Bank, N.A. Control tower for virtual rewards currency
US11386223B1 (en) 2016-07-01 2022-07-12 Wells Fargo Bank, N.A. Access control tower
US12130937B1 (en) 2016-07-01 2024-10-29 Wells Fargo Bank, N.A. Control tower for prospective transactions
US11074325B1 (en) * 2016-11-09 2021-07-27 Wells Fargo Bank, N.A. Systems and methods for dynamic bio-behavioral authentication
US11556936B1 (en) 2017-04-25 2023-01-17 Wells Fargo Bank, N.A. System and method for card control
US11062388B1 (en) 2017-07-06 2021-07-13 Wells Fargo Bank, N.A Data control tower
US11687929B2 (en) * 2018-03-23 2023-06-27 American Express Travel Related Services Co., Inc. Authenticated secure online and offline transactions
US20200133254A1 (en) 2018-05-07 2020-04-30 Strong Force Iot Portfolio 2016, Llc Methods and systems for data collection, learning, and streaming of machine signals for part identification and operating characteristics determination using the industrial internet of things
US20230119584A1 (en) * 2018-11-02 2023-04-20 Verona Holdings Sezc Pricing analytics for cryptographic tokens that link to real world objects
AU2019372344A1 (en) 2018-11-02 2021-05-27 William Edward Quigley A tokenization platform
US12450593B2 (en) 2018-11-02 2025-10-21 Verona Holdings Sezc Integrating cryptographic tokens representing real world items into media streams
US20230124806A1 (en) * 2018-11-02 2023-04-20 Verona Holdings Sezc Interfacing a digital token transaction system with a virtual reality system
US12469023B2 (en) 2018-11-02 2025-11-11 Verona Holdings Sezc Configuring a set of digital tokens with a temporal attribute that determines a timing of redemption of the set of digital tokens for a corresponding set of items
EP3909223B1 (en) 2019-01-13 2024-08-21 Strong Force Iot Portfolio 2016, LLC Monitoring and managing industrial settings
US20230117430A1 (en) * 2019-09-26 2023-04-20 Verona Holdings Sezc Initiating a workflow in a food delivery system based on a recognized activity in a digital token transaction system
CA3155654A1 (en) 2019-09-26 2021-04-01 Lukasz Jakub SLIWKA Distributed ledger lending systems having a smart contract architecture and methods therefor
WO2021092260A1 (en) 2019-11-05 2021-05-14 Strong Force Vcn Portfolio 2019, Llc Control tower and enterprise management platform for value chain networks
AU2020379834A1 (en) * 2019-11-05 2022-06-09 Strong Force Vcn Portfolio 2019, Llc Control tower and enterprise management platform for value chain networks
FR3104866B1 (fr) * 2019-12-13 2023-11-24 Accumulateurs Fixes Plateforme de services pour les systèmes de contrôle-commande industriels et son procédé d’utilisation.
US10992606B1 (en) 2020-09-04 2021-04-27 Wells Fargo Bank, N.A. Synchronous interfacing with unaffiliated networked systems to alter functionality of sets of electronic assets
US12067580B2 (en) * 2020-09-24 2024-08-20 The Toronto-Dominion Bank Management of programmatic and compliance workflows using robotic process automation
US11961094B2 (en) * 2020-11-15 2024-04-16 Morgan Stanley Services Group Inc. Fraud detection via automated handwriting clustering
US11546338B1 (en) 2021-01-05 2023-01-03 Wells Fargo Bank, N.A. Digital account controls portal and protocols for federated and non-federated systems and devices
US20230043731A1 (en) * 2021-08-06 2023-02-09 Salesforce.Com, Inc. Database system public trust ledger architecture
US12298971B1 (en) * 2021-09-24 2025-05-13 Arizona Board Of Regents Acting For And On Behalf Of Northern Arizona University Multicore data downscaling and routing visualization systems and related methods
JP2024536256A (ja) * 2021-09-29 2024-10-04 インタートラスト テクノロジーズ コーポレイション 信頼された台帳を使用する暗号トークン権利管理システム及び方法
WO2023069689A2 (en) 2021-10-22 2023-04-27 William Edward Quigley Crawling and clustering of distributed ledger data, push-based advertising using digital tokens, and smart contract escrow to mitigate risk for digital token sales
KR102377859B1 (ko) * 2021-10-28 2022-03-23 극동에너지 주식회사 태양광 패널 관리 시스템.
US11792289B2 (en) * 2021-11-22 2023-10-17 International Business Machines Corporation Live socket redirection
CA3238747A1 (en) * 2021-11-23 2023-06-01 Charles Howard CELLA Transaction platforms where systems include sets of other systems
US11553008B1 (en) * 2021-12-30 2023-01-10 Netskope, Inc. Electronic agent scribe and communication protections
US12236422B2 (en) * 2022-01-05 2025-02-25 Mastercard International Incorporated Computer-implemented methods and systems for authentic user-merchant association and services
US12511347B2 (en) * 2022-01-18 2025-12-30 Dell Products L.P. Intelligent order problem learning and mitigation in automated order processing system
US12086792B2 (en) * 2022-01-20 2024-09-10 VocaLink Limited Tokenized control of personal data
US11954916B2 (en) * 2022-02-07 2024-04-09 GM Global Technology Operations LLC Systems and methods for classifying detected objects in an image at an automated driving system
US20230252088A1 (en) * 2022-02-07 2023-08-10 Escrowtab, Inc. Systems and methods for secure document management
CN117980940A (zh) * 2022-02-17 2024-05-03 尤都素克·林万东 收集资产或不动产信息并实时匹配出价和要价之间可能性的系统和方法
US12147566B2 (en) * 2022-03-01 2024-11-19 Arm Limited Controlling personal information
US12205084B1 (en) 2022-04-12 2025-01-21 Wells Fargo Bank, N.A. Systems and methods for private network issuance of digital currency
US12033120B1 (en) * 2022-04-12 2024-07-09 Wells Fargo Bank, N.A. Systems and methods for private network issuance of digital currency
US11836690B1 (en) 2022-04-12 2023-12-05 Wells Fargo Bank, N.A. Systems and methods for private network issuance of digital currency
US12155641B1 (en) 2022-04-15 2024-11-26 Wells Fargo Bank, N.A. Network access tokens and meta-application programming interfaces for enhanced inter-enterprise system data promulgation and profiling
US20230360157A1 (en) * 2022-05-03 2023-11-09 Snowcrash Inc. PROVENANCE FOR LICENSING OF ARTIST-SPECIFIC ASSETS USING NON-FUNGIBLE TOKENS (NFTs)
US20230367643A1 (en) * 2022-05-12 2023-11-16 Bank Of America Corporation System for identification and management of layered dependent resource distribution devices
US20230394582A1 (en) * 2022-05-20 2023-12-07 Foreside Financial Group, LLC User interface for guiding actions for desired impact
US12526155B2 (en) 2022-06-06 2026-01-13 Salesforce, Inc. Multi-signature wallets in public trust ledger actions via a database system
US12462251B2 (en) * 2022-06-10 2025-11-04 Oracle International Corporation Automatic modification of transaction constraints
US20230421368A1 (en) * 2022-06-23 2023-12-28 Bank Of America Corporation Smart chip enabled one-time password resource distribution card
US12045879B2 (en) * 2022-06-28 2024-07-23 Capital One Services, Llc Systems and methods for securing risk in blockchain networks
US12493864B2 (en) * 2022-07-13 2025-12-09 Adeia Guides Inc. Systems and methods for completing payment transactions initiated through a first device using a second device
US12199978B2 (en) * 2022-07-15 2025-01-14 Mastercard International Incorporated Systems, methods, and non-transitory computer-readable media for biometrically confirming trusted engagement
US20240046249A1 (en) * 2022-08-02 2024-02-08 WENEW, Inc. Methods and systems for linking digital wallets on a blockchain network
US12387200B2 (en) * 2022-08-03 2025-08-12 Bank Of America Corporation System and method for parsing and tokenization of designated electronic resource segments via a machine learning engine
US12417262B2 (en) * 2022-09-20 2025-09-16 Dish Network L.L.C. Systems and methods for 3D printing of limited edition virtual items
US11924054B1 (en) 2022-09-27 2024-03-05 Schneider Electric (Australia) Pty Ltd. Managing remote terminal communications
US20240119529A1 (en) * 2022-10-05 2024-04-11 Morgan Stanley Services Group Inc. Machine learning based personalized ethical interest and sensitivity profile generation for investment management
US11734677B1 (en) * 2022-10-20 2023-08-22 Touch Point Worldwide, Inc. Systems and methods for exchanging electronic tokens
US12425440B1 (en) * 2022-10-20 2025-09-23 Tanium Inc. Assessment of the enterprise network of another organization
US12602674B2 (en) * 2022-10-20 2026-04-14 Skillz Platform Inc. System and method for remotely modifying cloud-based client applications
US12488321B2 (en) 2022-11-08 2025-12-02 Dell Products L.P. Smart contract deployment for DCF trust services billing
US12105761B2 (en) * 2022-11-10 2024-10-01 Palo Psifiakes Technologie Epe System and method for web crawling and content summarization
US12380430B2 (en) 2022-11-30 2025-08-05 Salesforce, Inc. Intermediary roles in public trust ledger actions via a database system
US12470320B2 (en) * 2022-12-07 2025-11-11 Xerox Corporation System and method for estimating errors in a sensor network implementing high frequency (HF) communication channels
US12165035B2 (en) 2022-12-13 2024-12-10 Citibank, N.A. Systems and methods for responding to predicted events in time-series data using synthetic profiles created by artificial intelligence models trained on non-homogonous time-series data
US11704540B1 (en) * 2022-12-13 2023-07-18 Citigroup Technology, Inc. Systems and methods for responding to predicted events in time-series data using synthetic profiles created by artificial intelligence models trained on non-homogenous time series-data
KR102741929B1 (ko) * 2022-12-21 2024-12-16 주식회사 수퍼트리 토너먼트 게임 서비스에서 사용자의 프로필 정보가 포함된 nft의 거래를 가능하게 하는 서비스 제공 서버 및 그 동작 방법
US12568152B2 (en) 2022-12-27 2026-03-03 Dell Products L.P. Data confidence fabrics and a sidecar proxy
US20240333743A1 (en) * 2022-12-30 2024-10-03 Darktrace Holdings Limited Generation of embeddings and use thereof for detection and cyber security analysis
US11907230B1 (en) * 2023-01-10 2024-02-20 Dell Products L.P. System and method for distributed management of hardware based on intent
US12124351B2 (en) 2023-01-10 2024-10-22 Dell Products L.P. System and method for distributed management of hardware based on performance validation
US11929891B1 (en) 2023-01-10 2024-03-12 Dell Products L.P. System and method for distributed management of hardware through relationship management
US12309162B2 (en) 2023-01-10 2025-05-20 Dell Products L.P. System and method for distributed management of hardware with intermittent connectivity
US12360810B2 (en) 2023-01-10 2025-07-15 Dell Products L.P. System and method for distributed management of configurable hardware to satisfy user intent
US20240243926A1 (en) * 2023-01-18 2024-07-18 Bank Of America Corporation System for a secure modular cloud-enabled resource exchange apparatus
AU2024218399A1 (en) * 2023-02-10 2025-01-30 1Inch Limited Dynamic multi-path transfers
JP7725515B2 (ja) * 2023-03-13 2025-08-19 株式会社東芝 モデル利活用システム、モデル利活用方法及びプログラム
US12566876B2 (en) * 2023-03-30 2026-03-03 Zoom Communications, Inc. Protecting sensitive information shared to a video conference
US12547940B2 (en) * 2023-03-30 2026-02-10 Sap Se Machine learning integration in low-code no-code application development
US12217287B2 (en) * 2023-03-31 2025-02-04 Intuit Inc. Brand engine for extracting and presenting brand data with user interfaces
US20240340230A1 (en) * 2023-04-10 2024-10-10 Dish Wireless L.L.C. Framework for centralized monitoring the health of remote packet data nodes of roaming peering partners over public cloud
US20240346506A1 (en) * 2023-04-13 2024-10-17 Capital One Services, Llc Systems and methods for outlier detection using unsupervised machine learning models trained on oversampled data
US12561686B2 (en) 2023-04-13 2026-02-24 Capital One Services, Llc Systems and methods for outlier detection using unsupervised machine learning models trained on balanced data
US12210839B1 (en) * 2023-05-01 2025-01-28 Pontimyx Corporation Multilevel data analysis
US12348653B2 (en) * 2023-05-10 2025-07-01 Taisys Technologies Co., Ltd. Method and system for decentralized identity generation
US20250328447A1 (en) * 2024-04-23 2025-10-23 Trayport Limited Method and system for determining user engagement based on user interactions
US12609934B2 (en) * 2023-05-18 2026-04-21 Pure Storage, Inc. Service mesh-based control of access to a storage application
US20240396719A1 (en) * 2023-05-24 2024-11-28 Richard D'Souza Quantum key distribution (qkd) based secure communication system using artificial intelligence
US12034726B1 (en) * 2023-05-31 2024-07-09 Cloudflare, Inc. Logging access types based on inserting tenant control headers into requests
JP2024179701A (ja) * 2023-06-15 2024-12-26 トヨタ自動車株式会社 制御装置及び制御方法
US20240420205A1 (en) * 2023-06-16 2024-12-19 Adobe Inc. Using generative artificial intelligence to optimize product search queries
CN121693898A (zh) * 2023-06-22 2026-03-17 瑞典爱立信有限公司 用于以安全方式确定和共享网络资源的强健性级别的代理
US20240428275A1 (en) * 2023-06-22 2024-12-26 Microsoft Technology Licensing, Llc Foundation model integrations for personalized engagement with software applications
US12614147B2 (en) 2023-06-26 2026-04-28 Ingram Micro Inc. Systems and methods for alerts and notifications in an advanced distribution platform
US20250045786A1 (en) * 2023-07-11 2025-02-06 Ingram Micro Inc. Systems and methods for distribution management including special pricing determination
US20250022046A1 (en) * 2023-07-10 2025-01-16 Vitaliy Grigorov Auction system and method
US20250018287A1 (en) * 2023-07-11 2025-01-16 Sony Interactive Entertainment LLC Event driven dynamic switching between game modes
US20250018286A1 (en) * 2023-07-11 2025-01-16 Sony Interactive Entertainment LLC Manual switching between game modes with fade
US20250018288A1 (en) * 2023-07-11 2025-01-16 Sony Interactive Entertainment LLC Dynamic switching between game modes driven by frame delta
WO2025017579A1 (en) * 2023-07-15 2025-01-23 Jio Platforms Limited Method and system for unified data ingestion in a network performance management system
US12526321B2 (en) * 2023-07-18 2026-01-13 Project Bordeaux, Inc. Electronic communication lists monitoring and tracking
CN116975561B (zh) * 2023-07-19 2024-04-05 快速直接(深圳)精密制造有限公司 一种基于step格式的车床工艺识别方法
US20250028977A1 (en) * 2023-07-20 2025-01-23 Bank Of America Corporation Artificial intelligence-based auditors of artificial intelligence
WO2025022221A1 (en) * 2023-07-22 2025-01-30 Kshitij Prasad Tiwari System and method for generating personalized user feed
CN117635179A (zh) * 2023-07-25 2024-03-01 北京壹清能环科技有限公司 一种碳交易价格预测方法、装置及存储介质
CN117131445B (zh) * 2023-07-28 2024-07-23 深圳市财富趋势科技股份有限公司 一种异常交易检测方法及系统
US12481978B2 (en) 2023-07-31 2025-11-25 Dell Products L.P. Third-party proxies for crypto-based DCF
US12562889B2 (en) 2023-07-31 2026-02-24 Dell Products L.P. Global topics in a distributed ledger to share trust provider information
CN116680011B (zh) * 2023-07-31 2023-11-03 和创（北京）科技股份有限公司 一种aPaaS平台能力控制方法及系统
US20250045686A1 (en) * 2023-07-31 2025-02-06 Capital One Services, Llc Systems and methods for transaction acceptance modeling
US12518257B2 (en) 2023-07-31 2026-01-06 Dell Products L.P. Smart contract invocation during DCF annotation
US12549391B2 (en) * 2023-07-31 2026-02-10 Dell Products L.P. Subscription-based model with protection against billing avoidance
US20250053611A1 (en) * 2023-08-11 2025-02-13 Taiwan Semiconductor Manufacturing Company, Ltd. Methods and apparatuses for convolution of input data
US12204509B1 (en) * 2023-08-14 2025-01-21 Oracle International Corporation Auto-scaling for semantic deduplication of event logs
US12093230B1 (en) * 2023-08-14 2024-09-17 Oracle International Corporation Semantic deduplication of event logs
US12309152B2 (en) * 2023-08-15 2025-05-20 Citibank, N.A. Access control for requests to services
CN116797323B (zh) * 2023-08-21 2023-11-14 北京嗨飞科技有限公司 一种数据处理方法、装置及设备
WO2025042417A1 (en) 2023-08-23 2025-02-27 Yuga Labs, Inc. Blockchain-based metadata handling protocols
US20250069142A1 (en) * 2023-08-25 2025-02-27 Tata Consultancy Services Limited Interface layout to discover and optimize economics of information exchange of digitally virtualized physical space
CN116824512B (zh) * 2023-08-28 2023-11-07 西华大学 一种27.5kV可视化接地刀闸状态识别方法及装置
CN116821774B (zh) * 2023-08-28 2023-11-28 山东理工职业学院 一种基于人工智能的发电故障诊断方法
EP4517564A1 (de) 2023-08-29 2025-03-05 Siemens Aktiengesellschaft Verfahren zum verwalten von lizenzen, system, computerprogramm - produkt und computerlesbares medium
WO2025046283A1 (en) * 2023-08-31 2025-03-06 EL-DAR AL-KHASSEH LTITWER AL-OMRANI Ltd. Tokenomic data structures for enabling smart contract functionality
US20250078190A1 (en) * 2023-08-31 2025-03-06 EL-DAR AL-KHASSEH LTITWER AL-OMRANI Ltd. Tokenized asset ownership and transference
US20250078049A1 (en) * 2023-08-31 2025-03-06 EL-DAR AL-KHASSEH LTITWER AL-OMRANI Ltd. Execution of asset tokenization and ownership with machine learning technology
US20250086580A1 (en) * 2023-09-12 2025-03-13 Segmatics, Inc. Methods and systems for workflow management via control data structures
EP4524798A1 (en) * 2023-09-14 2025-03-19 Bull Sas Digital asset management system, corresponding method and computer program
WO2025064722A1 (en) * 2023-09-19 2025-03-27 Ge Xiaolin Systems and methods for hybrid artificial intelligence enhancement and optimization
CN116956332B (zh) * 2023-09-20 2024-01-05 深圳市智慧城市科技发展集团有限公司 Bim数据处理方法、设备及计算机可读存储介质
US20250104047A1 (en) * 2023-09-21 2025-03-27 The Toronto-Dominion Bank Partial exchange of nft via nft interaction
US12355770B2 (en) * 2023-10-03 2025-07-08 strongDM, Inc. Identity and activity based network security policies
US12537694B1 (en) * 2023-10-06 2026-01-27 The Bank Of New York Mellon System and method for validating an instruction with multiple digital signatures
CN119809503B (zh) * 2023-10-11 2025-11-18 北京京东远升科技有限公司 资源配置方法及装置、电子设备和计算机可读存储介质
US20250131093A1 (en) * 2023-10-19 2025-04-24 Royal Bank Of Canada System and method for saas data control platform
CN117171578B (zh) * 2023-11-03 2024-02-06 成都方顷科技有限公司 一种基于大数据分析的机场智慧台站管理方法及系统
CN117408675A (zh) * 2023-11-03 2024-01-16 南京鼎华智能系统有限公司 车间保修系统以及车间保修方法
US20250147939A1 (en) * 2023-11-07 2025-05-08 Wells Fargo Bank, N.A. Simplification and duplication prevention for unified data catalog
CN117196591B (zh) * 2023-11-07 2024-02-09 成都理工大学 一种设备失效模式预判与剩余寿命预测耦合系统及方法
US12482465B1 (en) * 2023-11-09 2025-11-25 Amazon Technologies, Inc. Speech processing utilizing customized embeddings
CN117237114B (zh) * 2023-11-10 2024-03-08 深圳市迪博企业风险管理技术有限公司 一种基于孪生进化的融资性贸易合规检测方法
CN117273553B (zh) * 2023-11-22 2024-02-02 青州市华康生物科技有限公司 一种基于糖浆浓度检测的生产异常监测系统
EP4564739A1 (en) * 2023-11-29 2025-06-04 Airbus Operations GmbH Filter device and method for communication between a trusted domain and an untrusted domain, and computer system
EP4564733A1 (en) * 2023-11-30 2025-06-04 Mastercard International Incorporated Method & tool for analysing a network of information
US12541798B1 (en) * 2023-11-30 2026-02-03 Veeva Systems Inc. Systems and methods for querying data records of a repository
US20250182013A1 (en) * 2023-12-01 2025-06-05 Saudi Arabian Oil Company System and method configured to predict problems with projects and to generate early alerts using artificial intelligence
US20250190840A1 (en) * 2023-12-07 2025-06-12 Amazon Technologies, Inc. Management of queues for various quantum processing units provided by a quantum computing service
US20250191011A1 (en) * 2023-12-07 2025-06-12 Dish Wireless L.L.C. Machine learning-based selection of wireless retail locations
WO2025128768A1 (en) * 2023-12-13 2025-06-19 Invisible Holdings Llc Aggregation and transfer of property data
US20250199510A1 (en) * 2023-12-18 2025-06-19 UiPath, Inc. Automatic annotations and technical specification generation for robotic process automation workflows using artificial intelligence (ai)
CN117871994B (zh) * 2023-12-22 2024-07-23 湖南奕坤科技有限公司 一种用于plc电柜的快速故障检测方法及系统
CN117560401B (zh) * 2024-01-10 2024-03-19 北京飞渡科技股份有限公司 一种基于物联网的大规模iot设备数据监测方法
WO2025160388A1 (en) * 2024-01-26 2025-07-31 Strong Force TX Portfolio 2018, LLC Artificial intelligence driven systems of systems for converged technology stacks
CN117648388B (zh) * 2024-01-29 2024-04-12 成都七柱智慧科技有限公司 一种可视化的安全实时的数据仓库实现方法及其系统
WO2025165632A1 (en) * 2024-01-30 2025-08-07 Flannery James Patrick System and methods for blockchain-based distributed risk transfer for providing financial coverage
US12143518B1 (en) * 2024-02-05 2024-11-12 Driss El Majdoubi Systems and methods for secure networks
CN117729053B (zh) * 2024-02-07 2024-05-03 国开启科量子技术(安徽)有限公司 基于量子隐形传态的防伪验证方法、设备及存储介质
US20250274439A1 (en) * 2024-02-28 2025-08-28 Anecdotes.ai, LTD System and method for collecting evidences from a private infrastructure
US20250278707A1 (en) * 2024-03-01 2025-09-04 Wells Fargo Bank, N.A. Protection architecture using proof of usage on distributed ledgers
WO2025188577A1 (en) * 2024-03-04 2025-09-12 Chitaley Aniruddha D Methods and systems of facilitating managing interactions of entities
CN117874733B (zh) * 2024-03-12 2024-05-24 北京营加品牌管理有限公司 一种交易执行方法以及系统
EP4617972A1 (en) * 2024-03-15 2025-09-17 Tata Consultancy Services Limited Method and system for anomalous price variance based recommendation of alternate buying options
US12608709B2 (en) 2024-03-15 2026-04-21 Degensoft Ltd. Decentralized systems and methods for response generation to API calls
US20250299126A1 (en) * 2024-03-19 2025-09-25 The Boeing Company Workflow engine for predictive assembly
US20250307971A1 (en) * 2024-03-27 2025-10-02 Adeia Guides Inc. Techniques for generating customized event media to facilitate event engagement
CN117973636B (zh) * 2024-03-28 2024-05-31 南京信息工程大学 一种通用交通气象短临预报方法、装置及存储介质
US20250321951A1 (en) * 2024-04-11 2025-10-16 Dell Products L.P. Property graph-based comparison mechanism to evaluate blockchain requests
US12443886B1 (en) * 2024-04-15 2025-10-14 NeuRealities, Inc. Techniques for adjusting mixed reality graphical environments using machine learning
US12466065B2 (en) * 2024-04-16 2025-11-11 Fanuc Corporation Path dataset generation
TWI883920B (zh) * 2024-04-16 2025-05-11 中國鋼鐵股份有限公司 儲能系統的監測方法與儲能監測系統
US12574234B2 (en) 2024-04-18 2026-03-10 Degensoft Ltd Secure cross-chain atomic swaps
WO2025219958A1 (en) * 2024-04-19 2025-10-23 Winzo Games Private Limited Method and system for enabling accountable interaction in an application
KR102789229B1 (ko) * 2024-04-29 2025-04-01 주식회사 인터엑스 제조 디지털 트랜스포메이션을 위한 데이터 활용 방법
KR102789230B1 (ko) * 2024-04-29 2025-04-01 주식회사 인터엑스 제조 디지털 트랜스포메이션을 위한 데이터 교환 방법
KR102789228B1 (ko) * 2024-04-29 2025-04-01 주식회사 인터엑스 제조 디지털 트랜스포메이션을 위한 데이터 수집 및 관리 방법
CN118350056B (zh) * 2024-05-07 2024-09-13 中域高科（武汉）信息技术有限公司 变电站虚拟监控后台的信息自动验收方法及系统
CN118246345B (zh) * 2024-05-22 2024-07-26 云南云金地科技有限公司 一种三生空间多目标协同优化方法
US12298950B1 (en) 2024-06-06 2025-05-13 IP Holdings 2017 LLC Centralized data platform for disparate applications
US20250385850A1 (en) * 2024-06-12 2025-12-18 Cisco Technology, Inc. Telemetry-based device power consumption prediction
KR102771713B1 (ko) * 2024-06-19 2025-02-20 이종찬 실시간 검색 데이터셋을 반영한 정기/비정기 파인-튜닝 데이터셋 자동 수집 및 가공 방법
WO2026006028A1 (en) * 2024-06-26 2026-01-02 Istari Digital, Inc. Discontinuous access and latency-tolerant coordination across decentralized digital model platforms
CN118394967B (zh) * 2024-06-26 2024-09-06 科学城(广州)数字科技集团有限公司 一种基于互联网园区智能管理系统及方法
US20260004289A1 (en) * 2024-06-26 2026-01-01 Alchemy Insights, Inc. Off-Chain Gas Management System and Method
US20260005841A1 (en) * 2024-07-01 2026-01-01 Bank Of America Corporation Quantum-based distributed ledger
WO2026024918A1 (en) * 2024-07-23 2026-01-29 Strong Force Ee Portfolio 2022, Llc Ai-based energy edge platforms, systems, and methods
US20260030191A1 (en) * 2024-07-25 2026-01-29 Microchip Touch Solutions Limited SYSTEM AND METHODS FOR DYNAMIC MIGRATION OF ENDPOINTS ACROSS PCIe HOSTS
US12288103B1 (en) 2024-07-26 2025-04-29 The Huntington National Bank Systems and methods to facilitate adaptive resource capacity prediction and control using cloud infrastructures with a capacity prediction interface
US12326840B1 (en) 2024-07-26 2025-06-10 The Huntington National Bank Systems and methods to facilitate testing within sub-environments with respect to resource capacity data corresponding to cloud resources
US20240420846A1 (en) * 2024-08-17 2024-12-19 IQSpark LLC Medical Companion Application with Integrated AI Technologies, Extended Reality, and Decentralized Health Data Marketplace and Related Methods
US20260057442A1 (en) 2024-08-21 2026-02-26 Villani Analytics, LLC. Chat tool for financial planning & analysis functions
US20260087521A1 (en) * 2024-09-20 2026-03-26 Microsoft Technology Licensing, Llc Dynamic ad audience optimization and stage transition framework
TWI904863B (zh) * 2024-09-23 2025-11-11 國立臺北科技大學 用於偵測詐騙之語音辨識方法
US12585637B1 (en) * 2024-09-23 2026-03-24 T-Mobile Usa, Inc. Bulk modification of cluster configurations and systems and methods of the same
TWI910855B (zh) * 2024-10-04 2026-01-01 中華電信股份有限公司 耗電量估算系統及耗電量估算方法
US12608419B1 (en) 2024-10-22 2026-04-21 Sound Patrol Inc. Artificial intelligence systems and methods for detecting musical infringement in symbolic music
CN119030803B (zh) * 2024-10-29 2025-02-07 成都安准网络安全技术有限公司 一种基于大数据模型的网络安全管理方法及系统
CN120002627A (zh) * 2024-12-27 2025-05-16 青岛大学 个体化虚拟腹腔镜手术机器人的路径标记与自动规划方法
US12518109B1 (en) 2025-01-14 2026-01-06 OpenAi OPCo, LLC. Language model automations
CN119539458B (zh) * 2025-01-20 2025-05-13 厦门佳智科技有限公司 一种隔热片打磨机的智能控制方法
US12461902B1 (en) * 2025-02-05 2025-11-04 Trayport Limited Method and system for data storage and retrieval
CN119646861B (zh) * 2025-02-19 2025-04-29 成都城投优联科技有限公司 基于报表生成的数据安全管理系统及方法
US12602560B1 (en) * 2025-02-21 2026-04-14 Bank Of America Corporation QR code verification engine
CN119721778B (zh) * 2025-02-27 2025-06-10 莆田学院 一种电子商务经营数据分析评估方法
TWI908625B (zh) * 2025-03-06 2025-12-11 南亞塑膠工業股份有限公司 基於串聯集成模型的化工製程優化系統及其方法
CN119891358B (zh) * 2025-03-18 2025-05-20 南方电网科学研究院有限责任公司 一种基于不平衡残差信息学习的暂态电压稳定评估方法
US12432242B1 (en) 2025-03-28 2025-09-30 strongDM, Inc. Anomaly detection in managed networks
AU2025202797B1 (en) * 2025-04-22 2025-12-18 Tayco, Dominic Thaddeus MR System and Method for Issuing and Managing Blockchain-Based Cross-Jurisdictional Regulatory Compliance Credentials for Wholesale Investors
US12554723B1 (en) 2025-06-28 2026-02-17 IP Holdings 2017 LLC Platform for agentic AI creation, contextualized enterprise data interaction, and enterprise memory
KR102884210B1 (ko) * 2025-08-07 2025-11-11 (주)노르마 유전 알고리즘을 활용하여 최적화된 양자 회로를 활용하여 신용 평가 모델을 학습시키는 신용 평가 방법
KR102884208B1 (ko) * 2025-08-07 2025-11-11 (주)노르마 양자 학습 회로를 활용하여 신용을 평가하는 신용 평가 방법
US12603921B1 (en) 2025-11-19 2026-04-14 strongDM, Inc. Indexing entities and attributes for policy enforcement

Citations (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100250497A1 (en) * 2007-01-05 2010-09-30 Redlich Ron M Electromagnetic pulse (EMP) hardened information infrastructure with extractor, cloud dispersal, secure storage, content analysis and classification and method therefor
US20170041296A1 (en) * 2015-08-05 2017-02-09 Intralinks, Inc. Systems and methods of secure data exchange
US20190068627A1 (en) * 2017-08-28 2019-02-28 Oracle International Corporation Cloud based security monitoring using unsupervised pattern recognition and deep learning
US20200005264A1 (en) * 2018-06-29 2020-01-02 Itron, Inc. Operating smart utility meters using distributed ledgers
US20200285991A1 (en) * 2019-03-08 2020-09-10 Tata Consultancy Services Limited Immutable records based generation of machine learning models for dynamically triggering actions
US20200348662A1 (en) * 2016-05-09 2020-11-05 Strong Force Iot Portfolio 2016, Llc Platform for facilitating development of intelligence in an industrial internet of things system
US20200394220A1 (en) * 2019-06-11 2020-12-17 International Business Machines Corporation Database asset fulfillment chaincode deployment
US20210272109A1 (en) * 2020-02-27 2021-09-02 Symon Okhale Adeji Synqvault one act
US11139955B1 (en) * 2018-02-12 2021-10-05 Winklevoss Ip, Llc Systems, methods, and program products for loaning digital assets and for depositing, holding and/or distributing collateral as a token in the form of digital assets on an underlying blockchain
US11257089B2 (en) * 2018-11-28 2022-02-22 Dmg Blockchain Solutions Inc. Cryptographic taint tracking
US20220108262A1 (en) * 2020-10-04 2022-04-07 Strong Force Iot Portfolio 2016, Llc Industrial digital twin systems and methods with echelons of executive, advisory and operations messaging and visualization
US11308487B1 (en) * 2018-02-12 2022-04-19 Gemini Ip, Llc System, method and program product for obtaining digital assets
US11334883B1 (en) * 2018-03-05 2022-05-17 Gemini Ip, Llc Systems, methods, and program products for modifying the supply, depositing, holding and/or distributing collateral as a stable value token in the form of digital assets
US11522700B1 (en) * 2018-02-12 2022-12-06 Gemini Ip, Llc Systems, methods, and program products for depositing, holding and/or distributing collateral as a token in the form of digital assets on an underlying blockchain

Family Cites Families (173)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5167009A (en) * 1990-08-03 1992-11-24 E. I. Du Pont De Nemours & Co. (Inc.) On-line process control neural network using data pointers
AU2274501A (en) 1999-12-17 2001-06-25 Eldering, Charles A. Electronic asset registration method
EP1287470A4 (en) * 2000-05-04 2005-07-13 American Int Group Inc METHOD AND SYSTEM FOR INTRODUCING AND GIVEN SAYING
US7937470B2 (en) 2000-12-21 2011-05-03 Oracle International Corp. Methods of determining communications protocol latency
US7092986B2 (en) * 2002-02-07 2006-08-15 Institute For Information Industry Transparent mobile IPv6 agent
US8224872B2 (en) * 2004-06-25 2012-07-17 International Business Machines Corporation Automated data model extension through data crawler approach
US7401731B1 (en) * 2005-05-27 2008-07-22 Jpmorgan Chase Bank, Na Method and system for implementing a card product with multiple customized relationships
US8108689B2 (en) * 2005-10-28 2012-01-31 Panasonic Corporation Obfuscation evaluation method and obfuscation method
US7735726B2 (en) * 2005-11-17 2010-06-15 Target Brands, Inc. Voucher system and method of use
US8601155B2 (en) 2006-08-16 2013-12-03 Oracle America, Inc. Telemetry stream performance analysis and optimization
US9405792B2 (en) * 2007-08-14 2016-08-02 John Nicholas and Kristin Gross Trust News aggregator and search engine using temporal decoding
US7908234B2 (en) 2008-02-15 2011-03-15 Yahoo! Inc. Systems and methods of predicting resource usefulness using universal resource locators including counting the number of times URL features occur in training data
EP3832975B1 (en) 2009-05-29 2025-04-02 Alcatel Lucent System and method for accessing private digital content
US9608826B2 (en) * 2009-06-29 2017-03-28 Jpmorgan Chase Bank, N.A. System and method for partner key management
US10565229B2 (en) 2018-05-24 2020-02-18 People.ai, Inc. Systems and methods for matching electronic activities directly to record objects of systems of record
US8560420B2 (en) * 2009-12-07 2013-10-15 Predictive Technologies Group, Llc Calculating predictive technical indicators
US8412560B1 (en) * 2010-03-05 2013-04-02 Amazon Technologies, Inc. Price optimization using current offers
US8544104B2 (en) * 2010-05-10 2013-09-24 International Business Machines Corporation Enforcement of data privacy to maintain obfuscation of certain data
US10263888B2 (en) * 2010-09-30 2019-04-16 Trading Technologies International, Inc. Sticky order routers
EP3726451B1 (en) 2012-09-12 2025-12-24 IEX Group, Inc. Transmission latency leveling apparatuses, methods and systems
US9633076B1 (en) * 2012-10-15 2017-04-25 Tableau Software Inc. Blending and visualizing data from multiple data sources
US9378065B2 (en) 2013-03-15 2016-06-28 Advanced Elemental Technologies, Inc. Purposeful computing
US9892460B1 (en) * 2013-06-28 2018-02-13 Winklevoss Ip, Llc Systems, methods, and program products for operating exchange traded products holding digital math-based assets
AU2014321533A1 (en) * 2013-09-17 2016-04-21 Iex Group, Inc. Techniques for facilitating electronic trading
EP2874073A1 (en) 2013-11-18 2015-05-20 Fujitsu Limited System, apparatus, program and method for data aggregation
WO2015091583A1 (en) * 2013-12-20 2015-06-25 Koninklijke Philips N.V. Operator lifting in cryptographic algorithm
US9917781B2 (en) 2014-06-05 2018-03-13 KEMP Technologies Inc. Methods for intelligent data traffic steering
US10275772B2 (en) * 2014-06-16 2019-04-30 Bank Of America Corporation Cryptocurrency risk detection system
TW201728124A (zh) * 2014-09-16 2017-08-01 科勞簡尼克斯股份有限公司 以彈性地定義之通信網路控制器為基礎之網路控制、操作及管理
US10176032B2 (en) 2014-12-01 2019-01-08 Uptake Technologies, Inc. Subsystem health score
US9875510B1 (en) 2015-02-03 2018-01-23 Lance Kasper Consensus system for tracking peer-to-peer digital records
WO2016132234A1 (en) * 2015-02-17 2016-08-25 Domus Semo Sancus Network based money and value transfer service with a digital wallet
EP3269090B1 (en) * 2015-02-27 2019-10-02 Royal Bank Of Canada Coordinated processing of data by networked computing resources
US20170046670A1 (en) * 2015-06-02 2017-02-16 Elwha Llc Machine/Article/Composition/Process State(s) for Tracking Philanthropic And/or Other Efforts
WO2016209213A1 (en) 2015-06-23 2016-12-29 Hewlett Packard Enterprise Development Lp Recommending analytic tasks based on similarity of datasets
CA2901718C (en) * 2015-06-25 2022-08-30 Dundas Data Visualization, Inc. Systems and methods for intelligent data preparation and visualization
US20240223615A1 (en) * 2015-10-28 2024-07-04 Qomplx Llc System and method for data set creation with crowd-based reinforcement
US11388198B2 (en) 2015-10-28 2022-07-12 Qomplx, Inc. Collaborative database and reputation management in adversarial information environments
US20170161746A1 (en) 2015-12-04 2017-06-08 Xor Data Exchange, Inc Compromised Identity Exchange Systems and Methods
US10599872B2 (en) * 2015-12-04 2020-03-24 Early Warning Services, Llc Systems and methods of determining compromised identity information
US10235076B2 (en) * 2015-12-16 2019-03-19 Accenture Global Solutions Limited Data pipeline architecture for cloud processing of structured and unstructured data
WO2017127850A1 (en) * 2016-01-24 2017-07-27 Hasan Syed Kamran Computer security based on artificial intelligence
CA3015447C (en) * 2016-02-22 2021-09-14 Tata Consultancy Services Limited Systems and methods for computing data privacy-utility tradeoff
US10754334B2 (en) 2016-05-09 2020-08-25 Strong Force Iot Portfolio 2016, Llc Methods and systems for industrial internet of things data collection for process adjustment in an upstream oil and gas environment
US20200225655A1 (en) 2016-05-09 2020-07-16 Strong Force Iot Portfolio 2016, Llc Methods, systems, kits and apparatuses for monitoring and managing industrial settings in an industrial internet of things data collection environment
US12610034B2 (en) * 2016-07-26 2026-04-21 Qcify Inc. Real-time quality data marketplace
US20180041426A1 (en) * 2016-08-08 2018-02-08 Layer3 TV, Inc. Dynamic route selection for routing data in a content delivery system
US10523716B1 (en) * 2016-09-23 2019-12-31 Amazon Technologies Inc. Immutable accounts
US11023463B2 (en) 2016-09-26 2021-06-01 Splunk Inc. Converting and modifying a subquery for an external data system
US11663227B2 (en) 2016-09-26 2023-05-30 Splunk Inc. Generating a subquery for a distinct data intake and query system
US11243963B2 (en) 2016-09-26 2022-02-08 Splunk Inc. Distributing partial results to worker nodes from an external data system
US11615104B2 (en) 2016-09-26 2023-03-28 Splunk Inc. Subquery generation based on a data ingest estimate of an external data system
US11126632B2 (en) 2016-09-26 2021-09-21 Splunk Inc. Subquery generation based on search configuration data from an external data system
US11604795B2 (en) 2016-09-26 2023-03-14 Splunk Inc. Distributing partial results from an external data system between worker nodes
US10956415B2 (en) 2016-09-26 2021-03-23 Splunk Inc. Generating a subquery for an external data system using a configuration file
US11593377B2 (en) 2016-09-26 2023-02-28 Splunk Inc. Assigning processing tasks in a data intake and query system
US10740844B2 (en) * 2016-09-26 2020-08-11 Shapeshift Ag System and method of managing trustless asset portfolios
US11580107B2 (en) 2016-09-26 2023-02-14 Splunk Inc. Bucket data distribution for exporting data to worker nodes
US10360191B2 (en) 2016-10-07 2019-07-23 International Business Machines Corporation Establishing overlay trust consensus for blockchain trust validation system
US10152240B2 (en) 2016-10-31 2018-12-11 Chicago Mercantile Exchange Inc. Resource allocation based on transaction processor classification
US10701163B2 (en) 2016-12-16 2020-06-30 Visa International Service Association Lines prediction model
US10791134B2 (en) 2016-12-21 2020-09-29 Threat Stack, Inc. System and method for cloud-based operating system event and data access monitoring
US20180247191A1 (en) 2017-02-03 2018-08-30 Milestone Entertainment Llc Architectures, systems and methods for program defined entertainment state system, decentralized cryptocurrency system and system with segregated secure functions and public functions
US10320819B2 (en) 2017-02-27 2019-06-11 Amazon Technologies, Inc. Intelligent security management
EP3593305A4 (en) * 2017-03-08 2020-10-21 IP Oversight Corporation SYSTEM AND METHOD FOR CREATING SECURE TOKENS OF COMMODITY ASSETS FROM RESERVES
EP4220464A1 (en) * 2017-03-22 2023-08-02 Visa International Service Association Privacy-preserving machine learning
EP3616383A4 (en) * 2017-04-28 2020-04-08 Anonos Inc. SYSTEMS AND METHODS FOR IMPLEMENTING CENTRALIZED PRIVACY CONTROL IN DECENTRALIZED SYSTEMS
US11726822B2 (en) 2017-06-05 2023-08-15 Umajin Inc. Systems and methods for providing digital twin-enabled applications
US20190012660A1 (en) * 2017-07-06 2019-01-10 Robert Masters Systems and methods for providing an architecture for an internet-based marketplace
US12184781B2 (en) 2017-07-10 2024-12-31 Burstiq, Inc. Systems and methods for accessing digital assets in a blockchain using owner consent contracts
US11468186B2 (en) * 2017-10-30 2022-10-11 Equifax Inc. Data protection via aggregation-based obfuscation
WO2019094729A1 (en) * 2017-11-09 2019-05-16 Strong Force Iot Portfolio 2016, Llc Methods and systems for the industrial internet of things
WO2019101724A1 (en) * 2017-11-24 2019-05-31 British Telecommunications Public Limited Company Access control for digital assets
US10929053B2 (en) 2017-12-08 2021-02-23 Pure Storage, Inc. Safe destructive actions on drives
US11551212B2 (en) * 2018-01-10 2023-01-10 Rajeev Malhotra Methods and systems for management of a blockchain-based computer-enabled networked ecosystem
US11831409B2 (en) 2018-01-12 2023-11-28 Nok Nok Labs, Inc. System and method for binding verifiable claims
EP3514715A1 (en) 2018-01-22 2019-07-24 Alitheon, Inc. Secure digital fingerprint key object database
CA2993064A1 (en) * 2018-01-26 2019-07-26 Erik Dean Lindquist Methods and systems for enabling interoperability of independent hash-based authentication technologies
US20190236559A1 (en) 2018-01-31 2019-08-01 Salesforce.Com, Inc. Systems, methods, and apparatuses for implementing smart flow contracts using distributed ledger technologies in a cloud based computing environment
US11475442B1 (en) 2018-02-12 2022-10-18 Gemini Ip, Llc System, method and program product for modifying a supply of stable value digital asset tokens
US11164254B1 (en) * 2018-02-14 2021-11-02 Equity Shift, Inc. Blockchain instrument for transferable equity
US11700265B2 (en) * 2018-03-06 2023-07-11 Americorp Investments Llc Customized view of restricted information recorded into a blockchain
US11951400B2 (en) * 2018-03-14 2024-04-09 Sony Interactive Entertainment LLC Secure decentralized video game transaction platform
US11528611B2 (en) 2018-03-14 2022-12-13 Rose Margaret Smith Method and system for IoT code and configuration using smart contracts
JP2021520010A (ja) * 2018-03-30 2021-08-12 エクスポジション パーク ホールディングス エスイーゼットシー ブロックチェーン融資トランザクションシステムおよび方法
US11334543B1 (en) 2018-04-30 2022-05-17 Splunk Inc. Scalable bucket merging for a data intake and query system
US12412120B2 (en) * 2018-05-06 2025-09-09 Strong Force TX Portfolio 2018, LLC Systems and methods for controlling rights related to digital knowledge
US11669914B2 (en) * 2018-05-06 2023-06-06 Strong Force TX Portfolio 2018, LLC Adaptive intelligence and shared infrastructure lending transaction enablement platform responsive to crowd sourced information
WO2019217323A1 (en) * 2018-05-06 2019-11-14 Strong Force TX Portfolio 2018, LLC Methods and systems for improving machines and systems that automate execution of distributed ledger and other transactions in spot and forward markets for energy, compute, storage and other resources
US11544782B2 (en) * 2018-05-06 2023-01-03 Strong Force TX Portfolio 2018, LLC System and method of a smart contract and distributed ledger platform with blockchain custody service
WO2019213752A1 (en) * 2018-05-11 2019-11-14 Echoai Corp. A method and system for managing digital assets in a blockchain
US11163840B2 (en) * 2018-05-24 2021-11-02 Open Text Sa Ulc Systems and methods for intelligent content filtering and persistence
EP3575980A3 (en) 2018-05-29 2020-03-04 Accenture Global Solutions Limited Intelligent data quality
CN112534774B (zh) * 2018-06-04 2025-03-11 强力交易投资组合2018有限公司 去中心化的防止欺诈的安全系统
US10812254B2 (en) * 2018-07-30 2020-10-20 International Business Machines Corporation Identity confidence score based on blockchain based attributes
KR102619524B1 (ko) * 2018-08-01 2023-12-29 리지뷰 디지털 엘엘씨 디지털 화폐를 사용하는 거래를 용이하게 하기 위한 시스템 및 방법
US20200065802A1 (en) * 2018-08-27 2020-02-27 Digital Asset (Switzerland) GmbH Eligibility of a digital asset for a transaction
US11763335B2 (en) * 2018-08-27 2023-09-19 Gemini Ip, Llc Real-time distribution of cryptocurrency rewards for a loyalty program
US11818204B2 (en) * 2018-08-29 2023-11-14 Credit Suisse Securities (Usa) Llc Systems and methods for calculating consensus data on a decentralized peer-to-peer network using distributed ledger
US20200104922A1 (en) * 2018-09-28 2020-04-02 Strike Derivatives Inc. Electronic trade processing system and method
US20200118131A1 (en) * 2018-10-11 2020-04-16 International Business Machines Corporation Database transaction compliance
US10936585B1 (en) 2018-10-31 2021-03-02 Splunk Inc. Unified data processing across streaming and indexed data sets
US11546373B2 (en) * 2018-11-20 2023-01-03 CipherTrace, Inc. Cryptocurrency based malware and ransomware detection systems and methods
US11257069B2 (en) * 2018-12-06 2022-02-22 Wells Fargo Bank, N.A. Systems and methods for an all-in-one mobile wallet with administrator portal
US20220138842A1 (en) * 2018-12-11 2022-05-05 Wells Fargo Bank, N.A. Custodial account management tool
US11244313B2 (en) * 2019-01-31 2022-02-08 Salesforce.Com, Inc. Systems, methods, and apparatuses for implementing declarative smart actions for coins and assets transacted onto a blockchain using distributed ledger technology (DLT)
US20200250717A1 (en) * 2019-01-31 2020-08-06 EMC IP Holding Company LLC Proof-of-value provenance for data marketplace environment
US12093942B1 (en) 2019-02-22 2024-09-17 Gemini Ip, Llc Systems, methods, and program products for modifying the supply, depositing, holding, and/or distributing collateral as a stable value token in the form of digital assets
US10901983B2 (en) * 2019-03-01 2021-01-26 Wanchain Ltd. System and method for universal blockchain interoperability
EP3709309A1 (en) * 2019-03-11 2020-09-16 Koninklijke Philips N.V. Medical data collection for machine learning
US11381657B2 (en) 2019-04-05 2022-07-05 Citrix Systems, Inc. Enhanced file sharing systems and methods
US11430066B2 (en) * 2019-04-29 2022-08-30 Securrency, Inc. Systems, methods, and storage media for managing digital liquidity tokens in a distributed ledger platform
JP2022536447A (ja) * 2019-04-29 2022-08-17 セキュレンシー、インコーポレイテッド 分散台帳プラットフォームでデジタル流動性トークンを管理するためのシステム、方法、及び記憶媒体
WO2020220216A1 (en) 2019-04-29 2020-11-05 Splunk Inc. Search time estimate in data intake and query system
US11099902B1 (en) 2019-05-10 2021-08-24 Innovium, Inc. Parallelized ingress compute architecture for network switches in distributed artificial intelligence and other applications
WO2020232137A1 (en) * 2019-05-14 2020-11-19 Equifax Inc. Data protection via attributes-based aggregation
US20220351188A1 (en) 2019-05-16 2022-11-03 Sony Group Corporation Digital asset transfer method, digital asset transfer device, and program
US11526610B2 (en) * 2019-05-21 2022-12-13 Veracode, Inc. Peer-to-peer network for blockchain security
US10984518B2 (en) 2019-05-24 2021-04-20 Continental Mapping Consultants, Llc Methods and systems for assessing the quality of geospatial data
US20210374741A1 (en) * 2019-06-01 2021-12-02 Bee Mortgage App, Inc. Compliance controller for the integration of legacy systems in smart contract asset control
CN111937020A (zh) * 2019-06-06 2020-11-13 海付移通科技香港有限公司 一种数字货币的聚合支付方法、系统及边缘服务器
KR102818463B1 (ko) 2019-07-25 2025-06-10 삼성전자주식회사 마스터 지능 소자 및 이의 제어 방법
TW202143136A (zh) * 2019-08-09 2021-11-16 英商路昂有限公司 使用者媒體平台伺服器系統
US11385933B2 (en) 2019-08-09 2022-07-12 Kyndryl, Inc. Priority determination of computer resources based on configured dynamic logic
US11961140B2 (en) 2019-08-13 2024-04-16 Chicago Mercantile Exchange Inc. Randomization of orders at matching in electronic trading systems
US11093292B2 (en) 2019-09-27 2021-08-17 International Business Machines Corporation Identifying recurring actions in a hybrid integration platform to control resource usage
US11106477B2 (en) 2019-09-27 2021-08-31 Amazon Technologies, Inc. Execution of owner-specified code during input/output path to object storage service
US11269808B1 (en) 2019-10-21 2022-03-08 Splunk Inc. Event collector with stateless data ingestion
US11671483B2 (en) 2019-10-30 2023-06-06 Telefonaktiebolaget Lm Ericsson (Publ) In-band protocol-based in-network computation offload framework
EP4052207A1 (en) * 2019-11-01 2022-09-07 JPMorgan Chase Bank, N.A. Systems and methods for cross-ecosystem aggregation of assets using distributed ledgers
US12561600B2 (en) * 2019-11-22 2026-02-24 Google Llc Linear time algorithms for privacy preserving convex optimization
KR102541781B1 (ko) 2019-12-26 2023-06-08 서강대학교 산학협력단 블록체인 네트워크에서의 처리 성능 향상 방법
US12282875B2 (en) * 2019-12-30 2025-04-22 Nb Ventures, Inc. Linkedchain, control tower and blockchain for enterprise applications
US11941520B2 (en) * 2020-01-09 2024-03-26 International Business Machines Corporation Hyperparameter determination for a differentially private federated learning process
US12014065B2 (en) 2020-02-11 2024-06-18 Pure Storage, Inc. Multi-cloud orchestration as-a-service
US11399305B2 (en) * 2020-02-27 2022-07-26 At&T Intellectual Property I, L.P. Network edge cloud adjustment
US12307453B2 (en) 2020-02-28 2025-05-20 Polymath Inc. Cryptographic encryption protocol for data types and values
US20210295431A1 (en) * 2020-03-19 2021-09-23 Vollinger Corp Asset usage rights token for connected ecosystems
BR112022019902A2 (pt) * 2020-03-30 2022-11-22 Blockquake Ip Holdings Llc Sistema e método de verificação automatizada de conheça a sua transação em transações de ativos digitais
EP4150552A4 (en) * 2020-05-11 2024-05-22 Liquidx, Inc. SYSTEMS AND METHODS FOR DIGITALIZATION AND TRADING OF COMMERCIAL FINANCIAL ASSETS
US20210406276A1 (en) 2020-06-26 2021-12-30 Bank Of America Corporation System for automated data lineage and movement detection
US12579284B2 (en) 2020-07-06 2026-03-17 Dogwood Logic, Inc. Traceable decentralized control of network access to private information
WO2022020609A1 (en) * 2020-07-22 2022-01-27 ZUZLab, Inc. Method and system for defining, creating, managing, and transacting multiple classes of digital objects
US20230043702A1 (en) 2020-07-27 2023-02-09 New York Digital Investment Group Multi-modal routing engine and processing architecture for currency orchestration of transactions
WO2022027027A1 (en) * 2020-07-27 2022-02-03 New York Digital Investiment Group Llc Cryptocurrency payment and distribution platform
US10878505B1 (en) 2020-07-31 2020-12-29 Agblox, Inc. Curated sentiment analysis in multi-layer, machine learning-based forecasting model using customized, commodity-specific neural networks
US20220044316A1 (en) * 2020-08-04 2022-02-10 International Business Machines Corporation Blockchain implemented transfer of multi-asset digital wallets
US11636396B2 (en) 2020-08-21 2023-04-25 Paypal, Inc. Child tokens for improved transaction data storage, and reduced transaction information payloads based on transaction predictions for quicker computerized payment processing
US20220076250A1 (en) * 2020-09-10 2022-03-10 International Business Machines Corporation Blockchain enabled smart compliance
EP3975092B1 (en) 2020-09-29 2025-04-30 MasterCard International Incorporated Method and system for detecting fraudulent transactions
US11620303B1 (en) 2020-10-09 2023-04-04 Splunk Inc. Security essentials and information technology essentials for a data intake and query system
CN116472696A (zh) * 2020-10-15 2023-07-21 信升亚洲(金融顾问)有限公司 数字资产兑换系统、数字钱包和数字资产兑换架构
US20220122721A1 (en) 2020-10-21 2022-04-21 Spa Space App Inc. Machine learning methods for analyzing user information to match users with appropriate therapists and treatment facilities
US20220156698A1 (en) * 2020-11-15 2022-05-19 Pricewaterhousecoopers Llp Systems, methods, and user interfaces for a web-based personalized upskilling platform including visualizing role data and workforce data
KR102221052B1 (ko) 2020-11-30 2021-02-25 윤동권 Ｓｄｎ 오픈플로우 프로토콜을 지원하는 네트워크 장비의 장애처리 시스템
US12067141B2 (en) * 2021-03-31 2024-08-20 Collibra Belgium Bv Systems and methods for an on-demand, secure, and predictive value-added data marketplace
US20220327529A1 (en) * 2021-03-31 2022-10-13 Williams Richard K Advanced Transactional Protocols And Ecosystem For Smart Contract Authoring And Deployment
US20220377070A1 (en) * 2021-05-20 2022-11-24 Jose Roberto Rosas Bustos Systems and methods for transaction management in a cloudless infrastructure of computing devices
EP4348981A1 (en) 2021-05-25 2024-04-10 Homebase ApS A method for managing a digital identity
US20220414492A1 (en) 2021-06-23 2022-12-29 Joni Jezewski Additional Solution Automation & Interface Analysis Implementations
US20220414621A1 (en) * 2021-06-29 2022-12-29 E2Interactive, Inc. Blockchain and non-fungible token application in a gaming environment to store and track digital assets
AU2021107272A4 (en) * 2021-08-25 2021-12-09 Ishan Bhond An internet of things enabled food and drugs supply chain management using blockchain
US20230073859A1 (en) * 2021-09-08 2023-03-09 Ebay Inc. Digital Twin NFT Listing
US20230092436A1 (en) * 2021-09-23 2023-03-23 International Business Machines Corporation Framework for demaraction of digital assets
GB2611084A (en) 2021-09-27 2023-03-29 Custodiex Ltd A security system
US11487850B1 (en) 2021-10-12 2022-11-01 Adidas Ag Activation architecture for processing digital assets and related physical products
US20230137867A1 (en) 2021-10-28 2023-05-04 Capital One Services, Llc Generating non-fungible tokens (nfts) representing digital assets
US12500811B2 (en) 2021-10-29 2025-12-16 Oracle International Corporation Secure bi-directional network connectivity system between private networks
CA3238747A1 (en) * 2021-11-23 2023-06-01 Charles Howard CELLA Transaction platforms where systems include sets of other systems
US20230334168A1 (en) * 2022-04-13 2023-10-19 University College Dublin, National University Of Ireland, Dublin Tiered data sharing privacy assurance
US12074807B2 (en) 2022-08-26 2024-08-27 Ciena Corporation Detecting shortfalls in an agreement between a publisher and a subscriber
US20240232922A9 (en) * 2022-10-20 2024-07-11 Gulp Data, Inc. Systems, methods, and devices for automatic dataset valuation
US12105761B2 (en) 2022-11-10 2024-10-01 Palo Psifiakes Technologie Epe System and method for web crawling and content summarization

Patent Citations (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100250497A1 (en) * 2007-01-05 2010-09-30 Redlich Ron M Electromagnetic pulse (EMP) hardened information infrastructure with extractor, cloud dispersal, secure storage, content analysis and classification and method therefor
US20170041296A1 (en) * 2015-08-05 2017-02-09 Intralinks, Inc. Systems and methods of secure data exchange
US20200348662A1 (en) * 2016-05-09 2020-11-05 Strong Force Iot Portfolio 2016, Llc Platform for facilitating development of intelligence in an industrial internet of things system
US20190068627A1 (en) * 2017-08-28 2019-02-28 Oracle International Corporation Cloud based security monitoring using unsupervised pattern recognition and deep learning
US11522700B1 (en) * 2018-02-12 2022-12-06 Gemini Ip, Llc Systems, methods, and program products for depositing, holding and/or distributing collateral as a token in the form of digital assets on an underlying blockchain
US11308487B1 (en) * 2018-02-12 2022-04-19 Gemini Ip, Llc System, method and program product for obtaining digital assets
US11139955B1 (en) * 2018-02-12 2021-10-05 Winklevoss Ip, Llc Systems, methods, and program products for loaning digital assets and for depositing, holding and/or distributing collateral as a token in the form of digital assets on an underlying blockchain
US11334883B1 (en) * 2018-03-05 2022-05-17 Gemini Ip, Llc Systems, methods, and program products for modifying the supply, depositing, holding and/or distributing collateral as a stable value token in the form of digital assets
US20200005264A1 (en) * 2018-06-29 2020-01-02 Itron, Inc. Operating smart utility meters using distributed ledgers
US11257089B2 (en) * 2018-11-28 2022-02-22 Dmg Blockchain Solutions Inc. Cryptographic taint tracking
US20200285991A1 (en) * 2019-03-08 2020-09-10 Tata Consultancy Services Limited Immutable records based generation of machine learning models for dynamically triggering actions
US20200394220A1 (en) * 2019-06-11 2020-12-17 International Business Machines Corporation Database asset fulfillment chaincode deployment
US20210272109A1 (en) * 2020-02-27 2021-09-02 Symon Okhale Adeji Synqvault one act
US20220108262A1 (en) * 2020-10-04 2022-04-07 Strong Force Iot Portfolio 2016, Llc Industrial digital twin systems and methods with echelons of executive, advisory and operations messaging and visualization

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Method And System For Purposeful Computing (Year: 2014) *
System And Method For Binding A Veritable Statement (Year: 2019) *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230141966A1 (en) * 2021-11-10 2023-05-11 International Business Machines Corporation Using Device-Bound Credentials for Enhanced Security of Authentication in Native Applications
US11943370B2 (en) * 2021-11-10 2024-03-26 International Business Machines Corporation Using device-bound credentials for enhanced security of authentication in native applications
US20240380481A1 (en) * 2023-05-13 2024-11-14 Cisco Technology, Inc. Backscatter device onboarding
US12489523B2 (en) * 2023-05-13 2025-12-02 Cisco Technology, Inc. Backscatter device onboarding
TWI905573B (zh) * 2023-11-24 2025-11-21 合作金庫商業銀行股份有限公司 婚後共享信託管理系統
US12609816B2 (en) 2024-08-22 2026-04-21 Bank Of America Corporation Dynamic creation of contextual protective shield to protect cards using neuromorphic tiny AI module and dynamic short code hashing

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