Artificial intelligence-driven creative content management systems, platforms and methods

Abstract

A system for enabling distributed media transactions utilizes edge devices with adaptive networking capabilities to optimize media content delivery and transaction processing. The system deploys artificial intelligence models on edge devices to intelligently manage networking operations, including protocol selection, traffic routing, and resource allocation. Local transaction processing capabilities at the edge enable efficient media transactions without relying solely on centralized infrastructure. A blockchain-based transaction layer provides secure, distributed handling of media transactions across the network of edge devices, ensuring transparency and immutability. The system further implements generative AI caching at the network layer, storing AI outputs, features, or model states to reduce computational overhead and improve response times for transaction-related media handling.

Description

Docket: 16606-12POAARTIFICIAL INTELLIGENCE-DRIVEN CREATIVE CONTENT MANAGEMENT SYSTEMS, PLATFORMS AND METHODSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to: U.S. Provisional Patent Application No. 63 / 728,883, filed 6 December 2024, and U.S. Provisional Patent ApplicationNo. 63 / 813,508, filed 28 May 2025. Each patent application referenced above is hereby incorporated by reference as if fully set forth herein in its entirety.FIELD

[0002] The present disclosure relates to Al-driven creative content management systems used in the creation, distribution, and / or management of creative works.BACKGROUND

[0003] Media rights management is becoming increasingly more complex. For example, divisions of revenue, tracking generative Al-based derivative works, and other considerations are difficult to manage. Similarly, governance and resource optimization are increasingly complex. Accordingly, there is a need for improved media rights management systems.SUMMARY

[0004] In some aspects, the techniques described herein relate to a system for enabling distributed media transactions, including: a plurality of edge devices configured to provide adaptive networking capabilities including at least one of adaptive selection of networking protocols, adaptive routing of network traffic, adaptive block sizing, adaptive data rate, adaptive error correction, or adaptive storage of data; a set of artificial intelligence models executed on the plurality of edge devices and configured to support networking operations; a local transaction processing capability implemented on the plurality of edge devices and configured to process and manage media transactions locally; a blockchain-based transaction layer configured to handle distributed media transactions across the plurality of edge devices; and a generative Al caching capability implemented at a network layer and configured to perform at least one of output caching, feature caching, or model state caching to support transaction-related media handling.

[0005] In some aspects, the techniques described herein relate to a system, wherein at least one model of the set of artificial intelligence models is configured to perform intelligent routing of transaction data through cellular, WiFi, Open Radio Access Network (ORAN), Bluetooth, or loT communication protocols based on network conditions.

[0006] In some aspects, the techniques described herein relate to a system, wherein at least one model of the set of artificial intelligence models is configured to perform predictive network traffic management for allocating bandwidth to transaction workflows.

[0007] In some aspects, the techniques described herein relate to a system, wherein at least one model of the set of artificial intelligence models is configured to perform anomaly detection operations.

[0008] In some aspects, the techniques described herein relate to a system, wherein the plurality of edge devices includes networking devices selected from routers, switches, edge devices, loT systems, mesh-networking nodes, networking chips, and networking systems-on-chip that integrate networking capabilities with artificial intelligence capabilities for media content handling.

[0009] In some aspects, the techniques described herein relate to a system, wherein the system includes a content delivery network implemented at the edge that uses cached generative Al outputs, features, or model states to support delivery of generative Al media content.Docket: 16606-12POA

[0010] In some aspects, the techniques described herein relate to a system, wherein blockchain-based media rights transactions are orchestrated using a set of artificial intelligence models in accordance with a media rights infrastructure configured for automated transaction orchestration.

[0011] In some aspects, the techniques described herein relate to a system, wherein the system employs containerization and microservices architectures to allow rapid deployment and scaling of transaction-related functionality across the distributed edge devices.

[0012] In some aspects, the techniques described herein relate to a system, wherein the system further includes sensor fusion capabilities configured to integrate loT data, social data, web data, or crowdsourced data to support analytics related to media transactions.

[0013] In some aspects, the techniques described herein relate to a system, wherein the system uses adaptive networking at the edge in combination with generative Al caching to reduce computational resources and energy consumption associated with generative Al workloads that are part of transaction workflows.

[0014] In some aspects, the techniques described herein relate to a method for enabling distributed media transactions, including: providing, by a plurality of edge devices, adaptive networking capabilities including at least one of adaptive selection of networking protocols, adaptive routing of network traffic, adaptive block sizing, adaptive data rate, adaptive error correction, or adaptive storage of data; executing a set of artificial intelligence models on the plurality of edge devices to support networking operations; processing and managing media transactions locally on the plurality of edge devices using a local transaction processing capability; handling distributed media transactions across the plurality of edge devices using a blockchain-based transaction layer; and performing, at a network layer, at least one of output caching, feature caching, or model state caching using a generative Al caching capability to support transaction-related media handling.

[0015] In some aspects, the techniques described herein relate to a method, wherein at least one model of the set of artificial intelligence models performs intelligent routing of transaction data through cellular, WiFi, Open Radio Access Network (ORAN), Bluetooth, or loT communication protocols based on network conditions.

[0016] In some aspects, the techniques described herein relate to a method, wherein at least one model of the set of artificial intelligence models performs predictive network traffic management for allocating bandwidth to transaction workflows.

[0017] In some aspects, the techniques described herein relate to a method, wherein at least one model of the set of artificial intelligence models performs anomaly detection operations.

[0018] In some aspects, the techniques described herein relate to a method, wherein the plurality of edge devices includes networking devices selected from routers, switches, edge devices, loT systems, mesh-networking nodes, networking chips, and networking systems-on-chip that integrate networking capabilities with artificial intelligence capabilities for media content handling.

[0019] In some aspects, the techniques described herein relate to a method, further including implementing a content delivery network at the edge that uses cached generative Al outputs, features, or model states to support delivery of generative Al media content.

[0020] In some aspects, the techniques described herein relate to a method, farther including orchestrating blockchainbased media rights transactions using a set of artificial intelligence models in accordance with a media rights infrastructure configured for automated transaction orchestration.Docket: 16606-12POA

[0021] In some aspects, the techniques described herein relate to a method, further including employing containerization and microservices architectures to allow rapid deployment and scaling of transaction-related functionality across the distributed edge devices.

[0022] In some aspects, the techniques described herein relate to a method, further including integrating loT data, social data, web data, or crowdsourced data using sensor fusion capabilities to support analytics related to media transactions.

[0023] In some aspects, the techniques described herein relate to a method, further including using adaptive networking at the edge in combination with generative Al caching to reduce computational resources and energy consumption associated with generative Al workloads that are part of transaction workflows.

[0024] In some aspects, the techniques described herein relate to a computer-implemented system for automated media rights transaction orchestration, including: a transaction management module executed on a plurality of distributed processing nodes and configured to orchestrate media rights transactions; a governance engine configured to define and enforce one or more workflow rules associated with a media rights transaction managed by the transaction management module; a set of artificial intelligence models trained to detect anomalous or fraudulent transaction behavior based on historical transaction data and stakeholder trust scores, wherein outputs from the artificial intelligence models are provided to the transaction management module; a blockchain interface configured to generate and digitally sign a blockchain transaction for distributing a royalty payment to a stakeholder according to a smart contract; and a control processor configured to coordinate with the transaction management module to execute the governance workflow prior to authorizing the blockchain transaction, wherein execution of the governance workflow includes verifying account credentials, evaluating the stakeholder trust score, and confirming approval of the transaction before transmission to a blockchain network.

[0025] In some aspects, the techniques described herein relate to a system, wherein the governance engine is further configured to dynamically update workflow rules based on regulatory compliance or stakeholder policy changes.

[0026] In some aspects, the techniques described herein relate to a system, wherein the set of artificial intelligence models includes an ensemble of supervised and unsupervised models trained on historical royalty and licensing data.

[0027] In some aspects, the techniques described herein relate to a system, wherein the blockchain interface implements a hybrid on-chain / off-chain validation protocol to record verified transaction hashes while storing detailed data in a secure off-chain repository.

[0028] In some aspects, the techniques described herein relate to a system, wherein the control processor executes a robotic process automation routine to initiate approvals and notify stakeholders prior to transaction settlement.

[0029] In some aspects, the techniques described herein relate to a system, wherein the governance workflow includes a fraud detection layer configured to flag anomalies in payment routing or rights ownership records.

[0030] In some aspects, the techniques described herein relate to a system, wherein each stakeholder trust score is calculated using historical transaction reliability, dispute records, and authentication metadata.

[0031] In some aspects, the techniques described herein relate to a system, wherein smart contract templates are automatically generated and digitally signed using a secure key management service.

[0032] In some aspects, the techniques described herein relate to a system, wherein the system maintains an immutable audit trail accessible to authorized auditors for compliance review.

[0033] In some aspects, the techniques described herein relate to a system, wherein the orchestration module integrates with a digital rights management subsystem to enforce license terms during media playback or redistribution.Docket: 16606-12POA

[0034] In some aspects, the techniques described herein relate to a computer-implemented method for automated media rights transaction orchestration, including: orchestrating media rights transactions using a transaction management module executed on a plurality of distributed processing nodes; defining and enforcing one or more workflow rules associated with a media rights transaction managed by the transaction management module using a governance engine; detecting anomalous or fraudulent transaction behavior based on historical transaction data and stakeholder trust scores using a set of artificial intelligence models, wherein outputs from the artificial intelligence models are provided to the transaction management module; generating and digitally signing a blockchain transaction for distributing a royalty payment to a stakeholder according to a smart contract using a blockchain interface; and coordinating, by a control processor, with the transaction management module to execute a governance workflow prior to authorizing the blockchain transaction, wherein executing the governance workflow includes verifying account credentials, evaluating the stakeholder trust score, and confirming approval of the transaction before transmission to a blockchain network.

[0035] In some aspects, the techniques described herein relate to a method, further including dynamically updating workflow rules based on regulatory compliance or stakeholder policy changes using the governance engine.

[0036] In some aspects, the techniques described herein relate to a method, wherein the set of artificial intelligence models includes an ensemble of supervised and unsupervised models trained on historical royalty and licensing data.

[0037] In some aspects, the techniques described herein relate to a method, wherein the blockchain interface implements a hybrid on-chain / off-chain validation protocol to record verified transaction hashes while storing detailed data in a secure off-chain repository.

[0038] In some aspects, the techniques described herein relate to a method, further including executing, by the control processor, a robotic process automation routine to initiate approvals and notify stakeholders prior to transaction settlement.

[0039] In some aspects, the techniques described herein relate to a method, wherein the governance workflow includes a fraud detection layer configured to flag anomalies in payment routing or rights ownership records.

[0040] In some aspects, the techniques described herein relate to a method, wherein each stakeholder trust score is calculated using historical transaction reliability, dispute records, and authentication metadata.

[0041] In some aspects, the techniques described herein relate to a method, further including automatically generating and digitally signing smart contract templates using a secure key management service.

[0042] In some aspects, the techniques described herein relate to a method, further including maintaining an immutable audit trail accessible to authorized auditors for compliance review.

[0043] In some aspects, the techniques described herein relate to a method, further including integrating with a digital rights management subsystem to enforce license terms during media playback or redistribution.

[0044] In some aspects, the techniques described herein relate to a computer-implemented system for media transaction decision support and forecasting, including: a data acquisition subsystem configured to collect historical transaction records, current market indicators, and predictive economic data relating to media assets; a digital twin simulation module configured to simulate transactional scenarios based on contextual and operational parameters; a set of artificial intelligence models including at least one contextual forecasting model configured to: receive simulated transaction results from the digital twin simulation module, and predict financial outcomes and risk exposure for a proposed media transaction based on the simulated transaction results; and a user interface configured to present the predicted financial outcomes, risk exposure, and recommended strategies for execution.Docket: 16606-12POA

[0045] In some aspects, the techniques described herein relate to a system, wherein the data acquisition subsystem further collects real-time streaming, licensing, and sales data from third-party APIs.

[0046] In some aspects, the techniques described herein relate to a system, wherein the digital twin simulation module models at least one of: contract structures, revenue flows, or market responses for different licensing strategies.

[0047] In some aspects, the techniques described herein relate to a system, wherein the contextual forecasting model includes a recurrent neural network trained on time-series media transaction data.

[0048] In some aspects, the techniques described herein relate to a system, wherein at least one model of the set of artificial intelligence models computes scenario probabilities and expected value distributions for competing transaction proposals.

[0049] In some aspects, the techniques described herein relate to a system, wherein the system further includes a recommendation engine configured to rank alternative transaction pathways based on predicted risk and return.

[0050] In some aspects, the techniques described herein relate to a system, wherein the user interface displays at least one interactive dashboard including at least one of: sensitivity analyses, confidence intervals, or trend forecasts.

[0051] In some aspects, the techniques described herein relate to a system, wherein all forecast and decision data are recorded on a distributed ledger.

[0052] In some aspects, the techniques described herein relate to a system, wherein the digital twin simulation module is configured to perform what-if analysis by modeling multiple alternative scenarios and comparing predicted outcomes for each scenario.

[0053] In some aspects, the techniques described herein relate to a system, wherein the system further includes a robotic process automation module configured to automate execution of selected transaction strategies based on the predicted financial outcomes.

[0054] In some aspects, the techniques described herein relate to a computer-implemented method for media transaction decision support and forecasting, including: collecting historical transaction records, current market indicators, and predictive economic data relating to media assets using a data acquisition subsystem; simulating transactional scenarios based on contextual and operational parameters using a digital twin simulation module; receiving simulated transaction results from the digital twin simulation module using at least one contextual forecasting model of a set of artificial intelligence models; predicting financial outcomes and risk exposure for a proposed media transaction based on the simulated transaction results using the at least one contextual forecasting model; and presenting the predicted financial outcomes, risk exposure, and recommended strategies for execution via a user interface.

[0055] In some aspects, the techniques described herein relate to a method, wherein the data acquisition subsystem further collects real-time streaming, licensing, and sales data from third-party APIs.

[0056] In some aspects, the techniques described herein relate to a method, wherein the digital twin simulation module models at least one of: contract structures, revenue flows, or market responses for different licensing strategies.

[0057] In some aspects, the techniques described herein relate to a method, wherein the contextual forecasting model includes a recurrent neural network trained on time-series media transaction data.

[0058] In some aspects, the techniques described herein relate to a method, further including computing scenario probabilities and expected value distributions for competing transaction proposals using at least one model of the set of artificial intelligence models.Docket: 16606-12POA

[0059] In some aspects, the techniques described herein relate to a method, further including ranking alternative transaction pathways based on predicted risk and return using a recommendation engine.

[0060] In some aspects, the techniques described herein relate to a method, wherein the user interface displays at least one interactive dashboard including at least one of: sensitivity analyses, confidence intervals, or trend forecasts.

[0061] In some aspects, the techniques described herein relate to a method, further including recording all forecast and decision data on a distributed ledger.

[0062] In some aspects, the techniques described herein relate to a method, further including performing what-if analysis by modeling multiple alternative scenarios and comparing predicted outcomes for each scenario using the digital twin simulation module.

[0063] In some aspects, the techniques described herein relate to a method, further including automating execution of selected transaction strategies based on the predicted financial outcomes using a robotic process automation module.

[0064] In some aspects, the techniques described herein relate to a system for managing personalized and tokenized media experiences, including: a personalization engine configured to generate personalized media content based on a user profile including demographic, preference, and behavioral data; a tokenization module configured to issue a blockchain token encoding access rights to the personalized media content; a transaction module configured to: validate token ownership via a smart contract recorded on a blockchain, and enforce access terms and usage conditions associated with the token; and a rights management processor configured to: record token transfer events on the blockchain, and automatically distribute royalties to stakeholders based on the access terms and usage conditions associated with transferred tokens.

[0065] In some aspects, the techniques described herein relate to a system, wherein the personalization engine includes a set of artificial intelligence models, including at least one of: a collaborative filtering model, a content-based filtering model, a hybrid model, a recurrent neural network, or a convolutional neural network configured to analyze the user profile and generate the personalized media content.

[0066] In some aspects, the techniques described herein relate to a system, wherein the user profile includes psychometric personality classification data determined by analyzing a user's cross-platform behavior, including at least one of: content consumption patterns, interactive gaming choices, social media content, or user-generated content.

[0067] In some aspects, the techniques described herein relate to a system, wherein the personalized media content includes at least one of: Al-generated music tailored to user preferences, personalized video content incorporating user-specified themes, personalized gaming experiences, or Al-generated text content customized to user reading preferences.

[0068] In some aspects, the techniques described herein relate to a system, wherein the tokenization module is configured to issue non- fungible tokens (NFTs) that encode unique access rights to specific personalized derivative works, wherein eachNFT provides provable access to the personalized media content.

[0069] In some aspects, the techniques described herein relate to a system, wherein the system further includes a digital rights management subsystem operatively connected to the transaction module and configured to enforce license terms during media playback by verifying token ownership before permitting access to the personalized media content.

[0070] In some aspects, the techniques described herein relate to a system, wherein the rights management processor is configured to identify stakeholders by analyzing the personalized media content to determine underlying creative works that influenced generation of the personalized media content, and to automatically add identified stakeholders to a royalty apportionment schedule.Docket: 16606-12POA

[0071] In some aspects, the techniques described herein relate to a system, wherein the blockchain token encodes tiered access rights including at least one of: time-limited access, geographic restrictions, device- specific permissions, or usage-based limitations, and wherein the transaction module enforces the tiered access rights through conditional smart contract execution.

[0072] In some aspects, the techniques described herein relate to a system, wherein the personalization engine is configured to generate the personalized media content in real-time based on immediate contextual factors, including user location, time of day, current user activity, and physiological data from wearable devices.

[0073] In some aspects, the techniques described herein relate to a system, wherein the system maintains a crossplatform user profile by aggregating user behavioral data from linear media platforms, interactive gaming platforms, social media platforms, and metaverse environments, and wherein the personalization engine uses the cross-platform user profile to generate personalized media content consistent across multiple platforms.

[0074] In some aspects, the techniques described herein relate to a method for managing personalized and tokenized media experiences, including: generating personalized media content based on a user profile including demographic, preference, and behavioral data using a personalization engine; issuing a blockchain token encoding access rights to the personalized media content using a tokenization module; validating token ownership via a smart contract recorded on a blockchain using a transaction module; enforcing access terms and usage conditions associated with the token using the transaction module; recording token transfer events on the blockchain using a rights management processor; and automatically distributing royalties to stakeholders based on the access terms and usage conditions associated with transferred tokens using the rights management processor.

[0075] In some aspects, the techniques described herein relate to a method, wherein the personalization engine includes a set of artificial intelligence models, including at least one of: a collaborative filtering model, a content-based filtering model, a hybrid model, a recurrent neural network, or a convolutional neural network configured to analyze the user profile and generate the personalized media content.

[0076] In some aspects, the techniques described herein relate to a method, wherein the user profile includes psychometric personality classification data determined by analyzing a user's cross-platform behavior, including at least one of: content consumption patterns, interactive gaming choices, social media content, or user-generated content.

[0077] In some aspects, the techniques described herein relate to a method, wherein the personalized media content includes at least one of: Al-generated music tailored to user preferences, personalized video content incorporating user-specified themes, personalized gaming experiences, or Al-generated text content customized to user reading preferences.

[0078] In some aspects, the techniques described herein relate to a method, wherein issuing the blockchain token includes issuing non- fungible tokens (NFTs) that encode unique access rights to specific personalized derivative works, wherein eachNFT provides provable access to the personalized media content.

[0079] In some aspects, the techniques described herein relate to a method, further including enforcing license terms during media playback by verifying token ownership before permitting access to the personalized media content using a digital rights management subsystem operatively connected to the transaction module.

[0080] In some aspects, the techniques described herein relate to a method, further including identifying stakeholders by analyzing the personalized media content to determine underlying creative works that influenced generation of the personalized media content, and automatically adding identified stakeholders to a royalty apportionment schedule using the rights management processor.Docket: 16606-12POA

[0081] In some aspects, the techniques described herein relate to a method, wherein the blockchain token encodes tiered access rights including at least one of: time-limited access, geographic restrictions, device- specific permissions, or usage-based limitations, and wherein the transaction module enforces the tiered access rights through conditional smart contract execution.

[0082] In some aspects, the techniques described herein relate to a method, wherein generating the personalized media content includes generating the personalized media content in real-time based on immediate contextual factors, including user location, time of day, current user activity, and physiological data from wearable devices.

[0083] In some aspects, the techniques described herein relate to a method, further including maintaining a crossplatform user profile by aggregating user behavioral data from linear media platforms, interactive gaming platforms, social media platforms, and metaverse environments, and wherein generating the personalized media content uses the cross-platform user profile to generate personalized media content consistent across multiple platforms.

[0084] In some aspects, the techniques described herein relate to a distributed computing system for edge-enabled media transactions and delivery, including: a plurality of edge nodes configured to receive media transaction requests from end users and process the requests locally; a set of artificial intelligence models deployed on the edge nodes and configured to: authenticate user identity for the transaction requests, select personalized media content based on user profile data, and determine applicable digital rights management restrictions; a cache management subsystem deployed on the edge nodes and configured to store and serve Al-generated media content based on the personalized selections; a blockchain ledger interface configured to record completed transactions including user authentication events, content delivery events, and rights enforcement events; and a content delivery network controller configured to coordinate content distribution to the edge nodes and enforce geographic access restrictions based on licensing rights.

[0085] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence models deployed on the edge nodes include adaptive networking capabilities configured to intelligently route transaction data through optimal network paths selected from cellular networks, WiFi networks, Open Radio Access Network (ORAN), Bluetooth protocols, and loT communication protocols based on real-time network conditions.

[0086] In some aspects, the techniques described herein relate to a system, wherein the cache management subsystem is configured to implement multiple caching layers, including output caching for storing previously generated Al content, feature caching for storing preprocessed input data, and model state caching for storing intermediate Al model states.

[0087] In some aspects, the techniques described herein relate to a system, wherein the plurality of edge nodes includes at least one of: network routers, network switches, edge computing devices, loT systems, mesh networking nodes, networking chipsets, or networking systems-on-chip (SoCs)

[0088] In some aspects, the techniques described herein relate to a system, wherein the blockchain ledger interface is configured to record authentication events, content delivery events, and digital rights enforcement events as immutable transaction records on a distributed blockchain network.

[0089] In some aspects, the techniques described herein relate to a system, wherein the set of artificial intelligence models includes authentication models configured to validate user credentials using at least one of machine learningbased pattern recognition, biometric authentication, hardware token authentication, or multi-factor authentication protocols.Docket: 16606-12POA

[0090] In some aspects, the techniques described herein relate to a system, wherein the cache management subsystem is configured to implement predictive content pre-fetching by analyzing user behavior patterns to anticipate content requests and proactively cache anticipated content at edge nodes proximate to end users.

[0091] In some aspects, the techniques described herein relate to a system, wherein the blockchain ledger interface is configured to execute smart contracts that automatically apportion and distribute royalty payments to multiple stakeholders according to predefined royalty schedules embedded in the smart contracts.

[0092] In some aspects, the techniques described herein relate to a system, wherein local processing of media transaction data at the edge nodes reduces network latency by performing authentication, personalization, and digital rights management operations proximate to end users rather than at centralized servers.

[0093] In some aspects, the techniques described herein relate to a system, wherein the system is configured to deploy containerized microservices on the plurality of edge nodes to enable scalable deployment and independent scaling of authentication modules, personalization engines, and digital rights management subsystems based on demand at individual edge nodes.

[0094] In some aspects, the techniques described herein relate to a method for edge-enabled media transactions and delivery, including: receiving media transaction requests from end users at a plurality of edge nodes; processing the requests locally at the plurality of edge nodes; authenticating user identity for the transaction requests using a set of artificial intelligence models deployed on the edge nodes; selecting personalized media content based on user profile data using the set of artificial intelligence models; determining applicable digital rights management restrictions using the set of artificial intelligence models; storing and serving Al-generated media content based on the personalized selections using a cache management subsystem deployed on the edge nodes; recording completed transactions including user authentication events, content delivery events, and rights enforcement events using a blockchain ledger interface; and coordinating content distribution to the edge nodes and enforcing geographic access restrictions based on licensing rights using a content delivery network controller.

[0095] In some aspects, the techniques described herein relate to a method, wherein the artificial intelligence models deployed on the edge nodes include adaptive networking capabilities configured to intelligently route transaction data through optimal network paths selected from cellular networks, WiFi networks, Open Radio Access Network (ORAN), Bluetooth protocols, and loT communication protocols based on real-time network conditions.

[0096] In some aspects, the techniques described herein relate to a method, wherein the cache management subsystem is configured to implement multiple caching layers, including output caching for storing previously generated Al content, feature caching for storing preprocessed input data, and model state caching for storing intermediate Al model states.

[0097] In some aspects, the techniques described herein relate to a method, wherein the plurality of edge nodes includes at least one of: network routers, network switches, edge computing devices, loT systems, mesh networking nodes, networking chipsets, or networking systems-on-chip (SoCs).

[0098] In some aspects, the techniques described herein relate to a method, wherein recording completed transactions includes recording authentication events, content delivery events, and digital rights enforcement events as immutable transaction records on a distributed blockchain network using the blockchain ledger interface.

[0099] In some aspects, the techniques described herein relate to a method, wherein the set of artificial intelligence models includes authentication models configured to validate user credentials using at least one of machine leaming-Docket: 16606-12POA based pattern recognition, biometric authentication, hardware token authentication, or multi-factor authentication protocols.

[0100] In some aspects, the techniques described herein relate to a method, further including implementing predictive content pre-fetching by analyzing user behavior patterns to anticipate content requests and proactively cache anticipated content at edge nodes proximate to end users using the cache management subsystem.

[0101] In some aspects, the techniques described herein relate to a method, further including executing smart contracts that automatically apportion and distribute royalty payments to multiple stakeholders according to predefined royalty schedules embedded in the smart contracts using the blockchain ledger interface.

[0102] In some aspects, the techniques described herein relate to a method, wherein local processing of media transaction data at the edge nodes reduces network latency by performing authentication, personalization, and digital rights management operations proximate to end users rather than at centralized servers.

[0103] In some aspects, the techniques described herein relate to a method, further including deploying containerized microservices on the plurality of edge nodes to enable scalable deployment and independent scaling of authentication modules, personalization engines, and digital rights management subsystems based on demand at individual edge nodes. Media usage monitoring oracle

[0104] In some aspects, the techniques described herein relate to a system for blockchain-based royalty verification and distribution, including: a data collection module configured to monitor one or more content distribution services to collect usage event data indicating instances of streaming, downloading, or public performance of a creative work; a blockchain oracle interface configured to provide the usage event data from off-chain content distribution services to a smart contract that manages royalty distribution on a blockchain; a smart contract execution module configured to: receive the usage event data from the blockchain oracle interface, calculate royalty amounts due to stakeholders based on the usage event data, and execute automated royalty distribution transactions according to a predefined royalty apportionment schedule; and a verification processor configured to reconcile royalty payments recorded on the blockchain with the collected usage event data to confirm accuracy of payments to stakeholders.

[0105] In some aspects, the techniques described herein relate to a system, wherein the data collection module is configured to monitor usage event data from multiple off-chain content distribution services.

[0106] In some aspects, the techniques described herein relate to a system, wherein the usage event data includes at least one of: streaming counts with associated timestamps, download transaction records, public performance logs, radio airplay data, synchronization licensing usage, or geographic distribution metrics.

[0107] In some aspects, the techniques described herein relate to a system, wherein the smart contract execution module is configured to calculate royalty amounts using a predefined royalty apportionment schedule that specifies percentage allocations for each stakeholder, and wherein the smart contract automatically transfers cryptocurrency payments to blockchain wallet addresses of the stakeholders.

[0108] In some aspects, the techniques described herein relate to a system, wherein the blockchain oracle interface is configured to provide the usage event data to the smart contract at periodic intervals, including at least one of: realtime continuous monitoring, hourly updates, daily batch processing, or monthly reconciliation cycles.

[0109] In some aspects, the techniques described herein relate to a system, wherein the verification processor is configured to identify discrepancies by comparing cumulative usage event data collected from the off-chain content distribution services against cumulative royalty payment amounts recorded on the blockchain, and to generate discrepancy alerts when differences exceed a predetermined threshold.Docket: 16606-12POA

[0110] In some aspects, the techniques described herein relate to a system, wherein the system further includes a stakeholder identification module configured to analyze the creative work to identity all entities entitled to receive royalties.[OHl] In some aspects, the techniques described herein relate to a system, wherein the smart contract execution module is configured to implement minimum payment thresholds whereby royalty amounts are accumulated until reaching a minimum threshold before executing distribution transactions.

[0112] In some aspects, the techniques described herein relate to a system, wherein the blockchain oracle interface is configured to cryptographically sign the usage event data before transmitting to the smart contract.

[0113] In some aspects, the techniques described herein relate to a system, wherein the verification processor is configured to maintain an immutable audit trail on the blockchain recording all usage events, calculated royalty amounts, payment distributions, and verification checks for compliance review by authorized auditors.

[0114] In some aspects, the techniques described herein relate to a method for blockchain-based royalty verification and distribution, including: monitoring one or more content distribution services to collect usage event data indicating instances of streaming, downloading, or public performance of a creative work using a data collection module; providing the usage event data from the off-chain content distribution services to a smart contract that manages royalty distribution on a blockchain using a blockchain oracle interface; receiving the usage event data from the blockchain oracle interface using a smart contract execution module; calculating royalty amounts due to stakeholders based on the usage event data using the smart contract execution module; executing automated royalty distribution transactions according to a predefined royalty apportionment schedule using the smart contract execution module; and reconciling royalty payments recorded on the blockchain with the collected usage event data to confirm accuracy of payments to stakeholders using a verification processor.

[0115] In some aspects, the techniques described herein relate to a method, wherein monitoring one or more content distribution services includes monitoring usage event data from multiple off-chain content distribution services.

[0116] In some aspects, the techniques described herein relate to a method, wherein the usage event data includes at least one of: streaming counts with associated timestamps, download transaction records, public performance logs, radio airplay data, synchronization licensing usage, or geographic distribution metrics.

[0117] In some aspects, the techniques described herein relate to a method, wherein calculating royalty amounts includes calculating royalty amounts using a predefined royalty apportionment schedule that specifies percentage allocations for each stakeholder, and wherein the smart contract automatically transfers cryptocurrency payments to blockchain wallet addresses of the stakeholders.

[0118] In some aspects, the techniques described herein relate to a method, wherein providing the usage event data to the smart contract includes providing the usage event data at periodic intervals, including at least one of: real-time continuous monitoring, hourly updates, daily batch processing, or monthly reconciliation cycles.

[0119] In some aspects, the techniques described herein relate to a method, wherein reconciling royalty payments includes identifying discrepancies by comparing cumulative usage event data collected from the off-chain content distribution services against cumulative royalty payment amounts recorded on the blockchain, and generating discrepancy alerts when differences exceed a predetermined threshold.

[0120] In some aspects, the techniques described herein relate to a method, further including analyzing the creative work to identity all entities entitled to receive royalties using a stakeholder identification module.Docket: 16606-12POA

[0121] In some aspects, the techniques described herein relate to a method, wherein executing automated royalty distribution transactions includes implementing minimum payment thresholds whereby royalty amounts are accumulated until reaching a minimum threshold before executing distribution transactions.

[0122] In some aspects, the techniques described herein relate to a method, wherein providing the usage event data includes cryptographically signing the usage event data before transmitting to the smart contract using the blockchain oracle interface.

[0123] In some aspects, the techniques described herein relate to a method, further including maintaining an immutable audit trail on the blockchain recording all usage events, calculated royalty amounts, payment distributions, and verification checks for compliance review by authorized auditors using the verification processor. Al-driven digital audio workstation

[0124] In some aspects, the techniques described herein relate to a digital audio workstation (DAW) system, including one or more processors and non-transitory memory storing instructions that, when executed, cause the DAW system to: receive one or more media assets for use in an audiovisual project; analyze the media assets using a set of artificial intelligence models to identify ownership information, contributor information, royalty-relevant metadata, and license- related obligations associated with the one or more media assets; monitor changes to the audiovisual project, including additions, deletions, edits, or transformations of the media assets, and update a media asset usage record reflecting a contribution of each media asset to the audiovisual project; compute one or more royalty allocation values for a proj ect output based on the ownership information, contributor information, royalty-relevant metadata, and the media asset usage record; and generate royalty-tracking output identifying stakeholders and corresponding royalty allocation values associated with the project output.

[0125] In some aspects, the techniques described herein relate to a system, wherein the set of artificial intelligence models includes at least one neural-network classifier trained to detect contributor identities based on audio fingerprints or embedded metadata patterns.

[0126] In some aspects, the techniques described herein relate to a system, wherein the set of artificial intelligence models further includes a large-language-model configured to interpret license text, usage restrictions, or contractual royalty terms extracted from unstructured documents.

[0127] In some aspects, the techniques described herein relate to a system, wherein updating the media asset usage record includes generating time-coded contribution weights that reflect temporal portions of the audiovisual project in which each media asset appears.

[0128] In some aspects, the techniques described herein relate to a system, wherein monitoring changes to the audiovisual project further includes detecting effect-chain modifications, track-level transformations, or timeline rearrangements and incorporating such modifications into the media asset usage record.

[0129] In some aspects, the techniques described herein relate to a system, wherein computing the royalty allocation values includes applying a rules engine configured to enforce royalty-splitting formulas, stakeholder- specific weighting factors, or usage-dependent revenue schedules.

[0130] In some aspects, the techniques described herein relate to a system, wherein the royalty- tracking output includes a visual representation within a DAW interface indicating stakeholder allocation values that update in real time as project edits are performed.Docket: 16606-12POA

[0131] In some aspects, the techniques described herein relate to a system, wherein the royalty-tracking output is exported in a structured file format selected from JavaScript Object Notation (JSON), XML, or comma-separated values (CSV) for interoperability with external rights-management systems.

[0132] In some aspects, the techniques described herein relate to a system, wherein the set of artificial intelligence models is further configured to identify potential licensing or usage conflicts by detecting media assets exceeding allowable usage parameters defined by royalty-relevant metadata.

[0133] In some aspects, the techniques described herein relate to a system, wherein the DAW system further maintains an audit log recording media-asset imports, metadata revisions, usage-record updates, royalty-allocation computations, and generated royalty-tracking output.

[0134] In some aspects, the techniques described herein relate to a method performed by a digital audio workstation (DAW) system, including: receiving one or more media assets for use in an audiovisual project; analyzing the media assets using a set of artificial intelligence models to identity ownership information, contributor information, royaltyrelevant metadata, and license-related obligations associated with the one or more media assets; monitoring changes to the audiovisual project, including additions, deletions, edits, or transformations of the media assets, and updating a media asset usage record reflecting a contribution of each media asset to the audiovisual project; computing one or more royalty allocation values for a project output based on the ownership information, contributor information, royalty-relevant metadata, and the media asset usage record; and generating royalty-tracking output identifying stakeholders and corresponding royalty allocation values associated with the project output.

[0135] In some aspects, the techniques described herein relate to a method, wherein the set of artificial intelligence models includes at least one neural-network classifier trained to detect contributor identities based on audio fingerprints or embedded metadata patterns.

[0136] In some aspects, the techniques described herein relate to a method, wherein the set of artificial intelligence models further includes a large-language-model configured to interpret license text, usage restrictions, or contractual royalty terms extracted from unstructured documents.

[0137] In some aspects, the techniques described herein relate to a method, wherein updating the media asset usage record includes generating time-coded contribution weights that reflect temporal portions of the audiovisual project in which each media asset appears.

[0138] In some aspects, the techniques described herein relate to a method, wherein monitoring changes to the audiovisual project further includes detecting effect-chain modifications, track-level transformations, or timeline rearrangements and incorporating such modifications into the media asset usage record.

[0139] In some aspects, the techniques described herein relate to a method, wherein computing the royalty allocation values includes applying a rules engine configured to enforce royalty-splitting formulas, stakeholder- specific weighting factors, or usage-dependent revenue schedules.

[0140] In some aspects, the techniques described herein relate to a method, wherein the royalty-tracking output includes a visual representation within a DAW interface indicating stakeholder allocation values that update in real time as project edits are performed.

[0141] In some aspects, the techniques described herein relate to a method, wherein the royalty-tracking output is exported in a structured file format selected from JavaScript Object Notation (JSON), XML, or comma-separated values (CSV) for interoperability with external rights-management systems.Docket: 16606-12POA

[0142] In some aspects, the techniques described herein relate to a method, wherein the set of artificial intelligence models is further configured to identify potential licensing or usage conflicts by detecting media assets exceeding allowable usage parameters defined by royalty-relevant metadata.

[0143] In some aspects, the techniques described herein relate to a method, further including maintaining an audit log recording media-asset imports, metadata revisions, usage-record updates, royalty-allocation computations, and generated royalty-tracking output. Royalties for training models on right holder content

[0144] In some aspects, the techniques described herein relate to a system for managing royalties associated with training an artificial intelligence model on media content, the system including: a data collection system configured to receive creative work data associated with a plurality of stakeholders and to identify stakeholder information for the creative work data; an intelligence system configured to train a generative artificial intelligence model using the creative work data; a transaction management system configured to generate a model royalty stack associated with the generative artificial intelligence model, a model-royalty stack including stakeholder identifiers and corresponding royalty apportionment percentages based on the creative work data used to train the model ; the transaction management system further configured to parameterize a royalty apportionment smart contract using the model royalty stack and to deploy a royalty-apportionment smart contract to a blockchain network; the transaction management system further configured to configure a blockchain oracle corresponding to the royalty apportionment smart contract, the blockchain oracle being operable to provide off-chain usage data for the generative artificial intelligence model to the royalty apportionment smart contract; and wherein the royalty apportionment smart contract is executed on the blockchain network to distribute royalty payments to the stakeholders according to the royalty apportionment percentages in response to usage of the generative artificial intelligence model.

[0145] In some aspects, the techniques described herein relate to a system, wherein the data collection system is further configured to receive event data from one or more content distribution platforms indicating usage of media content associated with the stakeholders.

[0146] In some aspects, the techniques described herein relate to a system, wherein the data collection system is further configured to provide event data to the blockchain oracle for transmission to the royalty apportionment smart contract.

[0147] In some aspects, the techniques described herein relate to a system, wherein the intelligence system is further configured to generate attribution metadata identifying the stakeholders whose creative work data contributed to training the generative artificial intelligence model.

[0148] In some aspects, the techniques described herein relate to a system, wherein the transaction management system is further configured to generate the model-royalty stack by aggregating stakeholder identifiers and corresponding apportionment percentages derived from attribution metadata.

[0149] In some aspects, the techniques described herein relate to a system, wherein the royalty apportionment smart contract includes a plurality of blockchain addresses corresponding to the stakeholder identifiers and executable instructions for allocating royalty distributions among the blockchain addresses.

[0150] In some aspects, the techniques described herein relate to a system, wherein deploying the royalty apportionment smart contract to the blockchain network includes digitally signing the smart contract using a key associated with the transaction management system.

[0151] In some aspects, the techniques described herein relate to a system, wherein the blockchain oracle is further configured to validate the off-chain usage data before providing the usage data to the royalty apportionment smart contract.Docket: 16606-12POA

[0152] In some aspects, the techniques described herein relate to a system, wherein the royalty apportionment smart contract is further configured to automatically execute royalty distribution transactions in response to the usage data without requiring manual approval.

[0153] In some aspects, the techniques described herein relate to a system, wherein the transaction management system is further configured to maintain an immutable record of the model royalty stack and subsequent royalty distribution transactions for audit and compliance review.

[0154] In some aspects, the techniques described herein relate to a method for managing royalties associated with training an artificial intelligence model on media content, the method including: receiving creative work data associated with a plurality of stakeholders and identifying stakeholder information for the creative work data using a data collection system; training a generative artificial intelligence model using the creative work data with an intelligence system; generating a model royalty stack associated with the generative artificial intelligence model using a transaction management system, a model-royalty stack including stakeholder identifiers and corresponding royalty apportionment percentages based on the creative work data used to train the model; parameterizing a royalty apportionment smart contract using the model royalty stack and deploying a royalty-apportionment smart contract to a blockchain network using the transaction management system; configuring a blockchain oracle corresponding to the royalty apportionment smart contract using the transaction management system, the blockchain oracle being operable to provide off-chain usage data for the generative artificial intelligence model to the royalty apportionment smart contract; and executing the royalty apportionment smart contract on the blockchain network to distribute royalty payments to the stakeholders according to the royalty apportionment percentages in response to usage of the generative artificial intelligence model.

[0155] In some aspects, the techniques described herein relate to a method, further including receiving event data from one or more content distribution platforms indicating usage of media content associated with the stakeholders using the data collection system.

[0156] In some aspects, the techniques described herein relate to a method, further including providing event data to the blockchain oracle for transmission to the royalty apportionment smart contract using the data collection system.

[0157] In some aspects, the techniques described herein relate to a method, further including generating attribution metadata identifying the stakeholders whose creative work data contributed to training the generative artificial intelligence model using the intelligence system.

[0158] In some aspects, the techniques described herein relate to a method, wherein generating the model-royalty stack includes aggregating stakeholder identifiers and corresponding apportionment percentages derived from attribution metadata using the transaction management system.

[0159] In some aspects, the techniques described herein relate to a method, wherein the royalty apportionment smart contract includes a plurality of blockchain addresses corresponding to the stakeholder identifiers and executable instructions for allocating royalty distributions among the blockchain addresses.

[0160] In some aspects, the techniques described herein relate to a method, wherein deploying the royalty apportionment smart contract to the blockchain network includes digitally signing the smart contract using a key associated with the transaction management system.

[0161] In some aspects, the techniques described herein relate to a method, further including validating the off-chain usage data before providing the usage data to the royalty apportionment smart contract using the blockchain oracle.Docket: 16606-12POA

[0162] In some aspects, the techniques described herein relate to a method, wherein executing the royalty apportionment smart contract includes automatically executing royalty distribution transactions in response to the usage data without requiring manual approval.

[0163] In some aspects, the techniques described herein relate to a method, further including maintaining an immutable record of the model royalty stack and subsequent royalty distribution transactions for audit and compliance review using the transaction management system.BRIEF DESCRIPTION OF THE FIGURES

[0164] The disclosure and the following detailed description of certain embodiments thereof may be understood by reference to the following figures:

[0165] Fig. 1 is a schematic diagram of components of a platform for enabling intelligent transactions in accordance with embodiments of the present disclosure.

[0166] Figs. 2A and 2B are schematic diagrams of additional components of a platform for enabling intelligent transactions in accordance with embodiments of the present disclosure.Intelligence Services System FIGS.

[0167] Fig. 3 is a schematic view of an example of an intelligence services system according to some embodiments.

[0168] Fig. 4 is a schematic view of an example of a neural network according to some embodiments.

[0169] Fig. 5 is a schematic view of an example of a convolutional neural network according to some embodiments.

[0170] Fig. 6 is a schematic view of an example of a neural network according to some embodiments.

[0171] Fig. 7 is a diagram of an approach based on reinforcement learning according to some embodiments.

[0172] Fig. 8 depicts a block diagram of exemplary features, capabilities, and interfaces of a robust generative artificial intelligence platform.Enterprise Access Laver FIGS.

[0173] Fig. 9 is a schematic view of an example of an enterprise ecosystem, including an enterprise access layer.

[0174] Fig. 10 is a functional block diagram of an example implementation of an enterprise access layer.

[0175] Fig. 11 is a schematic view of examples of how the enterprise access layer of Fig. 10 may be integrated with portions of an enterprise ecosystem.

[0176] Fig. 12 is a schematic view of an example market orchestration system that includes an enterprise access layer.

[0177] Fig. 13 is a functional block diagram of an example implementation of an intelligence system.

[0178] Fig. 14 is a functional block diagram of an example implementation of a data pool system.

[0179] Fig. 15 is a functional block diagram of an example implementation of a scoring system.

[0180] Fig. 16 is a simplified diagram of a determination of attention by a machine learning model in accordance with some embodiments.

[0181] Fig. 17 is a simplified diagram of a transformer model in accordance with some embodiments.Integrated Al convergence System of Systems FIGS.

[0182] Fig. 18 is a simplified diagram of financial infrastructure systems in accordance with some embodiments.

[0183] Fig. 19 is a simplified diagram of a configured artificial intelligence system (CAIS) in accordance with some embodiments.

[0184] Fig. 20 is a simplified diagram illustrating a KYX system in accordance with some embodiments.

[0185] Fig. 21 is a diagram that illustrates an exemplary embodiment of a system of models architecture within the intelligence system of the configured artificial intelligence system.Docket: 16606-12POA

[0186] Fig. 22 is a diagram that illustrates an exemplary embodiment of chipset architectures for systems of models.

[0187] Fig. 23 is a simplified diagram illustrating an example artificial neural network with multiple layers.

[0188] Fig. 24 is a simplified diagram illustrating an example of a training and inference of an example artificial neural network.

[0189] Fig. 25 is a simplified diagram illustrating an example of a determination of attention by a machine learning model.

[0190] Fig. 26 is a simplified block diagram of a first transformer model.

[0191] Fig. 27 is a simplified block diagram of a second transformer model.

[0192] Fig. 28 is a high-level schematic of an exemplary system in which a large language model including a retrieval component that provides a RAG capability.

[0193] Fig. 29 is a simplified diagram illustrating tool use by an example Al agent.

[0194] Fig. 30 is a simplified diagram illustrating an example scenario featuring an Al agent featuring an agent loop.

[0195] Fig. 31 is a simplified diagram illustrating a matrix for organizing and interconnecting various features of Al agent understanding.

[0196] Fig. 32 is a simplified diagram illustrating an agentic Al understanding Matrix in accordance with some embodiments.

[0197] FIG. 33 illustrates an example method for deploying a transacting Al agent on behalf of a party according to some embodiments of the present disclosure.

[0198] FIG. 34 is a diagram depicting an example configuration of a game-theoretic transacting agent according to some embodiments of the present disclosure.

[0199] FIG. 35 is a diagram depicting an example of an agent facing marketplace according to some embodiments of the present disclosure.

[0200] Fig. 36 is a simplified schematic illustrating a hyperpersonalized agent system in accordance with some embodiments.

[0201] Fig. 37 is a simplified diagram illustrating a game theory based agent system in accordance with some embodiments.

[0202] Fig. 38 is a simplified diagram illustrating a transaction orchestration system in accordance with some embodiments.

[0203] Fig. 39 is a simplified schematic of an agentic marketplace system in accordance with some embodiments.

[0204] Fig. 40 is a simplified schematic illustrating an agent-assisted transaction resolution system in accordance with some embodiments.

[0205] Fig. 41 is a simplified diagram of a customer vector system in accordance with some embodiments.

[0206] Fig. 42 is a simplified diagram of an opportunity vector system in accordance with some embodiments.

[0207] Fig. 43 is a simplified diagram of a configured vectorization system in accordance with some embodiments.

[0208] Fig. 44 is a simplified schematic of a cross-border payment treasury management system.

[0209] Fig. 45 is a simplified diagram of a stable-value token governance system 4500 in accordance with some embodiments.

[0210] Fig. 46 is a simplified schematic of an identity verification behavioral biometrics system in accordance with some embodiments.

[0211] Fig. 47 is a simplified diagram of a deepfake fraud identification system in accordance with some embodiments.Docket: 16606-12POA

[0212] Fig. 48 is a simplified diagram of a regulatory and compliance digital twin system in accordance with some embodiments.

[0213] Fig. 49 illustrates an enterprise Al cost optimization system in accordance with some embodiments.Media Rights Management Figs.

[0214] Fig. 51 is a schematic diagram of additional components of a platform for enabling intelligent transactions in accordance with embodiments of the present disclosure.

[0215] Fig. 52 is a schematic diagram of a system including a smart contract wrapper.

[0216] Fig. 53 is a schematic flow diagram of a method for executing a smart contract wrapper.

[0217] Fig. 54 is a schematic flow diagram of a method for updating an aggregate IP stack.

[0218] Fig. 55 is a schematic flow diagram of a method for adding assets and entities.

[0219] Fig. 56 is a schematic flow diagram of a method for updating an aggregate IP stack.

[0220] Fig. 57 is a schematic diagram of a system for analyzing and reporting on an aggregate stack of IP.

[0221] Fig. 58 is a schematic flow diagram of a method for analyzing and reporting on an aggregate stack of IP.

[0222] Fig. 59 is a simplified diagram of a system configured for media rights management in accordance with some embodiments.

[0223] Fig. 60 is a schematic diagram of an Al subsystem integrator system in accordance with some embodiments.

[0224] Fig. 61 is a schematic diagram of an Al model-driven edge system with intelligent edge capabilities for distributed media content delivery and transactions in accordance with some embodiments.

[0225] Like reference symbols in the various drawings indicate like elements.DETAILED DESCRIPTIONTransaction platform

[0226] Referring to Figs. 1, 2A and 2B, a set of systems, methods, components, modules, machines, articles, blocks, circuits, services, programs, applications, hardware, software and other elements are provided, collectively referred to herein interchangeably as the system 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. As used herein, “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; anDocket: 16606-12POA 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 all platform components, and 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 Facebook™ and Twitter™), Internet of Things (loT) data sources (including from sensors, cameras, data collectors, and instrumented machines and systems), such as loT sources that provide information about machines and systems that enable transactions and machines and systems that are involved in production and consumption of resources. External data sources 182 may include behavioral data sources, such as automated agent behavioral data sources 188 (such as tracking and reporting on behavior of automated agents that are used for conversation and dialog management, agents used for control functions for machines and systems, agents used for purchasing and sales, agents used for data collection, agents used for advertising, and others), human behavioral data sources (such as data sources tracking online behavior, mobility behavior, energy consumption behavior, energy production behavior, network utilization behavior, compute and processing behavior, resource consumption behavior, resource production behavior, purchasing behavior, attention behavior, social behavior, and others), and entity behavioral data sources 190 (such as behavior of business organizations and other entities, such as purchasing behavior, consumption behavior, production behavior, market activity, merger and acquisition behavior, transaction behavior, location behavior, and others). The loT, social and behavioral data from and about sensors, machines, humans, entities, and automated agents may collectively be used to populate expert systems, machine learning systems, and other intelligent systems and engines described throughout this disclosure, such as being provided as inputs to deep learning systems and being provided as feedback or outcomes for purposes of training, supervision, and iterative improvement of systems for prediction, forecasting, classification, automation and control. The data may be organized as a stream of events. The data may be stored in a distributed ledger or other distributed system. The data may be stored in a knowledge graph where nodes represent entities and links represent relationships. The external data sources may be queried via various database query functions. The external data sources 182 may be accessed via APIs, brokers, connectors, protocols like REST and SOAP, and other data ingestion and extraction techniques. Data may be enriched with metadata and may be subject to transformation and loading into suitable forms for consumption by the engines, such as by cleansing, normalization, de- duplication, and the like.

[0227] 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),Docket: 16606-12POA 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 associated with loT data for crypto transaction 295 and social data for crypto transaction 193. The platform 100 may make use of assets 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.

[0228] Referring to the block diagrams of Figs. 2A and 2B, further details and additional components of the platform 100 and interactions among them are depicted. 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. For example, 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). In embodiments, energy storage capacity may be transacted on an energy storage forward market 174 or an energy storage market 178.

[0229] The set of forward purchase and sale machines 110 may include an energy purchase and sale machine 104 for purchasing or selling energy, such as in an energy spot market 148 or an energy forward market 122. The energy purchase and sale machine 104 may use an expert system, neural network or other intelligence to determine timing of purchases, such as based on current and anticipated state information with respect to pricing and availability of energy and based on current and anticipated state information with respect to needs for energy, including needs for energy to perform computing tasks, cryptocurrency mining, data collection actions, and other work, such as work done by automated agents and systems and work required for humans or entities based on their behavior. For example, 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. Continuing the example, 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. Sales may also be conducted by an expert system operating on the various data sources described herein, including training on outcomes and human supervision.

[0230] 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 ofDocket: 16606-12POA 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. Sales may also be conducted by an expert system operating on the various data sources described herein, including training on outcomes and human supervision.

[0231] 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, based on an understanding of supply and demand, which is determined by processing inputs from the various data sources. For example, the attention purchase and sale machine 112 may purchase advertising space in a forward market for advertising based on learning from a wide range of inputs about market conditions, behavior data, and data regarding activities of agents 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. Sales may also be conducted by an expert system operating on the various data sources described herein, including training on outcomes and human supervision.

[0232] 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, based on an understanding of supply and demand, which is determined by processing inputs from the various data sources. For example, 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,Docket: 16606-12POA 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. Sales may also be conducted by an expert system operating on the various data sources described herein, including training on outcomes and human supervision.

[0233] 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, based on an understanding of supply and demand, which is determined by processing inputs from the various data sources. For example, 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. Sales may also be conducted by an expert system operating on the various data sources described herein, including training on outcomes and human supervision.

[0234] 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. 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, based on an understanding of supply and demand, which is determined by processing inputs from the various data sources. For example, the bandwidth purchase and sale machine 120 may purchase or reserve bandwidth on a network resource for a futureDocket: 16606-12POA 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. Sales may also be conducted by an expert system operating on the various data sources described herein, including training on outcomes and human supervision.

[0235] 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, based on an understanding of supply and demand, which is determined by processing inputs from the various data sources. For example, 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. Sales may also be conducted by an expert system operating on the various data sources described herein, including training on outcomes and human supervision.

[0236] In embodiments, the intelligent resource allocation and coordination system 168, including the intelligent resource purchasing system 164, the intelligent sale engine 172 and the automated spot market testing and arbitrage transaction execution engine 194, 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). For example, 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 theDocket: 16606-12POA platform, such as by tracking activities of various engines and agents, as well as by taking inputs from external data sources 182. In embodiments, 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. For example, as the energy for computational tasks becomes a significant fraction of an enterprise’s energy usage, 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 attentionbased exchanges of value, such as in advertising markets, micro-transaction markets, and others.

[0237] Referring still to Figs. 2A and 2B, 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 136 (such as for intelligent cryptocurrency execution) or for other purposes. Each of the set of intelligent forecasting engines 192 may use data that is tracked, aggregated, processed, or handled within the platform 100, such as by the data aggregation system 144, as well as input data from external data sources 182, such as social media data sources 180, automated agent behavioral data sources 188, human behavioral data sources 184, entity behavioral data sources 190 and loT data sources 198. These collective inputs may be used to forecast attributes, such as using a model (e.g., Bayesian, regression, or other statistical model), a rule, or an expert system, such as a machine learning system that has one or more classifiers, pattern recognizers, and predictors, such as any of the expert systems described throughout this disclosure. In embodiments, 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. In each case, 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.Docket: 16606-12POA

[0238] The intelligent forecasting engines 192 may also include a range of engines that provide forecasts at least in part based on human behavior, such as behavior of consumers and users, such as purchasing behavior, shopping behavior, sales behavior, product interaction behavior, energy utilization behavior, mobility behavior, activity level behavior, activity type behavior, transactional behavior, and other human behavior. These may include an energy price forecasting engine 235 using human behavior, a network spectrum price forecasting engine 237 using human behavior, a REC price forecasting engine 239 using human behavior, a compute price forecasting engine 241 using human behavior, and a network spectrum price forecasting engine 243 using human behavior.

[0239] Referring still to Figs. 2A and 2B, the platform 100 may include a set of intelligent transaction engines 136 that automate execution of transactions in forward markets 130 and / or spot markets 170 based on the determination that favorable conditions exist, such as by the intelligent resource allocation and coordination system 168 and / or with the use of forecasts from the intelligent forecasting engines 192. The intelligent transaction engines 136 may be configured to automatically execute transactions, using available market interfaces, such as APIs, connectors, ports, network interfaces, and the like, in each of the markets noted above. In embodiments, the intelligent transaction engines may execute transactions based on event streams that come from external data sources, such as loT data sources 198 and social media data sources 180. The engines may include, for example, an loT forward energy transaction engine 195 and / or an loT compute market transaction engine 106, either or both of which may use data from the Internet of Things to determine timing and other attributes for market transaction in a market for one or more of the resources described herein, such as an energy market transaction, a compute resource transaction or other resource transaction. loT data may include instrumentation and controls data for one or more machines (optionally coordinated as a fleet) that use or produce energy or that use or have compute resources, weather data that influences energy prices or consumption (such as wind data influencing production of wind energy), sensor data from energy production environments, sensor data from points of use for energy or compute resources (such as vehicle traffic data, network traffic data, IT network utilization data, Internet utilization and traffic data, camera data from work sites, smart building data, smart home data, and the like), and other data collected by or transferred within the Internet of Things, including data stored in loT platforms and of cloud services providers like Amazon, IBM, and others. The intelligent transaction engines 136 may include engines that use social data to determine timing of other attributes for a market transaction in one or more of the resources described herein, such as a social data forward energy transaction engine 199 and / or a social data compute market transaction engine 116. Social data may include data from social networking sites (e.g., Facebook™, YouTube™, Twitter™, Snapchat™, Instagram™, and others), data from websites, data from e- commerce sites, and data from other sites that contain information that may be relevant to determining or forecasting behavior of users or entities, such as data indicating interest or attention to particular topics, goods or services, data indicating activity types and levels such as may be observed by machine processing of image data showing individuals engaged in activities, including travel, work activities, leisure activities, and the like. Social data may be supplied to machine learning, such as for learning user behavior or entity behavior at a social data market predictor 186, and / or as an input to an expert system, a model, or the like, such as one for determining, based on the social data, the parameters for a transaction. For example, an event or set of events in a social data stream may indicate the likelihood of a surge of interest in an online resource, a product, or a service, and compute resources, bandwidth, storage, or the like may be purchased in advance (avoiding surge pricing) to accommodate the increased interest reflected by the social data stream.Docket: 16606-12POANeural Net Systems

[0240] Embodiments of the present disclosure, including ones involving expert systems, self-organization, machine learning, artificial intelligence, and the like, may benefit from the use of a neural net, such as a neural net trained for pattern recognition, for classification of one or more parameters, characteristics, or phenomena, for support of autonomous control, and other purposes. References to a neural net throughout this disclosure should be understood to encompass a wide range of different types of neural networks, machine learning systems, artificial intelligence systems, and the like, such as feed forward neural networks, radial basis function neural networks, self-organizing neural networks (e.g., Kohonen self-organizing neural networks), recurrent neural networks, modular neural networks, artificial neural networks, physical neural networks, multi-layered neural networks, convolutional neural networks, hybrids of neural networks with other expert systems (e.g., hybrid fuzzy logic - neural network systems), Autoencoder neural networks, probabilistic neural networks, time delay neural networks, convolutional neural networks, regulatory feedback neural networks, radial basis function neural networks, recurrent neural networks, Hopfield neural networks, Boltzmann machine neural networks, self-organizing map (SOM) neural networks, learning vector quantization (LVQ) neural networks, fully recurrent neural networks, simple recurrent neural networks, echo state neural networks, long short-term memory neural networks, bi-directional neural networks, hierarchical neural networks, stochastic neural networks, genetic scale RNN neural networks, committee of machines neural networks, associative neural networks, physical neural networks, instantaneously trained neural networks, spiking neural networks, neocognitron neural networks, dynamic neural networks, cascading neural networks, neuro-fuzzy neural networks, compositional patternproducing neural networks, memory neural networks, hierarchical temporal memory neural networks, deep feed forward neural networks, gated recurrent unit (GCU) neural networks, auto encoder neural networks, variational auto encoder neural networks, de-noising auto encoder neural networks, sparse auto-encoder neural networks, Markov chain neural networks, restricted Boltzmann machine neural networks, deep belief neural networks, deep convolutional neural networks, de-convolutional neural networks, deep convolutional inverse graphics neural networks, generative adversarial neural networks, liquid state machine neural networks, extreme learning machine neural networks, echo state neural networks, deep residual neural networks, support vector machine neural networks, neural Turing machine neural networks, and / or holographic associative memory neural networks, or hybrids or combinations of the foregoing, or combinations with other expert systems, such as rule-based systems, model-based systems (including ones based on physical models, statistical models, flow-based models, biological models, biomimetic models, and the like).

[0241] In embodiments, exemplary neural networks have cells that are assigned functions and requirements. In embodiments, the various neural net examples may include back-fed data / sensor cells, data / sensor cells, noisy input cells, and hidden cells. The neural net components also include probabilistic hidden cells, spiking hidden cells, output cells, match input / output cells, recurrent cells, memory cells, different memory cells, kernels, and convolution or pool cells.

[0242] In embodiments, an exemplary perceptron neural network may connect to, integrate with, or interface with the platform 100. The platform may also be associated with further neural net systems, such as a feed-forward neural network, a radial basis neural network, a deep feed-forward neural network, a recurrent neural network, a long / short- term neural network, and a gated recurrent neural network. The platform may also be associated with further neural net systems such as an auto encoder neural network, a variational neural network, a denoising neural network, a sparse neural network, a Markov chain neural network, and a Hop field network neural network. The platform may further be associated with additional neural net systems such as a Boltzmann machine neural network, a restricted BoltzmannDocket: 16606-12POA machine neural network, a deep belief neural network, a deep convolutional neural network, a deconvolutional neural network, and a deep convolutional inverse graphics neural network. The platform may also be associated with further neural net systems such as a generative adversarial neural network, a liquid state machine neural network, an extreme learning machine neural network, an echo state neural network, a deep residual neural network, a Kohonen neural network, a support vector machine neural network, and a neural Turing machine neural network.

[0243] The foregoing neural networks may have a variety of nodes or neurons, which may perform a variety of functions on inputs, such as inputs received from sensors or other data sources, including other nodes. Functions may involve weights, features, feature vectors, and the like. Neurons may include perceptrons, neurons that mimic biological functions (such as those of the human senses of touch, vision, taste, hearing, and smell), and the like. Continuous neurons, such as with sigmoidal activation, may be used in the context of various forms of neural networks, such as where back propagation is involved.

[0244] In many embodiments, an expert system or neural network may be trained, such as by a human operator or supervisor, or based on a data set, model, or the like. Training may include presenting the neural network with one or more training data sets that represent values, such as sensor data, event data, parameter data, and other types of data (including the many types described throughout this disclosure), as well as one or more indicators of an outcome, such as an outcome of a process, an outcome of a calculation, an outcome of an event, an outcome of an activity, or the like. Training may include training in optimization, such as training a neural network to optimize one or more systems based on one or more optimization approaches, such as Bayesian approaches, parametric Bayes classifier approaches, k-nearest-neighbor classifier approaches, iterative approaches, interpolation approaches, Pareto optimization approaches, algorithmic approaches, and the like. Feedback may be provided in a process of variation and selection, such as with a genetic algorithm that evolves one or more solutions based on feedback through a series of rounds.

[0245] In embodiments, a plurality of neural networks may be deployed in a cloud platform that receives data streams and other inputs collected (such as by mobile data collectors) in one or more transactional environments and transmitted to the cloud platform over one or more networks, including using network coding to provide efficient transmission. In the cloud platform, optionally using massively parallel computational capability, a plurality of different neural networks of various types (including modular forms, structure-adaptive forms, hybrids, and the like) may be used to undertake prediction, classification, control functions, and provide other outputs as described in connection with expert systems disclosed throughout this disclosure. The different neural networks may be structured to compete with each other (optionally including use evolutionary algorithms, genetic algorithms, or the like), such that an appropriate type of neural network, with appropriate input sets, weights, node types and functions, and the like, may be selected, such as by an expert system, for a specific task involved in a given context, workflow, environment process, system, or the like.

[0246] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a feed forward neural network, which moves information in one direction, such as from a data input, like a data source related to at least one resource or parameter related to a transactional environment, such as any of the data sources mentioned throughout this disclosure, through a series of neurons or nodes, to an output. Data may move from the input nodes to the output nodes, optionally passing through one or more hidden nodes, without loops. In embodiments, feed-forward neural networks may be constructed with various types of units, such as binary McCulloch-Pitts neurons, the simplest of which is a perceptron.Docket: 16606-12POA

[0247] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a capsule neural network, such as for prediction, classification, or control functions with respect to a transactional environment, such as relating to one or more of the machines and automated systems described throughout this disclosure.

[0248] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a radial basis function (RBF) neural network, which may be preferred in some situations involving interpolation in a multi-dimensional space (such as where interpolation is helpful in optimizing a multi-dimensional function, such as for optimizing a data marketplace as described here, optimizing the efficiency or output of a power generation system, a factory system, or the like, or other situation involving multiple dimensions. In embodiments, each neuron in the RBF neural network stores an example from a training set as a “prototype.” Linearity involved in the functioning of this neural network offers RBF the advantage of not typically suffering from problems with local minima or maxima.

[0249] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a radial basis function (RBF) neural network, such as one that employs a distance criterion with respect to a center (e.g., a Gaussian function). A radial basis function may be applied as a replacement for a hidden layer, such as a sigmoidal hidden layer transfer, in a multi-layer perceptron. An RBF network may have two layers, such as where an input is mapped onto each RBF in a hidden layer. In embodiments, an output layer may comprise a linear combination of hidden layer values representing, for example, a mean predicted output. The output layer value may provide an output that is the same as or similar to that of a regression model in statistics. In classification problems, the output layer may be a sigmoid function of a linear combination of hidden layer values, representing a posterior probability. Performance in both cases is often improved by shrinkage techniques, such as ridge regression in classical statistics. This corresponds to a prior belief in small parameter values (and therefore smooth output functions) in a Bayesian framework. RBF networks may avoid local minima, because the only parameters that are adjusted in the learning process are the linear mapping from the hidden layer to the output layer. Linearity ensures that the error surface is quadratic and therefore has a single minimum. In regression problems, this may be found in one matrix operation. In classification problems, the fixed non-linearity introduced by the sigmoid output junction may be handled using an iteratively re-weighted least squares function or the like. RBF networks may use kernel methods such as support vector machines (SVM) and Gaussian processes (where the RBF is the kernel function). A non-linear kernel function may be used to project the input data into a space where the learning problem may be solved using a linear model.

[0250] In embodiments, an RBF neural network may include an input layer, a hidden layer, and a summation layer. In the input layer, one neuron appears in the input layer for each predictor variable. In the case of categorical variables, N-l neurons are used, where N is the number of categories. The input neurons may, in embodiments, standardize the value ranges by subtracting the median and dividing by the interquartile range. The input neurons may then feed the values to each of the neurons in the hidden layer. In the hidden layer, a variable number of neurons may be used (determined by the training process). Each neuron may consist of a radial basis function that is centered on a point with as many dimensions as the number of predictor variables. The spread (e.g., radius) of the RBF junction may be different for each dimension. The centers and spreads may be determined by training. When presented with the vector of input values from the input layer, a hidden neuron may compute a Euclidean distance of the test case from the neuron’s center point and then apply the 1TB F kernel function to this distance, such as using the spread values. TheDocket: 16606-12POA resulting value may then be passed to the summation layer. In the summation layer, the value coming out of a neuron in the hidden layer may be multiplied by a weight associated with the neuron and may add to the weighted values of other neurons. This sum becomes the output. For classification problems, one output is produced (with a separate set of weights and summation units) for each target category. The value output for a category is the probability that the case being evaluated has that category. In training of an RBF, various parameters may be determined, such as the number of neurons in a hidden layer, the coordinates of the center of each hidden-layer function, the spread of each function in each dimension, and the weights applied to outputs as they pass to the summation layer. Training may be used by clustering algorithms (such as k-means clustering), by evolutionary approaches, and the like.

[0251] In embodiments, a recurrent neural network may have a time-varying, real- valued (more than just zero or one) activation (output). Each connection may have a modifiable real-valued weight. Some of the nodes are called labeled nodes, some output nodes, and others hidden nodes. For supervised learning in discrete time settings, training sequences of real-valued input vectors may become sequences of activations of the input nodes, one input vector at a time. At each time step, each non-input unit may compute its current activation as a nonlinear function of the weighted sum of the activations of all units from which it receives connections. The system may explicitly activate (independent of incoming signals) some output units at certain time steps.

[0252] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a self-organizing neural network, such as a Kohonen self-organizing neural network, such as for visualization of views of data, such as low-dimensional views of high-dimensional data. The self-organizing neural network may apply competitive learning to a set of input data, such as from one or more sensors or other data inputs from or associated with a transactional environment, including any machine or component that relates to the transactional environment. In embodiments, the self-organizing neural network may be used to identity structures in data, such as unlabeled data, such as in data sensed from a range of data sources, or sensors in or about a transactional environment, where sources of the data are unknown (such as where events may be coming from any of a range of unknown sources). The self-organizing neural network may organize structures or patterns in the data, such that they may be recognized, analyzed, and labeled, such as identifying market behavior structures as corresponding to other events and signals.

[0253] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a recurrent neural network, which may allow for a bi-directional flow of data, such as where connected units (e.g., neurons or nodes) form a directed cycle. Such a network may be used to model or exhibit dynamic temporal behavior, such as involved in dynamic systems, such as a wide variety of the automation systems, machines and devices described throughout this disclosure, such as an automated agent interacting with a marketplace for purposes of collecting data, testing spot market transactions, execution transactions, and the like, where dynamic system behavior involves complex interactions that a user may desire to understand, predict, control and / or optimize. For example, the recurrent neural network may be used to anticipate the state of a market, such as one involving a dynamic process or action, such as a change in state of a resource that is traded in or that enables a marketplace of transactional environment. In embodiments, the recurrent neural network may use internal memory to process a sequence of inputs, such as from other nodes and / or from sensors and other data inputs from or about the transactional environment, of the various types described herein. In embodiments, the recurrent neural network may also be used for pattern recognition, such as for recognizing a machine, component, agent, or other item based on a behavioral signature, a profile, a set of feature vectors (such as in an audio file or image), or the like. In a non-limiting example,Docket: 16606-12POA a recurrent neural network may recognize a shift in an operational mode of a marketplace or machine by learning to classify the shift from a training data set consisting of a stream of data from one or more data sources of sensors applied to or about one or more resources.

[0254] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a modular neural network, which may comprise a series of independent neural networks (such as ones of various types described herein) that are moderated by an intermediary. Each of the independent neural networks in the modular neural network may work with separate inputs, accomplishing subtasks that make up the task the modular network as a whole is intended to perform. For example, a modular neural network may comprise a recurrent neural network for pattern recognition, such as to recognize what type of machine or system is being sensed by one or more sensors that are provided as input channels to the modular network, and an RBF neural network for optimizing the behavior of the machine or system once understood. The intermediary may accept inputs of each of the individual neural networks, process them, and create output for the modular neural network, such as an appropriate control parameter, a prediction of state, or the like.

[0255] Combinations among any of the pairs, triplets, or larger combinations of the various neural network types described herein are encompassed by the present disclosure. This may include combinations where an expert system uses one neural network for recognizing a pattern (e.g., a pattern indicating a problem or fault condition) and a different neural network for self-organizing an activity or workflow based on the recognized pattern (such as providing an output governing autonomous control of a system in response to the recognized condition or pattern). This may also include combinations where an expert system uses one neural network for classifying an item (e.g., identifying a machine, a component, or an operational mode) and a different neural network for predicting a state of the item (e.g., a fault state, an operational state, an anticipated state, a maintenance state, or the like). Modular neural networks may also include situations where an expert system uses one neural network for determining a state or context (such as a state of a machine, a process, a workflow, a marketplace, a storage system, a network, a data collector, or the like) and a different neural network for self-organizing a process involving the state or context (e.g., a data storage process, a network coding process, a network selection process, a data marketplace process, a power generation process, a manufacturing process, a refining process, a digging process, a boring process, or other process described herein).

[0256] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a physical neural network where one or more hardware elements is used to perform or simulate neural behavior. In embodiments, one or more hardware neurons may be configured to stream voltage values, current values, or the like that represent sensor data, such as to calculate information from analog sensor inputs representing energy consumption, energy production, or the like, such as by one or more machines providing energy or consuming energy for one or more transactions. One or more hardware nodes may be configured to stream output data resulting from the activity of the neural net. Hardware nodes, which may comprise one or more chips, microprocessors, integrated circuits, programmable logic controllers, application- specific integrated circuits, field-programmable gate arrays, or the like, may be provided to optimize the machine that is producing or consuming energy, or to optimize another parameter of some part of a neural net of any of the types described herein. Hardware nodes may include hardware for acceleration of calculations (such as dedicated processors for performing basic or more sophisticated calculations on input data to provide outputs, dedicated processors for filtering or compressing data, dedicated processors for de-compressing data, dedicated processors for compression of specific file or data types (e.g., for handling image data, video streams, acoustic signals, thermal images, heat maps, or the like), and the like. A physicalDocket: 16606-12POA neural network may be embodied in a data collector, including one that may be reconfigured by switching or routing inputs in varying configurations, such as to provide different neural net configurations within the data collector for handling different types of inputs (with the switching and configuration optionally under control of an expert system, which may include a software-based neural net located on the data collector or remotely). A physical, or at least partially physical, neural network may include physical hardware nodes located in a storage system, such as for storing data within a machine, a data storage system, a distributed ledger, a mobile device, a server, a cloud resource, or in a transactional environment, such as for accelerating input / output functions to one or more storage elements that supply data to or take data from the neural net. A physical, or at least partially physical, neural network may include physical hardware nodes located in a network, such as for transmitting data within, to or from an industrial environment, such as for accelerating input / output functions to one or more network nodes in the network, accelerating relay functions, or the like. In embodiments of a physical neural network, an electrically adjustable resistance material may be used for emulating the function of a neural synapse. In embodiments, the physical hardware emulates the neurons, and software emulates the neural network between the neurons. In embodiments, neural networks complement conventional algorithmic computers. They are versatile and may be trained to perform appropriate functions without the need for any instructions, such as classification functions, optimization functions, pattern recognition functions, control functions, selection functions, evolution functions, and others.

[0257] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a multilayered feed-forward neural network, such as for complex pattern classification of one or more items, phenomena, modes, states, or the like. In embodiments, a multilayered feed forward neural network may be trained by an optimization technique, such as a genetic algorithm, such as to explore a large and complex space of options to find an optimum, or near-optimum, global solution. For example, one or more genetic algorithms may be used to train a multilayered feed forward neural network to classify complex phenomena, such as to recognize complex operational modes of machines, such as modes involving complex interactions among machines (including interference effects, resonance effects, and the like), modes involving non-linear phenomena, modes involving critical faults, such as where multiple, simultaneous faults occur, making root cause analysis difficult, and others. In embodiments, a multilayered feed forward neural network may be used to classify results from monitoring of a marketplace, such as monitoring systems, such as automated agents, that operate within the marketplace, as well as monitoring resources that enable the marketplace, such as computing, networking, energy, data storage, energy storage, and other resources.

[0258] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a feed-forward, back-propagation multi-layer perceptron (MLP) neural network, such as for handling one or more remote sensing applications, such as for taking inputs from sensors distributed throughout various transactional environments. In embodiments, the MLP neural network may be used for classification of transactional environments and resource environments, such as spot markets, forward markets, energy markets, renewable energy credit (REC) markets, networking markets, advertising markets, spectrum markets, ticketing markets, rewards markets, compute markets, and others mentioned throughout this disclosure, as well as physical resources and environments that produce them, such as energy resources (including renewable energy environments, mining environments, exploration environments, drilling environments, and the like, including classification of geological structures (including underground features and above ground features), classification of materials (including fluids, minerals, metals, and the like), and other problems. This may include fuzzy classification.Docket: 16606-12POA

[0259] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a structure-adaptive neural network, where the structure of a neural network is adapted, such as based on a rule, a sensed condition, a contextual parameter, or the like. For example, if a neural network does not converge on a solution, such as classifying an item or arriving at a prediction, when acting on a set of inputs after some amount of training, the neural network may be modified, such as from a feed forward neural network to a recurrent neural network, such as by switching data paths between some subset of nodes from unidirectional to bi-directional data paths. The structure adaptation may occur under control of an expert system, such as to trigger adaptation upon occurrence of a trigger, rule, or event, such as recognizing occurrence of a threshold (such as an absence of a convergence to a solution within a given amount of time) or recognizing a phenomenon as requiring different or additional structure (such as recognizing that a system is varying dynamically or in a non-linear fashion). In one nonlimiting example, an expert system may switch from a simple neural network structure like a feed forward neural network to a more complex neural network structure like a recurrent neural network, a convolutional neural network, or the like upon receiving an indication that a continuously variable transmission is being used to drive a generator, turbine, or the like in a system being analyzed.

[0260] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use an autoencoder, autoassociator or Diabolo neural network, which may be similar to a multilayer perceptron (MLP) neural network, such as where there may be an input layer, an output layer and one or more hidden layers connecting them. However, the output layer in the auto-encoder may have the same number of units as the input layer, where the purpose of the MLP neural network is to reconstruct its own inputs (rather than just emitting a target value). Therefore, the auto encoders may operate as an unsupervised learning model. An auto encoder may be used, for example, for unsupervised learning of efficient codings, such as for dimensionality reduction, for learning generative models of data, and the like. In embodiments, an auto-encoding neural network may be used to self-leam an efficient network coding for transmission of analog sensor data from a machine over one or more networks or of digital data from one or more data sources. In embodiments, an auto-encoding neural network may be used to self-leam an efficient storage approach for storage of streams of data.

[0261] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a probabilistic neural network (PNN), which, in embodiments, may comprise a multi-layer (e.g., four-layer) feed forward neural network, where layers may include input layers, hidden layers, pattem / summation layers and an output layer. In an embodiment of a PNN algorithm, a parent probability distribution function (PDF) of each class may be approximated, such as by a Parzen window and / or a non-parametric function. Then, using the PDF of each class, the class probability of a new input is estimated, and Bayes’ rule may be employed, such as to allocate it to the class with the highest posterior probability. A PNN may embody a Bayesian network and may use a statistical algorithm or analytic technique, such as Kernel Fisher discriminant analysis technique. The PNN may be used for classification and pattern recognition in any of a wide range of embodiments disclosed herein. In one non-limiting example, a probabilistic neural network may be used to predict a fault condition of an engine based on collection of data inputs from sensors and instruments for the engine.

[0262] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a time delay neural network (TDNN), which may comprise a feed forward architecture for sequential data that recognizes features independent of sequence position. In embodiments, to account for time shifts in data, delays are added to one or more inputs, or between one or more nodes, so that multiple data points (fromDocket: 16606-12POA distinct points in time) are analyzed together. A time delay neural network may form part of a larger pattern recognition system, such as using a perceptron network. In embodiments, a TDNN may be trained with supervised learning, such as where connection weights are trained with back propagation or under feedback. In embodiments, a TDNN may be used to process sensor data from distinct streams, such as a stream of velocity data, a stream of acceleration data, a stream of temperature data, a stream of pressure data, and the like, where time delays are used to align the data streams in time, such as to help understand patterns that involve understanding of the various streams (e.g., changes in price patterns in spot or forward markets).

[0263] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a convolutional neural network (referred to in some cases as a CNN, a ConvNet, a shift invariant neural network, or a space invariant neural network), wherein the units are connected in a pattern similar to the visual cortex of the human brain. Neurons may respond to stimuli in a restricted region of space, referred to as a receptive field. Receptive fields may partially overlap, such that they collectively cover the entire (e.g., visual) field. Node responses may be calculated mathematically, such as by a convolution operation, such as using multilayer perceptrons that use minimal preprocessing. A convolutional neural network may be used for recognition within images and video streams, such as for recognizing a type of machine in a large environment using a camera system disposed on a mobile data collector, such as on a drone or mobile robot. In embodiments, a convolutional neural network may be used to provide a recommendation based on data inputs, including sensor inputs and other contextual information, such as recommending a route for a mobile data collector. In embodiments, a convolutional neural network may be used for processing inputs, such as for natural language processing of instructions provided by one or more parties involved in a workflow in an environment. In embodiments, a convolutional neural network may be deployed with a large number of neurons (e.g., 100,000, 500,000 or more), with multiple (e.g., 4, 5, 6 or more) layers, and with many (e.g., millions) of parameters. A convolutional neural net may use one or more convolutional nets.

[0264] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a regulatory feedback network, such as for recognizing emergent phenomena (such as new types of behavior not previously understood in a transactional environment).

[0265] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a self-organizing map (SOM), involving unsupervised learning. A set of neurons may learn to map points in an input space to coordinates in an output space. The input space may have different dimensions and topology from the output space, and the SOM may preserve these while mapping phenomena into groups.

[0266] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a learning vector quantization neural net (LVQ). Prototypical representatives of the classes may parameterize, together with an appropriate distance measure, in a distance-based classification scheme.

[0267] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use an echo state network (ESN), which may comprise a recurrent neural network with a sparsely connected, random hidden layer. The weights of output neurons may be changed (e.g., the weights may be trained based on feedback). In embodiments, an ESN may be used to handle time series patterns, such as, in an example, recognizing a pattern of events associated with a market, such as the pattern of price changes in response to stimuli.

[0268] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a Bi-directional, recurrent neural network (BRNN), such as using a finite sequence of values (e.g., voltage values from a sensor) to predict or label each element of the sequence based on both the past and the futureDocket: 16606-12POA context of the element. This may be done by adding the outputs of two RNNs, such as one processing the sequence from left to right, the other one from right to left. The combined outputs are the predictions of target signals, such as ones provided by a teacher or supervisor. A bi-directional RNN may be combined with a long short-term memory RNN.

[0269] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a hierarchical RNN that connects elements in various ways to decompose hierarchical behavior, such as into useful subprograms. In embodiments, a hierarchical RNN may be used to manage one or more hierarchical templates for data collection in a transactional environment.

[0270] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a stochastic neural network, which may introduce random variations into the network. Such random variations may be viewed as a form of statistical sampling, such as Monte Carlo sampling.

[0271] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a genetic scale recurrent neural network. In such embodiments, an RNN (often an LSTM) is used where a series is decomposed into a number of scales where every scale informs the primary length between two consecutive points. A first order scale consists of a normal RNN, a second order consists of all points separated by two indices and so on. The Nth order RNN connects the first and last node. The outputs from all the various scales may be treated as a committee of members, and the associated scores may be used genetically for the next iteration.

[0272] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a committee of machines (CoM), comprising a collection of different neural networks that together “vote” on a given example. Because neural networks may suffer from local minima, starting with the same architecture and training, but using randomly different initial weights often gives different results. A CoM tends to stabilize the result.

[0273] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use an associative neural network (ASNN), such as involving an extension of a committee of machines that combines multiple feed forward neural networks and a k-nearest neighbor technique. It may use the correlation between ensemble responses as a measure of distance amid the analyzed cases for the kNN. This corrects the bias of the neural network ensemble. An associative neural network may have a memory that may coincide with a training set. If new data become available, the network instantly improves its predictive ability and provides data approximation (self-leams) without retraining. Another important feature of ASNN is the possibility to interpret neural network results by analysis of correlations between data cases in the space of models.

[0274] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use an instantaneously trained neural network (ITNN), where the weights of the hidden and the output layers are mapped directly from training vector data.

[0275] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a spiking neural network, which may explicitly consider the timing of inputs. The network input and output may be represented as a series of spikes (such as a delta function or more complex shapes). SNNs may process information in the time domain (e.g., signals that vary over time, such as signals involving dynamic behavior of markets or transactional environments). They are often implemented as recurrent networks.

[0276] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a dynamic neural network that addresses nonlinear multivariate behavior and includes learning ofDocket: 16606-12POA time-dependent behavior, such as transient phenomena and delay effects. Transients may include behavior of shifting market variables, such as prices, available quantities, available counterparties, and the like.

[0277] In embodiments, cascade correlation may be used as an architecture and supervised learning algorithm, supplementing adjustment of the weights in a network of fixed topology. Cascade-correlation may begin with a minimal network, then automatically trains, and adds new hidden units one by one, creating a multi-layer structure. Once a new hidden unit has been added to the network, its input-side weights may be frozen. This unit then becomes a permanent feature-detector in the network, available for producing outputs or for creating other, more complex feature detectors. The cascade-correlation architecture may learn quickly, determine its own size and topology, and retain the structures it has built even if the training set changes and requires no back-propagation.

[0278] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a neuro-fuzzy network, such as involving a fuzzy inference system in the body of an artificial neural network. Depending on the type, several layers may simulate the processes involved in a fuzzy inference, such as fuzzification, inference, aggregation and defuzzification. Embedding a fuzzy system in a general structure of a neural net as the benefit of using available training methods to find the parameters of a fuzzy system.

[0279] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a compositional pattern-producing network (CPPN), such as a variation of an associative neural network (ANN) that differs the set of activation functions and how they are applied. While typical ANNs often contain only sigmoid functions (and sometimes Gaussian functions), CPPNs may include both types of functions and many others. Furthermore, CPPNs may be applied across the entire space of possible inputs, so that they may represent a complete image. Since they are compositions of functions, CPPNs in effect encode images at infinite resolution and may be sampled for a particular display at whatever resolution is optimal.

[0280] This type of network may add new patterns without re-training. In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a one-shot associative memory network, such as by creating a specific memory structure, which assigns each new pattern to an orthogonal plane using adjacently connected hierarchical arrays.

[0281] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a hierarchical temporal memory (HTM) neural network, such as involving the structural and algorithmic properties of the neocortex. HTM may use a biomimetic model based on memory-prediction theory. HTM may be used to discover and infer the high-level causes of observed input patterns and sequences.Machine Learning System

[0282] In embodiments, the machine learning system may train models, such as predictive models (e.g., various types of neural networks, regression based models, and other machine-learned models). In embodiments, training can be supervised, semi-supervised, or unsupervised. In embodiments, training can be done using training data, which may be collected or generated for training purposes.

[0283] A facility output model (or prediction model) may be a model that receive facility attributes and outputs one or more predictions regarding the production or other output of a facility. Examples of predictions may be the amount of energy a facility will produce, the amount of processing the facility will undertake, the amount of data a network will be able to transfer, the amount of data that can be stored, the price of a component, service or the like (such as supplied to or provided by a facility), a profit generated by accomplishing a given tasks, the cost entailed in performing an action, and the like. In each case, the machine learning system optionally trains a model based on training data. InDocket: 16606-12POA embodiments, the machine learning system may receive vectors containing facility attributes (e.g., facility type, facility capability, obj ectives sought, constraints or rules that apply to utilization of resources or the facility, or the like), person attributes (e.g., role, components managed, and the like), and outcomes (e.g., energy produced, computing tasks completed, and financial results, among many others). Each vector corresponds to a respective outcome and the attributes of the respective facility and respective actions that led to the outcome. The machine learning system takes in the vectors and generates predictive model based thereon. In embodiments, the machine learning system may store the predictive models in the model datastore.

[0284] In embodiments, training can also be done based on feedback received by the system, which is also referred to as “reinforcement learning.” In embodiments, the machine learning system may receive a set of circumstances that led to a prediction (e.g., attributes of facility, attributes of a model, and the like) and an outcome related to the facility and may update the model according to the feedback.

[0285] In embodiments, training may be provided from a training data set that is created by observing actions of a set of humans, such as facility managers managing facilities that have various capabilities and that are involved in various contexts and situations. This may include use of robotic process automation to learn on a training data set of interactions of humans with interfaces, such as graphical user interfaces, of one or more computer programs, such as dashboards, control systems, and other systems that are used to manage an energy and compute management facility. Artificial Intelligence (Al) Systems

[0286] In embodiments, the Al system leverages the predictive models to make predictions regarding facilities. Examples of predictions include ones related to inputs to a facility (e.g., available energy, cost of energy, cost of compute resources, networking capacity and the like, as well as various market information, such as pricing information for end use markets), ones related to components or systems of a facility (including performance predictions, maintenance predictions, uptime / downtime predictions, capacity predictions and the like), ones related to functions or workflows of the facility (such as ones that involved conditions or states that may result in following one or more distinct possible paths within a workflow, a process, or the like), ones related to outputs of the facility, and others. In embodiments, the Al system receives a facility identifier. In response to the facility identifier, the Al system may retrieve attributes corresponding to the facility. In some embodiments, the Al system may obtain the facility attributes from a graph. Additionally or alternatively, the Al system may obtain the facility attributes from a facility record corresponding to the facility identifier, and the person attributes from a person record corresponding to the person identifier.

[0287] Examples of additional attributes that can be used to make predictions about a facility or a related process of system include: related facility information; owner goals (including financial goals); client goals; and many more additional or alternative attributes. In embodiments, the Al system may output scores for each possible prediction, where each prediction corresponds to a possible outcome. For example, in using a prediction model used to determine a likelihood that a hydroelectric source for a facility will produce 5 MW of power, the prediction model can output a score for a “will produce” outcome and a score for a “will not produce” outcome. The Al system may then select the outcome with the highest score as the prediction. Alternatively, the Al system may output the respective scores to a requesting system.Intelligence Services System

[0288] Fig. 3 illustrates an example intelligence system 300 (also referred to as “intelligence services,” an “intelligence services system,” or an “intelligence system”) according to some embodiments of the present disclosure. InDocket: 16606-12POA embodiments, the intelligence system 300 provides a framework for providing intelligence services to one or more intelligence service clients 336. In some embodiments, the intelligence system 300 framework may be adapted to be at least partially replicated in respective intelligence clients 336 (e.g., an enterprise access layer, a wallet system, a market orchestration system, a digital lending system, an asset-backed tokenization system, and / or the like). In these embodiments, an individual client 336 may include some or all of the capabilities of the intelligence system 300, whereby the intelligence system 300 is adapted for the specific functions performed by the subsystems of the intelligence client. Additionally or alternatively, in some embodiments, the intelligence system 300 may be implemented as a set of microservices, such that different intelligence clients 336 may leverage the intelligence system 300 via one or more APIs exposed to the intelligence clients. In these embodiments, the intelligence system 300 may be configured to perform various types of intelligence services that may be adapted for different intelligence clients 336. In either of these configurations, an intelligence service client 336 may provide an intelligence request to the intelligence system 300, whereby the request is to perform a specific intelligence task (e.g., a decision, a recommendation, a report, an instruction, a classification, a prediction, a training action, an NLP request, or the like). In response, the intelligence system 300 executes the requested intelligence task and returns a response to the intelligence service client 336. Additionally or alternatively, in some embodiments, the intelligence system 300 may be implemented using one or more specialized chips that are configured to provide Al assisted microservices such as image processing, diagnostics, location and orientation, chemical analysis, data processing, and so forth. Examples of Al-enabled chips are discussed elsewhere in the disclosure.

[0289] In embodiments, an intelligence system 300 may include an intelligence service controller 302 and artificial intelligence (Al) modules 304. In embodiments, an artificial intelligence system 300 receives an intelligence request from an intelligence service client 336 and any required data to process the request from the intelligence service client 336. In response to the request and the specific data, one or more implicated artificial intelligence modules 304 perform the intelligence task and output an “intelligence response”. Examples of intelligence modules 304 responses may include a decision (e.g., a control instruction, a proposed action, machine-generated text, and / or the like), a prediction (e.g., a predicted meaning of a text snippet, a predicted outcome associated with a proposed action, a predicted fault condition, and / or the like), a classification (e.g., a classification of an object in an image, a classification of a spoken utterance, a classified fault condition based on sensor data, and / or the like), and / or other suitable outputs of an artificial intelligence system.Artificial Intelligence Modules

[0290] In embodiments, artificial intelligence modules 304 may include an ML module 312, a rules-based module 328, an analytics module 318, an RPA module 316, a digital twin module 320, a machine vision module 322, an NLP module 324, and / or a neural network module 314. It is appreciated that the foregoing are non-limiting examples of artificial intelligence modules, and that some of the modules may be included or leveraged by other artificial intelligence modules. For example, the NLP module 324 and the machine vision module 322 may leverage different neural networks that are part of the neural network module 314 in performance of their respective functions.

[0291] It is further noted that in some scenarios, artificial intelligence modules 304 themselves may also be intelligence clients 336. For example, a rules-based module 328 for intelligence may request an intelligence task from an ML module 312 or a neural network module 314, such as requesting a classification of an object appearing in a video and / or a motion of the object. In this example, the rules-based module 328 for intelligence may be an intelligence service client 336 that uses the classification to determine whether to take a specified action. In another example, aDocket: 16606-12POA machine vision module 322 may request a digital twin of a specified environment from a digital twin module 320, such that the ML module 312 may request specific data from the digital twin as features to train a machine-learned model that is trained for a specific environment.

[0292] In embodiments, an intelligence task may require specific types of data to respond to the request. For example, a machine vision task requires one or more images (and potentially other data) to classify objects appearing in an image or set of images, to determine features within the set of images (such as locations of items, presence of faces, symbols or instructions, expressions, parameters of motion, changes in status, and many others), and the like. In another example, an NLP task requires audio of speech and / or text data (and potentially other data) to determine a meaning or other element of the speech and / or text. In yet another example, an Al-based control task (e.g., a decision on movement of a robot) may require environment data (e.g., maps, coordinates of known obstacles, images, and / or the like) and / or a motion plan to make a decision as to how to control the motion of a robot. In a platform-level example, an analytics-based reporting task may require data from a number of different databases to generate a report. Thus, in embodiments, tasks that can be performed by an intelligence system 300 may require, or benefit from, specific intelligence service inputs 332. In some embodiments, an intelligence system 300 may be configured to receive and / or request specific data from the intelligence service inputs 332 to perform a respective intelligence task. Additionally or alternatively, the requesting intelligence service client 336 may provide the specific data in the request. For instance, the intelligence system 300 may expose one or more APIs to the intelligence clients 336, whereby a requesting client 336 provides the specific data in the request via the API. Examples of intelligence service inputs may include, but are not limited to, sensors that provide sensor data, video streams, audio streams, databases, data feeds, human input, and / or other suitable data.

[0293] In embodiments, intelligence modules 304 includes and provides access to an ML module 312 that may be integrated into or be accessed by one or more intelligence clients 336. In embodiments, the ML module 312 may provide machine-based learning capabilities, features, functions, and algorithms for use by an intelligence service client 336, such as training ML models, leveraging ML models, reinforcing ML models, performing various clustering techniques, feature extraction, and / or the like. In an example, a machine learning module 312 may provide machine learning computing, data storage, and feedback infrastructure to a simulation system (e.g., as described above). The machine learning module 312 may also operate cooperatively with other modules, such as the rules-based module 328, the machine vision module 322, the RPA module 316, and / or the like.

[0294] The machine learning module 312 may define one or more machine learning models for performing analytics, simulation, decision making, and predictive analytics related to data processing, data analysis, simulation creation, and simulation analysis of one or more components or subsystems of an intelligence service client 336. In embodiments, the machine learning models are algorithms and / or statistical models that perform specific tasks without using explicit instructions, relying instead on patterns and inference. The machine learning models build one or more mathematical models based on training data to make predictions and / or decisions without being explicitly programmed to perform the specific tasks. In example implementations, machine learning models may perform classification, prediction, regression, clustering, anomaly detection, recommendation generation, and / or other tasks.

[0295] In embodiments, the machine learning models may perform various types of classification based on the input data. Classification is a predictive modeling problem where a class label is predicted for a given example of input data. For example, machine learning models can perform binary classification, multi-class or multi-label classification. In embodiments, the machine-learning model may output “confidence scores” that are indicative of a respectiveDocket: 16606-12POA confidence associated with the classification of the input into the respective class. In embodiments, the confidence scores can be compared to one or more thresholds to render a discrete categorical prediction. In embodiments, only a certain number of classes (e.g., one) with the relatively largest confidence scores can be selected to render a discrete categorical prediction.

[0296] In embodiments, machine learning models may output a probabilistic classification. For example, machine learning models may predict, given a sample input, a probability distribution over a set of classes. Thus, rather than outputting only the most likely class to which the sample input should belong, machine learning models can output, for each class, a probability that the sample input belongs to such class. In embodiments, the probability distribution over all possible classes can sum to one. In embodiments, a Softmax function, or other type of function or layer can be used to turn a set of real values respectively associated with the possible classes to a set of real values in the range (0, 1) that sum to one. In embodiments, the probabilities provided by the probability distribution can be compared to one or more thresholds to render a discrete categorical prediction. In embodiments, only a certain number of classes (e.g., one) with the relatively largest predicted probability can be selected to render a discrete categorical prediction.

[0297] In embodiments, machine learning models can perform regression to provide output data in the form of a continuous numeric value. As examples, machine learning models can perform linear regression, polynomial regression, or nonlinear regression. As described, in embodiments, a Softmax function or other function or layer can be used to squash a set of real values respectively associated with a two or more possible classes to a set of real values in the range (0, 1) that sum to one. For example, machine learning models can perform linear regression, polynomial regression, or nonlinear regression. As examples, machine learning models can perform simple regression or multiple regression. As described above, in some implementations, a Softmax function or other function or layer can be used to squash a set of real values respectively associated with a two or more possible classes to a set of real values in the range (0, 1) that sum to one.

[0298] In embodiments, machine learning models may perform various types of clustering. For example, machine learning models may identify one or more previously -defined clusters to which the input data most likely corresponds. In some implementations in which machine learning models performs clustering, machine learning models can be trained using unsupervised learning techniques.

[0299] In embodiments, machine learning models may perform anomaly detection or outlier detection. For example, machine learning models can identify input data that does not conform to an expected pattern or other characteristic (e.g., as previously observed from previous input data). As examples, the anomaly detection can be used for fraud detection or system failure detection.

[0300] In some implementations, machine learning models can provide output data in the form of one or more recommendations. For example, machine learning models can be included in a recommendation system or engine. As an example, given input data that describes previous outcomes for certain entities (e.g., a score, ranking, or rating indicative of an amount of success or enjoyment), machine learning models can output a suggestion or recommendation of one or more additional entities that, based on the previous outcomes, are expected to have a desired outcome

[0301] As described above, machine learning models can be or include one or more of various different types of machine-learned models. Examples of such different types of machine-learned models are provided below for illustration. One or more of the example models described below can be used (e.g., combined) to provide the output data in response to the input data. Additional models beyond the example models provided below can be used as well.Docket: 16606-12POA

[0302] In some implementations, machine learning models can be or include one or more classifier models such as, for example, linear classification models; quadratic classification models; etc. Machine learning models may be or include one or more regression models such as, for example, simple linear regression models; multiple linear regression models; logistic regression models; stepwise regression models; multivariate adaptive regression splines; locally estimated scatterplot smoothing models; etc.

[0303] In some examples, machine learning models can be or include one or more decision tree-based models such as, for example, classification and / or regression trees; chi-squared automatic interaction detection decision trees; decision stumps; conditional decision trees; etc.

[0304] Machine learning models may be or include one or more kernel machines. In some implementations, machine learning models can be or include one or more support vector machines. Machine learning models may be or include one or more instance-based learning models such as, for example, learning vector quantization models; self-organizing map models; locally weighted learning models; etc. In some implementations, machine learning models can be or include one or more nearest neighbor models such as, for example, k-nearest neighbor classifications models; k-nearest neighbors regression models; etc. Machine learning models can be or include one or more Bayesian models such as, for example, naive Bayes models; Gaussian naive Bayes models; multinomial naive Bayes models; averaged one- dependence estimators; Bayesian networks; Bayesian belief networks; hidden Markov models; etc.

[0305] Machine learning models may include one or more clustering models such as, for example, k-means clustering models; k-medians clustering models; expectation maximization models; hierarchical clustering models; etc.

[0306] In some implementations, machine learning models can perform one or more dimensionality reduction techniques such as, for example, principal component analysis; kernel principal component analysis; graph-based kernel principal component analysis; principal component regression; partial least squares regression; Sammon mapping; multidimensional scaling; projection pursuit; linear discriminant analysis; mixture discriminant analysis; quadratic discriminant analysis; generalized discriminant analysis; flexible discriminant analysis; autoencoding; etc.

[0307] In some implementations, machine learning models can perform or be subjected to one or more reinforcement learning techniques such as Markov decision processes; dynamic programming; Q functions or Q-leaming; value function approaches; deep Q-networks; dilferentiable neural computers; asynchronous advantage actor-critics; deterministic policy gradient; etc.

[0308] In embodiments, artificial intelligence modules 304 may include and / or provide access to a neural network module 314. In embodiments, the neural network module 314 is configured to train, deploy, and / or leverage artificial neural networks (or “neural networks”) on behalf of an intelligence service client 336. It is noted that in the description, the term machine learning model may include neural networks, and as such, the neural network module 314 may be part of the machine learning module 312. In embodiments, the neural network module 314 may be configured to train neural networks that may be used by the intelligence clients 336. Non-limiting examples of different types of neural networks may include any of the neural network types described throughout this disclosure and the documents incorporated herein by reference, including without limitation convolutional neural networks (CNN), deep convolutional neural networks (DCN), feed forward neural networks (including deep feed forward neural networks), recurrent neural networks (RNN) (including without limitation gated RNNs), long / short term memory (LTSM) neural networks, and the like, as well as hybrids or combinations of the above, such as deployed in series, in parallel, in acyclic (e.g., directed graph-based) flows, and / or in more complex flows that may include intermediate decision nodes, recursive loops, and the like, where a given type of neural network takes inputs from a data source or other neuralDocket: 16606-12POA network and provides outputs that are included within the input sets of another neural network until a flow is completed and a final output is provided. In embodiments, the neural network module 314 may be leveraged by other artificial intelligence modules 304, such as the machine vision module 322, the NLP module 324, the rules-based module 328, the digital twin module 320, and so on. Example applications of the neural network module 314 are described throughout the disclosure.

[0309] A neural network includes a group of connected nodes, which also can be referred to as neurons or perceptrons. A neural network can be organized into one or more layers. Neural networks that include multiple layers can be referred to as “deep” networks. A deep network can include an input layer, an output layer, and one or more hidden layers positioned between the input layer and the output layer. The nodes of the neural network can be connected or non- fully connected.

[0310] In embodiments, the neural networks can be or include one or more feed forward neural networks. In feed forward networks, the connections between nodes do not form a cycle. For example, each connection can connect a node from an earlier layer to a node from a later layer.

[0311] In embodiments, the neural networks can be or include one or more recurrent neural networks. In some instances, at least some of the nodes of a recurrent neural network can form a cycle. Recurrent neural networks can be especially useful for processing input data that is sequential in nature. In particular, in some instances, a recurrent neural network can pass or retain information from a previous portion of the input data sequence to a subsequent portion of the input data sequence through the use of recurrent or directed cyclical node connections.

[0312] In some examples, sequential input data can include time-series data (e.g., sensor data versus time or imagery captured at different times). For example, a recurrent neural network can analyze sensor data versus time to detect or predict a swipe direction, to perform handwriting recognition, etc. Sequential input data may include words in a sentence (e.g., for natural language processing, speech detection or processing, etc.); notes in a musical composition; sequential actions taken by a user (e.g., to detect or predict sequential application usage); sequential object states; etc. In some example embodiments, recurrent neural networks include long short-term (LSTM) recurrent neural networks; gated recurrent units; bi-direction recurrent neural networks; continuous time recurrent neural networks; neural history compressors; echo state networks; Elman networks; Jordan networks; recursive neural networks; Hopfield networks; fully recurrent networks; sequence-to-sequence configurations; etc.

[0313] In some examples, neural networks can be or include one or more non-recurrent sequence-to-sequence models based on self-attention, such as Transformer networks. Details of an exemplary transformer network can be found at http : / / papers. nips. cc / paper / 7181 -attention-is-all-you-need.pdf.

[0314] In embodiments, the neural networks can be or include one or more convolutional neural networks. In some instances, a convolutional neural network can include one or more convolutional layers that perform convolutions over input data using learned filters. Filters can also be referred to as kernels. Convolutional neural networks can be especially useful for vision problems such as when the input data includes imagery such as still images or video. However, convolutional neural networks can also be applied for natural language processing.

[0315] In embodiments, the neural networks can be or include one or more generative networks such as, for example, generative adversarial networks. Generative networks can be used to generate new data such as new images or other content.

[0316] In embodiments, the neural networks may be or include autoencoders. In some instances, the aim of an autoencoder is to learn a representation (e.g., a lower-dimensional encoding) for a set of data, typically for the purposeDocket: 16606-12POA of dimensionality reduction. For example, in some instances, an autoencoder can seek to encode the input data and then provide output data that reconstructs the input data from the encoding. Recently, the autoencoder concept has become more widely used for learning generative models of data. In some instances, the autoencoder can include additional losses beyond reconstructing the input data.

[0317] In embodiments, the neural networks may be or include one or more other forms of artificial neural networks such as, for example, deep Boltzmann machines; deep belief networks; stacked autoencoders; etc. Any of the neural networks described herein can be combined (e.g., stacked) to form more complex networks.

[0318] Fig. 4 illustrates an example neural network with multiple layers. Neural network 340 may include an input layer, a hidden layer, and an output layer with each layer comprising a plurality of nodes or neurons that respond to different combinations of inputs from the previous layers. The connections between the neurons have numeric weights that determine how much relative effect an input has on the output value of the node in question. Input layer may include a plurality of input nodes 342, 344, 346, 348 and 350 that may provide information from the outside world or input data (e.g., sensor data, image data, text data, audio data, etc.) to the neural network 340. The input data may be from different sources and may include library data xl, simulation data x2, user input data x3, training data x4 and outcome data x5. The input nodes 342, 344, 346, 348 and 350 may pass on the information to the next layer, and no computation may be performed by the input nodes. Hidden layers may include a plurality of nodes, such as nodes 352, 354, and 356. The nodes 352, 354, and 356 in the hidden layer may process the information from the input layer based on the weights of the connections between the input layer and the hidden layer and transfer information to the output layer. Output layers may include an output node 358 which processes information based on the weights of the connections between the hidden layer and the output layer and is responsible for computing and transferring information as an output 359 from the network to the outside world, such as recognizing certain objects or activities, or predicting a condition or an action.

[0319] In embodiments, a neural network 340 may include two or more hidden layers and may be referred to as a deep neural network. The layers are constructed so that the first layer detects a set of primitive patterns in the input (e.g., image) data, the second layer detects patterns of patterns and the third layer detects patterns of those patterns. In some embodiments, a node in the neural network 340 may have connections to all nodes in the immediately preceding layer and the immediate next layer. Thus, the layers may be referred to as fully-connected layers. In some embodiments, a node in the neural network 340 may have connections to only some of the nodes in the immediately preceding layer and the immediate next layer. Thus, the layers may be referred to as sparsely-connected layers. Each neuron in the neural network consists of a weighted linear combination of its inputs and the computation on each neural network layer may be described as a multiplication of an input matrix and a weight matrix. A bias matrix is then added to the resulting product matrix to account for the threshold of each neuron in the next level. Further, an activation function is applied to each resultant value, and the resulting values are placed in the matrix for the next layer. Thus, the output from a node i in the neural network may be represented as: yi= f ( xiwi+ bi)

[0320] where f is the activation function, £ xiwi is the weighted sum of input matrix and bi is the bias matrix.

[0321] The activation function determines the activity level or excitation level generated in the node as a result of an input signal of a particular size. The purpose of the activation function is to introduce non-linearity into the output of a neural network node because most real-world functions are non-linear and it is desirable that the neurons can learn these non-linear representations. Several activation functions may be used in an artificial neural network. One exampleDocket: 16606-12POA activation function is the sigmoid function a(x), which is a continuous S-shaped monotonically increasing function that asymptotically approaches fixed values as the input approaches plus or minus infinity. The sigmoid function o(x) takes a real-valued input and transforms it into a value between 0 and 1 : ff(x)=l / ( 1 +exp(-x)).

[0322] Another example activation function is the tanh function, which takes a real-valued input and transforms it into a value within the range of [-1 , 1 ] : tanh (x) =2a( 2x) — 1

[0323] A third example activation function is the rectified linear unit (ReLU) function. The ReLU function takes a real-valued input and thresholds it above zero (i.e., replacing negative values with zero): f(x)=max(0, x).

[0324] It will be apparent that the above activation functions are provided as examples and in various embodiments, neural network 340 may utilize a variety of activation functions including (but not limited to) identity, binary step, logistic, soft step, tan h, arctan, softsign, rectified linear unit (ReLU), leaky rectified linear unit, param eteric rectified linear unit, randomized leaky rectified linear unit, exponential linear unit, s-shaped rectified linear activation unit, adaptive piecewise linear, softplus, bent identity, softexponential, sinusoid, sine, gaussian, softmax, maxout, and / or a combination of activation functions.

[0325] In the example shown in Fig. 4, nodes 342, 344, 346, 348 and 350 in the input layer may take external inputs xl, x2, x3, x4 and x5, which may be numerical values depending upon the input dataset. It will be understood that even though only five inputs are shown in Fig. 4, in various implementations, a node may include tens, hundreds, thousands, or more inputs. As discussed above, no computation is performed on the input layer and thus the outputs from nodes 342, 344, 346, 348 and 350 of input layer are xl, x2, x3, x4 and x5, respectively, which are fed into the hidden layer. The output of node 352 in the hidden layer may depend on the outputs from the input layer (xl, x2, x3, x4 and x5) and weights associated with connections (wl, w2, w3, w4 and w5). Thus, the output from node 352 may be computed as:

[0326] Y352=f(xlwl+x2w2+x3w3+x4w4+x5w5 +b;,s ).

[0327] The outputs from the nodes 354 and 356 in the hidden layer may also be computed in a similar manner and then be fed to the node 358 in the output layer. Node 358 in the output layer may perform similar computations (using weights vl, v2 and v3 associated with the connections) as the nodes 352, 354 and 356 in the hidden layers:

[0328] ¥353— f(y352vl+y354v2+y356v3+b35s);

[0329] where Y340 is the output of the neural network 340.

[0330] As mentioned, the connections between nodes in the neural network have associated weights, which determine how much relative effect an input value has on the output value of the node in question. Before the network is trained, random values are selected for each of the weights. The weights are adjusted during the training process, and this adjustment of weights to determine the best set of weights thatmaximizes the accuracy of the neural network is referred to as training. For every input in a training dataset, the output of the artificial neural network may be observed and compared with the expected output, and the error between the expected output and the observed output may be propagated back to the previous layer. The weights may be adjusted accordingly based on the error. This process is repeated until the output error is below a predetermined threshold.

[0331] In embodiments, backpropagation (e.g., backward propagation of errors) is utilized with an optimization method such as gradient descent to adjust weights and update the neural network characteristics. Backpropagation mayDocket: 16606-12POA be a supervised training scheme that learns from labeled training data and errors at the nodes by changing parameters of the neural network to reduce the errors. For example, a result of forward propagation (e.g., output activation value(s)) determined using training input data is compared against a corresponding known reference output data to calculate a loss function gradient. The gradient may then be utilized in an optimization method to determine new updated weights in an attempt to minimize a loss function. For example, to measure error, the mean square error is determined using the equation:(eq. 1) E=(target-output)2

[0332] To determine the gradient for a weight “w,” a partial derivative of the error with respect to the weight may be determined, where:(eq. 2) gradient=3E / 3w

[0333] The calculation of the partial derivative of the errors with respect to the weights may flow backwards through the node levels of the neural network. Then a portion (e.g., ratio, percentage, etc.) of the gradient is subtracted from the weight to determine the updated weight. The portion may be specified as a learning rate “a.” Thus, an example equation of determining the updated weight is given by the formula:(eq. 3) wnew =wold -a3E / 3w

[0334] The learning rate must be selected such that it is not too small (e.g., a rate that is too small may lead to a slow convergence to the desired weights) and not too large (e.g., a rate that is too large may cause the weights to not converge to the desired weights).

[0335] After the weight adjustment, the network should perform better than before for the same input because the weights have now been adjusted to minimize the errors.

[0336] As mentioned, neural networks may include convolutional neural networks (CNN). A CNN is a specialized neural network for processing data having a known, grid-like topology, such as image data. Accordingly, CNNs are commonly used for classification, object recognition and computer vision applications, but they also may be used for other types of pattern recognition, such as speech and language processing.

[0337] A convolutional neural network learns highly non-linear mappings by interconnecting layers of artificial neurons arranged in many different layers with activation functions that make the layers dependent. It includes one or more convolutional layers, interspersed with one or more sub-sampling layers and non-linear layers, which are typically followed by one or more fully connected layers.

[0338] Referring to Fig. 5, a CNN 360 includes an input layer with an input image 362 to be classified by the CNN 360, a hidden layer which in turn includes one or more convolutional layers, interspersed with one or more activation or non-linear layers (e.g., ReLU) and pooling or sub-sampling layers and an output layer- typically including one or more fully connected layers. Input image 362 may be represented by a matrix of pixels and may have multiple channels. For example, a colored image may have red, green, and blue channels, each representing red, green, and blue (RGB) components of the input image. Each channel may be represented by a 2-D matrix of pixels having pixel values in the range of 0 to 255. A gray-scale image, on the other hand, may have only one channel. The following section describes processing of a single image channel using CNN 360. It will be understood that multiple channels may be processed in a similar manner.

[0339] As shown, input image 362 may be processed by the hidden layer, which includes sets of convolutional and activation layers 364 and 368, each followed by pooling layers 366 and 370.Docket: 16606-12POA

[0340] The convolutional layers of the convolutional neural network serve as feature extractors capable of learning and decomposing the input image into hierarchical features. The convolution layers may perform convolution operations on the input image, where a filter (also referred to as a kernel or feature detector) may slide over the input image at a certain step size (referred to as the stride). For every position (or step), element- wise multiplications between the filter matrix and the overlapped matrix in the input image may be calculated and summed to get a final value that represents a single element of an output matrix constituting a feature map. The feature map refers to image data that represents various features of the input image data and may have smaller dimensions as compared to the input image. The activation or non-linear layers use different non-linear trigger functions to signal distinct identification of likely features on each hidden layer. Non-linear layers use a variety of specific functions to implement the non-linear triggering, including the rectified linear units (ReLUs), hyperbolic tangent, absolute of hyperbolic tangent and sigmoid functions. In one implementation, a ReLU activation implements the function y=max(x, 0) and keeps the input and output sizes of a layer the same. The advantage of using ReLU is that the convolutional neural network is trained many times faster. ReLU is a non-continuous, non- saturating activation function that is linear with respect to the input if the input values are larger than zero and zero otherwise.

[0341] As shown in Fig. 5, the first convolution and activation layer 364 may perform convolutions on input image 362 using multiple filters, followed by a non-linearity operation (e.g., ReLU) to generate multiple output matrices (or feature maps) 372. The number of filters used may be referred to as the depth of the convolution layer. Thus, the first convolution and activation layer 364 in the example of Fig. 5 has a depth of three and generates three feature maps using three filters. Feature maps 372 may thenbe passed to the firstpooling layer that may sub-sample or down-sample the feature maps using a pooling function to generate output matrix 374. The pooling function replaces the feature map with a summary statistic to reduce the spatial dimensions of the extracted feature map, thereby reducing the number of parameters and computations in the network. Thus, the pooling layer reduces the dimensionality of the feature maps while retaining the most important information. The pooling function can also be used to introduce translation invariance into the neural network, such that small translations to the input do not change the pooled outputs. Different pooling functions may be used in the pooling layer, including max pooling, average pooling, and 12-norm pooling.

[0342] Output matrix 374 may then be processed by a second convolution and activation layer 368 to perform convolutions and non-linear activation operations (e.g., ReLU) as described above to generate feature maps 376. In the example shown in Fig. 5, the second convolution and activation layer 368 may have a depth of five. Feature maps 376 may then be passed to a pooling layer 370, where feature maps 376 may be subsampled or down-sampled to generate an output matrix 378.

[0343] Output matrix 378 generated by pooling layer 370 is then processed by one or more fully connected layer 380 that forms a part of the output layer of CNN 360. The fully connected layer 380 has a full connection with all the feature maps of the output matrix 378 of the pooling layer 370. In embodiments, the fully connected layer 380 may take the output matrix 378 generated by the pooling layer 370 as the input in vector form, and perform high-level determination to output a feature vector containing information of the structures in the input image. In embodiments, the fully-connected layer 380 may classify the object in the input image 362 into one of several categories using a Softmax function. The Softmax function may be used as the activation function in the output layer and takes a vector of real-valued scores and maps it to a vector of values between zero and one that sum to one. In embodiments, other classifiers, such as a support vector machine (SVM) classifier, may be used.Docket: 16606-12POA

[0344] In embodiments, one or more normalization layers may be added to the CNN 360 to normalize the output of the convolution filters. The normalization layer may provide whitening or lateral inhibition, avoid vanishing or exploding gradients, stabilize training, and enable learning with higher rates and faster convergence. In embodiments, the normalization layers are added after the convolution layer but before the activation layer.

[0345] CNN 360 may thus be seen as multiple sets of convolution, activation, pooling, normalization and fully connected layers stacked together to learn, enhance and extract implicit features and patterns in the input image 362. A layer, as used herein, can refer to one or more components that operate with a similar function by mathematical or other functional means to process received inputs to generate / derive outputs for a next layer with one or more other components for fiirther processing within CNN 360.

[0346] The initial layers of CNN 360, e.g., convolution layers, may extract low-level features such as edges and / or gradients from the input image 362. Subsequent layers may extract or detect progressively more complex features and paterns, such as the presence of curvatures and textures in image data, and so on. The output of each layer may serve as an input of a succeeding layer in CNN 360 to learn hierarchical feature representations from data in the input image 362. This allows convolutional neural networks to efficiently learn increasingly complex and abstract visual concepts.

[0347] Although only two convolution layers are shown in the example, the present disclosure is not limited to the example architecture, and CNN 360 architecture may comprise any number of layers in total, and any number of layers for convolution, activation and pooling. For example, there have been many variations and improvements over the basic CNN model described above. Some examples include Alexnet, GoogLeNet, VGGNet (that stacks many layers containing narrow convolutional layers followed by max pooling layers), Residual network or ResNet (that uses residual blocks and skip connections to learn residual mapping), DenseNet (that connects each layer of CNN to every other layer in a feed-forward fashion), Squeeze and excitation networks (that incorporate global context into features) and AmobeaNet (that uses evolutionary algorithms to search and find optimal architecture for image recognition).Training of a convolutional neural network

[0348] The training process of a convolutional neural network, such as CNN 360, may be similar to the training process discussed in Fig. 4 with respect to neural network 340.

[0349] In embodiments, all parameters and weights (including the weights in the filters and weights for the fully- connected layer are initially assigned (e.g., randomly assigned). Then, during training, a training image or images, in which the objects have been detected and classified, are provided as the input to the CNN 360, which performs the forward propagation steps. In other words, CNN 360 applies convolution, non-linear activation, and pooling layers to each training image to determine the classification vectors (i.e., detect and classify each training image). These classification vectors are compared with the predetermined classification vectors. The error (e.g., the squared sum of differences, log loss, softmax log loss) between the classification vectors of the CNN and the predetermined classification vectors is determined. This error is then employed to update the weights and parameters of the CNN in a backpropagation process, which may use gradient descent and may include one or more iterations. The training process is repeated for each training image in the training set.

[0350] The training process and inference process described above may be performed on hardware, software, or a combination of hardware and software. However, training a convolutional neural network like CNN 360 or using the trained CNN for inference generally requires significant amounts of computational power to perform, for example, the matrix multiplications or convolutions. Thus, specialized hardware circuits, such as graphic processing units (GPUs), tensor processing units (TPUs), neural network processing units (NPUs), FPGAs, ASICs, or other highly parallelDocket: 16606-12POA processing circuits may be used for training and / or inference. Training and inference may be performed on a cloud, in a data center, or on a device.Region-based CNNs ( RCNNs) and object detection

[0351] In embodiments, an obj ect detection model extends the functionality of CNN-based image classification neural network models by not only classifying objects but also determining their locations in an image in terms of bounding boxes. Region-based CNN (R-CNN) methods are used to extract regions of interest (ROI), where each ROI is a rectangle that may represent the boundary of an object in an image. Conceptually, R-CNN operates in two phases. In a first phase, region proposal methods generate all potential bounding box candidates in the image. In a second phase, for every proposal, a CNN classifier is applied to distinguish between objects. Alternatively, a fast R-CNN architecture can be used, which integrates the feature extractor and classifier into a unified network. Another faster R-CNN can be used, which incorporates a Region Proposal Network (RPN) and fast R-CNN into an end-to-end trainable framework. Mask R-CNN adds instance segmentation, while mesh R-CNN adds the ability to generate a 3D mesh from a 2D image.

[0352] Referring back to Fig. 3, in embodiments, the artificial intelligence modules 304 may provide access to and / or integrate a robotic process automation (RPA) module 316. The RPA module 316 may facilitate, among other things, computer automation of producing and validating workflows. The RPA module 316 provides automation of tasks performed by humans, such as receiving and reviewing written information, entering data into user interfaces, converting or otherwise processing data such as files or records, recording observations, generating documents such as reports, and communicating with other users by mechanisms such as email. In some cases, the tasks involve a workflow that includes a number of interrelated steps, contextual information that relates to the task, and interactions with other applications and humans. The RPA module 316 can be configured to receive or learn one or more such workflows on behalf of the human and in a manner similar to the actions and logic of the human, and can thereafter perform such workflows in response to various triggers, such as events. Examples of RPA modules 316 may encompass those in this disclosure and in the documents incorporated by reference herein and may involve automation of any of the wide range of value chain network activities or entities described therein.

[0353] In embodiments, an RPA module 316 is configured to receive or learn a robotic process automation workflow in a variety of ways. As a first example, in embodiments, the RPA module 316 can include a graphical user interface (GUI) that enables a user to specify the details of the robotic process automation workflow. The GUI can include components that represent different types of actions, such as receiving input from a user or application, converting or otherwise processing data, and providing input to an application. The GUI can receive, from the user, a selection of components representing actions that correspond to the steps of the workflow when performed by a human. The GUI can also receive, from the user, an interconnection of the selected components, such as a logical order in which the corresponding actions are to be performed, or a dependency of one component upon another component (e.g., a first component can output data that is received as input by another component). The GUI can include one or more templates, such as one or more sequences of actions that are performed together to complete a common workflow. The GUI can receive, from the user, a selection of a template, optionally including one or more details that adapt the selected template to a particular workflow performed by the human. Based on the input received from the user, the RPA module 316 can generate a robotic process automation workflow that can be executed to perform the workflow. The RPA module 316 can store the generated workflow for future use. For example, the RPA module 316 can executeDocket: 16606-12POA the compiled code or interpret the generated script to perform the workflow in a similar manner as performed by a human.

[0354] As a second example, in embodiments, an RPA module 316 is configured to receive or learn a workflow based on a set of rules. For example, the RPA module 316 can include a GUI that enables a user to specify the details of the robotic process automation workflow as a set of conditions and responsive actions. The GUI includes a set of components that respond to conditions to be monitored, such as a status of a resource or an occurrence of an event. The GUI for designing the workflows can include a set of components that represent actions to be taken in response to an occurrence of one of the conditions. The GUI can receive, from the user, a selection of components representing one or more of the conditions of a workflow, and a selection of one or more components representing the actions to be taken in response to the conditions. In some embodiments, the GUI can include one or more templates, such as one or more conditions associated with one or more actions that correspond to a common workflow. The GUI can receive, from the user, a selection of one of the templates, including one or more details that adapt the selected template to a particular workflow performed by the human. Based on the input received from the user, the RPA module 316 can generate a robotic process automation workflow that automates a set of tasks in response to one or more detected events. The RPA module 316 can store the generated workflow for future use. For example, the RPA module 316 can monitor the selected conditions and perform the selected actions in response to an occurrence of the selected actions, in a similar manner as performed by the human.

[0355] As a third example, in embodiments, an RPA module 316 is configured to learn a workflow by recording a set of actions performed by a human to complete the workflow. For example, the RPA module 316 can receive, from the user, an indication of a start of the workflow involving a device, such as a selection of a Start Recording button. The RPA module 316 can receive user input from the user, such as input to one or more human interaction devices (HIDs), such as a keyboard, a mouse, a touchscreen, a camera, or a microphone. Alternatively or additionally, the RPA module 316 can receive user input as a series of human interaction events reported by a device, such as an input layer of an operating system that receives and aggregates user input from one or more human input devices. Alternatively or additionally, the RPA module 316 can receive user input as a series of events reported by one or more applications, such as a web browser that reports a set of user input events. The RPA module 316 can record the user input as a sequence of inputs. The RPA module 316 can associate the recorded user input with contextual information, such as an identification of the application to which the user input was directed. The RPA module 316 can associate the recorded user input with other events, such as preceding events of an application that receives the user input (e.g., an indication by a web browser that a web page has been rendered and is available to receive user input) and / or responsive events of the application in response to receiving the user input (e.g., an action performed by a web page in response to receiving user input). The RPA module 316 can associate the recorded user input with other events occurring within the device, such as an action performed by another application or an operating system of the device in response to the user input. The RPA module 316 can receive, from the user, an indication of an end of the workflow, such as a selection of a Stop Recording button. The RPA module 316 can generate a workflow that includes a record of the observed user input, optionally in association with other data. The RPA module 316 can store the generated workflow for future use. For example, the RPA module 316 can replay the sequence of recorded user input to perform the workflow in a similar manner as performed by the human.

[0356] As a fourth example, in embodiments, an RPA module 316 is configured to learn a workflow by watching an interaction between a human and a device. For example, a human can perform a number of workflows using the deviceDocket: 16606-12POA over a period of time, such as a business day. The RPA module 316 can monitor the user input of the human and can identify, in the user input, one or more patterns of actions that are repeatedly performed by the human. The RPA module 316 can determine that a pattern of actions corresponds to a workflow performed by the human. In some embodiments, the RPA module 316 can identify variations among various instances of the actions when performed by the human during the workflow, such as different types of data entry that occur in different instances of the actions. The RPA module 316 can associate an action in the workflow with one or more parameters, wherein the parameters correspond to the different variations among the various instances of the action when performed by the human. In various embodiments, the RPA module 316 can determine a basis of each of the variations of the action that are associated with different variations of the action in the workflow. For example, the RPA module 316 can determine that when the workflow is performed by the human on behalf of a first user, the action is to be performed with a first data entry value, such as data entry including the name of the first user. When the workflow is performed by the human on behalf of a second user, the action is to be performed with a second data entry value, such as data entry including the name of the second user. The data entry can be represented in the workflow as a data entry parameter (e.g., a name of a user on whose behalf the workflow is performed), optionally with specific values that correspond to a context of the workflow (e.g., the names of the users on whose behalf the workflow can be performed). The RPA module 316 can generate a workflow that includes a sequence of commands that correspond to the pattern of actions performed by the user during the workflow, and, optionally, the parameters and / or parameter values of various actions of the workflow. The RPA module 316 can store the generated workflow for future use. For example, the RPA module 316 can replay the sequence of commands to replicate the pattern of actions that correspond to the workflow when performed in a similar manner as by the human.

[0357] In embodiments, the RPA module 316 can be implemented in a variety of architectures. As a first example, the RPA module 316 can be implemented on the same device that a human uses to perform a workflow, and / or that a user uses to specify the details of a workflow. The RPA module 316 can store one or more generated workflows on the device, and can perform the workflow on the same device. As a second example, the RPA module 316 can be implemented on a first device to replicate a workflow performed by a human on a second device. The RPA module 316 can monitor the interaction of the human with the second device while performing a task, generate and store a workflow on the first device, and execute the workflow on the first device to perform the task on the first device in a similar manner as performed by the user on the second device. As a third example, the RPA module 316 can be implemented on a first device to generate a workflow that corresponds to a task performed by the human on the first device, and can transmit the workflow to a second device. The workflow can cause the second device to perform the task on the second device in a similar manner as performed by the user on the first device. As a fourth example, the RPA module 316 can be implemented on a second device to receive a workflow that corresponds to a task performed by the human on a first device. The RPA module 316 workflow can execute the workflow on the second device to perform the task on the second device in a similar manner as performed by the user on the first device. In some embodiments, the RPA module 316 can be distributed over a set of two or more devices, such as a first portion of the RPA module 316 that executes on a first device to generate a workflow based on an interaction between a human and the first device, and a second portion of the RPA module 316 that executes on a second device to perform the workflow on the second device. In some embodiments, at least a portion of the RPA module 316 can be replicated over a plurality of devices, such as two or more devices that each perform (e.g., concurrently and / or consecutively) a workflow that was generated based on an interaction between a human and a first device. In some embodiments, different RPADocket: 16606-12POA modules 316 executing on each of a plurality of devices can interact to execute one or more workflows (e.g., a first RPA module 316 that executes on a first device to perform a first portion of a workflow, and a second RPA module 316 that executes on a second device to perform a second portion of the same workflow). Each RPA module 316 can operate in a particular role while performing at least a portion of a workflow, such as a first RPA module 316 that executes on a cloud edge device to receive an input of a workflow, a second RPA module 316 that executes on a cloud server to process the input of the workflow, and a third RPA module 316 that executes on another cloud edge device to present an output of the workflow.

[0358] In embodiments, an RPA module 316 can perform a workflow in response to a variety of triggers. The RPA module 316 can perform a workflow in response to a request of a user, such as a request to execute code or run a particular script in order to perform a learned workflow. The RPA module 316 can perform a workflow in response to a detection of a pattern of activity by a human (e.g., a second workflow that is to be performed by the RPA module 316 in response to a completion of a first workflow by a human). The RPA module 316 can perform at least a portion of a workflow in lieu of a human performing at least a portion of the workflow. For example, the RPA module 316 can detect a start of a workflow by a human, and can suggest to the human that the RPA module 316 perform the rest of the workflow. Upon receiving an acceptance of the suggestion, the RPA module 316 can perform the entire workflow in lieu of the human, and / or one or more remaining steps of the workflow following the initial steps performed by the human. The RPA module 316 can perform a workflow in response to an occurrence of a type of data (e.g., the device receiving a file that includes particular data type, such as a particular type of document or a particular type of image). The RPA module 316 can perform a workflow in response to receiving a message through a communication channel such as email, telephone, text message, gesture input received by a camera or haptic input device, or voice input received by a microphone. The RPA module 316 can perform a workflow in response to receiving a request from an operating system or an application executing on the device (e.g., a request from a spreadsheet application in response to a user entering a certain type of data). The RPA module 316 can perform a workflow in response to a detected event. For example, when a device recognizes a presence of a particular human (e.g., when a camera of a device recognizes a face of the human), the RPA module 316 can perform a workflow that involves displaying a report for the human. The RPA module 316 can perform a workflow at a scheduled interval, such as once per hour or once per day. The RPA module 316 can perform a workflow in response to a request received from another workflow executed on the same device or another device (e.g., a second workflow that is to be performed upon completion of a first workflow).

[0359] In embodiments, an RPA module 316 can perform a workflow based on a variety of inputs. The RPA module 316 can perform a workflow based on one or more details of a trigger of the workflow. For example, if the workflow is being performed in response to a request of a user to perform the workflow, the RPA module 316 can perform the workflow based on one or more details of the request. For example, if the workflow was triggered by a request of a user to process a particular document, the RPA module 316 can perform the workflow based on one or more details of the document. If the workflow is being performed in response to a message or telephone call, the RPA module 316 can perform the workflow based on an identity of the sender of the message or the identity of the caller. If the workflow is being performed as a daily instance based on a schedule, the RPA module 316 can perform the workflow based on the day of the week on which the workflow is being performed. If a workflow is being performed in response to a detection of a condition, the RPA module 316 can perform the workflow based on one or more details of the condition. For example, if the condition is a storage capacity of a device that exceeds a storage capacity threshold, the RPADocket: 16606-12POA module 316 can perform the workflow based on a severity of the storage capacity condition (e.g., a remaining storage capacity of the device). The RPA module 316 can perform a workflow based on a data source, such as one or more files of a file system, one or more rows or records of a database, or one or more messages received by a network interface. If the RPA module 316 is performing a workflow in response to one or more events, the RPA module 316 can perform the workflow based on one or more details of the event. For example, if the RPA module 316 is performing a second workflow in response to a completion of a first workflow on the same device or another device, the RPA module 316 can perform the workflow based on a date or time of the completion of the first workflow, a result of the first workflow, and / or an output of the first workflow. The RPA module 316 can perform a workflow based on one or more contextual details. For example, the RPA module 316 can perform a workflow based on a detected number and identities of humans who are present in the proximity of a device. The RPA module 316 can perform a workflow based on data associated with an application executing on the device. For example, if the RPA module 316 performs the workflow based on a loading of a web page, the RPA module 316 can perform the workflow based on data scraped from the contents of the web page. The RPA module 316 can perform the workflow based on observation of human actions that involve interactions with hardware elements, with software interfaces, and with other elements. Observations may include field observations as humans perform real tasks, as well as observations of simulations or other activities in which a human performs an action with the explicit intent to provide a training data set or input for the RPA module 316, such as where a human tags or labels a training data set with features that assist the RPA module 316 in learning to recognize or classify features or objects, among many other examples.

[0360] In embodiments, an RPA module 316 can interact with one or more applications while performing the workflow. For example, the RPA module 316 can extract data from a variable or an object of an application, such as text content of a textbox in a web form or the contents of cells in a spreadsheet. The RPA module 316 can extract data stored within an application (e.g., by inspecting a memory space of the application). The RPA module 316 can analyze data generated as output by the application (e.g., one or more files generated by the application, one or more rows or records of a spreadsheet generated by the application, or one or more network communication messages received and / or transmitted by the application over a network). The RPA module 316 can invoke an application programming interface (API) of the application to request data from the application, and can receive and analyze data provided by the application in response to the invocation of the API. The RPA module 316 can examine one or more properties of the device on which the application is executing (e.g., a portion of a display of the devices that includes a graphical user interface of the application) to extract data from the application. Alternatively or additionally, the RPA module 316 can provide data to an application and / or modify a behavior of an application while performing the workflow. For example, the RPA module 316 can generate user input that is directed to an application (e.g., simulating a human interaction device (HID), such as a keyboard, to generate keystrokes that are delivered to the application as user input). The RPA module 316 can directly transmit and / or modify data of the application (e.g., altering HTML data stored in a rendered web page to modifying the contents of the textbox, or directly modifying data in the memory space of an application). The RPA module 316 can request the operating system to interact with and / or modify the behavior of an application (e.g., requesting that the device start, activate, suspend, resume, close, or terminate an application). The RPA module 316 can invoke an API of the application to provide data to the application (e.g., invoking an API of a spreadsheet to request the entry of data into a particular cell). The RPA module 316 can invoke code associated with an application to provide data and / or modify the behavior of the application (e.g., executing code that is encoded in an application-specific programming language and embedded in a document used by an application or invoking a storedDocket: 16606-12POA procedure of a database associated with the application). The RPA module 316 can cause or allow an interaction with an application to be visible to a human (e.g., the RPA module 316 can provide user input that simulates a user visually activating a spreadsheet application and visually typing data into various cells of the spreadsheet application). The RPA module 316 can hide an interaction with an application from a human (e.g., visually hiding a window of an application while entering data into one or more textboxes of the window of the application).

[0361] In embodiments, an RPA module 316 can utilize a variety of logical processes while performing a workflow. The RPA module 316 can retrieve, interpret, analyze, convert, validate, aggregate, partition, render, store, and / or otherwise process data that was received and / or is associated with the workflow. The RPA module 316 can transmit the data to another workflow, application, or device for processing or storage, and / or can query or receive the data from another workflow, application, or device. The RPA module 316 can apply an optical character recognition (OCR) process to an image (e.g., a picture of a form or a document) to determine and extract text content from the image. The RPA module 316 can apply a computer vision process to an image (e.g., a photograph captured by a camera) to determine and extract image data from the image, such as detecting, recognizing, classifying, and / or localizing one or more objects. The RPA module 316 can apply a speech recognition process to a sound input (e.g., a voice input from a telephone call or a microphone) to determine and extract voice content from the image, such as one or more voice commands. The RPA module 316 can apply a gesture recognition process to an input device (e. g. , a camera, proximity sensor, or inertial measurement unit that detects movement of a hand) to determine one or more gestures performed by a human. The RPA module 316 can apply a pattern recognition process to data to detect one or more patterns in the data (e.g., analyzing sensor data from a machine to detect one or more occurrences of an event associated with the machine, such as a movement of a moving part of the machine).

[0362] In embodiments, the RPA module 316 performs a workflow in cooperation with a human or another workflow. For example, a workflow can include one or more human portions to be performed by a human and one or more automated portions to be performed by the RPA module 316. The RPA module 316 can first perform an automated portion and deliver a result of the automated portion to the human so that the human can perform a human portion based on the result. The RPA module 316 can receive a result of a human portion of the workflow and can perform an automated portion of the workflow on the result of the human portion of the workflow. The RPA module 316 can perform the automated portion of the workflow concurrently with a human performing a human portion of the workflow, and can then combine a result of the automated portion of the workflow with a result of the human portion of the workflow. The RPA module 316 can perform a first automated portion of the workflow, present a result of the first automated portion to a human for review and validation, and can perform a second automated portion of the workflow based on the review and validation of the result of the first automated portion based on a result of the review and validation by the human.

[0363] In embodiments, an RPA module 316 may learn to perform certain tasks based on the learned patterns and processes. The RPA module 316 can use one or more artificial intelligence modules 304 to perform one or more steps of a workflow. For example, an RPA module 316 can perform a data classification step on input data by applying a classification neural network to the input data. An RPA module 316 can perform a pattern recognition step on input data by applying a pattern recognition neural network to the input data. An RPA module 316 can perform a computer vision processing step and / or an optical character recognition step of a workflow by applying one or CNNs 360 to an image. An RPA module 316 can perform a sequential analysis step involving time series data by applying one or more recurrent neural networks (RNNs) to the time series data. An RPA module 316 can perform one or more naturalDocket: 16606-12POA language processing steps on a natural-language expression (e.g., a natural-language document or a natural-language voice input) by applying one or more transformer-based neural networks to the natural-language expression.

[0364] In various embodiments, the RPA module 316 uses one or more artificial intelligence modules 304 that are untrained. For example, the one or more artificial intelligence modules 304 can include a k-nearest-neighbor model that determines a classification of a received input based on a proximity of the received input to a collection of other inputs with known classifications. The k-nearest-neighbor model then classifies the received input according to a majority of the known classifications of the determined k inputs that are closest to the received input.

[0365] In various embodiments, the RPA module 316 uses one or more artificial intelligence modules 304 that are trained in an unsupervised manner. For example, the workflow can include an anomaly detection step, such as determining a portion of a form that includes handwritten text. An anomaly detection algorithm can partition the form into a collection of symbols, and can compare the symbols to distinguish between symbols that occur with a high frequency (e.g., machine-printed characters in a font) from symbols that occur with a low frequency (e.g. , hand-printed characters that are unique or at least highly variable). The anomaly detection algorithm can therefore partition the form into regions that include machine-printed characters and regions that include hand-printed characters. The RPA module 316 can then process each region of the document with either an OCR module that is configured to recognize machine- printed characters in a font or an OCR module that is configured to recognize hand-printed characters.

[0366] In various embodiments, the RPA module 316 uses one or more artificial intelligence modules 304 that are specifically designed and / or trained for the workflow. For example, the workflow can be associated with a training data set, and the RPA module 316 can train one or more machine learning models to perform the processing of the workflow based on the training data set. In various embodiments, the RPA module 316 uses one or more pretrained artificial intelligence modules 304 to perform the processing of the workflow. For example, the RPA module 316 can receive a partially pretrained natural language processing (NLP) machine learning model that is generally trained to recognize sentence structure and word meaning. The RPA module 316 can adapt the partially pretrained NLP machine learning model based on natural-language expressions that are more specifically associated with the workflow. The adaptation can involve applying transfer learning to an artificial intelligence module 304 (e.g., more specifically training one or more classification layers in a classification portion of the NLP machine learning model while holding other portions of the NLP machine learning model constant). The adaptation can involve retraining an artificial intelligence module 304 (e.g., retraining an entirety of an NLP machine learning model based on natural-language expressions that are associated with a workflow). The adaptation can involve generating an ensemble of artificial intelligence modules 304 to perform the workflow (e.g., two or more artificial intelligence modules 304, each of which performs classification of data in a different way, wherein an output classification of the workflow is based on a consensus of the two or more artificial intelligence modules 304). The artificial intelligence modules 304 can include a random forest, in which each of one or more decision trees analyses an input data according to different criteria, and an output of the random forest is based on a consensus of the decision trees. The artificial intelligence modules 304 can include a stacking ensemble, in which each of two or more machine learning models processes data to generate an output, and another machine learning model determines which output, among the outputs of the two or more machine learning models, is to be used as the output of processing the data.

[0367] In embodiments, the RPA module 316 generates one or more outputs or results of a workflow. The RPA module 316 can generate, as output, data that can be stored by the device (e.g., as a file in a file system or as a row or record in a database). The RPA module 316 can generate, as output, data that is included in another data set (e.g., text enteredDocket: 16606-12POA into fields of a form, numbers entered into cells of a spreadsheet, or text entered into textboxes of a web page). The RPA module 316 can generate, as output, data that is transmitted to another device (e.g., a submission of form data of a web page to a webserver). The RPA module 316 can generate, as output, data that is communicated to one or more users (e.g., a visual notification of a result displayed for a user of the device, or a message that is transmitted to a user by a communication channel such as email, text message, or voice output). The RPA module 316 can generate, as output, data that modifies a behavior of an application (e.g., a command to start, activate, suspend, resume, close, or terminate an application). The RPA module 316 can generate, as output, data that modifies a behavior of the device or another device (e.g., a command that controls a machine, such as a printer, a camera, a device, or an industrial manufacturing device). The RPA module 316 can generate, as output, data that reflects an initial, current, or final status of the workflow (e.g., a dashboard that shows a progress of the workflow to completion, or a result of the workflow in combination with the results of other workflows). The RPA module 316 can generate, as output, one or more events (e.g., notifications to a human, an application, an operating system of the device, or another device as to the progression, completion, and / or results of the workflow). The events can be received and further processed by the RPA module 316 or another RPA module executing on the same device or another device. For example, upon completion of a first workflow, the RPA module 316 can initiate a second workflow based on a result and / or output of the first workflow. The RPA module 316 can generate, as output, documentation of one or more results of the workflow. For example, the RPA module 316 can update a log to document the results and / or output of the workflow, including one or more errors, exceptions, validation failures that occurred during the workflow.

[0368] In embodiments, the RPA module 316 modifies a workflow based on a performance of the workflow. For example, the RPA module 316 can request review, by a user, of one or more results of the workflow, including one or more errors, exceptions, validation failures that occurred during the workflow. The RPA module 316 can deactivate one or more steps or modules of the workflow that resulted in an error, exception, or validation failure. The RPA module 316 can automatically adjust the workflow to perform future instances of the workflow based on the completed instance of the workflow. For example, the RPA module 316 can update the workflow to improve an efficiency of the workflow, to add or remove functions to the workflow, to adjust functions of the workflow to perform differently, to log one or more instances and / or parameters of the workflow, and / or to eliminate or reduce one or more logical faults in the workflow. The RPA module 316 can update one or more artificial intelligence modules 304 associated with the workflow. For example, the RPA module 316 can generate or add one or more machine learning models to the workflow to improve processing of the workflow. The RPA module 316 can remove one or more machine learning models to improve efficiency of the workflow. The RPA module 316 can redesign and / or retrain one or more machine learning models based on a result of the workflow. The RPA module 316 can add one or more machine learning models to an existing ensemble of machine learning models.Analytics Module

[0369] In embodiments, the artificial intelligence modules 304 may include and / or provide access to an analytics module 318. In embodiments, an analytics module 318 is configured to perform various analytical processes on data output from value chain entities or other data sources. In example embodiments, analytics produced by the analytics module 318 may facilitate quantification of system performance as compared to a set of goals and / or metrics. The goals and / or metrics may be preconfigured, determined dynamically from operating results, and the like. Examples of analytics processes that can be performed by an analytics module 318 are discussed below and in the document incorporated herein by reference. In some example implementations, analytics processes may include tracking goalsDocket: 16606-12POA and / or specific metrics that involve coordination of value chain activities and demand intelligence, such as involving forecasting demand for a set of relevant items by location and time (among many others).Digital Twin Module

[0370] In embodiments, artificial intelligence modules 304 may include and / or provide access to a digital twin module 320. The digital twin module 320 may encompass any of a wide range of features and capabilities described herein In embodiments, a digital twin module 320 may be configured to provide, among other things, execution environments for and different types of digital twins, such as twins of physical environments, twins of robot operating units, logistics twins, executive digital twins, organizational digital twins, role-based digital twins, and the like. In embodiments, the digital twin module 320 may be configured in accordance with digital twin systems and / or modules described elsewhere throughout the disclosure. In example embodiments, a digital twin module 320 may be configured to generate digital twins that are requested by intelligence clients 336. Further, the digital twin module 320 may be configured with interfaces, such as APIs and the like, for receiving information from external data sources. For instance, the digital twin module 320 may receive real-time data from sensor systems of a machinery, vehicle, robot, or other device, and / or sensor systems of the physical environment in which a device operates. In embodiments, the digital twin module 320 may receive digital twin data from other suitable data sources, such as third-party services (e.g., weather services, traffic data services, logistics systems and databases, and the like. In embodiments, the digital twin module 320 may include digital twin data representing features, states, or the like of value chain network entities, such as supply chain infrastructure entities, transportation or logistic entities, containers, goods, or the like, as well as demand entities, such as customers, merchants, stores, points-of-sale, points-of-use, and the like. The digital twin module 320 may be integrated with or into, linked to, or otherwise interact with an interface (e.g., a control tower or dashboard), for coordination of supply and demand, including coordination of automation within supply chain activities and demand management activities.

[0371] In embodiments, a digital twin module 320 may provide access to and manage a library of digital twins. Artificial intelligence modules 304 may access the library to perform functions, such as a simulation of actions in a given environment in response to certain stimuli.Machine Vision Module

[0372] In embodiments, artificial intelligence modules 304 may include and / or provide access to a machine vision module 322. In embodiments, a machine vision module 322 is configured to process images (e.g., captured by a camera) to detect and classify objects in the image. In embodiments, the machine vision module 322 receives one or more images (which may be frames of a video feed or single still shot images) and identifies “blobs” in an image (e.g., using edge detection techniques or the like). The machine vision module 322 may then classify the blobs. In some embodiments, the machine vision module 322 leverages one or more machine-learned image classification models and / or neural networks (e.g., convolutional neural networks) to classify the blobs in the image. In some embodiments, the machine vision module 322 may perform feature extraction on the images and / or the respective blobs in the image prior to classification. In some embodiments, the machine vision module 322 may leverage classification made in a previous image to affirm or update classifications) from the previous image. For example, if an object that was detected in a previous frame was classified with a lower confidence score (e.g., the object was partially occluded or out of focus), the machine vision module 322 may affirm or update the classification if the machine vision module 322 is able to determine a classification of the object with a higher degree of confidence. In embodiments, the machine vision module 322 is configured to detect occlusions, such as objects that may be occluded by another object. InDocket: 16606-12POA embodiments, the machine vision module 322 receives additional input to assist in image classification tasks, such as from a radar, a sonar, a digital twin of an environment (which may show locations of known objects), and / or the like. In some embodiments, a machine vision module 322 may include or interface with a liquid lens. In these embodiments, the liquid lens may facilitate improved machine vision (e.g., when focusing at multiple distances is necessitated by the environment and job of a robot) and / or other machine vision tasks that are enabled by a liquid lens.Natural Language Processing Module

[0373] In embodiments, the artificial intelligence modules 304 may include and / or provide access to a natural language processing (NLP) module 324. In embodiments, an NLP module 324 performs natural language tasks on behalf of an intelligence service client 336. Examples of natural language processing techniques may include, but are not limited to, speech recognition, speech segmentation, speaker diarization, text-to-speech, lemmatization, morphological segmentation, parts-of-speech tagging, stemming, syntactic analysis, lexical analysis, and the like. In embodiments, the NLP module 324 may enable voice commands that are received from a human. In embodiments, the NLP module 324 receives an audio stream (e.g., from a microphone) and may perform voice-to-text conversion on the audio stream to obtain a transcription of the audio stream. The NLP module 324 may process text (e.g., a transcription of the audio stream) to determine a meaning of the text using various NLP techniques (e.g., NLP models, neural networks, and / or the like). In embodiments, the NLP module 324 may determine an action or command that was spoken in the audio stream based on the results of the NLP. In embodiments, the NLP module 324 may output the results of the NLP to an intelligence service client 336.

[0374] In embodiments, the NLP module 324 provides an intelligence service client 336 with the ability to parse one or more conversational voice instructions provided by a human user to perform one or more tasks, as well as communicate with the human user. The NLP module 324 may perform speech recognition to recognize the voice instructions, natural language understanding to parse and derive meaning from the instructions, and natural language generation to generate a voice response for the user upon processing of the user instructions. In some embodiments, the NLP module 324 enables an intelligence service client 336 to understand the instructions and, upon successful completion of the task by the intelligence service client 336, provide a response to the user. In embodiments, the NLP module 324 may formulate and ask questions to a user if the context of the user request is not completely clear. In embodiments, the NLP module 324 may utilize inputs received from one or more sensors, including vision sensors, location-based data (e.g., GPS data), to determine context information associated with processed speech or text data.

[0375] In embodiments, the NLP module 324 uses neural networks when performing NLP tasks, such as recurrent neural networks, long short term memory (LSTMs), gated recurrent unit (GRUs), transformer neural networks, convolutional neural networks and / or the like.

[0376] Fig. 6 illustrates an example neural network for implementing NLP module 324. In the illustrated example, the example neural network is a transformer neural network. In the example, the transformer neural network includes three input stages and five output stages to transform an input sequence into an output sequence. The example transformer includes an encoder 382 and a decoder 384. The encoder 382 processes input, and the decoder 384 generates output probabilities, for example. The encoder 382 includes three stages, and the decoder 384 includes five stages. Encoder 382 stage 1 represents an input as a sequence of positional encodings added to embedded inputs. Encoder 382 stages 2 and 3 include N layers (e.g., N=6, etc.) in which each layer includes a position-wise feedforward neural network (FNN) and an attention-based sublayer. Each attention-based sublayer of encoder 382 stage 2 includes four linear projections and multi-head attention logic to be added and normalized to be provided to the position- wise FNN ofDocket: 16606-12POA encoder 382 stage 3. Encoder 382 stages 2 and 3 employ a residual connection followed by a normalization layer at their output.

[0377] The example decoder 384 processes an output embedding as its input with the output embedding shifted right by one position to help ensure that a prediction for position i is dependent on positions previous to / less than i. In stage 2 of the decoder 384, masked multi-head attention is modified to prevent positions from attending to subsequent positions. Stages 3-4 of the decoder 384 include N layers (e.g., N=6, etc.) in which each layer includes a position-wise FNN and two attention-based sublayers. Each attention-based sublayer of decoder 384 stage 3 includes four linear projections and multi-head attention logic to be added and normalized to be provided to the position- wise FNN of decoder 384 stage 4. Decoder 384 stages 2-4 employ a residual connection followed by a normalization layer at their output. Decoder 384 stage 5 provides a linear transformation followed by a so Umax function to normalize a resulting vector of K numbers into a probability distribution, including K probabilities proportional to exponentials of the K input numbers.

[0378] Additional examples of neural networks may be found elsewhere in the disclosure.Rules-Based Module

[0379] Referring back to Fig. 3, in embodiments, artificial intelligence modules 304 may also include and / or provide access to a rules-based module 328 that may be integrated into or be accessed by an intelligence service client 336. In some embodiments, a rules-based module 328 may be configured with programmatic logic that defines a set of rules and other conditions that trigger certain actions that may be performed in connection with an intelligence client. In embodiments, the rules-based module 328 may be configured with programmatic logic that receives input and determines whether one or more rules are met based on the input. If a condition is met, the rules-based module 328 determines an action to perform, which may be output to a requesting intelligence service client 336. The data received by the rules-based engine may be received from an intelligence service input 332 source and / or may be requested from another module in artificial intelligence modules 304, such as the machine vision module 322, the neural network module 314, the ML module 312, and / or the like. For example, a rules-based module 328 may receive classifications of objects in a field of view of a mobile system (e.g., robot, autonomous vehicle, or the like) from a machine vision system and / or sensor data from a lidar sensor of the mobile system and, in response, may determine whether the mobile system should continue in its path, change its course, or stop. In embodiments, the rules-based module 328 may be configured to make other suitable rules-based decisions on behalf of a respective client 336, examples of which are discussed throughout the disclosure. In some embodiments, the rules-based engine may apply governance standards and / or analysis modules, which are described in greater detail below.Intelligence Services Controller and Analysis Management Module

[0380] In embodiments, artificial intelligence modules 304 interface with an intelligence service controller 302, which is configured to determine a type of request issued by an intelligence service client 336 and, in response, may determine a set of governance standards and / or analyses that are to be applied by the artificial intelligence modules 304 when responding to the request. In embodiments, the intelligence service controller 302 may include an analysis management module 306, a set of analysis modules 308, and a governance library 310.

[0381] In embodiments, an intelligence service controller 302 is configured to determine a type of request issued by an intelligence service client 336 and, in response, may determine a set of governance standards and / or analyses that are to be applied by the artificial intelligence modules 304 when responding to the request. In embodiments, the intelligence service controller 302 may include an analysis management module 306, a set of analysis modules 308,Docket: 16606-12POA and a governance library 310. In embodiments, the analysis management module 306 receives an artificial intelligence module 304 request and determines the governance standards and / or analyses implicated by the request. In embodiments, the analysis management module 306 may determine the governance standards that apply to the request based on the type of decision that was requested and / or whether certain analyses are to be performed with respect to the requested decision. For example, a request for a control decision that results in an intelligence service client 336 performing an action may implicate a certain set of governance standards that apply, such as safety standards, legal standards, quality standards, or the like, and / or may implicate one or more analyses regarding the control decision, such as a risk analysis, a safety analysis, an engineering analysis, or the like.

[0382] In some embodiments, the analysis management module 306 may determine the governance standards that apply to a decision request based on one or more conditions. Non-limiting examples of such conditions may include the type of decision that is requested, a geolocation in which a decision is being made, an environment that the decision will alfect, current or predicted environmental conditions of the environment and / or the like. In embodiments, the governance standards may be defined as a set of standards libraries stored in a governance library 310. In embodiments, standards libraries may define conditions, thresholds, rules, recommendations, or other suitable parameters by which a decision may be analyzed. Examples of standards libraries may include a legal standards library, a regulatory standards library, a quality standards library, an engineering standards library, a safety standards library, a financial standards library, and / or other suitable types of standards libraries. In embodiments, the governance library 310 may include an index that indexes certain standards defined in the respective standards library based on different conditions. Examples of conditions may be a jurisdiction or geographic areas to which certain standards apply, environmental conditions to which certain standards apply, device types to which certain standards apply, materials or products to which certain standards apply, and / or the like.

[0383] In some embodiments, the analysis management module 306 may determine the appropriate set of standards that must be applied with respect to a particular decision and may provide the appropriate set of standards to the artificial intelligence modules 304, such that the artificial intelligence modules 304 leverages the implicated governance standards when determining a decision. In these embodiments, the artificial intelligence modules 304 may be configured to apply the standards in the decision-making process, such that a decision output by the artificial intelligence modules 304 is consistent with the implicated governance standards. It is appreciated that the standards libraries in the governance library may be defined by the platform provider, customers, and / or third parties. The standards may be government standards, industry standards, customer standards, or other suitable sources. In embodiments, each set of standards may include a set of conditions that implicate the respective set of standards, such that the conditions may be used to determine which standards to apply, given a situation.

[0384] In some embodiments, the analysis management module 306 may determine one or more analyses that are to be performed with respect to a particular decision and may provide corresponding analysis modules 308 that perform those analyses to the artificial intelligence modules 304, such that the artificial intelligence modules 304 leverage the corresponding analysis modules 308 to analyze a decision before outputting the decision to the requesting client. In embodiments, the analysis modules 308 may include modules that are configured to perform specific analyses with respect to certain types of decisions, whereby the respective modules are executed by a processing system that hosts the instance of the intelligence system 300. Non-limiting examples of analysis modules 308 may include risk analysis module(s), security analysis module(s), decision tree analysis module(s), ethics analysis module(s), failure mode andDocket: 16606-12POA effects (FMEA) analysis module(s), hazard analysis module(s), quality analysis module(s), safety analysis module(s), regulatory analysis module(s), legal analysis module(s), and / or other suitable analysis modules.

[0385] In some embodiments, the analysis management module 306 is configured to determine which types of analyses to perform based on the type of decision that was requested by an intelligence service client 336. In some of these embodiments, the analysis management module 306 may include an index or other suitable mechanism that identifies a set of analysis modules 308 based on a requested decision type. In these embodiments, the analysis management module 306 may receive the decision type and may determine a set of analysis modules 308 that are to be executed based on the decision type. Additionally or alternatively, one or more governance standards may define when a particular analysis is to be performed. For example, the engineering standards may define what scenarios necessitate a FMEA analysis. In this example, the engineering standards may have been implicated by a request for a particular type of decision and the engineering standards may define scenarios when an FMEA analysis is to be performed. In this example, artificial intelligence modules 304 may execute a safety analysis module and / or a risk analysis module and may determine an alternative decision if the action would violate a legal standard or a safety standard. In response to analyzing a proposed decision, artificial intelligence modules 304 may selectively output the proposed condition based on the results of the executed analyses. If a decision is allowed, artificial intelligence modules 304 may output the decision to the requesting intelligence service client 336. If the proposed configuration is flagged by one or more of the analyses, artificial intelligence modules 304 may determine an alternative decision and execute the analyses with respect to the alternate proposed decision until a conforming decision is obtained.

[0386] It is noted here that in some embodiments, one or more analysis modules 308 may themselves be defined in a standard, and one or more relevant standards used together may comprise a particular analysis. For example, the applicable safety standard may call for a risk analysis that can use one or more allowable methods. In this example, an ISO standard for overall process and documentation, and an ASTM standard for a narrowly defined procedure may be employed to complete the risk analysis required by the safety governance standard.

[0387] As mentioned, the foregoing framework of an intelligence system 300 may be applied in and / or leveraged by various entities of a value chain. For example, in some embodiments, a platform-level intelligence system may be configured with the entire capabilities of the intelligence system 300, and certain configurations of the intelligence system 300 may be provisioned for respective value chain entities. Furthermore, in some embodiments, an intelligence service client 336 may be configured to escalate an intelligence system task to a higher-level value chain entity (e.g., edge-level or the platform-level) when the intelligence service client 336 cannot perform the task autonomously. It is noted that in some embodiments, an intelligence service controller 302 may direct intelligence tasks to a lower-level component. Furthermore, in some implementations, an intelligence system 300 may be configured to output default actions when a decision cannot be reached by the intelligence system 300 and / or a higher or lower-level intelligence system. In some of these implementations, the default decisions may be defined in a rule and / or in a standards library. Reinforcement Learning to determine optimal policy

[0388] Reinforcement learning (RL) is a machine learning technique where an agent iteratively learns an optimal policy through interactions with the environment. In RL, the agent must discover correct actions by trial-and-error so as to maximize some notion of long-term reward. Specifically, in a system employing RL, there exist two entities: (1) an environment and (2) an agent. The agent is a computer program component that is connected to its environment such that it can sense the state of the environment as well as execute actions on the environment. On each step of interaction, the agent senses the current state of the environment, s, and chooses an action to take, a. The action changesDocket: 16606-12POA the state of the environment, and the value of this state transition is communicated to the agent by a reward signal, r, where the magnitude of r indicates the desirability of an action. Overtime, the agent builds a policy, it, which specifies the action the agent will take for each state of the environment.

[0389] Formally, in reinforcement learning, there exists a discrete set of environment states, S; a discrete set of agent actions, A; and a set of scalar reinforcement signals, R. After learning, the system creates a policy, it, that defines the value of taking action asA in state ssS. The policy defines Qit(s, a) as the expected return value for starting from s, taking action a, and following policy it.

[0390] The reinforcement learning agent is trained in a policy through iterative exposure to various states, having the agent select an action as per the policy and providing a reward based on a function designed to reward desirable behavior. Based on the reward feedback, the system may “learn” the policy and becomes trained in producing desirable actions. For example, for navigation policy, RL agent may evaluate its state repeatedly (e.g., location, distance from a target object), select an action (e.g., provide input to the motors for movement towards the target object), evaluate the action using a reward signal, which provides an indication of the success of the action, (e.g., a reward of +10 if movement reduces the distance between a mobile system and a target object and -10 if the movement increases the distance). Similarly, the RL agent may be trained in a grasping policy by iteratively obtaining images of a target obj ect to be grasped, attempting to grasp the object, evaluating the attempt, and then executing the subsequent iteration using the evaluation of the attempt of the preceding iteration(s) to assist in determining the next attempt.

[0391] There may be several approaches for training the RL agent in a policy. Imitation learning is a key approach in which the agent learns from state / action pairs where the actions are those that would be chosen by an expert (e.g., a human) in response to an observed state. Imitation learning not just solves sample-inefficiency or computational feasibility problems, but also makes the training process safer. The RL agent may derive multiple examples of the state / action pairs by observing a human (e.g., navigating towards and grasping a target object), and uses them as a basis for training the policy. Behavior cloning (BC), which focuses on learning the expert’s policy using supervised learning, is an example of an imitation learning approach.

[0392] Value based learning approach aims to find a policy comprising a sequence of actions that maximizes the expectation value of future reward (or minimizes the expected cost). The RL agent may learn the value / cost function and then derive a policy with respect to the same. Two different expectation values are often referred to: the state value V(s) and the action value Q (s,a), respectively. The state value function V(s) represents the value associated with the agent at each state, whereas the action value function Q(s,a) represents the value associated with the agent at state s and performing action a. The value-based learning approach works by approximating optimal value (V* or Q*) and then deriving an optimal policy. For example, the optimal value function Q*(s, a) may be identified by finding the sequence of actions that maximizes the state-action value function Q (s, a). The optimal policy for each state can be derived by identifying the highest valued action that can be taken from each state.

[0393] 7t*(s)=argmax Q*(s,a)

[0394] To iteratively calculate the value function as actions within the sequence are executed and the mobile system transitions from one state to another, the Bellman Optimality equation may be applied. The optimal value function Q*(s,a) obeys the Bellman Optimality equation and can be expressed as:(eq. 4) Q*(st, at) = E [rt+l+y max Q*(st+1 ,at+l)]Docket: 16606-12POA

[0395] Policy based learning approach directly optimizes the policy function n using a suitable optimization technique (e.g., stochastic gradient descent) to fine tune a vector of parameters without calculating a value function. The policybased learning approach is typically effective in high-dimensional or continuous action spaces.

[0396] Fig. 7 illustrates an approach based on reinforcement learning and including evaluation of various states, actions and rewards in determining optimal policy for executing one or more tasks by a mobile system.

[0397] At 402, a reinforcement learning agent (e.g., of the intelligence services system 300) receives sensor information, including a plurality of images captured by the mobile system in the environment. The analysis of one or more of these images may enable the agent to determine a first state associated with the mobile system at 404. The data representing the first state may include information about the environment, such as images, sounds, temperature or time and information about the mobile system, including its position, speed, internal state (e.g., battery life, clock setting), etc.

[0398] At 406, 408, and 410, various potential actions responsive to the state may be determined. Some examples of potential actions include providing control instructions to actuators, motors, wheels, wings flaps, or other components that controls the agent's speed, acceleration, orientation, or position; changing the agent's internal settings, such as putting certain components into a sleep mode to conserve battery life; changing the direction if the agent is in danger of colliding with an obstacle object; acquiring or transmitting data; attempting to grasp a target object and the like.

[0399] At 412, 414 and 416, expected rewards may be determined for each of the potential actions based on a reward function. For each of the determined potential actions, an expected reward may be determined based on a reward function. The reward may be predicated on a desired outcome, such as avoiding an obstacle, conserving power, or acquiring data. If the action yields the desired outcome (e.g., avoiding the obstacle), the reward is high; otherwise, the reward may be low.

[0400] The agent may also look to the future to analyze whether there may be opportunities for realizing higher rewards in the future. At 418, 420, and 422, the agent may determine future states resulting from potential actions, respectively, at 406, 408, and 410.

[0401] For each of the future states predicted at 418, 420, and 422, one or more future actions may be determined and evaluated. At 424, 426, and 428, for example, values or other indicators of expected rewards associated with one or more of the future actions may be developed. The expected rewards associated with one or more future actions may be evaluated by comparing the values of reward functions associated with each future action.

[0402] At 430, an action may be selected based on a comparison of expected current and future rewards.

[0403] In embodiments, the reinforcement learning agent may be pre-trained through simulations in a digital twin system. In embodiments, the reinforcement agent may be pre-trained using behavior cloning. In embodiments, the reinforcement agent may be trained using a deep reinforcement learning algorithm selected from Deep Q-Network (DQN), double deep Q-Network (DDQN), Deep Deterministic Policy Gradient (DDPG), soft actor critic (SAC), advantage actor critic (A2C), asynchronous advantage actor critic (A3C), proximal policy optimization (PPO), trust region policy optimization (TRPO).

[0404] In embodiments, the reinforcement learning agent may look to balance exploitation (of current knowledge) with exploration (of uncharted territory) while traversing the action space. For example, the agent may follow an s- greedy policy by randomly selecting exploration occasionally with probability s while taking the optimal action most of the time with probability 1-s, where s is a parameter satisfying 0<s<l .Docket: 16606-12POAGenerative Al systems

[0405] In example embodiments, a generative artificial intelligence engine (GATE) may be combined with a machine learning system in a transaction environment. Input to the GAIE may include images, video, audio, text, programmatic code, data, and the like. Outputs from a GAIE may include structured and organized prose, images, video, audio content, software / programming source code, formatted data (e.g., arrays), algorithms, definitions, context-specific structures (e.g., smart contacts, transaction platform configuration data sets, and the like), machine language-based data (e.g., API-formatted content), and the like. For GAIE instances in which the models are designed to process text data, the GAIE may interface to other programmatic systems (such as traditional machine learning engines) to process other forms of data into text data. In example embodiments, the other programmatic systems, including systems executing machine learning algorithms, may produce textual based (optionally at volume) that may be consumed by GAIE. For example, consider such another system building a series of one thousand text-based observations on the other-formatted data; this may be a useful input for a GAIE model to learn and process (e.g., summarize) into text- formatted output information. In example embodiments, an interface between the GAIE and its combined machine learning system may be extended to include a dialogue between the systems, where the GAIE includes and / or accesses a capability to ask the machine learning system specific questions to facilitate the refining of its knowledge. For example, the dialogue capability may include a request of the machine learning system to provide an assessment of current market trading positions. In another example, the dialogue capability may encode numeric outputs from the machine learning engine into text (e.g., words, such as high, medium, low) that may be input for interpretation by the GAIE.

[0406] Referring to Fig. 8, a platform 800 for the application of generative Al may include a robust task-agnostic nexttoken prediction Al engine 802 that operates to predict a next token given a set of inputs encoded as embedded tokens. A robust task-agnostic next-token prediction Al engine 802 may include deep learning models, which use multilayered neural networks to process, analyze, and make predictions with complex data, such as language. An objective of the robust next-token prediction Al engine 802 may include data science modeling through, among other things, use of topic-specific embeddings, attention mechanisms, and decoder-only transformer models. Capabilities of such an engine 802 may include a pre-training capability to facilitate configuring next- token prediction for specific subject matter (e.g., marketplace item valuation), a tokenizing capability to facilitate converting complex terms into actionable tokens (e.g., converting compound chemical names into fundamental elements), access to distributed training (e.g., data-parallel training and / or model-parallel training, and the like), few-shot learning to reduce training demand for updates, such as new business intelligence data, and the like. In general, the next-token prediction Al engine 802 may combine large language modeling techniques and decoder-only transformer models to generate powerful foundation models for next-token prediction Al content generation.

[0407] In example embodiments, the next-token prediction Al engine 802 may be structured with a machine learning (sparse Multi-Layer Perceptron) architecture configured to sparsely activate conditional computation using, for example, mixture-of-experts (MoE) techniques. A machine learning architecture may be configured with expert modules that may be used to process inputs and a gating function that may facilitate assigning expert modules to process portion(s) of input tokens. A machine learning architecture may further include a combination of deterministic routing of input tokens to expert modules and learned routing that uses a portion of input tokens to predict the expert modules for a set of input tokens.Docket: 16606-12POA

[0408] A G AIE may be trained to operate within a domain, such as written language, computer programming language, subject matter-specific domains (e.g., a software-orchestrated marketplace domain), and the like, to generate content (constructs) that comply with rules of the domain. In general, a GAIE may generate content for any topic for which the GAIE is trained. So, for example, a GAIE may be trained on a topic of pig farmers and may therefore generate language-based descriptions, images, contracts, breeding guidance, textual output, and the like for any of a potentially wide range of pig farmer sub-topics.

[0409] Adapting a generative Al engine for subject matter- specific applications may include pretraining a next-token prediction Al model-based system through the use of, for example, in-context (e.g., application, domain, topicspecific) examples that are responsive to a corresponding prompt. While the next-token predictive capabilities of the underlying next-token prediction Al engine may remain unaffected by this pre-training, subject matter- specific pretrained instances may be developed / deployed.

[0410] In example embodiments, a platform 800 for the application of generative Al may include a set of subject matter-specific pretrained examples and prompts 804. This set of examples and prompts 804 may be configured by analyzing (e.g., by a human expert and / or computer-based expert and / or digital twin) information that characterizes various aspects of the domain to generate example prompts and preferred and / or correct responses. Pretraining may also include training the next-token prediction Al engine 802 by sampling some text (e.g., prompt / response sets) from the set of subject matter-specific pretrained examples and prompts 804 and training it to predict a next word, object, and / or term. Pretraining may also include sampling some images, contracts, architectures, and the like to predict a next token. These prompt-response sub-sets may facilitate pre-training the prediction Al engine 802 for predicting a next token (e.g., word, object, image element, and the like) for various aspects.

[0411] When an instance is implemented for textual generation, such a GAIE instance may be referred to as a natural language generation system that constructs words (e.g., from sub-word tokens), sentences, and paragraphs for a target subject and / or domain.

[0412] In example embodiments, real-world instances of the platform 800 may require ongoing updates to facilitate the platform 800 being responsive as aspects of a domain (e.g., a business entity in the domain) change, such as business goals change, newproducts are released, competitors merge, new markets emerge, and the like. In this regard, training the platform 800 with in-context prompts and examples may be automated and repeated as new data is released for an enterprise to prevent snapshot-in-time data aging-based errors. The platform 800 for the application of generative Al may include an ongoing pre-training module 828 that processes new and updated content into prompt and / or response sets and interactively iterates through rounds of pre- training. New and updated data and / or information may regularly be found in various subject matter specific information sets, such as: a dataset of medical records (e.g., to assist with medical diagnoses), a dataset of legal documents and court decisions (e.g., to provide legal advice), a release of a new product (e.g., images of the product), or a financial dataset such as SEC filings or analyst reports. In example embodiments, uses of the platform 800 may include applying the pre-training and optimizing techniques to a range of different domains (e.g., medical diagnosis, business operation, marketplace operation, and the like) to produce a fine-tuned domain specific token-predictive engine including ongoing refinement through (daily) in-context pretraining.

[0413] In example embodiments, an ongoing pre-training module 828 may work with the next-token prediction Al engine 802 to update a set of subject matter specific tokens that may be maintained in a subj ect matter specific instance token storage facility 808. This subject matter specific instance token storage facility 808 may be referenced by aDocket: 16606-12POA subject matter specific instance of the next-token prediction Al engine 802 during an operational mode (e.g., when processing inputs / prompts). In example embodiments, the platform 800 may include a plurality of sets of subject matter specific tokens that may be maintained by corresponding ongoing pre-training modules 828.

[0414] Training, however, may not ensure that the responses to prompts are correct every time. In general, a business entity is likely to be less interested in a tool that provides answers that are probably right and may differ from time to time. A product that can provide accurate responses (e.g., including taking actions) based on what the end-user wants vastly increases the potential use cases and product value. A high level of accuracy and integration with operational systems may enable such a tool to go beyond just generating new content to be more productive; through integration with workflows, it may facilitate automating workflow actions. In this regard, the platform 800 for the application of generative Al may also include a pre-training optimizing engine 806 that may work cooperatively with the ongoing pre-training module 828 to further refine accuracy of responses to prompts for a domain. The pre-training optimizing engine 806 may facilitate improved accuracy of in-context responses, task-specific fine-tuning, and for sparse model variants of the platform 800, enrich few-shot learning capabilities. In example embodiments, fine tuning may further benefit the platform by reducing bias that may be present in the training data. This may be essential to ensure subject matter specific jargon is adapted as training data changes (e.g., in the digital marketing / promotional space, ensure that “influencer” is replaced with “creator”). Further, a pre-training optimizing engine 806 may provide a wider range of prompts and responses based on user preferences (e.g., speaking styles) to enrich the platform’s ability to provide usercentric responses. In example embodiments, user-centric responses may include fine tuning the platform 800 for different roles in an organization. As an example, when a user in a financial planning role inquires about a business development topic, responses may be directed toward the financial planning role (e.g., as compared to a customer / client inquiry about that topic).

[0415] A platform 800 for the application of generative Al may be used to produce text-based content for a multinational entity with employees who speak different languages. While the platform 800 may be trained (and pre-trained) to operate interactively in a plurality of languages, generating automated content may benefit from use of a neural machine translation module 810. In example embodiments, a portion of the entity in a first jurisdiction may produce content in a first language and resulting recurring generated output (e.g., types of reports and the like) may be generated in the first language. However, employees who speak a second language may benefit from the type of report when translated into the employee’s native language. Therefore, associating the neural machine translation module 810 with the platform may prove valuable while reducing compute demand for the platform 800.

[0416] Emerging next-token prediction Al systems feature increasingly adaptable next token prediction capabilities. These capabilities may be further adapted to assist in closed problem set solution prediction, such as allocation of resources, deployment of a robotic fleet and the like. To achieve greater prediction capabilities, a subject matter specific next-token prediction Al-based engine, such as the platform 800 for the application of generative Al, may include a solution-predictive engine 812 that leverages next-token (e.g., next word) predictive capabilities to predict a most-likely solution to a closed solution-set problem. This may be accomplished optionally through the use of sets of problem domain-specific pre-training prompts and examples. Such examples may be adapted for different user preferences. In example embodiments, each user in a closed problem set environment may generate prompts and responses that may enable the platform 800 to respond to the user based on the user’s inquiry style. Alternatively, the solution prediction engine 812 may adapt a user’s prompt and / or configure a prompt based on user preferences toDocket: 16606-12POA attempt to deliver responses that are consistent with a user’s preferences (e.g., engineering-based responses for an engineer role-user and legal-based responses for a lawyer).

[0417] For more complex analysis and decision making / predicting, a formal logic-based Al system 814 may be incorporated into and / or be referenced by the subject matter specific platform 800.

[0418] Further, the basic concepts of next-token prediction of a generative Al engine, such as the platform 800 for subject matter based application of generative Al may be applied to analyzed expressions of images, audio (e.g., encoded text), video (e.g., sequences of related images), programmatic code (domain-specific text with readily understood rules), and the like. Therefore, a next-token prediction Al platform (e.g., platform 800) may further include an image / video analysis engine 816 (optionally NN -based) that adds a spatial aspect to the next-token predictive capabilities of a next-token prediction Al system. Images used for training may include 3D CAD images (for a domain that includes physical devices such as vehicles), radiologic images (for a medical analysis domain), business performance graphs, schematics, and the like. In example embodiments, aspects of the underlying task-agnostic nexttoken prediction Al engine 802 may be adapted (e.g., different embeddings, neural network structures and the like) for different input formats, such as images, temporal-spatial content, and the like.

[0419] The platform 800 may further include an expert review and approval portal 818 through which an expert (e.g., human / digital twin, and the like) can review, edit, and approve content generated. Examples include review and adaptation by a subject matter specific data story expert; a data scientist, and the like. The expert review and approval portal 818 may operate cooperatively with, for example, the pre-training optimizing engine 806 that may receive and analyze expert feedback (e.g., edits to the content and the like) for opportunities to further optimize the platform 800.

[0420] The platform 800 may further include a training data generation facility 820 that may generate natural language prompts, such as subject matter specific prompts that may be applied by, for example, the pre-training optimizing engine 806 to increase platform response accuracy and / or efficiency while fine tuning a subject matter specific instance.

[0421] In example embodiments, the platform 800 may further be configured to access a corpus of domain and / or problem relevant content as a step in responding to a prompt. In example embodiments, the platform may be pretrained on the content of the corpus. While the content of the corpus may not be directly included in the response, such as if it provides a level of detail beyond what the platform 800 has been trained to provide in a response, it may be cited in the response to facilitate identifying and expressing sources from which a response is derived. These external source references may be handled via a citation module 822.

[0422] Business decisions are often context-based. Understanding both the context for a decision and aspects and / or assumptions of the decision process may prove highly valuable for evaluating, for example, competing decisions and / or recommendations. Context may include both tangible and intangible factors. An intangible factor may include historical interactions between parties involved in the evaluation process, for example. A decision process may include not only assumptions on which a decision or recommendation is based, but also criteria by which tangible factors are processed, evaluated, analyzed, and the like. To provide such context for generated output of the platform 800, an interpretability engine 824 may be incorporated into and / or be accessible to the platform 800. An objective of use of the interpretability engine 824 may be to generate additional content that reflects context for, among other things, how the next-token prediction Al instance operates and / or generates a corresponding output.

[0423] In example embodiments, the next-token predictive capabilities of a next-token prediction Al engine 802 may be utilized for developing a set of emergent data science predictive and / or interpretive skills. While such a platformDocket: 16606-12POA may be trained directly on various data sets, context for elements and results in such data sets may be a rich source of complementary training data. By associating data elements with descriptions thereof, the platform 800 may gain data science capabilities, such as to group by or pivot categorical sums, infer feature importance, derive correlations, predict unseen test cases, and the like. In this regard, a data science emergent skill development system 826 may be utilized by the platform to enhance further subject matter specific applicability and utility.

[0424] While only a few embodiments of the disclosure have been shown and described, it will be obvious to those skilled in the art that many changes and modifications may be made thereunto without departing from the spirit and scope of the disclosure as described in the following claims. All patent applications and patents, both foreign and domestic, and all other publications referenced herein are incorporated herein in their entireties to the full extent permitted by law.

[0425] The methods and systems described herein may be deployed in part or in whole through machines that execute computer software, program codes, and / or instructions on a processor. The disclosure may be implemented as a method on the machine(s), as a system or apparatus as part of or in relation to the machine(s), or as a computer program product embodied in a computer readable medium executing on one or more of the machines. In embodiments, the processor may be part of a server, cloud server, client, network infrastructure, mobile computing platform, stationary computing platform, or other computing platforms. A processor may be any kind of computational or processing device capable of executing program instructions, codes, binary instructions and the like, including a central processing unit (CPU), a general processing unit (GPU), a logic board, a chip (e.g., a graphics chip, a video processing chip, a data compression chip, or the like), a chipset, a controller, a system-on-chip (e.g., an RF system on chip, an Al system on chip, a video processing system on chip, or others), an integrated circuit, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), an approximate computing processor, a quantum computing processor, a parallel computing processor, a neural network processor, or other type of processor. The processor may be or may include a signal processor, digital processor, data processor, embedded processor, microprocessor or any variant such as a coprocessor (math co-processor, graphic co-processor, communication co-processor, video co-processor, Al coprocessor, and the like) and the like that may directly or indirectly facilitate execution of program code or program instructions stored thereon. In addition, the processor may enable execution of multiple programs, threads, and codes. The threads may be executed simultaneously to enhance the performance of the processor and to facilitate simultaneous operations of the application. By way of implementation, methods, program codes, program instructions and the like described herein may be implemented in one or more threads. The thread may spawn other threads that may have assigned priorities associated with them; the processor may execute these threads based on priority or any other order based on instructions provided in the program code. The processor, or any machine utilizing one, may include non- transitory memory that stores methods, codes, instructions and programs as described herein and elsewhere. The processor may access a non-transitory storage medium through an interface that may store methods, codes, and instructions as described herein and elsewhere. The storage medium associated with the processor for storing methods, programs, codes, program instructions or other type of instructions capable of being executed by the computing or processing device may include but may not be limited to one or more of a CD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache, network-attached storage, server-based storage, and the like.

[0426] A processor may include one or more cores that may enhance speed and performance of a multiprocessor. In embodiments, the process may be a dual core processor, quad core processors, other chip-level multiprocessor and the like that combine two or more independent cores (sometimes called a die).Docket: 16606-12POA

[0427] The methods and systems described herein may be deployed in part or in whole through machines that execute computer software on various devices including a server, client, firewall, gateway, hub, router, switch, infrastructure- as-a-service, platform-as-a-service, or other such computer and / or networking hardware or system. The software may be associated with a server that may include a file server, print server, domain server, internet server, intranet server, cloud server, infrastructure-as-a-service server, platform-as-a-service server, web server, and other variants such as secondary server, host server, distributed server, failover server, backup server, server farm, and the like. The server may include one or more of memories, processors, computer readable media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other servers, clients, machines, and devices through a wired or a wireless medium, and the like. The methods, programs, or codes as described herein and elsewhere may be executed by the server. In addition, other devices required for execution of methods as described in this application may be considered as a part of the infrastructure associated with the server.

[0428] The server may provide an interface to other devices including, without limitation, clients, other servers, printers, database servers, print servers, file servers, communication servers, distributed servers, social networks, and the like. Additionally, this coupling and / or connection may facilitate remote execution of programs across the network. The networking of some or all of these devices may facilitate parallel processing of a program or method at one or more locations without deviating from the scope of the disclosure. In addition, any of the devices attached to the server through an interface may include at least one storage medium capable of storing methods, programs, code and / or instructions. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for program code, instructions, and programs.

[0429] The software program may be associated with a client that may include a file client, print client, domain client, internet client, intranet client and other variants such as secondary client, host client, distributed client and the like. The client may include one or more of memories, processors, computer readable media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other clients, servers, machines, and devices through a wired or a wireless medium, and the like. The methods, programs, or codes as described herein and elsewhere may be executed by the client. In addition, other devices required for the execution of methods as described in this application may be considered as a part of the infrastructure associated with the client.

[0430] The client may provide an interface to other devices including, without limitation, servers, other clients, printers, database servers, print servers, file servers, communication servers, distributed servers and the like. Additionally, this coupling and / or connection may facilitate remote execution of programs across the network. The networking of some or all of these devices may facilitate parallel processing of a program or method at one or more locations without deviating from the scope of the disclosure. In addition, any of the devices attached to the client through an interface may include at least one storage medium capable of storing methods, programs, applications, code and / or instructions. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for program code, instructions, and programs.

[0431] The methods and systems described herein may be deployed in part or in whole through network infrastructures. The network infrastructure may include elements such as computing devices, servers, routers, hubs, firewalls, clients, personal computers, communication devices, routing devices and other active and passive devices, modules and / or components as known in the art. The computing and / or non-computing device(s) associated with the network infrastructure may include, apart from other components, a storage medium such as flash memory, buffer,Docket: 16606-12POA stack, RAM, ROM and the like. The processes, methods, program codes, instructions described herein and elsewhere may be executed by one or more of the network infrastructural elements. The methods and systems described herein may be adapted for use with any kind of private, community, or hybrid cloud computing network or cloud computing environment, including those which involve features of software as a service (SaaS), platform as a service (PaaS), and / or infrastructure as a service (laaS).

[0432] The methods, program codes, and instructions described herein and elsewhere may be implemented on a cellular network with multiple cells. The cellular network may either be frequency division multiple access (FDMA) network or code division multiple access (CDMA) network. The cellular network may include mobile devices, cell sites, base stations, repeaters, antennas, towers, and the like. The cell network may be a GSM, GPRS, 3G, 4G, 5G, LTE, EVDO, mesh, or other network types.

[0433] The methods, program codes, and instructions described herein and elsewhere may be implemented on or through mobile devices. The mobile devices may include navigation devices, cell phones, mobile phones, mobile personal digital assistants, laptops, palmtops, netbooks, pagers, electronic book readers, music players and the like. These devices may include, apart from other components, a storage medium such as flash memory, buffer, RAM, ROM and one or more computing devices. The computing devices associated with mobile devices may be enabled to execute program codes, methods, and instructions stored thereon. Alternatively, the mobile devices may be configured to execute instructions in collaboration with other devices. The mobile devices may communicate with base stations interfaced with servers and configured to execute program codes. The mobile devices may communicate on a peer-to- peer network, mesh network, or other communications network. The program code may be stored on the storage medium associated with the server and executed by a computing device embedded within the server. The base station may include a computing device and a storage medium. The storage device may store program codes and instructions executed by the computing devices associated with the base station.

[0434] The computer software, program codes, and / or instructions may be stored and / or accessed on machine readable media that may include: computer components, devices, and recording media that retain digital data used for computing for some interval of time; semiconductor storage known as random access memory (RAM); mass storage typically for more permanent storage, such as optical discs, forms of magnetic storage like hard disks, tapes, drums, cards and other types; processor registers, cache memory, volatile memory, non-volatile memory; optical storage such as CD, DVD; removable media such as flash memory (e.g., USB sticks or keys), floppy disks, magnetic tape, paper tape, punch cards, standalone RAM disks, Zip drives, removable mass storage, off-line, and the like; other computer memory such as dynamic memory, static memory, read / write storage, mutable storage, read only, random access, sequential access, location addressable, file addressable, content addressable, network attached storage, storage area network, bar codes, magnetic ink, network-attached storage, network storage, NVME-accessible storage, PCIE connected storage, distributed storage, and the like.

[0435] The methods and systems described herein may transform physical and / or intangible items from one state to another. The methods and systems described herein may also transform data representing physical and / or intangible items from one state to another.

[0436] The elements described and depicted herein, including in flow charts and block diagrams throughout the figures, imply logical boundaries between the elements. However, according to software or hardware engineering practices, the depicted elements and the functions thereof may be implemented on machines through computer executable code using a processor capable of executing program instructions stored thereon as a monolithic softwareDocket: 16606-12POA structure, as standalone software modules, or as modules that employ external routines, code, services, and so forth, or any combination of these, and all such implementations may be within the scope of the disclosure. Examples of such machines may include, but may not be limited to, personal digital assistants, laptops, personal computers, mobile phones, other handheld computing devices, medical equipment, wired or wireless communication devices, transducers, chips, calculators, satellites, tablet PCs, electronic books, gadgets, electronic devices, devices, artificial intelligence, computing devices, networking equipment, servers, routers and the like. Furthermore, the elements depicted in the flow chart and block diagrams or any other logical component may be implemented on a machine capable of executing program instructions. Thus, while the foregoing drawings and descriptions set forth functional aspects of the disclosed systems, no particular arrangement of software for implementing these functional aspects should be inferred from these descriptions unless explicitly stated or otherwise clear from the context. Similarly, it will be appreciated that the various steps identified and described in the disclosure may be varied, and that the order of steps may be adapted to particular applications of the techniques disclosed herein. All such variations and modifications are intended to fall within the scope of this disclosure. As such, the depiction and / or description of an order for various steps should not be understood to require a particular order of execution for those steps, unless required by a particular application, or explicitly stated or otherwise clear from the context.

[0437] The methods and / or processes described in the disclosure, and steps associated therewith, may be realized in hardware, software or any combination of hardware and software suitable for a particular application. The hardware may include a general-purpose computer and / or dedicated computing device or specific computing device or particular aspect or component of a specific computing device. The processes may be realized in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable devices, along with internal and / or external memory. The processes may also, or instead, be embodied in an application specific integrated circuit, a programmable gate array, programmable array logic, or any other device or combination of devices that may be configured to process electronic signals. It will further be appreciated that one or more of the processes may be realized as a computer executable code capable of being executed on a machine-readable medium.

[0438] The computer executable code may be created using a structured programming language such as C, an object oriented programming language such as C++, or any other high-level or low-level programming language (including assembly languages, hardware description languages, and database programming languages and technologies) that may be stored, compiled or interpreted to run on one of the devices described in the disclosure, as well as heterogeneous combinations of processors, processor architectures, or combinations of different hardware and software, or any other machine capable of executing program instructions. Computer software may employ virtualization, virtual machines, containers, dock facilities, portainers, and other capabilities.

[0439] Thus, in one aspect, methods described in the disclosure and combinations thereof may be embodied in computer executable code that, when executing on one or more computing devices, performs the steps thereof. In another aspect, the methods may be embodied in systems that perform the steps thereof and may be distributed across devices in a number of ways, or all of the functionality may be integrated into a dedicated, standalone device or other hardware. In another aspect, the means for performing the steps associated with the processes described in the disclosure may include any of the hardware and / or software described in the disclosure. All such permutations and combinations are intended to fall within the scope of the disclosure.

[0440] While the disclosure has been disclosed in connection with the preferred embodiments shown and described in detail, various modifications and improvements thereon will become readily apparent to those skilled in the art.Docket: 16606-12POAAccordingly, the spirit and scope of the disclosure is not to be limited by the foregoing examples, but is to be understood in the broadest sense allowable by law.

[0441] The use of the terms “a” and “an” and “the” and similar referents in the context of describing the disclosure (especially in the context of the following claims) is to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “with,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitations of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the disclosure and does not pose a limitation on the scope of the disclosure unless otherwise claimed. The term “set” may include a set with a single member. No language in the specification should be construed as indicating any non-claim ed element as essential to the practice of the disclosure.

[0442] While the foregoing written description enables one skilled to make and use what is considered presently to be the best mode thereof, those skilled in the art will understand and appreciate the existence of variations, combinations, and equivalents of the specific embodiment, method, and examples herein. The disclosure should therefore not be limited by the above-described embodiment, method, and examples, but by all embodiments and methods within the scope and spirit of the disclosure.

[0443] All documents referenced herein are hereby incorporated by reference as if fully set forth herein.Enterprise Access Laver - Introduction

[0444] One environment that can utilize the functionality of an access layer is an enterprise. An enterprise generally refers to an organization with a particular overarching purpose, goal, or objective. For instance, a purpose may be to produce and market a particular set of one or more product lines, to undertake a charitable activity, to provide a public service, or other purpose. To achieve its purpose, an enterprise may have a structure that includes various business units, such as executive officers, aboard of trustees or directors, divisions, departments, managers and other job roles, facilities and other assets, a wide array of projects, activities, processes and workflows, etc. Some enterprises span multiple business sectors and therefore have business units, such as divisions, that can be dedicated to a particular business sector.

[0445] Enterprises, usually by their size and nature, can have a wide array of resources and assets. For instance, their resources may include raw materials, equipment, devices, systems, products (e.g., parts, components, sub-assemblies, assemblies), capital, knowledge, and technology among others. Some examples of knowledge resources include resources that are customer-based (e.g., customer lists or customer transactional history such as order history, contact information, demand frequency, etc.), vender / supplier-based (e.g., suppliers, procurement information, supply transactional history, etc.), process-based (e.g., formulations, procedures such as standard operating procedures, technical data sheets, process reports such as material compliance reports or quality reports, or other memorialized process expertise), and research-based (e.g., research and development information or reports). Enterprise resources may also include human resources, including expertise and knowledge of enterprise personnel and contractors, or personnel and contractors of customers, suppliers, vendors, partners, etc. Technology resources may include resourcesDocket: 16606-12POA such as inventions, trade secrets, designs, proprietary information of the enterprise (e.g., proprietary software or processes), etc.

[0446] In some embodiments, some or all of the resources of the enterprise may be represented in some digital form (e.g., a particular file format), such that these resources may undergo management and processing actions such as being copied, edited, shared, transferred, exchanged, updated, recorded, monitored, accessed, extracted, transformed, loaded, compressed, decompressed, deleted, obsoleted or otherwise processed, such as in digital form or between digital form and another form (such as where knowledge of an expert worker or other individual is accessed by querying the worker through a crowdsourcing system). Even resources that have not had a conventional digital format (e.g., physical goods or equipment) may be represented in a digital format. For example, a non-fungible token may be used to represent resources that are not digital. Additionally or alternatively, some aspect of a resource (e.g., a physical good) can be represented as a digital form or via a digital proxy. For instance, a physical resource may have an associated digital certificate of authenticity, proof of purchase, deed, or a title.

[0447] Due to the expanding evolution of digital assets, it is inevitable that enterprises demand an efficient and robust manner of managing digital assets. For example, just as enterprises have historically and efficiently engaged in the transaction of physical goods and the logistics involved in those transactions, enterprises will likely need to address similar aspects for digital transactions. Furthermore, with digital assets, there may be different issues that need to be addressed due to the digital nature of these assets when compared to physical assets. For instance, although unauthentic copies of physical goods are feasible, often, depending on the physical good, the energy, expertise, or equipment needed to generate a copy of physical goods can by itself inhibit copying and help promote the authenticity of a physical asset. In comparison, a digital asset may be easier to replicate. For example, computing has predominantly evolved with a particular simplicity to read / write functionality; making digital files / formats in many cases effortless to duplicate often with minimal loss. Ease of duplication can result in complications, such as where a digital asset is copied and widely distributed and some copies are subsequently modified, making it difficult to determine which versions, among many, are valid. Problems of provenance and validity are compounded with the increasing presence of dynamic digital such as smart contracts and dynamic objects, that are serially updated without human intervention through a network, often by linkage to other dynamic objects that are of uncertain provenance.

[0448] Another aspect that is different between physical assets and digital assets is interoperability. Interoperability refers to the ability of systems to exchange and use information. For a physical asset, supply chains are typically structured by participating enterprises to facilitate structural interoperability (such as among the component parts of a system), chemical operability (such as among constituent ingredients in a recipe), etc. For digital assets (such term including physical assets that have a digital component or capability (such as smart devices and systems)), interoperability may have a variety of different issues. For example, having the computing resources to interact with a digital asset may not be cost prohibitive. Therefore, there may be a large number of entities that are able to cooperate with regard to a digital asset. Additionally, the number of entities is fairly elastic because it may quickly increase or decrease depending on the scarcity or demand for the digital asset (e.g., due to its low-cost barrier to entry). Yet a potential outgrowth of the large number of entities that are able to interact with a digital asset is that the access point should have the capability to accommodate for variance between the entities an...

Claims

Docket: 16606-12POACLAIMSWhat is claimed is:Al model-driven edge system with intelligent edge capabilities for distributed media content delivery and transactions1. A system for enabling distributed media transactions, comprising: a plurality of edge devices configured to provide adaptive networking capabilities including at least one of adaptive selection of networking protocols, adaptive routing of network traffic, adaptive block sizing, adaptive data rate, adaptive error correction, or adaptive storage of data; a set of artificial intelligence models executed on the plurality of edge devices and configured to support networking operations; a local transaction processing capability implemented on the plurality of edge devices and configured to process and manage media transactions locally; a blockchain-based transaction layer configured to handle distributed media transactions across the plurality of edge devices; and a generative Al caching capability implemented at a network layer and configured to perform at least one of output caching, feature caching, or model state caching to support transaction-related media handling.

2. The system of claim 1 , wherein at least one model of the set of artificial intelligence models is configured to perform intelligent routing of transaction data through cellular, WiFi, Open Radio Access Network (ORAN), Bluetooth, or loT communication protocols based on network conditions.

3. The system of claim 1 , wherein at least one model of the set of artificial intelligence models is configured to perform predictive network traffic management for allocating bandwidth to transaction workflows.

4. The system of claim 1 , wherein at least one model of the set of artificial intelligence models is configured to perform anomaly detection operations.

5. The system of claim 1, wherein the plurality of edge devices comprise networking devices selected from routers, switches, edge devices, loT systems, mesh-networking nodes, networking chips, and networking systems-on-chip that integrate networking capabilities with artificial intelligence capabilities for media content handling.

6. The system of claim 1, wherein the system includes a content delivery network implemented at the edge that uses cached generative Al outputs, features, or model states to support delivery of generative Al media content.

7. The system of claim 1, wherein blockchain-based media rights transactions are orchestrated using a set of artificial intelligence models in accordance with a media rights infrastructure configured for automated transaction orchestration.

8. The system of claim 1, wherein the system employs containerization and microservices architectures to allow rapid deployment and scaling of transaction-related functionality across the distributed edge devices.

9. The system of claim 1, wherein the system further comprises sensor fusion capabilities configured to integrate loT data, social data, web data, or crowdsourced data to support analytics related to media transactions.

10. The system of claim 1, wherein the system uses adaptive networking at the edge in combination with generative Al caching to reduce computational resources and energy consumption associated with generative Al workloads that are part of transaction workflows.

11. A method for enabling distributed media transactions, comprising:Docket: 16606-12POA providing, by a plurality of edge devices, adaptive networking capabilities including at least one of adaptive selection of networking protocols, adaptive routing of network traffic, adaptive block sizing, adaptive data rate, adaptive error correction, or adaptive storage of data; executing a set of artificial intelligence models on the plurality of edge devices to support networking operations; processing and managing media transactions locally on the plurality of edge devices using a local transaction processing capability; handling distributed media transactions across the plurality of edge devices using a blockchain-based transaction layer; and performing, at a network layer, at least one of output caching, feature caching, or model state caching using a generative Al caching capability to support transaction-related media handling.

12. The method of claim 11, wherein at least one model of the set of artificial intelligence models performs intelligent routing of transaction data through cellular, WiFi, Open Radio Access Network (ORAN), Bluetooth, or loT communication protocols based on network conditions.

13. The method of claim 11 , wherein at least one model of the set of artificial intelligence models performs predictive network traffic management for allocating bandwidth to transaction workflows.

14. The method of claim 11, wherein at least one model of the set of artificial intelligence models performs anomaly detection operations.

15. The method of claim 11, wherein the plurality of edge devices comprise networking devices selected from routers, switches, edge devices, loT systems, mesh-networking nodes, networking chips, and networking systems-on-chip that integrate networking capabilities with artificial intelligence capabilities for media content handling.

16. The method of claim 11, further comprising implementing a content delivery network at the edge that uses cached generative Al outputs, features, or model states to support delivery of generative Al media content.

17. The method of claim 11, further comprising orchestrating blockchain-based media rights transactions using a set of artificial intelligence models in accordance with a media rights infrastructure configured for automated transaction orchestration.

18. The method of claim 11, further comprising employing containerization and microservices architectures to allow rapid deployment and scaling of transaction-related functionality across the distributed edge devices.

19. The method of claim 11 , further comprising integrating loT data, social data, web data, or crowdsourced data using sensor fusion capabilities to support analytics related to media transactions.

20. The method of claim 11, further comprising using adaptive networking at the edge in combination with generative Al caching to reduce computational resources and energy consumption associated with generative Al workloads that are part of transaction workflows.Automated Media Rights Transaction Orchestration System21. A computer-implemented system for automated media rights transaction orchestration, comprising: a transaction management module executed on a plurality of distributed processing nodes and configured to orchestrate media rights transactions; a governance engine configured to define and enforce one or more workflow rules associated with a media rights transaction managed by the transaction management module;Docket: 16606-12POA a set of artificial intelligence models trained to detect anomalous or fraudulent transaction behavior based on historical transaction data and stakeholder trust scores, wherein outputs from the artificial intelligence models are provided to the transaction management module; a blockchain interface configured to generate and digitally sign a blockchain transaction for distributing a royalty payment to a stakeholder according to a smart contract; and a control processor configured to coordinate with the transaction management module to execute the governance workflow prior to authorizing the blockchain transaction, wherein execution of the governance workflow comprises verifying account credentials, evaluating the stakeholder trust score, and confirming approval of the transaction before transmission to a blockchain network.

22. The system of claim 21, wherein the governance engine is further configured to dynamically update workflow rules based on regulatory compliance or stakeholder policy changes.

23. The system of claim 21 , wherein the set of artificial intelligence models comprises an ensemble of supervised and unsupervised models trained on historical royalty and licensing data.

24. The system of claim 21, wherein the blockchain interface implements a hybrid on-chain / off-chain validation protocol to record verified transaction hashes while storing detailed data in a secure off-chain repository.

25. The system of claim 21, wherein the control processor executes a robotic process automation routine to initiate approvals and notify stakeholders prior to transaction settlement.

26. The system of claim 21, wherein the governance workflow includes a fraud detection layer configured to flag anomalies in payment routing or rights ownership records.

27. The system of claim 21, wherein each stakeholder trust score is calculated using historical transaction reliability, dispute records, and authentication metadata.

28. The system of claim 21, wherein smart contract templates are automatically generated and digitally signed using a secure key management service.

29. The system of claim 21, wherein the system maintains an immutable audit trail accessible to authorized auditors for compliance review.

30. The system of claim 21, wherein the orchestration module integrates with a digital rights management subsystem to enforce license terms during media playback or redistribution.

31. A computer- implemented method for automated media rights transaction orchestration, comprising: orchestrating media rights transactions using a transaction management module executed on a plurality of distributed processing nodes; defining and enforcing one or more workflow rules associated with a media rights transaction managed by the transaction management module using a governance engine; detecting anomalous or fraudulent transaction behavior based on historical transaction data and stakeholder trust scores using a set of artificial intelligence models, wherein outputs from the artificial intelligence models are provided to the transaction management module; generating and digitally signing a blockchain transaction for distributing a royalty payment to a stakeholder according to a smart contract using a blockchain interface; and coordinating, by a control processor, with the transaction management module to execute a governance workflow prior to authorizing the blockchain transaction, wherein executing the governance workflow comprisesDocket: 16606-12POA verifying account credentials, evaluating the stakeholder trust score, and confirming approval of the transaction before transmission to a blockchain network.

32. The method of claim 31 , further comprising dynamically updating workflow rules based on regulatory compliance or stakeholder policy changes using the governance engine.

33. The method of claim 31 , wherein the set of artificial intelligence models comprises an ensemble of supervised and unsupervised models trained on historical royalty and licensing data.

34. The method of claim 31, wherein the blockchain interface implements a hybrid on-chain / off-chain validation protocol to record verified transaction hashes while storing detailed data in a secure off-chain repository.

35. The method of claim 31, further comprising executing, by the control processor, a robotic process automation routine to initiate approvals and notify stakeholders prior to transaction settlement.

36. The method of claim 31, wherein the governance workflow includes a fraud detection layer configured to flag anomalies in payment routing or rights ownership records.

37. The method of claim 31, wherein each stakeholder trust score is calculated using historical transaction reliability, dispute records, and authentication metadata.

38. The method of claim 31, further comprising automatically generating and digitally signing smart contract templates using a secure key management service.

39. The method of claim 31, further comprising maintaining an immutable audit trail accessible to authorized auditors for compliance review.

40. The method of claim 31, further comprising integrating with a digital rights management subsystem to enforce license terms during media playback or redistribution.Media Transaction Decision Support and Forecasting Engine41. A computer-implemented system for media transaction decision support and forecasting, comprising: a data acquisition subsystem configured to collect historical transaction records, current market indicators, and predictive economic data relating to media assets; a digital twin simulation module configured to simulate transactional scenarios based on contextual and operational parameters; a set of artificial intelligence models including at least one contextual forecasting model configured to: receive simulated transaction results from the digital twin simulation module, and predict financial outcomes and risk exposure for a proposed media transaction based on the simulated transaction results; and a user interface configured to present the predicted financial outcomes, risk exposure, and recommended strategies for execution.

42. The system of claim 41, wherein the data acquisition subsystem further collects real-time streaming, licensing, and sales data from third-party APIs.

43. The system of claim 41, wherein the digital twin simulation module models at least one of: contract structures, revenue flows, or market responses for different licensing strategies.

44. The system of claim 41 , wherein the contextual forecasting model comprises a recurrent neural network trained on time-series media transaction data.

45. The system of claim 41, wherein at least one model of the set of artificial intelligence models computes scenario probabilities and expected value distributions for competing transaction proposals.Docket: 16606-12POA46. The system of claim 41, wherein the system further comprises a recommendation engine configured to rank alternative transaction pathways based on predicted risk and return.

47. The system of claim 41, wherein the user interface displays at least one interactive dashboard including at least one of: sensitivity analyses, confidence intervals, or trend forecasts.

48. The system of claim 41, wherein all forecast and decision data are recorded on a distributed ledger.

49. The system of claim 41, wherein the digital twin simulation module is configured to perform what-if analysis by modeling multiple alternative scenarios and comparing predicted outcomes for each scenario.

50. The system of claim 41 , wherein the system further comprises a robotic process automation module configured to automate execution of selected transaction strategies based on the predicted financial outcomes.

51. A computer-implemented method for media transaction decision support and forecasting, comprising: collecting historical transaction records, current market indicators, and predictive economic data relating to media assets using a data acquisition subsystem; simulating transactional scenarios based on contextual and operational parameters using a digital twin simulation module; receiving simulated transaction results from the digital twin simulation module using at least one contextual forecasting model of a set of artificial intelligence models; predicting financial outcomes and risk exposure for a proposed media transaction based on the simulated transaction results using the at least one contextual forecasting model; and presenting the predicted financial outcomes, risk exposure, and recommended strategies for execution via a user interface.

52. The method of claim 51, wherein the data acquisition subsystem further collects real-time streaming, licensing, and sales data from third-party APIs.

53. The method of claim 51, wherein the digital twin simulation module models at least one of: contract structures, revenue flows, or market responses for different licensing strategies.

54. The method of claim 51, wherein the contextual forecasting model comprises a recurrent neural network trained on time-series media transaction data.

55. The method of claim 51, further comprising computing scenario probabilities and expected value distributions for competing transaction proposals using at least one model of the set of artificial intelligence models.

56. The method of claim 51 , further comprising ranking alternative transaction pathways based on predicted risk and return using a recommendation engine.

57. The method of claim 51, wherein the user interface displays at least one interactive dashboard including at least one of: sensitivity analyses, confidence intervals, or trend forecasts.

58. The method of claim 51, further comprising recording all forecast and decision data on a distributed ledger.

59. The method of claim 51 , further comprising performing what-if analysis by modeling multiple alternative scenarios and comparing predicted outcomes for each scenario using the digital twin simulation module.

60. The method of claim 51, further comprising automating execution of selected transaction strategies based on the predicted financial outcomes using a robotic process automation module.Personalized and Tokenized Media Experience Transaction Framework61. A system for managing personalized and tokenized media experiences, comprising:Docket: 16606-12POA a personalization engine configured to generate personalized media content based on a user profile including demographic, preference, and behavioral data; a tokenization module configured to issue a blockchain token encoding access rights to the personalized media content; a transaction module configured to: validate token ownership via a smart contract recorded on a blockchain, and enforce access terms and usage conditions associated with the token; and a rights management processor configured to: record token transfer events on the blockchain, and automatically distribute royalties to stakeholders based on the access terms and usage conditions associated with transferred tokens.

62. The system of claim 61, wherein the personalization engine comprises a set of artificial intelligence models including at least one of: a collaborative filtering model, a content-based filtering model, a hybrid model, a recurrent neural network, or a convolutional neural network configured to analyze the user profile and generate the personalized media content.

63. The system of claim 61, wherein the user profile comprises psychometric personality classification data determined by analyzing a user's cross-platform behavior including at least one of: content consumption patterns, interactive gaming choices, social media content, or user-generated content.

64. The system of claim 61, wherein the personalized media content comprises at least one of: Al-generated music tailored to user preferences, personalized video content incorporating user-specified themes, personalized gaming experiences, or Al-generated text content customized to user reading preferences.

65. The system of claim 61, wherein the tokenization module is configured to issue non- fungible tokens (NFTs) that encode unique access rights to specific personalized derivative works, wherein each NFT provides provable access to the personalized media content.

66. The system of claim 61, wherein the system further comprises a digital rights management subsystem operatively connected to the transaction module and configured to enforce license terms during media playback by verifying token ownership before permitting access to the personalized media content.

67. The system of claim 61, wherein the rights management processor is configured to identify stakeholders by analyzing the personalized media content to determine underlying creative works that influenced generation of the personalized media content, and to automatically add identified stakeholders to a royalty apportionment schedule.

68. The system of claim 61, wherein the blockchain token encodes tiered access rights including at least one of: timelimited access, geographic restrictions, device-specific permissions, or usage-based limitations, and wherein the transaction module enforces the tiered access rights through conditional smart contract execution.

69. The system of claim 61, wherein the personalization engine is configured to generate the personalized media content in real-time based on immediate contextual factors including user location, time of day, current user activity, and physiological data from wearable devices.

70. The system of claim 61 , wherein the system maintains a cross-platform user profile by aggregating user behavioral data from linear media platforms, interactive gaming platforms, social media platforms, and metaverse environments, and wherein the personalization engine uses the cross-platform user profile to generate personalized media content consistent across multiple platforms.Docket: 16606-12POA71. A method for managing personalized and tokenized media experiences, comprising: generating personalized media content based on a user profile including demographic, preference, and behavioral data using a personalization engine; issuing a blockchain token encoding access rights to the personalized media content using a tokenization module; validating token ownership via a smart contract recorded on a blockchain using a transaction module; enforcing access terms and usage conditions associated with the token using the transaction module; recording token transfer events on the blockchain using a rights management processor; and automatically distributing royalties to stakeholders based on the access terms and usage conditions associated with transferred tokens using the rights management processor.

72. The method of claim 71, wherein the personalization engine comprises a set of artificial intelligence models including at least one of: a collaborative filtering model, a content-based filtering model, a hybrid model, a recurrent neural network, or a convolutional neural network configured to analyze the user profile and generate the personalized media content.

73. The method of claim 71, wherein the user profile comprises psychometric personality classification data determined by analyzing a user's cross-platform behavior including at least one of: content consumption patterns, interactive gaming choices, social media content, or user-generated content.

74. The method of claim 71, wherein the personalized media content comprises at least one of: Al-generated music tailored to user preferences, personalized video content incorporating user-specified themes, personalized gaming experiences, or Al-generated text content customized to user reading preferences.

75. The method of claim 71, wherein issuing the blockchain token comprises issuing non-fungib le tokens (NFTs) that encode unique access rights to specific personalized derivative works, wherein each NFT provides provable access to the personalized media content.

76. The method of claim 71, further comprising enforcing license terms during media playback by verifying token ownership before permitting access to the personalized media content using a digital rights management subsystem operatively connected to the transaction module.

77. The method of claim 71, further comprising identifying stakeholders by analyzing the personalized media content to determine underlying creative works that influenced generation of the personalized media content, and automatically adding identified stakeholders to a royalty apportionment schedule using the rights management processor.

78. The method of claim 71 , wherein the blockchain token encodes tiered access rights including at least one of: timelimited access, geographic restrictions, device-specific permissions, or usage-based limitations, and wherein the transaction module enforces the tiered access rights through conditional smart contract execution.

79. The method of claim 71, wherein generating the personalized media content comprises generating the personalized media content in real-time based on immediate contextual factors including user location, time of day, current user activity, and physiological data from wearable devices.

80. The method of claim 71, further comprising maintaining a cross-platform user profile by aggregating user behavioral data from linear media platforms, interactive gaming platforms, social media platforms, and metaverse environments, and wherein generating the personalized media content uses the cross-platform user profile to generate personalized media content consistent across multiple platforms.Docket: 16606-12POAEdge-Enabled Media Transaction and Delivery System81. A distributed computing system for edge-enabled media transactions and delivery, comprising: a plurality of edge nodes configured to receive media transaction requests from end users and process the requests locally; a set of artificial intelligence models deployed on the edge nodes and configured to: authenticate user identity for the transaction requests, select personalized media content based on user profile data, and determine applicable digital rights management restrictions; a cache management subsystem deployed on the edge nodes and configured to store and serve Al-generated media content based on the personalized selections; a blockchain ledger interface configured to record completed transactions, including user authentication events, content delivery events, and rights enforcement events; and a content delivery network controller configured to coordinate content distribution to the edge nodes and enforce geographic access restrictions based on licensing rights.

82. The system of claim 81, wherein the artificial intelligence models deployed on the edge nodes comprise adaptive networking capabilities configured to intelligently route transaction data through optimal network paths selected from cellular networks, WiFi networks, Open Radio Access Network (ORAN), Bluetooth protocols, and loT communication protocols based on real-time network conditions.

83. The system of claim 81, wherein the cache management subsystem is configured to implement multiple caching layers including output caching for storing previously generated Al content, feature caching for storing preprocessed input data, and model state caching for storing intermediate Al model states.

84. The system of claim 81, wherein the plurality of edge nodes comprises at least one of: network routers, network switches, edge computing devices, loT systems, mesh networking nodes, networking chipsets, or networking systems- on-chip (SoCs)85. The system of claim 81, wherein the blockchain ledger interface is configured to record authentication events, content delivery events, and digital rights enforcement events as immutable transaction records on a distributed blockchain network.

86. The system of claim 81, wherein the set of artificial intelligence models includes authentication models configured to validate user credentials using at least one of machine learning-based pattern recognition, biometric authentication, hardware token authentication, or multi-factor authentication protocols.

87. The system of claim 81 , wherein the cache management subsystem is configured to implement predictive content pre-fetching by analyzing user behavior patterns to anticipate content requests and proactively cache anticipated content at edge nodes proximate to end users.

88. The system of claim 81, wherein the blockchain ledger interface is configured to execute smart contracts that automatically apportion and distribute royalty payments to multiple stakeholders according to predefined royalty schedules embedded in the smart contracts.

89. The system of claim 81, wherein local processing of media transaction data at the edge nodes reduces network latency by performing authentication, personalization, and digital rights management operations proximate to end users rather than at centralized servers.Docket: 16606-12POA90. The system of claim 81, wherein the system is configured to deploy containerized microservices on the plurality of edge nodes to enable scalable deployment and independent scaling of authentication modules, personalization engines, and digital rights management subsystems based on demand at individual edge nodes.

91. A method for edge-enabled media transactions and delivery, comprising: receiving media transaction requests from end users at a plurality of edge nodes; processing the requests locally at the plurality of edge nodes; authenticating user identity for the transaction requests using a set of artificial intelligence models deployed on the edge nodes; selecting personalized media content based on user profile data using the set of artificial intelligence models; determining applicable digital rights management restrictions using the set of artificial intelligence models; storing and serving Al-generated media content based on the personalized selections using a cache management subsystem deployed on the edge nodes; recording completed transactions including user authentication events, content delivery events, and rights enforcement events using a blockchain ledger interface; and coordinating content distribution to the edge nodes and enforcing geographic access restrictions based on licensing rights using a content delivery network controller.

92. The method of claim 91 , wherein the artificial intelligence models deployed on the edge nodes comprise adaptive networking capabilities configured to intelligently route transaction data through optimal network paths selected from cellular networks, WiFi networks, Open Radio Access Network (ORAN), Bluetooth protocols, and loT communication protocols based on real-time network conditions.

93. The method of claim 91, wherein the cache management subsystem is configured to implement multiple caching layers including output caching for storing previously generated Al content, feature caching for storing preprocessed input data, and model state caching for storing intermediate Al model states.

94. The method of claim 91, wherein the plurality of edge nodes comprises at least one of: network routers, network switches, edge computing devices, loT systems, mesh networking nodes, networking chipsets, or networking systems- on-chip (SoCs).

95. The method of claim 91, wherein recording completed transactions comprises recording authentication events, content delivery events, and digital rights enforcement events as immutable transaction records on a distributed blockchain network using the blockchain ledger interface.

96. The method of claim 91 , wherein the set of artificial intelligence models includes authentication models configured to validate user credentials using at least one of machine learning-based pattern recognition, biometric authentication, hardware token authentication, or multi- factor authentication protocols.

97. The method of claim 91, further comprising implementing predictive content pre-fetching by analyzing user behavior patterns to anticipate content requests and proactively cache anticipated content at edge nodes proximate to end users using the cache management subsystem.

98. The method of claim 91, further comprising executing smart contracts that automatically apportion and distribute royalty payments to multiple stakeholders according to predefined royalty schedules embedded in the smart contracts using the blockchain ledger interface.Docket: 16606-12POA99. The method of claim 91, wherein local processing of media transaction data at the edge nodes reduces network latency by performing authentication, personalization, and digital rights management operations proximate to end users rather than at centralized servers.

100. The method of claim 91 , further comprising deploying containerized microservices on the plurality of edge nodes to enable scalable deployment and independent scaling of authentication modules, personalization engines, and digital rights management subsystems based on demand at individual edge nodes.Media usage monitoring oracle101. A system forblockchain-based royalty verification and distribution, comprising: a data collection module configured to monitor one or more content distribution services to collect usage event data indicating instances of streaming, downloading, or public performance of a creative work; a blockchain oracle interface configured to provide the usage event data from off-chain content distribution services to a smart contract that manages royalty distribution on a blockchain; a smart contract execution module configured to: receive the usage event data from the blockchain oracle interface, calculate royalty amounts due to stakeholders based on the usage event data, and execute automated royalty distribution transactions according to a predefined royalty apportionment schedule; and a verification processor configured to reconcile royalty payments recorded on the blockchain with the collected usage event data to confirm accuracy of payments to stakeholders.

102. The system of claim 101, wherein the data collection module is configured to monitor usage event data from multiple off-chain content distribution services.

103. The system of claim 101, whereinthe usage event data comprises at least one of: streaming counts with associated timestamps, download transaction records, public performance logs, radio airplay data, synchronization licensing usage, or geographic distribution metrics.

104. The system of claim 101, wherein the smart contract execution module is configured to calculate royalty amounts using a predefined royalty apportionment schedule that specifies percentage allocations for each stakeholder, and wherein the smart contract automatically transfers cryptocurrency payments to blockchain wallet addresses of the stakeholders.

105. The system of claim 101, wherein the blockchain oracle interface is configured to provide the usage event data to the smart contract at periodic intervals including at least one of: real-time continuous monitoring, hourly updates, daily batch processing, or monthly reconciliation cycles.

106. The system of claim 101, wherein the verification processor is configured to identify discrepancies by comparing cumulative usage event data collected from the off-chain content distribution services against cumulative royalty payment amounts recorded on the blockchain, and to generate discrepancy alerts when differences exceed a predetermined threshold.

107. The system of claim 101, wherein the system further comprises a stakeholder identification module configured to analyze the creative work to identify all entities entitled to receive royalties.Docket: 16606-12POA108. The system of claim 101, wherein the smart contract execution module is configured to implement minimum payment thresholds whereby royalty amounts are accumulated until reaching a minimum threshold before executing distribution transactions.

109. The system of claim 101, wherein the blockchain oracle interface is configured to cryptographically sign the usage event data before transmitting to the smart contract.

110. The system of claim 101, wherein the verification processor is configured to maintain an immutable audit trail on the blockchain recording all usage events, calculated royalty amounts, payment distributions, and verification checks for compliance review by authorized auditors.

111. A method for blockchain-based royalty verification and distribution, comprising: monitoring one or more content distribution services to collect usage event data indicating instances of streaming, downloading, or public performance of a creative work using a data collection module; providing the usage event data from the off-chain content distribution services to a smart contract that manages royalty distribution on a blockchain using a blockchain oracle interface; receiving the usage event data from the blockchain oracle interface using a smart contract execution module; calculating royalty amounts due to stakeholders based on the usage event data using the smart contract execution module; executing automated royalty distribution transactions according to a predefined royalty apportionment schedule using the smart contract execution module; and reconciling royalty payments recorded on the blockchain with the collected usage event data to confirm accuracy of payments to stakeholders using a verification processor.

112. The method of claim 111, wherein monitoring one or more content distribution services comprises monitoring usage event data from multiple off-chain content distribution services.

113. The method of claim 111, wherein the usage event data comprises at least one of: streaming counts with associated timestamps, download transaction records, public performance logs, radio airplay data, synchronization licensing usage, or geographic distribution metrics.

114. The method of claim 111, wherein calculating royalty amounts comprises calculating royalty amounts using a predefined royalty apportionment schedule that specifies percentage allocations for each stakeholder, and wherein the smart contract automatically transfers cryptocurrency payments to blockchain wallet addresses of the stakeholders.

115. The method of claim 111, wherein providing the usage event data to the smart contract comprises providing the usage event data at periodic intervals including at least one of: real-time continuous monitoring, hourly updates, daily batch processing, or monthly reconciliation cycles.

116. The method of claim 111, wherein reconciling royalty payments comprises identifying discrepancies by comparing cumulative usage event data collected from the off-chain content distribution services against cumulative royalty payment amounts recorded on the blockchain, and generating discrepancy alerts when differences exceed a predetermined threshold.

117. The method of claim 111, further comprising analyzing the creative work to identify all entities entitled to receive royalties using a stakeholder identification module.

118. The method of claim 111, wherein executing automated royalty distribution transactions comprises implementing minimum payment thresholds whereby royalty amounts are accumulated until reaching a minimum threshold before executing distribution transactions.Docket: 16606-12POA119. The method of claim 111, wherein providing the usage event data comprises cryptographically signing the usage event data before transmitting to the smart contract using the blockchain oracle interface.

120. The method of claim 111, further comprising maintaining an immutable audit trail on the blockchain recording all usage events, calculated royalty amounts, payment distributions, and verification checks for compliance review by authorized auditors using the verification processor.Al-driven digital audio workstation121. A digital audio workstation (DAW) system, comprising one or more processors and non-transitory memory storing instructions that, when executed, cause the DAW system to: receive one or more media assets for use in an audiovisual project; analyze the media assets using a set of artificial intelligence models to identify ownership information, contributor information, royalty-relevant metadata, and license-related obligations associated with the one or more media assets; monitor changes to the audiovisual project, including additions, deletions, edits, or transformations of the media assets, and update a media asset usage record reflecting a contribution of each media asset to the audiovisual project; compute one or more royalty allocation values for a project output based on the ownership information, contributor information, royalty-relevant metadata, and the media asset usage record; and generate royalty-tracking output identifying stakeholders and corresponding royalty allocation values associated with the project output.

122. The system of claim 121, wherein the set of artificial intelligence models comprises at least one neural-network classifier trained to detect contributor identities based on audio fingerprints or embedded metadata patterns.

123. The system of claim 121, wherein the set of artificial intelligence models further comprises a large-language- model configured to interpret license text, usage restrictions, or contractual royalty terms extracted from unstructured documents.

124. The system of claim 121, wherein updating the media asset usage record comprises generating time-coded contribution weights that reflect temporal portions of the audiovisual project in which each media asset appears.

125. The system of claim 121, wherein monitoring changes to the audiovisual project further comprises detecting effect-chain modifications, track-level transformations, or timeline re-arrangements and incorporating such modifications into the media asset usage record.

126. The system of claim 121, wherein computing the royalty allocation values comprises applying a rules engine configured to enforce royalty-splitting formulas, stakeholder-specific weighting factors, or usage-dependent revenue schedules.

127. The system of claim 121, wherein the royalty-tracking output includes a visual representation within a DAW interface indicating stakeholder allocation values that update in real time as project edits are performed.

128. The system of claim 121, wherein the royalty -tracking output is exported in a structured file format selected from JavaScript Obj ect Notation (J SON), XML, or comma-separated values (C SV) for interoperability with external rights- management systems.

129. The system of claim 121, wherein the set of artificial intelligence models is further configured to identify potential licensing or usage conflicts by detecting media assets exceeding allowable usage parameters defined by royaltyrelevant metadata.Docket: 16606-12POA130. The system of claim 121, wherein the DAW system further maintains an audit log recording media-asset imports, metadata revisions, usage-record updates, royalty-allocation computations, and generated royalty-tracking output.

131. A method performed by a digital audio workstation (DAW) system, comprising: receiving one ormore media assets for use in an audiovisual project; analyzing the media assets using a set of artificial intelligence models to identify ownership information, contributor information, royalty-relevant metadata, and license-related obligations associated with the one or more media assets; monitoring changes to the audiovisual project, including additions, deletions, edits, or transformations of the media assets, and updating a media asset usage record reflecting a contribution of each media asset to the audiovisual project; computing one or more royalty allocation values for a project output based on the ownership information, contributor information, royalty-relevant metadata, and the media asset usage record; and generating royalty-tracking output identifying stakeholders and corresponding royalty allocation values associated with the project output.

132. The method of claim 131, wherein the set of artificial intelligence models comprises at least one neural-network classifier trained to detect contributor identities based on audio fingerprints or embedded metadata patterns.

133. The method of claim 131, wherein the set of artificial intelligence models further comprises a large-language- model configured to interpret license text, usage restrictions, or contractual royalty terms extracted from unstructured documents.

134. The method of claim 131, wherein updating the media asset usage record comprises generating time-coded contribution weights that reflect temporal portions of the audiovisual project in which each media asset appears.

135. The method of claim 131, wherein monitoring changes to the audiovisual project further comprises detecting effect-chain modifications, track-level transformations, or timeline re-arrangements and incorporating such modifications into the media asset usage record.

136. The method of claim 131, wherein computing the royalty allocation values comprises applying a rules engine configured to enforce royalty-splitting formulas, stakeholder-specific weighting factors, or usage-dependent revenue schedules.

137. The method of claim 131, wherein the royalty-tracking output includes a visual representation within a DAW interface indicating stakeholder allocation values that update in real time as project edits are performed.

138. The method of claim 131, wherein the royalty -tracking output is exported in a structured file format selected from JavaScript Obj ect Notation (J SON), XML, or comma-separated values (C SV) for interoperability with external rights- management systems.

139. The method of claim 131, wherein the set of artificial intelligence models is further configured to identify potential licensing or usage conflicts by detecting media assets exceeding allowable usage parameters defined by royaltyrelevant metadata.

140. The method of claim 131, further comprising maintaining an audit log recording media-asset imports, metadata revisions, usage-record updates, royalty-allocation computations, and generated royalty-tracking output.Royalties for training models on right holder content141. A system for managing royalties associated with training an artificial intelligence model on media content, the system comprising:Docket: 16606-12POA a data collection system configured to receive creative work data associated with a plurality of stakeholders and to identify stakeholder information for the creative work data; an intelligence system configured to train a generative artificial intelligence model using the creative work data; a transaction management system configured to generate a model royalty stack associated with the generative artificial intelligence model, a model-royalty stack comprising stakeholder identifiers and corresponding royalty apportionment percentages based on the creative work data used to train the model; the transaction management system further configured to parameterize a royalty apportionment smart contract using the model royalty stack and to deploy a royalty-apportionment smart contract to a blockchain network; the transaction management system further configured to configure a blockchain oracle corresponding to the royalty apportionment smart contract, the blockchain oracle being operable to provide off-chain usage data for the generative artificial intelligence model to the royalty apportionment smart contract; and wherein the royalty apportionment smart contract is executed on the blockchain network to distribute royalty payments to the stakeholders according to the royalty apportionment percentages in response to usage of the generative artificial intelligence model.

142. The system of claim 141, wherein the data collection system is further configured to receive event data from one or more content distribution platforms indicating usage of media content associated with the stakeholders.

143. The system of claim 141, wherein the data collection system is further configured to provide event data to the blockchain oracle for transmission to the royalty apportionment smart contract.

144. The system of claim 141, wherein the intelligence system is further configured to generate attribution metadata identifying the stakeholders whose creative work data contributed to training the generative artificial intelligence model.

145. The system of claim 141, wherein the transaction management system is further configured to generate the modelroyalty stack by aggregating stakeholder identifiers and corresponding apportionment percentages derived from attribution metadata.

146. The system of claim 141, wherein the royalty apportionment smart contract comprises a plurality of blockchain addresses corresponding to the stakeholder identifiers and executable instructions for allocating royalty distributions among the blockchain addresses.

147. The system of claim 141, wherein deploying the royalty apportionment smart contract to the blockchain network comprises digitally signing the smart contract using a key associated with the transaction management system.

148. The system of claim 141, wherein the blockchain oracle is further configured to validate the off-chain usage data before providing the usage data to the royalty apportionment smart contract.

149. The system of claim 141, wherein the royalty apportionment smart contract is further configured to automatically execute royalty distribution transactions in response to the usage data without requiring manual approval.

150. The system of claim 141, wherein the transaction management system is further configured to maintain an immutable record of the model royalty stack and subsequent royalty distribution transactions for audit and compliance review.

151. A method for managing royalties associated with training an artificial intelligence model on media content, the method comprising:Docket: 16606-12POA receiving creative work data associated with a plurality of stakeholders and identifying stakeholder information for the creative work data using a data collection system; training a generative artificial intelligence model using the creative work data with an intelligence system; generating a model royalty stack associated with the generative artificial intelligence model using a transaction management system, a model-royalty stack comprising stakeholder identifiers and corresponding royalty apportionment percentages based on the creative work data used to train the model; parameterizing a royalty apportionment smart contract using the model royalty stack and deploying a royaltyapportionment smart contract to a blockchain network using the transaction management system; configuring a blockchain oracle corresponding to the royalty apportionment smart contract using the transaction management system, the blockchain oracle being operable to provide off-chain usage data for the generative artificial intelligence model to the royalty apportionment smart contract; and executing the royalty apportionment smart contract on the blockchain network to distribute royalty payments to the stakeholders according to the royalty apportionment percentages in response to usage of the generative artificial intelligence model.

152. The method of claim 151, further comprising receiving event data from one or more content distribution platforms indicating usage of media content associated with the stakeholders using the data collection system.

153. The method of claim 151, further comprising providing event data to the blockchain oracle for transmission to the royalty apportionment smart contract using the data collection system.

154. The method of claim 151, further comprising generating attribution metadata identifying the stakeholders whose creative work data contributed to training the generative artificial intelligence model using the intelligence system.

155. The method of claim 151, wherein generating the model-royalty stack comprises aggregating stakeholder identifiers and corresponding apportionment percentages derived from attribution metadata using the transaction management system.

156. The method of claim 151, wherein the royalty apportionment smart contract comprises a plurality of blockchain addresses corresponding to the stakeholder identifiers and executable instructions for allocating royalty distributions among the blockchain addresses.

157. The method of claim 151, wherein deploying the royalty apportionment smart contract to the blockchain network comprises digitally signing the smart contract using a key associated with the transaction management system.

158. The method of claim 151, further comprising validating the off-chain usage data before providing the usage data to the royalty apportionment smart contract using the blockchain oracle.

159. The method of claim 151, wherein executing the royalty apportionment smart contract comprises automatically executing royalty distribution transactions in response to the usage data without requiring manual approval.

160. The method of claim 151, further comprising maintaining an immutable record of the model royalty stack and subsequent royalty distribution transactions for audit and compliance review using the transaction management system.

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