US2023206261A1PendingUtilityA1

Transaction platforms where systems include sets of other systems

Assignee: STRONG FORCE TX PORTFOLIO 2018 LLCPriority: Nov 23, 2021Filed: Mar 7, 2023Published: Jun 29, 2023
Est. expiryNov 23, 2041(~15.3 yrs left)· nominal 20-yr term from priority
G06Q 30/0202G06F 21/6245G06Q 20/367G06Q 40/04G06N 20/00G06Q 20/405G06N 3/08G06Q 40/03G06Q 30/0609H04L 43/0852H04L 63/00G06F 21/44G06Q 20/36G06Q 20/382G06Q 20/0855G06Q 20/3674G06Q 20/40G06Q 20/4016G06Q 30/0209G06Q 40/00H04L 9/50H04L 2209/56H04L 63/10G06Q 20/223G06Q 20/3672G06Q 30/0206G06Q 20/10G06Q 20/3825G06Q 20/3829G06Q 2220/00G06Q 40/06G06Q 30/0241G06Q 30/0201G06Q 40/08
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Claims

Abstract

Systems and methods for transaction platforms include various systems interacting with each other and transacting in various ways. 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 marketplace opportunity data, wherein the marketplace opportunity data includes information related to a set of assets of one or more types; determining configuration parameters to be implemented in the new marketplace; determining the feasibility of implementing the configuration parameters in the new marketplace; determining data resources to support the new marketplace; determining an architecture of the new marketplace; determining the configuration of the data resources in a data model for the marketplace; configuring a marketplace object; connecting selected data resources to populate the marketplace object; and launching the new marketplace.

Claims

exact text as granted — not AI-modified
What is claimed is: 
     
         1 . A system comprising:
 a machine learning system that trains a set of machine-learned models to generate a market prediction using training data comprising 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, wherein the storage hardware includes instructions that when executed by the processor perform operations, and wherein the operations include:
 monitoring a plurality of public market participants via an interface system of a network access layer, wherein 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, wherein 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, wherein 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 the monitored public market participant that involves the asset available in the digital wallet system satisfies the asset control criteria, generating a message data packet requesting an actual transaction with the monitored public market participant involving the asset available, wherein the message data packet is configured for communication via the interface system. 
   
     
     
         2 . The system of  claim 1 , wherein the asset is available in a hot wallet of the digital wallet system. 
     
     
         3 . The system of  claim 1 , wherein the asset is available in a cold wallet of the digital wallet system. 
     
     
         4 . The system of  claim 1 , wherein the asset is available in a custodial wallet of the digital wallet system. 
     
     
         5 . The system of  claim 1 , wherein 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.   
     
     
         6 . The system of  claim 5 , wherein 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. 
     
     
         7 . The system of  claim 5 , wherein 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.   
     
     
         8 . The system of  claim 5 , wherein 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. 
     
     
         9 . The system of  claim 5 , wherein 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. 
     
     
         10 . The system of  claim 1 , wherein the market prediction is a prediction for a parameter of demand in a forward market for an asset. 
     
     
         11 . The system of  claim 1 , wherein the market prediction is a prediction for a parameter of supply in a forward market for an asset. 
     
     
         12 . The system of  claim 1 , wherein the market prediction is a prediction of a set of terms and/or conditions for a smart contract. 
     
     
         13 . The system of  claim 1 , wherein the market prediction is based at least in part on crowdsourced data. 
     
     
         14 . The system of  claim 1 , wherein 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. 
     
     
         15 . The system of  claim 1 , wherein the artificial intelligence system includes at least one of a recurrent neural network, a convolutional neural network, or a combination thereof. 
     
     
         16 . The system of  claim 14 , wherein the set of Internet of Things systems includes a set of smart home Internet of Things devices or a set of workplace Internet of Things devices. 
     
     
         17 . The system of  claim 14 , wherein the set of Internet of Things systems includes a set of Internet of Things device to monitor a set of consumer goods stores. 
     
     
         18 . A system, comprising:
 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 comprising 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, wherein the storage hardware includes instructions that when executed by the processor perform operations, and wherein the operations include:
 monitoring a plurality of public market participants via an interface system of a network access layer, wherein 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, wherein 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, wherein 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 the monitored public market participant that involves the asset available in the digital wallet system satisfies the asset control criteria, generating a message data packet requesting an actual transaction with the monitored public market participant involving the asset available, wherein the message data packet is configured for communication via the interface system. 
   
     
     
         19 . The system of  claim 18 , wherein the asset is available in a hot wallet of the digital wallet system, a cold wallet of the digital wallet system, or a custodial wallet of the digital wallet system. 
     
     
         20 . The system of  claim 18 , wherein the operations further comprise:
 receiving a response message from the monitored public market participant; and   determining that the response message indicates an acknowledgment to fulfill the request for the actual transaction; and   facilitating fulfillment of the actual transaction.   
     
     
         21 . The system of  claim 20 , wherein 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. 
     
     
         22 . The system of  claim 20 , wherein 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.   
     
     
         23 . The system of  claim 20 , wherein 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. 
     
     
         24 . The system of  claim 20 , wherein 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. 
     
     
         25 . The system of  claim 18 , further comprising a security monitoring system for monitoring assets and/or collateral based on the data collected by the Internet of Things data collection platform. 
     
     
         26 . The system of  claim 25 , wherein 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. 
     
     
         27 . The system of  claim 26 , wherein the data collected by the Internet of Things data collection platform is image data, sensor data, or location data. 
     
     
         28 . The system of  claim 25 , further comprising 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. 
     
     
         29 . The system of  claim 18 , wherein 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.

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