Systems, methods, kits, and apparatuses for know your model systems in value chain networks

Abstract

A value chain network control tower system comprises a processor and memory configured to execute a know your model system that manages the complete lifecycle of Al models in enterprise environments. The know your model system performs model intake and registration actions including model documentation collection, registration procedures, metadata collection, input / output interface standardization, legal and licensing validation checks, and security validation. The system conducts comprehensive model evaluation and risk assessment actions by analyzing foundational properties, task performance, safety and risk management, alignment and compliance characteristics, operational metrics, and tooling transparency capabilities. The know your model system executes model deployment actions through automated environment validation, predeployment approval processes, and controlled production deployment with continuous monitoring.

Description

SYSTEMS, METHODS, KITS, AND APPARATUSESFOR KNOW YOUR MODEL SYSTEMS IN VALUE CHAIN NETWORKS CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U S Provisional Application No 63 / 674,513, filed 23 July 2024 and U.S Provisional Application No. 63 / 848,406, filed 22 July 2025.

[0002] This application is also a continuation-in-part and claims priority to U.S. Patent Application Serial No. 19 / 277,338, filed 22 July 2025, which is a continuation of International Application No. PCT / US25 / 13044, filed 24 January 2025, which claims the benefit of the following U.S. Patent Applications: Serial No. 63 / 625,597, filed 26 January 2024; Serial No. 63 / 639,916, filed 29 April 2024; and Serial No. 63 / 638,590, filed 25 April 2024.

[0003] This application is also a continuation-in-part and claims priority to U.S. Patent Application Serial No. 19 / 277,330, filed 22 July 2025, which is a continuation of International Application No. PCT / US24 / 44898, filed 30 August 2024 which claims the benefit of priority to the following U S. Patent Applications: Serial No. 63 / 535,748, filed 31 August 2023; Serial No. 63 / 536,171, filed 1 September 2023; Serial No 63 / 610,894, filed 15 December 2023; Serial No. 63 / 621,549, filed 16 January 2024; Serial No. 63 / 625,597, filed 26 January 2024; and Serial No. 63 / 638,590, filed 25 April 2024

[0004] All the foregoing applications are hereby incorporated by reference as if fully set forth herein in their entirety.BACKGROUND

[0005] The rapid proliferation of artificial intelligence systems across enterprise environments has created unprecedented challenges in data governance, model management, and autonomous system oversight Organizations are increasingly deploying complex Al models and autonomous agents to perform critical business functions, yet they lack comprehensive frameworks to understand, monitor, and control these systems effectively.

[0006] Enterprises face significant difficulties in ensuring data quality and trustworthiness across their operations. Organizations struggle to track the origins and transformations of data as it flows through complex systems, making it nearly impossible to assess data reliability or identify potential contamination sources. This challenge is compounded by the integration of multiple data sources.

[0007] Organizations deploying Al models face substantial challenges in managing the complete lifecycle of these systems, from initial development through deployment and ongoing maintenance.

[0008] Model governance presents particular difficulties as organizations struggle to maintain visibility into model behavior, performance characteristics, and decision-making processes.

[0009] The deployment of autonomous Al agents introduces unique challenges related to control, monitoring, and accountability. Unlike traditional software systems, Al agents can make independent decisions, take actions, and interact with other systems in ways that may be difficult to predict or control. Organizations lack comprehensive frameworks for understanding agent reasoning processes, monitoring agent actions, and ensuring agents operate within acceptable parameters.SUMMARY

[0010] In some aspects, the techniques described herein relate to a value chain network control tower system, including: a processor and memory configured to execute a first set of know your model Al -based learning models, wherein the first set of know your model Al-based learning models is configured to: perform at least one model intake and registration action associated with a second set of Al-based learning models; perform at least one modelevaluation and risk assessment action associated with the second set of Al-based learning models; and perform at least one model deployment action associated with the second set of Al -based learning models

[0011] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the first set of know your model Al-based learning models is further configured to perform at least one model monitoring and observability action associated with the second set of Al-based learning models.

[0012] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the first set of know your model Al -based learning models is further configured to perform at least one model updating and retraining action associated with the second set of Al-based learning models.

[0013] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the first set of know your model Al-based learning models includes at least one of: a linear classification model, a regression model, a decision tree-based model, a kernel machine, a support vector machine, an instance-based learning model, a nearest neighbor model, a Bayesian model, a clustering model, a convolutional neural network, a deep convolutional neural network, a feed forward neural network, a recurrent neural network, a long / short term memory neural network, a transformer model, a large language model, or a self- attention model.

[0014] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the second set of Al-based learning models includes at least one of: a linear classification model, a regression model, a decision tree-based model, a kernel machine, a support vector machine, an instance-based learning model, a nearest neighbor model, a Bayesian model, a clustering model, a convolutional neural network, a deep convolutional neural network, a feed forward neural network, a recurrent neural network, a long / short term memory' neural network, a transformer model, a large language model, or a self-attention model

[0015] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the second set of Al-based learning models is associated with at least one of: a demand prediction, a product lifecycle management action, an inventory management action, a robotic fleet management action, a shipping container fleet management action, a logistics action, a demand shaping action, or a procurement action.

[0016] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the at least one model intake and registration action includes at least one of: model documentation collection, model registration procedures, model metadata collection, input and output interface standardization, legal and licensing validation checks, model card collection and management, cataloging and tagging operations, API schema conformance and endpoint definition, model interface wrapping, endpoint configuration testing in sandbox or staging environments, telemetry hook integration, invocation method registration including assigning resource identifiers and configuring API endpoints, or security and privacy validation.

[0017] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the at least one model evaluation and risk assessment action includes at least one of: evaluation of foundational properties, evaluation of task performance, evaluation of safety and risk management, evaluation of alignment and compliance, evaluation of operational metrics, or evaluation of tooling and transparency

[0018] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the at least one model deployment action includes at least one of: production integration, API disclosure, scaling, selection of usage caps and quotas, cost and latency budgeting, selecting region- specific deployment strategies for data sovereignty, model endpoint wrapping, containerized environment hosting, standardized API access configuration, authentication via identity and access management systems or API keys, throttling policy application,metrics logging for monitoring and observability services, automatic scaling of model inference based on demand, elastic inference backend provisioning with scalable GPU-backed infrastructure clusters, horizontal scaling tuned to provider service level agreements, budget control implementation with alert triggering and usage stopping at thresholds, region- specific model deployment for data residency and compliance requirements, or geo-fencing enforcement by location-aware routing layers.

[0019] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the at least one model monitoring and observability action includes at least one of: logging of API calls, capturing payload sizes, capturing error responses, capturing latency buckets, inference latency tracking, token use per invocation tracking, model-specific usage metric tracking, model throughput tracking, model GPU utilization tracking, request rate tracking, data drift detection, monitoring prompt types, comparing model output distributions over time, implementing audit trails, implementing real-time feedback loops, abuse and misuse detection, or detection of anomalous usage.

[0020] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the at least one model updating and retraining action includes at least one of: data pipeline refresh actions, fine-tuning and customization actions, model iteration and versioning actions, model retraining actions, model comparison with stored responses and metrics comparison, updating a retrieval augmented generation knowledge base, or conducting A / B testing between model versions.

[0021] In some aspects, the techniques described herein relate to a value chain network control tower system, further including a digital twin system, wherein the digital twin system is configured to generate a digital twin of the second set of Al-based learning models

[0022] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the at least one model evaluation and risk assessment action includes performing digital twin simulation using the digital twin and a simulation system.

[0023] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the first set of know your model Al-based learning models is configured to perform at least one of ensemble model management including dynamic weighting or selection optimization for the second set of Al-based learning models

[0024] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the first set of know your model Al-based learning models is configured to integrate with at least one external regulatory compliance framework to ensure adherence to industry-specific Al governance standards for the second set of Al-based learning models.

[0025] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the first set of know your model Al-based learning models is configured to generate at least one of: a model performance metric or a compliance report for regulatory auditing of the second set of Al-based learning models.

[0026] In some aspects, the techniques described herein relate to a method for operating a value chain network control tower, including: executing, via a processor and memory, a first set of know your model Al -based learning models, wherein the first set of know your model Al-based learning models are configured to: perform at least one model intake and registration action associated with a second set of Al-based learning models; perform at least one model evaluation and risk assessment action associated with the second set of Al-based learning models; and perform at least one model deployment action associated with the second set of Al -based learning models

[0027] In some aspects, the techniques described herein relate to a method wherein the first set of know your model Al-based learning models is further configured to perform at least one model monitoring and observability action associated with the second set of Al -based learning models.

[0028] In some aspects, the techniques described herein relate to a method wherein the first set of know your model Al-based learning models is further configured to perform at least one model updating and retraining action associated with the second set of Al-based learning models.

[0029] In some aspects, the techniques described herein relate to a method, wherein the first set of know your model Al-based learning models includes at least one of: a linear classification model, a regression model, a decision treebased model, a kernel machine, a support vector machine, an instance-based learning model, a nearest neighbor model, a Bayesian model, a clustering model, a convolutional neural network, a deep convolutional neural network, a feed forward neural network, a recurrent neural network, a long / short term memory neural network, a transformer model, a large language model, or a self-attention model.

[0030] In some aspects, the techniques described herein relate to a method, wherein the second set of Al-based learning models includes at least one of: a linear classification model, a regression model, a decision tree-based model, a kernel machine, a support vector machine, an instance-based learning model, a nearest neighbor model, a Bayesian model, a clustering model, a convolutional neural network, a deep convolutional neural network, a feed forward neural network, a recurrent neural network, a long / short term memory neural network, a transformer model, a large language model, or a self-attention model.

[0031] In some aspects, the techniques described herein relate to a method, wherein the second set of Al-based learning models is associated with at least one of: a demand prediction, a product lifecycle management action, an inventory management action, a robotic fleet management action, a shipping container fleet management action, a logistics action, a demand shaping action, or a procurement action.

[0032] In some aspects, the techniques described herein relate to a method, wherein the at least one model intake and registration action includes at least one of: model documentation collection, model registration procedures, model metadata collection, input and output interface standardization, legal and licensing validation checks, model card collection and management, cataloging and tagging operations, API schema conformance and endpoint definition, model interface wrapping, endpoint configuration testing in sandbox or staging environments, telemetry hook integration, invocation method registration including assigning resource identifiers and configuring API endpoints, or security and privacy validation.

[0033] In some aspects, the techniques described herein relate to a method, wherein the at least one model evaluation and risk assessment action includes at least one of: evaluation of foundational properties, evaluation of task performance, evaluation of safety and risk management, evaluation of alignment and compliance, evaluation of operational metrics, or evaluation of tooling and transparency.

[0034] In some aspects, the techniques described herein relate to a method, wherein the at least one model deployment action includes at least one of: production integration, API disclosure, scaling, selection of usage caps and quotas, cost and latency budgeting, selecting region- specific deployment strategies for data sovereignty, model endpoint wrapping, containerized environment hosting, standardized API access configuration, or authentication via identity and access management systems or API keys, throttling policy application, metrics logging for monitoring and observability services, automatic scaling of model inference based on demand, elastic inference backend provisioning with scalable GPU-backed infrastructure clusters, horizontal scaling tuned to provider service level agreements,budget control implementation with alert triggering and usage stopping at thresholds, region-specific model deployment for data residency and compliance requirements, or geo-fencing enforcement by location-aware routing layers.

[0035] In some aspects, the techniques described herein relate to a method, wherein the at least one model monitoring and observability action includes at least one of: logging of API calls, capturing payload sizes, capturing error responses, capturing latency buckets, inference latency tracking, token use per invocation tracking, model-specific usage metric tracking, model throughput tracking, model GPU utilization tracking, request rate tracking, data drift detection, monitoring prompt types, comparing model output distributions over time, implementing audit trails, implementing real-time feedback loops, abuse and misuse detection, or detection of anomalous usage.

[0036] In some aspects, the techniques described herein relate to a method, wherein the at least one model updating and retraining action includes at least one of: data pipeline refresh actions, fine-tuning and customization actions, model iteration and versioning actions, model retraining actions, model comparison with stored responses and metrics comparison, updating a retrieval augmented generation knowledge base, or conducting A / B testing between model versions.

[0037] In some aspects, the techniques described herein relate to a method, further including generating a digital twin of the second set of Al-based learning models using a digital twin system.

[0038] In some aspects, the techniques described herein relate to a method, wherein the at least one model evaluation and risk assessment action includes performing digital twin simulation using the digital twin and a simulation system.

[0039] In some aspects, the techniques described herein relate to a method wherein the first set of know your model Al-based learning models is further configured to perform ensemble model management including dynamic weighting and selection optimization for the second set of Al-based learning models.

[0040] In some aspects, the techniques described herein relate to a robotic fleet management system, including: a processor and memory' configured to execute a know your robot system, wherein the know your robot system is configured to: establish an initial connection with a candidate robotic system; perform at least one discovery and authentication action for the candidate robotic system: receive and process at least one capability declaration from the candidate robotic system; execute at least one compliance validation action for the candidate robotic system; perform at least one contextual configuration and operational parameterization action associated with the candidate robotic system; and perform at least one deployment action for the candidate robotic system.

[0041] In some aspects, the techniques described herein relate to a robotic fleet management system, wherein the at least one discovery and authentication action includes at least one of: receiving discovery packets including cryptographic identity credentials, device serial numbers, or pre-provisioned public key infrastructure certificates via secure networking protocols; or performing hardware attestation using trusted platform modules or secure enclave cryptographic signatures.

[0042] In some aspects, the techniques described herein relate to a robotic fleet management system, wherein the capability declaration includes a machine-readable manifest describing at least one of: a supported robotic tasks, a hardware specification, an operational constraint, an energy management detail, a capability profile, or a certified Al model signature.

[0043] In some aspects, the techniques described herein relate to a robotic fleet management system, wherein the at least one compliance validation action includes at least one of: comparing a received capability profile against a stored organizational governance policy or external regulatory frameworks, verifying that an Al model hash matches anapproved production model recorded in a compliance ledger, or confirming geolocation constraints for a restricted area.

[0044] In some aspects, the techniques described herein relate to a robotic fleet management system, wherein the at least one contextual configuration and operational parameterization action includes at least one of: transmitting contextual data including environment maps, dynamic scheduling parameters, or region-specific task priority lists; and deploying operational restrictions including geofencing boundaries, maximum acceleration limits, or time- constrained task permissions.

[0045] In some aspects, the techniques described herein relate to a robotic fleet management system, wherein the know your robot system is further configured to: integrate the candidate robotic system into a centralized orchestration and control plane that exposes at least one of: a dynamic task assignment API, a real-time telemetry collection service, or a health status subscription channel.

[0046] In some aspects, the techniques described herein relate to a robotic fleet management system, wherein the know your robot system is further configured to: perform continuous monitoring of tire candidate robotic system postdeployment by streaming real-time health telemetry.

[0047] In some aspects, the techniques described herein relate to a robotic fleet management system, wherein the know your robot system is further configured to: integrate with a digital twin system to generate a digital twin of the candidate robotic system for simulation-based testing or performance validation.

[0048] In some aspects, the techniques described herein relate to a robotic fleet management system, wherein the know your robot system is further configured to integrate with a know your model system to perform at least one of: a model intake and registration action, a model evaluation and risk assessment action, or a model deployment action for a set of models associated with the candidate robotic system.

[0049] In some aspects, the techniques described herein relate to a robotic fleet management system, wherein the know your robot system is further configured to integrate with a know your sensor system to perform at least one of: a sensor validation and authentication action, a sensor monitoring and anomaly detection action, or a sensor calibration and optimization action for a set of sensors associated with the candidate robotic system.

[0050] In some aspects, the techniques described herein relate to a method for executing a know your robot system by a robotic fleet management system, including: establishing an initial connection with a candidate robotic system; performing at least one discovery and authentication action for the candidate robotic system; receiving and processing at least one capability declaration from the candidate robotic system; executing at least one compliance validation action for the candidate robotic system; performing at least one contextual configuration and operational parameterization action associated with the candidate robotic system; and performing at least one deployment action for the candidate robotic system.

[0051] In some aspects, the techniques described herein relate to a method, wherein the at least one discovery and authentication action includes at least one of: receiving discovery packets including cryptographic identity credentials, device serial numbers, or pre-provisioned public key infrastructure certificates via secure networking protocols; or performing hardware attestation using trusted platform modules or secure enclave cryptographic signatures.

[0052] In some aspects, the techniques described herein relate to a method, wherein the at least one capability declaration includes a machine-readable manifest describing at least one of: supported robotic tasks, a hardware specification, an operational constraint, an energy management detail, a capability profile, or a certified Al model signature.

[0053] In some aspects, the techniques described herein relate to a method, wherein the at least one compliance validation action includes at least one of: comparing a received capability profile against a stored organizational governance policy or external regulatory frameworks, verifying that an Al model hash matches an approved production model recorded in a compliance ledger, or confirming geolocation constraints for a restricted area

[0054] In some aspects, the techniques described herein relate to a method, wherein the at least one contextual configuration and operational parameterization action includes at least one of: transmitting contextual data including environment maps, dynamic scheduling parameters, or region- specific task priority lists; and deploying operational restrictions including geofencing boundaries, maximum acceleration limits, or time-constrained task permissions.

[0055] In some aspects, the techniques described herein relate to a method, further including integrating the candidate robotic system into a centralized orchestration and control plane that exposes at least one of: a dynamic task assignment API, a real-time telemetry collection service, or a health status subscription channel.

[0056] In some aspects, the techniques described herein relate to a method, further including: performing continuous monitoring of the candidate robotic system post-deployment by streaming real-time health telemetry.

[0057] In some aspects, the techniques described herein relate to a method, further including: integrating with a digital twin system to generate a digital twin of the candidate robotic system for simulation-based testing or performance validation.

[0058] In some aspects, the techniques described herein relate to a method, further including: integrating w ith a know your model system to perform at least one of: a model intake and registration action, a model evaluation and risk assessment action, or a model deployment action for a set of models associated with the candidate robotic system

[0059] Tn some aspects, the techniques described herein relate to a method, further including: integrating with a know your sensor system to perform at least one of a sensor validation and authentication action, a sensor monitoring and anomaly detection action, or a sensor calibration and optimization action for a set of sensors associated with the candidate robotic system

[0060] In some aspects, the techniques described herein relate to a shipping container fleet management system, including: a processor and memory configured to execute a know your shipping container system, wherein the know your shipping container system is configured to: establish an initial connection with a candidate shipping container; perform at least one discovery and authentication action for the candidate shipping container; receive and process at least one capability declaration from the candidate shipping container; execute at least one compliance validation action for the candidate shipping container; perform at least one contextual configuration and operational parameterization action associated with the candidate shipping container; and perform at least one deployment action for the candidate shipping container.

[0061] In some aspects, the techniques described herein relate to a shipping container fleet management system, wherein the at least one discovery and authentication action includes at least one of: receiving discovery packets including cryptographic identity credentials, container identification numbers, geolocation data, or pre-provisioned public key infrastructure certificates via secure networking protocols; or performing hardware attestation using trusted platform modules or secure enclax e cryptographic signatures.

[0062] In some aspects, the techniques described herein relate to a shipping container fleet management system, wherein the at least one capability declaration includes a machine-readable manifest including at least one of: supported cargo types, environmental sensors, security features, tracking capabilities, operational constraints, weightlimits, permissible routes, hazardous materials handling requirements, a capability profile, or certified sensor calibration data

[0063] In some aspects, the techniques described herein relate to a shipping container fleet management system, wherein the at least one compliance validation action includes at least one of comparing a received capability profile against stored organizational governance policies or external regulatory frameworks, verifying that declared cargo types comply with customs regulations, confirming route restrictions based on hazardous materials classifications, or enforcing security protocols including access control and tamper detection.

[0064] In some aspects, the techniques described herein relate to a shipping container fleet management system, wherein the at least one contextual configuration and operational parameterization action includes at least one of: transmitting contextual data including route plans, weather forecasts, port schedules, or destination facility requirements: and deploying operational restrictions including speed limits, geofencing boundaries for restricted areas, or temperature setpoints for sensitive cargo.

[0065] In some aspects, the techniques described herein relate to a shipping container fleet management system, wherein the know your shipping container system is further configured to: integrate the candidate shipping container into a centralized orchestration and control plane that exposes at least one of: a dynamic routing API, a real-time telemetry' collection service, or a health status subscription channel.

[0066] In some aspects, the techniques described herein relate to a shipping container fleet management system, wherein the know your shipping container system is further configured to: perform continuous monitoring of the candidate shipping container post-deployment by streaming real-time telemetry

[0067] Tn some aspects, the techniques described herein relate to a shipping container fleet management system, wherein the know your shipping container system is further configured to: integrate with a digital twin system to generate a digital twin of the candidate shipping container for simulation-based testing or performance validation

[0068] In some aspects, the techniques described herein relate to a shipping container fleet management system, wherein the know your shipping container system is further configured to: integrate with a know your model system to perfonn at least one of: a model intake and registration action, a model evaluation and risk assessment action, or a model deployment action for a set of Al models associated with the candidate shipping container

[0069] In some aspects, the techniques described herein relate to a shipping container fleet management system, wherein the know your shipping container system is further configured to: integrate with a know your sensor system to perfonn at least one of a sensor validation and authentication action, a sensor monitoring and anomaly detection action, or a sensor calibration and optimization action for a set of sensors associated with the candidate shipping container.

[0070] In some aspects, the techniques described herein relate to a method for executing a know your shipping container system by a shipping container fleet management system, including: establishing an initial connection with a candidate shipping container; performing at least one discovery and authentication action for the candidate shipping container; receiving and processing at least one capability declaration from the candidate shipping container; executing at least one compliance validation action for the candidate shipping container; performing at least one contextual configuration and operational parameterization action associated with the candidate shipping container; and performing at least one deployment action for the candidate shipping container.

[0071] In some aspects, the techniques described herein relate to a method, wherein the at least one discovery and authentication action includes at least one of: receiving discovery packets including cryptographic identity credentials,container identification numbers, geolocation data, or pre-provisioned public key infrastructure certificates via secure networking protocols; or performing hardware attestation using trusted platform modules or secure enclave cryptographic signatures.

[0072] In some aspects, the techniques described herein relate to a method, wherein the at least one capability declaration includes a machine-readable manifest describing at least one of: supported cargo types, environmental sensors, security features, tracking capabilities, operational constraints, weight limits, permissible routes, hazardous materials handling requirements, a capability profile, or certified sensor calibration data.

[0073] In some aspects, the techniques described herein relate to a method, wherein the at least one compliance validation action includes at least one of: comparing a received capability profile against stored organizational governance policies or external regulatory frameworks, verifying that declared cargo types comply with customs regulations, confinning route restrictions based on hazardous materials classifications, or enforcing security protocols including access control and tamper detection

[0074] In some aspects, the techniques described herein relate to a method, wherein the at least one contextual configuration and operational parameterization action includes at least one of: transmitting contextual data including route plans, weather forecasts, port schedules, or destination facility requirements; and deploying operational restrictions including speed limits, geofencing boundaries for restricted areas, or temperature setpoints for sensitive cargo.

[0075] In some aspects, the techniques described herein relate to a method, further including: integrating the candidate shipping container into a centralized orchestration and control plane that exposes at least one of: a dynamic routing API, a real-time telemetry collection sendee, and a health status subscription channel

[0076] In some aspects, the techniques described herein relate to a method, further including: performing continuous monitoring of the candidate shipping container post-deployment by streaming real-time telemetry.

[0077] In some aspects, the techniques described herein relate to a method, further including : integrating with a digital twin system to generate a digital twin of the candidate shipping container for simulation-based testing or performance validation.

[0078] In some aspects, the techniques described herein relate to a method, further including: integrating with a know your model system to perform at least one of: a model intake and registration action, a model evaluation and risk assessment action, or a model deployment action for a set of Al models associated with the candidate shipping container.

[0079] In some aspects, the techniques described herein relate to a method, further including: integrating with a know your sensor system to perform at least one of a sensor validation and authentication action, a sensor monitoring and anomaly detection action, or a sensor calibration and optimization action for a set of sensors associated with the candidate shipping container. VCN Control Tower System and Know Yom Digital Twin System

[0080] In some aspects, the techniques described herein relate to a value chain network control tower system, including: a processor and memory configured to execute a know your digital twin system, wherein the know your digital twin system is configured to: perform at least one digital twin generation action for a physical value chain network asset; perform at least one evaluation and risk assessment action for the digital twin; and perform at least one deployment action for the digital twin.

[0081] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the at least one digital twin generation action includes at least one of: generating an initial 3D representation of aphysical asset using photogrammetry, LiDAR scanning, or CAD model integration; comparing initial geometry' against real-time sensor data via a data reconciliation module; or performing model calibration using least-squares optimization to fine-tune a behavior model of the digital twin.

[0082] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the at least one evaluation and risk assessment action includes at least one of: executing anomaly detection using at least one recurrent neural network trained on historical operational data; performing a risk assessment by simulating at least one operational scenario; or generating a digital twin trust score

[0083] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the at least one deployment action includes at least one of: provisioning and scaling of the digital twin through containerized architecture; or implementing a federated learning framework for distributed training of digital twin behavioral models.

[0084] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the know your digital twin system supports value chain network digital tw in types including at least one of: a value chain network digital twin representing a value chain network infrastructure; a manufacturing plant digital twin; a distribution center digital twin; a transportation network digital twin; a retail point of sale digital twin; a warehouse digital twin; or a logistics system digital twin.

[0085] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the know your digital twin system is further configured to: create a network 71, wherein the know your digital twin system is further configured to: integrate with a standardized data ingestion pipeline compatible with at least one industrial protocol; facilitate a data exchange with at least one existing control system, SCADA system, or enterprise resource planning platform; or provide a configuration management module to centrally manage and deploy changes to the digital twin.

[0086] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the know your digital twin system is further configured to: provide a pre-built library of digital twin templates representing physical asset types.

[0087] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the know your digital twin system is further configured to: perform at least one monitoring and observability action for tire digital twin.

[0088] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the know your digital twin system is further configured to: perform at least one updating and retraining action for the digital twin.

[0089] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the at least one monitoring and observability action includes at least one of: continuously updating digital twin with at least one real-time data stream from the physical asset; detecting a deviation from a predicted behavior and automatically retraining a simulation engine; or providing at least one tool for version control to track changes to the digital twin.

[0090] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the at least one updating and retraining action includes at least one of: ingestion of new operational data; incorporating an improvement in sensor technology; or recalibrating a behavioral model against an updated physical asset specification.

[0091] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the know your digital twin system is further configured to: integrate with a know your model system to perform at least one of: a model intake and registration action, a model evaluation and risk assessment action, or a model deployment action for a set of models associated with the digital twin system

[0092] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the know your digital twin system is further configured to: integrate with a know your sensor system to perform at least one of: a sensor validation and authentication action, a sensor monitoring and anomaly detection action, or a sensor calibration and optimization action for a set of sensors associated with the physical asset represented by the digital twin.

[0093] In some aspects, the techniques described herein relate to a method for executing a know your digital twin system by a value chain network control tower system, including: performing at least one digital twin generation action for a physical value chain network asset; performing at least one evaluation and risk assessment action for the digital twin; or performing at least one deployment action for tire digital twin.

[0094] In some aspects, the techniques described herein relate to a method, wherein the at least one digital twin generation action includes at least one of: generating an initial 3D representation of a physical asset using photogrammetry', LiDAR scanning, or CAD model integration; comparing the initial geometry against real-time sensor data via a data reconciliation module; or performing model calibration using least-squares optimization to finetune a behavior model of the digital twin.

[0095] In some aspects, the techniques described herein relate to a method, wherein the at least one evaluation and risk assessment action includes at least one of: executing anomaly' detection using at least one recurrent neural network trained on historical operational data; performing a risk assessment by simulating at least one operational scenario; or generating a digital twin trust score.

[0096] In some aspects, the techniques described herein relate to a method, wherein the at least one deployment action includes at least one of: provisioning and scaling of the digital twin through containerized architecture; or implementing a federated learning framework for distributed training of digital twin behavioral models.

[0097] In some aspects, the techniques described herein relate to a method, wherein the know your digital twin system supports value chain network digital twin types including at least one of: a value chain network digital twin representing a value chain network infrastructure; a manufacturing plant digital twin; a distribution center digital twin; a transportation network digital twin; a retail point of sale digital twin; a warehouse digital twin; or a logistics system digital twin.

[0098] In some aspects, the techniques described herein relate to a method, further including: creating a network of interconnected digital twins representing related assets or components within the value chain network

[0099] In some aspects, the techniques described herein relate to a method, further including: integrating with a standardized data ingestion pipeline compatible with at least one industrial protocol; facilitating a data exchange with an existing control system, a SCADA system, or an enterprise resource planning platform; or providing a configuration management module to centrally manage and deploy' changes to the digital twin.

[0100] In some aspects, the techniques described herein relate to a method, further including: providing a pre-built library of digital twin templates representing physical asset types.

[0101] In some aspects, the techniques described herein relate to a method, further including: performing at least one monitoring and observability action for the digital twin

[0102] In some aspects, the techniques described herein relate to a method, further including: performing at least one updating and retraining action for the digital twin.

[0103] In some aspects, the techniques described herein relate to a method, wherein the at least one monitoring and observability action includes at least one of: continuously updating the digital twin with at least one real-time data stream from a physical asset; detecting a deviation from a predicted behavior and automatically retraining a simulation engine; or providing at least one tool for version control to track changes to the digital twin.

[0104] In some aspects, the techniques described herein relate to a method, wherein the at least one updating and retraining action includes at least one of: ingestion of new operational data; incorporating an improvement in a sensor technology; or recalibrating a behavioral model against an updated physical asset specification

[0105] In some aspects, the techniques described herein relate to a method, further including: integrating with a know your model system to perform at least one of: a model intake and registration action, a model evaluation and risk assessment action, or a model deployment action for a set of models associated with the digital twin system.

[0106] In some aspects, the techniques described herein relate to a method, further including: integrating with a know your sensor system to perform at least one of a sensor validation and authentication action, a sensor monitoring and anomaly detection action, or a sensor calibration and an optimization action for a set of sensors associated with the physical asset represented by the digital twin. VCN Control Tower System and Know Your Physical Al System

[0107] In some aspects, the techniques described herein relate to a value chain network control tower system, including: a processor and memory configured to execute a know your physical Al system, wherein the know your physical Al system is configured to: establish an initial connection with a candidate physical Al system; perform at least one discovery and authentication action for the candidate physical AT system; receive and process at least one capability declaration from the candidate physical Al system; execute at least one compliance validation action for the candidate physical Al system; perform at least one contextual configuration and operational parameterization action associated with the candidate physical Al system; and perform at least one deployment action for the candidate physical Al system.

[0108] In some aspects, the techniques described herein relate to a system, wherein the discovery and authentication action includes receiving from the candidate physical Al system at least one of: a discovery packet including cryptographic identity credentials, a device serial number, or a pre-provisioned public key infrastructure (PKI) certificate via a secure networking protocol

[0109] In some aspects, the techniques described herein relate to a system, wherein tire know your physical Al system is further configured to perform at least one of hardware attestation using trusted platform modules or secure enclave cryptographic signatures to verify device integrity or authenticity of the candidate physical Al system

[0110] In some aspects, the techniques described herein relate to a system, wherein the at least one capability declaration includes a machine-readable manifest describing at least one of: supported Al tasks, hardware specifications, operational constraints, or certified Al model signatures.[OHl] In some aspects, the techniques described herein relate to a system, wherein the at least one compliance validation action includes comparing a received capability profile against at least one of: a stored organizational governance policy or an external regulatory framework.

[0112] In some aspects, the techniques described herein relate to a system, wherein the contextual configuration and operational parameterization action includes securely transmitting via encrypted over-the-air (OTA) update channels at least one of: dynamic scheduling parameters or an interaction policy.

[0113] In some aspects, the techniques described herein relate to a system, wherein the know your physical Al system integrates the candidate physical Al system into a centralized orchestration and control plane that exposes at least one of: a dynamic task assignment API, a real-time telemetry collection service, or a health status subscription channel.

[0114] In some aspects, the techniques described herein relate to a system, wherein the know your physical Al system performs at least one of an initial sandbox validation or a simulation-based testing of the candidate physical Al system prior to deployment.

[0115] In some aspects, the techniques described herein relate to a system, wherein the know your physical Al system incorporates a distributed trust ledger component to immutably record onboarding steps, policy validations, operational events, or update transactions using blockchain or tamper-evident cryptographic data structures.

[0116] In some aspects, the techniques described herein relate to a system, wherein the know your physical Al system is further configured to: integrate with a digital twin system to generate a digital twin of the candidate physical Al system for at least one of: simulation-based testing or performance validation.

[0117] In some aspects, the techniques described herein relate to a system, wherein tire know your physical Al system is further configured to: integrate with a know your model system to perform at least one of: a model intake and registration action, a model evaluation and risk assessment action, or a model deployment action for a set of models associated with the candidate physical Al system.

[0118] In some aspects, the techniques described herein relate to a system, wherein the know your physical Al system is further configured to: integrate with a know your sensor system to perform at least one of action for a set of sensors associated with the candidate physical Al system wherein the action includes at least one of: a sensor validation and authentication action, a sensor monitoring and anomaly detection action, or a sensor calibration and optimization action

[0119] In some aspects, the techniques described herein relate to a method for executing a know your physical Al system by a value chain network control tower, including: establishing an initial connection with a candidate physical Al system: performing at least one discovery and authentication action for the candidate physical Al system: receiving and processing at least one capability declaration from the candidate physical Al system; executing at least one compliance validation action for the candidate physical Al system; performing at least one contextual configuration and operational parameterization action associated with the candidate physical Al system; and performing at least one deployment action for the candidate physical Al system.

[0120] In some aspects, the techniques described herein relate to a method, wherein the discovery and authentication action includes receiving from the candidate physical Al system at least one of: a discovery packet including cryptographic identity credentials, a device serial number, or a pre-provisioned public key infrastructure (PKI) certificate via a secure networking protocol

[0121] In some aspects, the techniques described herein relate to a method further including: performing hardware attestation using trusted platform modules or secure enclave cryptographic signatures to verify device integrity or authenticity of the candidate physical Al system

[0122] In some aspects, the techniques described herein relate to a method, wherein the at least one capability declaration includes a machine-readable manifest describing at least one of: supported Al tasks, hardware specifications, operational constraints, or certified Al model signatures.

[0123] In some aspects, the techniques described herein relate to a method, wherein the at least one compliance validation action includes comparing a received capability profile against at least one of: a stored organizational governance policy or an external regulatory framework.

[0124] In some aspects, the techniques described herein relate to a method, wherein the contextual configuration and operational parameterization action includes securely transmitting via encrypted over-the-air (OTA) update channels at least one of: dynamic scheduling parameters or an interaction policy.

[0125] In some aspects, the techniques described herein relate to a method, further including : integrating the candidate physical Al system into a centralized orchestration and control plane that exposes at least one of: a dynamic task assignment API, a real-time telemetry collection service, or a health status subscription channel.

[0126] In some aspects, the techniques described herein relate to a method, further including: performing initial sandbox validation and simulation-based testing of the candidate physical Al system prior to deployment.

[0127] In some aspects, the techniques described herein relate to a method, further including: incorporating a distributed trust ledger component to immutably record onboarding steps, policy validations, operational events, or update transactions using blockchain or tamper-evident cryptographic data structures.

[0128] In some aspects, the techniques described herein relate to a method, further including: integrating with a digital twin system to generate a digital twin of the candidate physical Al system for at least one of: simulation-based testing or performance validation.

[0129] In some aspects, the techniques described herein relate to a method, further including: integrating with a know your model system to perform at least one of: a model intake and registration action, a model evaluation and risk assessment action, or a model deployment action for a set of models associated with the candidate physical Al system

[0130] In some aspects, the techniques described herein relate to a method, further including: integrating with a know your sensor system to perform at least one action for a set of sensors associated with the candidate physical Al system wherein the action includes: a sensor validation and authentication action, a sensor monitoring and anomaly detection action, or a sensor calibration and optimization action.

[0131] In some aspects, the techniques described herein relate to a value chain network control tower system, including: a processor and memory configured to execute a know your model system, wherein the know your model system is configured to: perform at least one model intake and registration action associated with a set of candidate models; perform at least one model evaluation and risk assessment action associated with the set of candidate models; and perform at least one model deployment action associated with tire set of candidate models.

[0132] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the know your model system is further configured to perform at least one model monitoring and observability action associated with the set of candidate models.

[0133] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the know your model system is further configured to perform at least one model updating and retraining action associated with the set of candidate models

[0134] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the set of candidate models is associated with at least one of a demand prediction action, a product lifecycle management action, an inventory management action, a robotic fleet management action, a shipping container fleet management action, a logistics action, a demand shaping action, or a procurement action.

[0135] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the at least one model intake and registration action includes at least one of: model documentation collection, model registration procedures, model metadata collection, input and output interface standardization, legal and licensing validation checks, model card collection and management, cataloging and tagging operations, API schema conformance and endpoint definition, model interface wrapping, endpoint configuration testing in sandbox or staging environments, telemetry hook integration, invocation method registration including assigning resource identifiers and configuring API endpoints, or security and privacy validation.

[0136] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the at least one model evaluation and risk assessment action includes at least one of: evaluation of foundational properties, evaluation of task performance, evaluation of safety and risk management, evaluation of alignment and compliance, evaluation of operational metrics, or evaluation of tooling and transparency.

[0137] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the at least one model deployment action includes at least one of: production integration, API disclosure, scaling, selection of usage caps and quotas, cost and latency budgeting, selecting region- specific deployment strategies for data sovereignty, model endpoint wrapping, containerized environment hosting, standardized API access configuration, authentication via identity and access management systems or API keys, throttling policy application, metrics logging for monitoring and observability sendees, automatic scaling of model inference based on demand, elastic inference backend provisioning with scalable GPU-backed infrastructure clusters, horizontal scaling tuned to provider sendee level agreements, budget control implementation with alert triggering and usage stopping at thresholds, region-specific model deployment for data residency and compliance requirements, or geo-fencing enforcement by location-aware routing layers.

[0138] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the at least one model monitoring and observability action includes at least one of: logging of API calls, capturing payload sizes, capturing error responses, capturing latency buckets, inference latency tracking, token use per invocation tracking, model-specific usage metric tracking, model throughput tracking, model GPU utilization tracking, request rate tracking, data drift detection, monitoring prompt types, comparing model output distributions over time, implementing audit trails, implementing real-time feedback loops, abuse and misuse detection, or detection of anomalous usage.

[0139] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the at least one model updating and retraining action includes at least one of: data pipeline refresh actions, fine-tuning and customization actions, model iteration and versioning actions, model retraining actions, model comparison with stored responses and metrics comparison, updating a retrieval augmented generation knowledge base, or conducting A / B testing between model versions.

[0140] In some aspects, the techniques described herein relate to a value chain network control tower system, further including a digital twin system, wherein the digital twin system is configured to generate a digital twin of the set of candidate models.

[0141] In some aspects, the techniques described herein relate to a value chain network control tower system, wherein the at least one model evaluation and risk assessment action includes performing digital twin simulation using the digital twin and a simulation system.

[0142] In some aspects, the techniques described herein relate to a method for executing a know your model system by a value chain network control tower, including: performing at least one model intake and registration action associated with a set of candidate models; performing at least one model evaluation and risk assessment action associated with the set of candidate models; and performing at least one model deployment action associated with the set of candidate models.

[0143] In some aspects, the techniques described herein relate to a method, further including performing at least one model monitoring and observability action associated with the set of candidate models.

[0144] In some aspects, the techniques described herein relate to a method, further including performing at least one model updating and retraining action associated with the set of candidate models

[0145] In some aspects, the techniques described herein relate to a method, wherein the set of candidate models is associated with at least one of a demand prediction, a product lifecycle management action, an inventory management action, a robotic fleet management action, a shipping container fleet management action, a logistics action, a demand shaping action, or a procurement action.

[0146] In some aspects, the techniques described herein relate to a method, wherein the at least one model intake and registration action includes at least one of: model documentation collection, model registration procedures, model metadata collection, input and output interface standardization, legal and licensing validation checks, model card collection and management, cataloging and tagging operations, API schema conformance and endpoint definition, model interface wrapping, endpoint configuration testing in sandbox or staging environments, telemetry hook integration, invocation method registration including assigning resource identifiers and configuring API endpoints, or security and privacy validation

[0147] In some aspects, the techniques described herein relate to a method, wherein the at least one model evaluation and risk assessment action includes at least one of: evaluation of foundational properties, evaluation of task performance, evaluation of safety and risk management, evaluation of alignment and compliance, evaluation of operational metrics, or evaluation of tooling and transparency.

[0148] In some aspects, the techniques described herein relate to a method, wherein the at least one model deployment action includes at least one of: production integration, API disclosure, scaling, selection of usage caps and quotas, cost and latency budgeting, selecting region- specific deployment strategies for data sovereignty, model endpoint wrapping, containerized environment hosting, standardized API access configuration, authentication via identity and access management systems or API keys, throttling policy application, metrics logging for monitoring and observability services, automatic scaling of model inference based on demand, elastic inference backend provisioning with scalable GPU-backed infrastructure clusters, horizontal scaling tuned to provider service level agreements, budget control implementation with alert triggering and usage stopping at thresholds, region-specific model deployment for data residency and compliance requirements, or geo-fencing enforcement by location-aware routing layers.

[0149] In some aspects, the techniques described herein relate to a method, wherein the at least one model monitoring and observability action includes at least one of: logging of API calls, capturing payload sizes, capturing error responses, capturing latency buckets, inference latency tracking, token use per invocation tracking, model-specific usage metric tracking, model throughput tracking, model GPU utilization tracking, request rate tracking, data drift detection, monitoring prompt types, comparing model output distributions over time, implementing audit trails, implementing real-time feedback loops, abuse and misuse detection, or detection of anomalous usage.

[0150] In some aspects, the techniques described herein relate to a method, wherein the at least one model updating and retraining action includes at least one of: data pipeline refresh actions, fine-tuning and customization actions, model iteration and versioning actions, model retraining actions, model comparison with stored responses and metrics comparison, updating a retrieval augmented generation knowledge base, or conducting A / B testing between model versions.

[0151] In some aspects, the techniques described herein relate to a method, further including generating a digital twin of the set of candidate models using a digital twin system

[0152] In some aspects, the techniques described herein relate to a method, wherein the at least one model evaluation and risk assessment action includes performing digital twin simulation using the digital twin and a simulation system.

[0153] In some aspects, the techniques described herein relate to a value chain network control tower system, including: a processor and memory configured to execute a know your Al agent system, wherein the know your Al agent system is configured to: observe configuration, input, internal processing, reasoning, choices, actions, and output of an Al agent; rationalize associations and relationships between internal and external features of tire Al agent; verify accuracy and consistency of a conceptual model of one or more features of the Al agent; alter parameters, training, deployment, and interactions of the Al agent based on understanding derived from observation, rationalization, and verification; and regulate reasoning, actions, and outputs of the Al agent through enforcement of governance policies.

[0154] In some aspects, the techniques described herein relate to a system, wherein the know your Al agent system is configured to apply understanding functions during at least one of: a conception stage involving design and planning of the Al agent, a development stage involving training and testing of the Al agent, a deployment stage involving storage and provisioning of the Al agent for specific tasks, or an interaction stage involving communication and data exchange between the Al agent and other entities.

[0155] In some aspects, the techniques described herein relate to a system, wherein the know your Al agent system is configured to understand at least one of: performance of the Al agent including accuracy, efficiency, and reliability metrics, alignment of the Al agent with organizational objectives and ethical standards, context of the Al agent including operational environment and resource dependencies, or security of the Al agent including operational integrity and safeguarding measures.

[0156] In some aspects, the techniques described herein relate to a system, wherein the know your Al agent system implements architectural and organizational techniques including trust zones that segregate Al agents based on information sensitivity or processing priority.

[0157] In some aspects, the techniques described herein relate to a system, wherein the know your Al agent system implements supervision techniques including deployment of supervising Al agents configured to monitor at least one of reasoning, actions, interactions, or outputs of subject Al agents and generate assessments of compliance with governance requirements.

[0158] In some aspects, the techniques described herein relate to a system, wherein the know your Al agent system implements investigative techniques including passive preservation of logs and records documenting stages of at least one of conceptualization, development, deployment, or interactions of the Al agent for regulatory' compliance and retrospective analysis.

[0159] In some aspects, the techniques described herein relate to a system, wherein the know your Al agent system implements investigative techniques including at least one of active monitoring or analysis involving real-timeobservation of Al agent behavior, automated testing of Al agent responses, or proactive identification of potential issues or anomalies.

[0160] In some aspects, the techniques described herein relate to a system, wherein the know your Al agent system implements investigative techniques including at least one of review and reflection on a history of behavior of the Al agent over time to detect changes, trends, or inconsistencies in behavior including instances of drift

[0161] In some aspects, the techniques described herein relate to a system, wherein the know your Al agent system is configured to generate records including at least one of logs, reports, summaries, or annotations that preserve, describe, analyze, or document reasoning of Al agents in specific instances or general patterns of analysis.

[0162] In some aspects, the techniques described herein relate to a system, wherein the know your Al agent system is configured to take actions based on understanding of the Al agent including at least one of generating notifications and alerts in response to discovering performance errors, misalignment, misbehavior, adversarial attacks, or security vulnerabilities, or intervening in current processing of the Al agent to override, supersede, correct, cancel, rollback, or terminate roles or actions.

[0163] In some aspects, the techniques described herein relate to a method for executing a know your Al agent system by a value chain network control tower system, including: observing configuration, input, internal processing, reasoning, choices, actions, and output of an Al agent; rationalizing associations and relationships between internal and external features of the Al agent; verifying accuracy and consistency of a conceptual model of one or more features of the Al agent; altering parameters, training, deployment, and interactions of the Al agent based on understanding derived from observation, rationalization, and verification; and regulating reasoning, actions, and outputs of the Al agent through enforcement of governance policies

[0164] In some aspects, the techniques described herein relate to a method, further including applying understanding functions during at least one of: a conception stage involving design and planning of the Al agent, a development stage involving training and testing of the Al agent, a deployment stage involving storage and provisioning of the Al agent for specific tasks, or an interaction stage involving communication and data exchange between the Al agent and other entities.

[0165] In some aspects, the techniques described herein relate to a method, further including understanding at least one of: performance of the Al agent including at least one of accuracy, efficiency, and reliability metrics, alignment of the Al agent with organizational objectives and ethical standards, context of the Al agent including operational environment and resource dependencies, or security of the Al agent including operational integrity or safeguarding measures.

[0166] In some aspects, the techniques described herein relate to a method, further including implementing architectural and organizational techniques including trust zones that segregate Al agents based on information sensitivity or processing priority.

[0167] In some aspects, the techniques described herein relate to a method, further including implementing supervision techniques including deploying supervising Al agents configured to monitor reasoning, actions, interactions, or outputs of subject Al agents and generate assessments of compliance with governance requirements

[0168] In some aspects, the techniques described herein relate to a method, further including implementing investigative techniques including passive preservation of logs and records documenting stages of conceptualization, development, deployment, and interactions of the Al agent for regulatory compliance and retrospective analysis.

[0169] In some aspects, the techniques described herein relate to a method, further including implementing investigative techniques including active monitoring and analysis involving real-time observation of Al agent behavior, automated testing of Al agent responses, and proactive identification of potential issues or anomalies.

[0170] In some aspects, the techniques described herein relate to a method, further including implementing investigative techniques including review and reflection on a history of behavior of the Al agent over time to detect changes, trends, and inconsistencies in behavior including instances of drift.

[0171] In some aspects, the techniques described herein relate to a method, further including generating records including logs, reports, summaries, or annotations that preserve, describe, analyze, or document reasoning of Al agents in specific instances or general patterns of analysis.

[0172] In some aspects, the techniques described herein relate to a method, further including taking actions based on understanding of the Al agent including generating notifications and alerts in response to discovering performance errors, misalignment, misbehavior, adversarial attacks, or security vulnerabilities, and intervening in current processing of the Al agent to override, supersede, correct, cancel, rollback, or terminate roles or actions. VCN Control Tower System and Bias Detection System

[0173] In some aspects, the techniques described herein relate to a value chain network control tower system, including: a processor and memory configured to execute a bias detection system, wherein the bias detection system is configured to: analyze a machine learning model across multiple dimensions of bias using dynamically adaptable evaluation frameworks; generate interactive bias profiles that map detected issues to specific model components and operational impact vectors

[0174] Tn some aspects, the techniques described herein relate to a system, wherein the bias detection system includes a multi-source bias definition repository that stores parameterized definitions of biases.

[0175] In some aspects, the techniques described herein relate to a system, wherein the bias detection system includes an adaptive metric generator that synthesizes custom evaluation metrics by combining at least one base statistical measure with at least one context-aware adjustment factor.

[0176] In some aspects, the techniques described herein relate to a system, wherein the bias detection system includes a cross-domain evaluator engine that executes at least one bias detection protocol.

[0177] In some aspects, the techniques described herein relate to a system, wherein the bias detection system includes a dynamic reporting module that generates interactive bias profiles mapping detected issues to a specific model component.

[0178] In some aspects, the techniques described herein relate to a system, wherein the bias detection system implements artificial intelligence systems configured to detect bias in the machine learning model through machine learning algorithms that identify bias patterns.

[0179] In some aspects, the techniques described herein relate to a system, wherein the bias detection system implements artificial intelligence systems configured to detect at least one specific category' of bias in the machine learning model, wherein the specific category of bias includes at least one of: demographic bias, geographic bias, temporal bias, or domain-specific bias patterns.

[0180] In some aspects, the techniques described herein relate to a system, wherein the bias detection system implements multi-dimensional data bias checking that provides evaluation of potential biases across at least one of a demographic dimension or a geographic dimension.

[0181] In some aspects, the techniques described herein relate to a system, wherein the bias detection system implements automated alerting mechanisms.

[0182] In some aspects, the techniques described herein relate to a system, wherein the bias detection system integrates with at least one regulatory compliance framework

[0183] In some aspects, the techniques described herein relate to a method for detecting bias in a value chain network, including: analyzing a machine learning model across multiple dimensions of bias using dynamically adaptable evaluation frameworks; and generating interactive bias profiles that map detected issues to specific model components and operational impact vectors.

[0184] In some aspects, the techniques described herein relate to a method, further including storing parameterized definitions of biases in a multi-source bias definition repository.

[0185] In some aspects, the techniques described herein relate to a method, further including synthesizing custom evaluation metrics by combining at least one base statistical measure with at least one context-aware adjustment factor using an adaptive metric generator.

[0186] In some aspects, the techniques described herein relate to a method, further including executing at least one bias detection protocol using a cross-domain evaluator engine.

[0187] In some aspects, the techniques described herein relate to a method, further including generating interactive bias profiles mapping detected issues to a specific model component using a dynamic reporting module.

[0188] In some aspects, the techniques described herein relate to a method, further including detecting bias in the machine learning model through machine learning algorithms that identify bias patterns using artificial intelligence systems

[0189] In some aspects, the teclmiques described herein relate to a method, further including detecting at least one specific category of bias in the machine learning model using artificial intelligence systems, wherein the specific category of bias includes at least one of: demographic bias, geographic bias, temporal bias, or domain-specific bias patterns.

[0190] In some aspects, the teclmiques described herein relate to a method, further including implementing multidimensional data bias checking that provides evaluation of potential biases across at least one of a demographic dimension or a geographic dimensions.

[0191] In some aspects, the techniques described herein relate to a method, further including implementing automated alerting mechanisms.

[0192] In some aspects, the techniques described herein relate to a method, further including integrating with at least one regulatory' compliance framework.

[0193] In some aspects, the techniques described herein relate to a value chain network control tower system, including: a processor and memory' configured to execute a know your data system, wherein the know your data system is configured to: receive data instances from external data sources; extract metadata from the data instances to assess data provenance; evaluate trustworthiness of the data instances; detect anomalies in the data instances; validate integrity of the data instances by analyzing deviations across at least one historical dataset and at least one simulated environment; compare the data instances against historical data repositories to enable baseline comparisons for identifying statistical outliers; and generate data source scores for weighting the data instances.

[0194] In some aspects, the techniques described herein relate to a system, wherein the know your data system is further configured to maintain a threat intelligence database that stores indicators of detected anomalies in the data instances.

[0195] In some aspects, the techniques described herein relate to a system, wherein the know your data system is further configured to take mitigating actions upon detecting adversarial activity in data instances including at least one of data quarantine protocols or adaptive model reconfiguration routines.

[0196] In some aspects, the techniques described herein relate to a system, wherein the external data sources include at least one of loT sensor networks transmitting environmental measurements, user devices submitting real-time reports via API endpoints, or distributed ledger nodes broadcasting transaction records.

[0197] In some aspects, the techniques described herein relate to a system, wherein extracting metadata includes at least one of validating geolocation tags against GPS timestamps embedded in device-generated telemetry packets or verifying cryptographic signatures attached to industrial sensor readings using public keys associated with the sensors.

[0198] In some aspects, the techniques described herein relate to a system, wherein detecting anomalies includes applying statistical analysis or pattern recognition algorithms to identify deviations from expected input characteristics of data using at least one of data normalization pipelines or feature extraction modules.

[0199] In some aspects, the techniques described herein relate to a system, wherein the know your data system employs ensemble techniques that combine unsupervised and supervised models to flag discrepancies across comparison sources for anomaly detection.

[0200] In some aspects, the techniques described herein relate to a system, wherein the know your data system integrates with device registries that store at least one of cryptographic attestation keys or hardware profiles used during source verification of the data instances.

[0201] In some aspects, the techniques described herein relate to a system, wherein the data source scores are used to weight the data instances before transmission to Al decision-making models, and data instances scoring below a dynamically adjusted confidence threshold are disregarded.

[0202] In some aspects, the techniques described herein relate to a system, wherein the know your data system is configured to detect fake data injection attempts or sockpuppeting behavior in the data instances using pattern recognition and anomaly detection algorithms.

[0203] In some aspects, the techniques described herein relate to a method for managing data in a value chain network, including: receiving data instances from external data sources; extracting metadata from the data instances to assess data provenance; evaluating trustworthiness of the data instances; detecting anomalies in the data instances; validating integrity of the data instances by analyzing deviations across at least one historical dataset and at least one simulated environment; comparing the data instances against historical data repositories to enable baseline comparisons for identifying statistical outliers; and generating data source scores for weighting the data instances.

[0204] In some aspects, the techniques described herein relate to a method, further including maintaining a threat intelligence database that stores indicators of detected anomalies in the data instances

[0205] In some aspects, the techniques described herein relate to a method, further including taking mitigating actions upon detecting adversarial activity in data instances, including at least one of data quarantine protocols or adaptive model reconfiguration routines.

[0206] In some aspects, the techniques described herein relate to a method, wherein the external data sources include at least one of loT sensor networks transmitting environmental measurements, user devices submitting real-time reports via API endpoints, or distributed ledger nodes broadcasting transaction records.

[0207] In some aspects, the techniques described herein relate to a method, wherein extracting metadata includes at least one of validating geolocation tags against GPS timestamps embedded in device-generated telemetry packets or verifying cryptographic signatures attached to industrial sensor readings using public keys associated with the sensors.

[0208] In some aspects, the techniques described herein relate to a method, wherein detecting anomalies includes applying statistical analysis or pattern recognition algorithms to identify deviations from expected input characteristics of data using at least one of data normalization pipelines or feature extraction modules.

[0209] In some aspects, the techniques described herein relate to a method, further including employing ensemble techniques that combine unsupervised and supervised models to flag discrepancies across comparison sources for anomaly detection.

[0210] In some aspects, the techniques described herein relate to a method, further including integrating with device registries that store cryptographic attestation keys and hardware profiles used during source verification of the data instances.

[0211] In some aspects, the techniques described herein relate to a method, wherein the data source scores are used to weight the data instances before transmission to Al decision-making models, and data instances scoring below a dynamically adjusted confidence threshold are disregarded.

[0212] In some aspects, the techniques described herein relate to a method, further including detecting fake data injection attempts or sockpuppeting behavior in the data instances using at least one of pattern recognition or anomaly detection algorithms.

[0213] In some aspects, the techniques described herein relate to a procurement system, including: a processor and memory configured to execute a know your model system, wherein the know your model system is configured to: perform at least one model intake and registration action associated with a set of candidate models; perform at least one model evaluation and risk assessment action associated with the set of candidate models; and perform at least one model deployment action associated with the set of candidate models.

[0214] In some aspects, the techniques described herein relate to a procurement system, wherein the know your model system is further configured to perform at least one model monitoring and observability action associated with the set of candidate models

[0215] In some aspects, the techniques described herein relate to a procurement system, wherein the know your model system is further configured to perform at least one model updating and retraining action associated with the set of candidate models.

[0216] In some aspects, the techniques described herein relate to a procurement system, wherein the at least one model intake and registration action includes at least one of: model documentation collection, model registration procedures, model metadata collection, input and output interface standardization, legal and licensing validation checks, model card collection and management, cataloging and tagging operations, API schema conformance and endpoint definition, model interface wrapping, endpoint configuration testing in sandbox or staging environments, telemetry hook integration, invocation method registration including assigning resource identifiers and configuring API endpoints, or security and privacy validation.

[0217] In some aspects, the techniques described herein relate to a procurement system, wherein the at least one model evaluation and risk assessment action includes at least one of: evaluation of foundational properties, evaluation of task performance, evaluation of safety and risk management, evaluation of alignment and compliance, evaluation of operational metrics, or evaluation of tooling and transparency

[0218] In some aspects, the techniques described herein relate to a procurement system, wherein the at least one model deployment action includes at least one of: production integration, API disclosure, scaling, selection of usage caps and quotas, cost and latency budgeting, selecting region-specific deployment strategies for data sovereignty, model endpoint wrapping, containerized environment hosting, standardized API access configuration, authentication via identity and access management systems or API keys, throttling policy application, metrics logging for monitoring and observability services, automatic scaling of model inference based on demand, elastic inference backend provisioning with scalable GPU-backed infrastructure clusters, horizontal scaling tuned to provider service level agreements, budget control implementation with alert triggering and usage stopping at thresholds, region-specific model deployment for data residency and compliance requirements, or geo-fencing enforcement by location-aware routing layers.

[0219] In some aspects, the techniques described herein relate to a procurement system, wherein the at least one model monitoring and observability action includes at least one of: logging of API calls, capturing payload sizes, capturing error responses, capturing latency buckets, inference latency tracking, token use per invocation tracking, model-specific usage metric tracking, model throughput tracking, model GPU utilization tracking, request rate tracking, data drift detection, monitoring prompt types, comparing model output distributions over time, implementing audit trails, implementing real-time feedback loops, abuse and misuse detection, or detection of anomalous usage.

[0220] In some aspects, the techniques described herein relate to a procurement system, wherein the at least one model updating and retraining action includes at least one of: data pipeline refresh actions, fine-tuning and customization actions, model iteration and versioning actions, model retraining actions, model comparison with stored responses and metrics comparison, updating a retrieval augmented generation knowledge base, or conducting A / B testing between model versions.

[0221] In some aspects, the techniques described herein relate to a procurement system, further including a digital twin system, wherein the digital twin system is configured to generate a digital twin of tire set of candidate models.

[0222] In some aspects, the techniques described herein relate to a procurement system, wherein the at least one model evaluation and risk assessment action includes performing a simulation using the digital twin.

[0223] In some aspects, the techniques described herein relate to a method for executing a know your model system by a procurement system, including: performing at least one model intake and registration action associated with a set of candidate models; performing at least one model evaluation and risk assessment action associated with the set of candidate models; and performing at least one model deployment action associated with the set of candidate models

[0224] In some aspects, the techniques described herein relate to a method, further including performing at least one model monitoring and observability action associated with the set of candidate models.

[0225] In some aspects, the techniques described herein relate to a method, further including performing at least one model updating and retraining action associated with the set of candidate models

[0226] In some aspects, the techniques described herein relate to a method, wherein the at least one model intake and registration action includes at least one of: model documentation collection, model registration procedures, model metadata collection, input and output interface standardization, legal and licensing validation checks, model card collection and management, cataloging and tagging operations, API schema conformance and endpoint definition, model interface wrapping, endpoint configuration testing in sandbox or staging environments, telemetry hook integration, invocation method registration including assigning resource identifiers and configuring API endpoints, or security and privacy validation.

[0227] In some aspects, the techniques described herein relate to a method, wherein the at least one model evaluation and risk assessment action includes at least one of: evaluation of foundational properties, evaluation of task performance, evaluation of safety and risk management, evaluation of alignment and compliance, evaluation of operational metrics, or evaluation of tooling and transparency.

[0228] In some aspects, the techniques described herein relate to a method, wherein the at least one model deployment action includes at least one of: production integration, API disclosure, scaling, selection of usage caps and quotas, cost and latency budgeting, selecting region- specific deployment strategies for data sovereignty, model endpoint wrapping, containerized environment hosting, standardized API access configuration, authentication via identity' and access management systems or API keys, throttling policy application, metrics logging for monitoring and observability services, automatic scaling of model inference based on demand, elastic inference backend provisioning with scalable GPU-backed infrastructure clusters, horizontal scaling tuned to provider service level agreements, budget control implementation with alert triggering and usage stopping at thresholds, region-specific model deployment for data residency and compliance requirements, or geo-fencing enforcement by location-aware routing layers.

[0229] In some aspects, the techniques described herein relate to a method, wherein the at least one model monitoring and observability action includes at least one of: logging of API calls, capturing payload sizes, capturing error responses, capturing latency buckets, inference latency tracking, token use per invocation tracking, model-specific usage metric tracking, model throughput tracking, model GPU utilization tracking, request rate tracking, data drift detection, monitoring prompt types, comparing model output distributions over time, implementing audit trails, implementing real-time feedback loops, abuse and misuse detection, or detection of anomalous usage.

[0230] In some aspects, the techniques described herein relate to a method, wherein the at least one model updating and retraining action includes at least one of: data pipeline refresh actions, fine-tuning and customization actions, model iteration and versioning actions, model retraining actions, model comparison with stored responses and metrics comparison, updating a retrieval augmented generation knowledge base, or conducting A / B testing between model versions.

[0231] In some aspects, the techniques described herein relate to a method, further including generating a digital twin of the set of candidate models by a digital twin system.

[0232] In some aspects, the techniques described herein relate to a method, wherein the at least one model evaluation and risk assessment action includes performing a simulation using the digital twin.

[0233] In some aspects, the techniques described herein relate to a logistics system, including: a processor and memory configured to execute a know' your model system, wherein the know your model system is configured to: perform at least one model intake and registration action associated with a set of candidate models; perform at leastone model evaluation and risk assessment action associated with the set of candidate models; and perform at least one model deployment action associated with the set of candidate models.

[0234] In some aspects, the techniques described herein relate to a logistics system, wherein the know your model system is further configured to perform at least one model monitoring and observability action associated with the set of candidate models

[0235] In some aspects, the techniques described herein relate to a logistics system, wherein the know your model system is further configured to perform at least one model updating and retraining action associated with the set of candidate models.

[0236] In some aspects, the techniques described herein relate to a logistics system, wherein the at least one model intake and registration action includes at least one of: model documentation collection, model registration procedures, model metadata collection, input and output interface standardization, legal and licensing validation checks, model card collection and management, cataloging and tagging operations, API schema conformance and endpoint definition, model interface wrapping, endpoint configuration testing in sandbox or staging environments, telemetry hook integration, invocation method registration including assigning resource identifiers and configuring API endpoints, or security and privacy validation.

[0237] In some aspects, the techniques described herein relate to a logistics system, wherein the at least one model evaluation and risk assessment action includes at least one of: evaluation of foundational properties, evaluation of task performance, evaluation of safety and risk management, evaluation of alignment and compliance, evaluation of operational metrics, or evaluation of tooling and transparency

[0238] Tn some aspects, the techniques described herein relate to a logistics system, wherein the at least one model deployment action includes at least one of: production integration, API disclosure, scaling, selection of usage caps and quotas, cost and latency budgeting, selecting region-specific deployment strategies for data sovereignty, model endpoint wrapping, containerized environment hosting, standardized API access configuration, authentication via identity and access management systems or API keys, throttling policy application, metrics logging for monitoring and observability services, automatic scaling of model inference based on demand, elastic inference backend provisioning with scalable GPU-backed infrastructure clusters, horizontal scaling tuned to provider service level agreements, budget control implementation with alert triggering and usage stopping at thresholds, region-specific model deployment for data residency and compliance requirements, or geo-fencing enforcement by location-aware routing layers.

[0239] In some aspects, the techniques described herein relate to a logistics system, wherein the at least one model monitoring and observability action includes at least one of: logging of API calls, capturing payload sizes, capturing error responses, capturing latency buckets, inference latency tracking, token use per invocation tracking, modelspecific usage metric tracking, model throughput tracking, model GPU utilization tracking, request rate tracking, data drift detection, monitoring prompt types, comparing model output distributions over time, implementing audit trails, implementing real-time feedback loops, abuse and misuse detection, or detection of anomalous usage

[0240] In some aspects, the techniques described herein relate to a logistics system, wherein the at least one model updating and retraining action includes at least one of: data pipeline refresh actions, fine-tuning and customization actions, model iteration and versioning actions, model retraining actions, model comparison with stored responses and metrics comparison, updating a retrieval augmented generation knowledge base, or conducting A / B testing between model versions.

[0241] In some aspects, the techniques described herein relate to a logistics system, further including a digital twin system, wherein the digital twin system is configured to generate a digital twin of the set of candidate models.

[0242] In some aspects, the techniques described herein relate to a logistics system, wherein the at least one model evaluation and risk assessment action includes performing a simulation using the digital twin

[0243] In some aspects, the techniques described herein relate to a method for executing a know your model system by a logistics system, including: performing at least one model intake and registration action associated with a set of candidate models; performing at least one model evaluation and risk assessment action associated with the set of candidate models; and performing at least one model deployment action associated with the set of candidate models.

[0244] In some aspects, the techniques described herein relate to a method, further including performing at least one model monitoring and observability action associated with the set of candidate models.

[0245] In some aspects, the techniques described herein relate to a method, further including performing at least one model updating and retraining action associated with the set of candidate models

[0246] In some aspects, the techniques described herein relate to a method, wherein the at least one model intake and registration action includes at least one of: model documentation collection, model registration procedures, model metadata collection, input and output interface standardization, legal and licensing validation checks, model card collection and management, cataloging and tagging operations, API schema conformance and endpoint definition, model interface wrapping, endpoint configuration testing in sandbox or staging environments, telemetry hook integration, invocation method registration including assigning resource identifiers and configuring API endpoints, or security and privacy validation

[0247] Tn some aspects, the techniques described herein relate to a method, wherein the at least one model evaluation and risk assessment action includes at least one of: evaluation of foundational properties, evaluation of task performance, evaluation of safety and risk management, evaluation of alignment and compliance, evaluation of operational metrics, or evaluation of tooling and transparency.

[0248] In some aspects, the techniques described herein relate to a method, wherein the at least one model deployment action includes at least one of: production integration, API disclosure, scaling, selection of usage caps and quotas, cost and latency budgeting, selecting region- specific deployment strategies for data sovereignty, model endpoint wrapping, containerized environment hosting, standardized API access configuration, authentication via identity and access management systems or API keys, throttling policy application, metrics logging for monitoring and observability services, automatic scaling of model inference based on demand, elastic inference backend provisioning with scalable GPU-backed infrastructure clusters, horizontal scaling tuned to provider service level agreements, budget control implementation with alert triggering and usage stopping at thresholds, region-specific model deployment for data residency and compliance requirements, or geo-fencing enforcement by location-aware routing layers.

[0249] In some aspects, the techniques described herein relate to a method, wherein the at least one model monitoring and observability action includes at least one of: logging of API calls, capturing payload sizes, capturing error responses, capturing latency buckets, inference latency tracking, token use per invocation tracking, model-specific usage metric tracking, model throughput tracking, model GPU utilization tracking, request rate tracking, data drift detection, monitoring prompt types, comparing model output distributions over time, implementing audit trails, implementing real-time feedback loops, abuse and misuse detection, or detection of anomalous usage.

[0250] In some aspects, the techniques described herein relate to a method, wherein the at least one model updating and retraining action includes at least one of: data pipeline refresh actions, fine-tuning and customization actions, model iteration and versioning actions, model retraining actions, model comparison with stored responses and metrics comparison, updating a retrieval augmented generation knowledge base, or conducting A / B testing between model versions.

[0251] In some aspects, the techniques described herein relate to a method, further including generating a digital twin of the set of candidate models using a digital twin system

[0252] In some aspects, the techniques described herein relate to a method, wherein the at least one model evaluation and risk assessment action includes performing a simulation using the digital twin.

[0253] In some aspects, the techniques described herein relate to an inventory management system, including: a processor and memory configured to execute a know your model system, wherein the know your model system is configured to: perform at least one model intake and registration action associated with a set of candidate models; perform at least one model evaluation and risk assessment action associated with the set of candidate models; and perform at least one model deployment action associated with the set of candidate models.

[0254] In some aspects, the techniques described herein relate to an inventory' management system, wherein the know your model system is further configured to perform at least one model monitoring and observability action associated with the set of candidate models.

[0255] In some aspects, the techniques described herein relate to an inventory' management system, wherein the know your model system is further configured to perform at least one model updating and retraining action associated with the set of candidate models

[0256] In some aspects, the techniques described herein relate to an inventory management system, wherein the at least one model intake and registration action includes at least one of: model documentation collection, model registration procedures, model metadata collection, input and output interface standardization, legal and licensing validation checks, model card collection and management, cataloging and tagging operations, API schema conformance and endpoint definition, model interface wrapping, endpoint configuration testing in sandbox or staging environments, telemetry hook integration, invocation method registration including assigning resource identifiers and configuring API endpoints, or security and privacy validation.

[0257] In some aspects, the techniques described herein relate to an inventory management system, wherein the at least one model evaluation and risk assessment action includes at least one of: evaluation of foundational properties, evaluation of task performance, evaluation of safety and risk management, evaluation of alignment and compliance, evaluation of operational metrics, or evaluation of tooling and transparency.

[0258] In some aspects, the techniques described herein relate to an inventory management system, wherein the at least one model deployment action includes at least one of: production integration, API disclosure, scaling, selection of usage caps and quotas, cost and latency budgeting, selecting region-specific deployment strategies for data sovereignty, model endpoint wrapping, containerized environment hosting, standardized API access configuration, authentication via identity and access management systems or API keys, throttling policy application, metrics logging for monitoring and observability services, automatic scaling of model inference based on demand, elastic inference backend provisioning with scalable GPU-backed infrastructure clusters, horizontal scaling tuned to provider service level agreements, budget control implementation with alert triggering and usage stopping at thresholds, region-specific model deployment for data residency and compliance requirements, or geo-fencing enforcement by location- aware routing layers.

[0259] In some aspects, the techniques described herein relate to an inventory management system, wherein the at least one model monitoring and observability action includes at least one of: logging of API calls, capturing payload sizes, capturing error responses, capturing latency buckets, inference latency tracking, token use per invocation tracking, model-specific usage metric tracking, model throughput tracking, model GPU utilization tracking, request rate tracking, data drift detection, monitoring prompt types, comparing model output distributions over time, implementing audit trails, implementing real-time feedback loops, abuse and misuse detection, or detection of anomalous usage.

[0260] In some aspects, the techniques described herein relate to an inventory management system, wherein the at least one model updating and retraining action includes at least one of: data pipeline refresh actions, fine-tuning and customization actions, model iteration and versioning actions, model retraining actions, model comparison with stored responses and metrics comparison, updating a retrieval augmented generation knowledge base, or conducting A / B testing between model versions.

[0261] In some aspects, the techniques described herein relate to an inventory’ management system, further including a digital twin system, wherein the digital twin system is configured to generate a digital twin of the set of candidate models.

[0262] In some aspects, the techniques described herein relate to an inventory management system, wherein the at least one model evaluation and risk assessment action includes performing a simulation using the digital twin

[0263] Tn some aspects, the techniques described herein relate to a method for executing a know your model system by an inventory management system, including: performing at least one model intake and registration action associated with a set of candidate models; performing at least one model evaluation and risk assessment action associated with the set of candidate models: and performing at least one model deployment action associated with the set of candidate models.

[0264] In some aspects, the techniques described herein relate to a method, further including performing at least one model monitoring and observability action associated with the set of candidate models.

[0265] In some aspects, the techniques described herein relate to a method, further including performing at least one model updating and retraining action associated with the set of candidate models

[0266] In some aspects, the techniques described herein relate to a method, wherein the at least one model intake and registration action includes at least one of: model documentation collection, model registration procedures, model metadata collection, input and output interface standardization, legal and licensing validation checks, model card collection and management, cataloging and tagging operations, API schema conformance and endpoint definition, model interface wrapping, endpoint configuration testing in sandbox or staging environments, telemetry hook integration, invocation method registration including assigning resource identifiers and configuring API endpoints, or security and privacy validation

[0267] In some aspects, the techniques described herein relate to a method, wherein the at least one model evaluation and risk assessment action includes at least one of: evaluation of foundational properties, evaluation of task performance, evaluation of safety and risk management, evaluation of alignment and compliance, evaluation of operational metrics, or evaluation of tooling and transparency.

[0268] In some aspects, the techniques described herein relate to a method, wherein the at least one model deployment action includes at least one of: production integration, API disclosure, scaling, selection of usage caps and quotas, cost and latency budgeting, selecting region- specific deployment strategies for data sovereignty, model endpoint wrapping, containerized environment hosting, standardized API access configuration, authentication via identity and access management systems or API keys, throttling policy application, metrics logging for monitoring and observability services, automatic scaling of model inference based on demand, elastic inference backend provisioning with scalable GPU-backed infrastructure clusters, horizontal scaling tuned to provider service level agreements, budget control implementation with alert triggering and usage stopping at thresholds, region-specific model deployment for data residency and compliance requirements, or geo-fencing enforcement by location-aware routing layers.

[0269] In some aspects, the techniques described herein relate to a method, wherein the at least one model monitoring and observability action includes at least one of: logging of API calls, capturing payload sizes, capturing error responses, capturing latency buckets, inference latency tracking, token use per invocation tracking, model-specific usage metric tracking, model throughput tracking, model GPU utilization tracking, request rate tracking, data drift detection, monitoring prompt types, comparing model output distributions over time, implementing audit trails, implementing real-time feedback loops, abuse and misuse detection, or detection of anomalous usage.

[0270] In some aspects, the techniques described herein relate to a method, wherein the at least one model updating and retraining action includes at least one of: data pipeline refresh actions, fine-tuning and customization actions, model iteration and versioning actions, model retraining actions, model comparison with stored responses and metrics comparison, updating a retrieval augmented generation knowledge base, or conducting A / B testing between model versions.

[0271] In some aspects, the techniques described herein relate to a method, further including generating a digital twin of the set of candidate models by a digital twin system.

[0272] In some aspects, the techniques described herein relate to a method, wherein the at least one model evaluation and risk assessment action includes performing a simulation using the digital twin.

[0273] In some aspects, the techniques described herein relate to a digital product network system, including: a processor and memory configured to execute a know-' your physical Al system, wherein the know your physical Al system is configured to: establish an initial connection with a candidate physical Al system; perform at least one discovery and authentication action for the candidate physical Al system; receive and process at least one capability declaration from the candidate physical Al system; execute at least one compliance validation action for the candidate physical Al system; perform at least one contextual configuration and operational parameterization action associated with the candidate physical Al system; and perform at least one deployment action for the candidate physical Al system.

[0274] In some aspects, the techniques described herein relate to a digital product network system, wherein the discovery and authentication action includes receiving by the candidate physical Al system a discovery' packet including cryptographic identity' credentials, device serial number, or pre-provisioned public key infrastructure ( OKI ) certificate via a secure networking protocol

[0275] In some aspects, the techniques described herein relate to a digital product network system, wherein the know your physical Al system performs hardware attestation using trusted platform modules or secure enclave cryptographic signatures to verify device integrity or authenticity of the candidate physical Al system

[0276] In some aspects, the techniques described herein relate to a digital product network system, wherein the at least one capability declaration includes a machine-readable manifest describing at least one of: supported Al tasks, hardware specifications, operational constraints, or certified Al model signatures.

[0277] In some aspects, the techniques described herein relate to a digital product network system, wherein the at least one compliance validation action includes comparing a received capability profile against at least one of a stored organizational governance policy or an external regulatory framework.

[0278] In some aspects, the techniques described herein relate to a digital product network system, wherein the at least one contextual configuration and operational parameterization action includes securely transmitting via encrypted over-the-air (OTA) update charnels at least one of dynamic scheduling parameters or interaction policies.

[0279] In some aspects, the techniques described herein relate to a digital product network system, wherein the know your physical Al system integrates the candidate physical Al system into a centralized orchestration and control plane that exposes at least one of: a dynamic task assignment API, a real-time telemetry collection service, or a health status subscription channel.

[0280] In some aspects, the techniques described herein relate to a digital product network system, wherein the know your physical Al system performs initial sandbox validation and simulation-based testing of the candidate physical Al system prior to deployment.

[0281] In some aspects, the techniques described herein relate to a digital product network system, wherein the know your physical Al system incorporates a distributed trust ledger component to immutably record onboarding steps, policy validations, operational events, or update transactions using blockchain or tamper-evident cryptographic data structures

[0282] In some aspects, the techniques described herein relate to a digital product network system, wherein the know your physical Al system is further configured to integrate with a digital twin system to generate a digital twin of the candidate physical Al system for simulation-based testing or performance validation.

[0283] In some aspects, the techniques described herein relate to a digital product network system, wherein the know your physical Al system is further configured to integrate with a know your model system to perform at least one of: a model intake and registration action, a model evaluation and risk assessment action, or a model deployment action for a set of models associated with the candidate physical Al system.

[0284] In some aspects, the techniques described herein relate to a digital product network system, wherein the know your physical Al system is further configured to integrate with a know your sensor system to perform at least one of a sensor validation and authentication action, a sensor monitoring and anomaly detection action, or a sensor calibration and optimization action for a set of sensors associated with the candidate physical Al system

[0285] In some aspects, the techniques described herein relate to a method for executing a know your physical Al system by a digital product network, including: establishing an initial connection with a candidate physical Al system; performing at least one discovery and authentication action for the candidate physical Al system; receiving and processing at least one capability declaration from the candidate physical Al system; executing at least one compliance validation action for the candidate physical Al system; performing at least one contextual configuration and operational parameterization action associated with the candidate physical Al system; and performing at least one deployment action for the candidate physical Al system.

[0286] In some aspects, the techniques described herein relate to a method, wherein the at least one discovery and authentication action includes the candidate physical Al system broadcasting a discovery packet includingcryptographic identity credentials, device serial number, or pre-provisioned public key infrastructure (PKI) certificate via a secure networking protocol.

[0287] In some aspects, the techniques described herein relate to a method, wherein performing the at least one discovery and authentication action includes performing hardware attestation using trusted platform modules or secure enclave cryptographic signatures to verify device integrity or authenticity of the candidate physical Al system.

[0288] In some aspects, the techniques described herein relate to a method, wherein the at least one capability declaration includes a machine-readable manifest describing at least one of: supported Al tasks, hardware specifications, operational constraints, or certified Al model signatures.

[0289] In some aspects, the techniques described herein relate to a method, wherein the at least one compliance validation action includes comparing a received capability profile against at least one of a stored organizational governance policy or an external regulatory framework.

[0290] In some aspects, the techniques described herein relate to a method, wherein the at least one contextual configuration and operational parameterization action includes at least one of: securely transmitting dynamic scheduling parameters or interaction policies via encrypted over-the-air (OTA) update charnels.

[0291] In some aspects, the techniques described herein relate to a method, further including integrating the candidate physical Al system into a centralized orchestration and control plane that exposes at least one of: a dynamic task assignment API, a real-time telemetry collection service, or a health status subscription channel.

[0292] In some aspects, the techniques described herein relate to a method, further including performing initial sandbox validation and simulation-based testing of the candidate physical Al system prior to deployment

[0293] Tn some aspects, the techniques described herein relate to a method, further including immutably recording onboarding steps, policy validations, operational events, or update transactions using blockchain or tamper-evident cryptographic data structures via a distributed trust ledger component.

[0294] In some aspects, the techniques described herein relate to a method, further including integrating with a digital twin system to generate a digital twin of the candidate physical Al system for simulation-based testing or performance validation.

[0295] In some aspects, the techniques described herein relate to a method, further including integrating with a know your model system to perform at least one of: a model intake and registration action, a model evaluation and risk assessment action, or a model deployment action for a set of models associated with the candidate physical Al system.

[0296] In some aspects, the techniques described herein relate to a method, further including integrating with a know your sensor system to perform at least one of a sensor validation and authentication action, a sensor monitoring and anomaly detection action, or a sensor calibration and optimization action, for a set of sensors associated with the candidate physical Al system.

[0297] In some aspects, the techniques described herein relate to a value chain network system including: at least one processor; at least one memory storing instructions that, when executed by the at least one processor, cause the system to: receive a user prompt that includes a request involving at least one value chain network action; generate, based on the user prompt, an action plan using an Al agent that includes a large language model and a tool set; and automatically execute the at least one action in accordance with the action plan, wherein the Al agent interfaces with applications, devices, and resources to perform the at least one action.

[0298] In some aspects, the techniques described herein relate to a system, wherein the at least one action includes: raw material sourcing, supplier selection and qualification, supplier performance monitoring, contract negotiation andprocurement, logistics and inbound transportation, supplier risk management, inventory replenishment planning, component and part quality inspections, just-in-time delivery coordination, supply chain visibility and tracking, demand forecasting and sales planning, production scheduling, manufacturing and assembly operations, equipment maintenance and reliability, quality control and testing, process optimization and continuous improvement, waste reduction and lean practices, digital twin or simulation-driven planning, energy and resource management, finished goods inventory management, packaging and labeling, order fulfillment and warehouse operations, distribution network planning, transportation and logistics optimization, last-mile delivery management, returns and reverse logistics, export compliance and documentation, customer needs analysis, customization and configuration services, marketing and demand generation, channel partner management, sales enablement, customer relationship management, after-sales support and warranty services, field sendee and maintenance, customer feedback collection, loyalty program and retention strategies, enterprise resource planning integration, data governance and data quality management, cybersecurity and network protection, legal and compliance monitoring, financial planning and cost control, sustainability and ESG compliance, workforce training and talent development, risk assessment and business continuity plaiming, cross-organization collaboration platforms, Al-driven insights and predictive analytics, supplier and partner ecosystem innovation programs, real-time KPI and performance monitoring, scenario simulation and impact analysis, dynamic reconfiguration of resources, autonomous negotiation and contract execution, multi-party data exchange and trusted ledger operations, Al-based anomaly detection and mitigation, automated regulatory reporting and audit trails, procurement, fleet management, additive manufacturing, or continuous value stream mapping and optimization

[0299] In some aspects, the techniques described herein relate to a system, wherein the Al agent includes an Al agent process that includes an iterative agent loop that enables processing of workflows with automated task decomposition and planning.

[0300] In some aspects, the techniques described herein relate to a system, wherein the large language model is configured to determine actions and generate responses according to a system prompt that describes capabilities and use of the tools in the tool set.

[0301] In some aspects, the techniques described herein relate to a system, wherein the Al agent incorporates selfcritique capabilities to detect and correct hallucinations or fabricated facts during processing of user prompts or generation of outputs.

[0302] In some aspects, the techniques described herein relate to a system, wherein the system is integrated with a digital twin system configured to generate at least one digital twin of at least one value chain network asset or process.

[0303] In some aspects, the techniques described herein relate to a system, wherein the digital twin system is configured to execute at least one simulation on the at least one digital twin to perform scenario simulation or impact analysis for the value chain network.

[0304] In some aspects, the techniques described herein relate to a system, wherein the tool set includes at least one of: executable programs, APIs, or communication interfaces for performing value chain network tasks

[0305] In some aspects, the techniques described herein relate to a system, wherein the Al agent is configured to invoke a sequence of tools from the tool set to fulfill the request indicated in the user prompt.

[0306] In some aspects, the techniques described herein relate to a system, wherein the system includes a governance and analysis system configured to monitor and regulate the Al agent's performance of value chain network tasks to ensure compliance with organizational policies and regulatory frameworks.

[0307] In some aspects, the techniques described herein relate to a method for executing value chain network tasks by an Al agent, including: receiving a user prompt that includes a request involving at least one value chain network action; generating, based on the user prompt, an action plan using an Al agent that includes a large language model and a tool set; and automatically executing at least one action in accordance with the action plan, wherein the Al agent interfaces with at least one of applications, devices, and resources to perform the at least one action

[0308] In some aspects, the techniques described herein relate to a method, wherein the at least one action includes: raw material sourcing, supplier selection and qualification, supplier performance monitoring, contract negotiation and procurement, logistics and inbound transportation, supplier risk management, inventory replenishment planning, component and part quality inspections, just-in-time delivery coordination, supply chain visibility and tracking, demand forecasting and sales planning, production scheduling, manufacturing and assembly operations, equipment maintenance and reliability, quality control and testing, process optimization and continuous improvement, waste reduction and lean practices, digital twin or simulation-driven planning, energy and resource management, finished goods inventory management, packaging and labeling, order fulfillment and warehouse operations, distribution network planning, transportation and logistics optimization, last-mile delivery management, returns and reverse logistics, export compliance and documentation, customer needs analysis, customization and configuration sendees, marketing and demand generation, channel partner management, sales enablement, customer relationship management, after-sales support and warranty services, field service and maintenance, customer feedback collection, loyalty' program and retention strategies, enterprise resource planning integration, data governance and data quality management, cybersecurity and network protection, legal and compliance monitoring, financial planning and cost control, sustainability and ESG compliance, workforce training and talent development, risk assessment and business continuity planning, cross-organization collaboration platforms, Al-driven insights and predictive analytics, supplier and partner ecosystem innovation programs, real-time KPI and performance monitoring, scenario simulation and impact analysis, dynamic reconfiguration of resources, autonomous negotiation and contract execution, multi-party data exchange and trusted ledger operations, Al-based anomaly detection and mitigation, automated regulatory reporting and audit trails, procurement, fleet management, additive manufacturing, or continuous value stream mapping and optimization.

[0309] In some aspects, the techniques described herein relate to a method, wherein the Al agent includes an iterative agent loop that enables processing of workflows with automated task decomposition or planning.

[0310] In some aspects, the techniques described herein relate to a method, wherein the large language model is configured to determine actions and generate responses according to a system prompt that describes capabilities and use of the tools in the tool set.

[0311] In some aspects, the techniques described herein relate to a method, wherein the Al agent incorporates selfcritique capabilities to detect and correct hallucinations or fabricated facts during processing of user prompts or generation of outputs.

[0312] In some aspects, the techniques described herein relate to a method, further including integrating with a digital twin system configured to generate at least one digital twin of at least one value chain network asset or process

[0313] In some aspects, the techniques described herein relate to a method, further including executing at least one simulation on the at least one digital twin to perform scenario simulation and impact analysis for the value chain network.

[0314] In some aspects, the techniques described herein relate to a method, wherein the tool set includes at least one of: executable programs, APIs, and communication interfaces for performing value chain network tasks.

[0315] In some aspects, the techniques described herein relate to a method, wherein the Al agent is configured to invoke a sequence of tools from the tool set to fulfill the request indicated in the user prompt

[0316] In some aspects, the techniques described herein relate to a method, further including monitoring and regulating the Al agent's performance of value chain network tasks using a governance and analysis system to ensure compliance with organizational policies and regulatory frameworks.

[0317] In some aspects, the techniques described herein relate to a value chain network system including: at least one processor; at least one memory storing instructions that, when executed by the at least one processor, cause the system to: receive input data from one or more data sources; generate, based on the input data, an action plan using an Al agent that includes a large language model and a tool set; and automatically execute at least one action in accordance with the action plan, wherein the Al agent interfaces with at least one of applications, devices, and resources to perform the at least one action.

[0318] In some aspects, tire techniques described herein relate to a system, wherein the at least one action includes: raw material sourcing, supplier selection and qualification, supplier performance monitoring, contract negotiation and procurement, logistics and inbound transportation, supplier risk management, inventory' replenishment planning, component and part quality inspections, just-in-time delivery coordination, supply chain visibility and tracking, demand forecasting and sales planning, production scheduling, manufacturing and assembly operations, equipment maintenance and reliability, quality control and testing, process optimization and continuous improvement, waste reduction and lean practices, digital twin or simulation-driven planning, energy and resource management, finished goods inventory management, packaging and labeling, order fulfillment and warehouse operations, distribution network planning, transportation and logistics optimization, last-mile delivery management, returns and reverse logistics, export compliance and documentation, customer needs analysis, customization and configuration services, marketing and demand generation, channel partner management, sales enablement, customer relationship management, after-sales support and warranty services, field service and maintenance, customer feedback collection, loyalty program and retention strategies, enterprise resource planning integration, data governance and data quality management, cybersecurity and network protection, legal and compliance monitoring, financial planning and cost control, sustainability and ESG compliance, workforce training and talent development, risk assessment and business continuity plaiming, cross-organization collaboration platforms, Al-driven insights and predictive analytics, supplier and partner ecosystem innovation programs, real-time KPI and performance monitoring, scenario simulation and impact analysis, dynamic reconfiguration of resources, autonomous negotiation and contract execution, multi-party data exchange and trusted ledger operations, Al-based anomaly detection and mitigation, automated regulatory' reporting and audit trails, procurement, fleet management, additive manufacturing, or continuous value stream mapping and optimization.

[0319] In some aspects, the techniques described herein relate to a system, wherein the Al agent includes an iterative agent loop that enables processing of workflows with automated task decomposition or planning.

[0320] In some aspects, the techniques described herein relate to a system, wherein the large language model is configured to determine actions and generate responses according to a system prompt that describes capabilities and use of the tools in the tool set.

[0321] In some aspects, the techniques described herein relate to a system, wherein the Al agent incorporates selfcritique capabilities to detect and correct hallucinations or fabricated facts during processing of input data or generation of outputs.

[0322] In some aspects, the techniques described herein relate to a system, wherein the system is integrated with a digital twin system configured to generate at least one digital twin of at least one value chain network asset or process.

[0323] In some aspects, the techniques described herein relate to a system, wherein the digital twin system is configured to execute at least one simulation on the at least one digital twin to perform scenario simulation and impact analysis for the value chain network.

[0324] In some aspects, the techniques described herein relate to a system, wherein the tool set includes at least one of: executable programs, APIs, or communication interfaces for performing value chain network tasks.

[0325] In some aspects, the techniques described herein relate to a system, wherein the Al agent is configured to invoke a sequence of tools from the tool set to fulfill a request indicated in the input data.

[0326] In some aspects, the techniques described herein relate to a system, wherein the system includes a governance and analysis system configured to monitor and regulate the Al agent's performance to ensure compliance with organizational policies or regulatory frameworks.

[0327] In some aspects, the techniques described herein relate to a method for executing value chain network tasks, including: receiving input data from one or more data sources; generating, based on the input data, an action plan using an Al agent that includes a large language model and a tool set; and automatically executing at least one action in accordance with the action plan, wherein the Al agent interfaces with at least one of applications, devices, and resources to perform the at least one action

[0328] In some aspects, the techniques described herein relate to a method, wherein the at least one action includes: raw material sourcing, supplier selection and qualification, supplier performance monitoring, contract negotiation and procurement, logistics and inbound transportation, supplier risk management, inventory replenishment planning, component and part quality inspections, just-in-time delivery coordination, supply chain visibility and tracking, demand forecasting and sales planning, production scheduling, manufacturing and assembly operations, equipment maintenance and reliability, quality control and testing, process optimization and continuous improvement, waste reduction and lean practices, digital twin or simulation-driven planning, energy and resource management, finished goods inventory management, packaging and labeling, order fulfillment and warehouse operations, distribution network planning, transportation and logistics optimization, last-mile delivery management, returns and reverse logistics, export compliance and documentation, customer needs analysis, customization and configuration services, marketing and demand generation, channel partner management, sales enablement, customer relationship management, after-sales support and warranty services, field service and maintenance, customer feedback collection, loyalty program and retention strategies, enterprise resource planning integration, data governance and data quality management, cybersecurity and network protection, legal and compliance monitoring, financial planning and cost control, sustainability and ESG compliance, workforce training and talent development, risk assessment and business continuity planning, cross-organization collaboration platforms, Al-driven insights and predictive analytics, supplier and partner ecosystem innovation programs, real-time KPI and performance monitoring, scenario simulation and impact analysis, dynamic reconfiguration of resources, autonomous negotiation and contract execution, multi-party data exchange and trusted ledger operations, Al-based anomaly detection and mitigation, automated regulatoryreporting and audit trails, procurement, fleet management, additive manufacturing, or continuous value stream mapping and optimization.

[0329] In some aspects, the techniques described herein relate to a method, wherein the Al agent includes an iterative agent loop that enables processing of workflows with automated task decomposition or planning

[0330] In some aspects, the techniques described herein relate to a method, wherein the large language model is configured to determine actions and generate responses according to a system prompt that describes capabilities and use of the tools in the tool set.

[0331] In some aspects, the techniques described herein relate to a method, wherein the Al agent incorporates selfcritique capabilities to detect and correct hallucinations or fabricated facts during processing of input data or generation of outputs.

[0332] In some aspects, the techniques described herein relate to a method, further including integrating with a digital twin system configured to generate at least one digital twin of at least one value chain network asset or process

[0333] In some aspects, the techniques described herein relate to a method, further including executing at least one simulation on the at least one digital twin to perform scenario simulation and impact analysis for the value chain network.

[0334] In some aspects, the techniques described herein relate to a method, wherein the tool set includes at least one of: executable programs, APIs, or communication interfaces for performing value chain network tasks.

[0335] In some aspects, the techniques described herein relate to a method, wherein the Al agent is configured to invoke a sequence of tools from the tool set to fulfill a request indicated in the input data

[0336] Tn some aspects, the techniques described herein relate to a method, further including monitoring and regulating the Al agent's performance using a governance and analysis system to ensure compliance with organizational policies or regulatory frameworks.BRIEF DESCRIPTION OF THE DRAWINGS

[0337] The accompanying drawings, which are included to provide a better understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the many aspects of the disclosure. In the drawings:

[0338] FIG. 1 is a block diagram showing prior art relationships of various entities and facilities in a supply chain.

[0339] FIG. 2 is a block diagram showing components and interrelationships of systems and processes of a value chain network in accordance with the present disclosure.

[0340] FIG. 3 is another block diagram showing components and interrelationships of systems and processes of a value chain network in accordance with the present disclosure

[0341] FIG. 4 is a block diagram showing components and interrelationships of systems and processes of a digital products network of FIGS. 2 and 3 in accordance with the present disclosure.

[0342] FIG 5 is a block diagram showing components and interrelationships of systems and processes of a value chain network technology stack in accordance with the present disclosure.

[0343] FIG. 6 is a block diagram showing a platform and relationships for orchestrating controls of various entities in a value chain network in accordance with the present disclosure.

[0344] FIG. 7 is a block diagram showing components and relationships in embodiments of a value chain network management platform in accordance with the present disclosure.

[0345] FIG. 8 is a block diagram showing components and relationships of value chain entities managed by embodiments of a value chain network management platform in accordance with the present disclosure.

[0346] FIG. 9 is a block diagram showing network relationships of entities in a value chain network in accordance with the present disclosure

[0347] FIG. 10 is a block diagram showing a set of applications supported by unified data handling layers in a value chain network management platform in accordance with the present disclosure.

[0348] FIG. 11 is a block diagram showing components and relationships in embodiments of a value chain network management platform in accordance with the present disclosure.

[0349] FIG. 12 is a block diagram showing components and relationships of a data storage layer in embodiments of a value chain network management platform in accordance with the present disclosure.

[0350] FIG. 13 is a block diagram showing components and relationships of an adaptive intelligent systems layer in embodiments of a value chain network management platform in accordance with the present disclosure.

[0351] FIG. 14 is a block diagram that depicts providing adaptive intelligence systems for coordinated intelligence for sets of demand and supply applications for a category of goods in accordance with the present disclosure.

[0352] FIG. 15 is a block diagram that depicts providing hybrid adaptive intelligence systems for coordinated intelligence for sets of demand and supply applications or a category' of goods in accordance with the present disclosure.

[0353] FIG. 16 is a block diagram that depicts providing adaptive intelligence systems for predictive intelligence for sets of demand and supply applications for a category of goods in accordance with the present disclosure

[0354] FIG 17 is a block diagram that depicts providing adaptive intelligence systems for classification intelligence for sets of demand and supply applications for a category of goods in accordance with the present disclosure.

[0355] FIG. 18 is a block diagram that depicts providing adaptive intelligence systems to produce automated control signals for sets of demand and supply applications for a category of goods in accordance with the present disclosure.

[0356] FIG. 19 is a block diagram that depicts training artificial intelligence / machine learning systems to produce information routing recommendations for a selected value chain network in accordance with the present disclosure.

[0357] FIG. 20 is a block diagram that depicts a semi-sentient problem recognition system for recognition of pain points / problem states in a value chain network in accordance with the present disclosure.

[0358] FIG. 21 is a block diagram that depicts a set of artificial intelligence systems operating on value chain information to enable automated coordination of value chain activities for an enterprise in accordance with the present disclosure.

[0359] FIG. 22 is a block diagram showing components and relationships involved in integrating a set of digital twins in an embodiment of a value chain network management platform in accordance with the present disclosure.

[0360] FIG. 23 is a block diagram showing a set of digital twins involved in embodiments of a value chain network management platform in accordance with the present disclosure.

[0361] FIG 24 is a block diagram showing components and relationships of entity discovery and management systems in embodiments of a value chain network management platform in accordance with the present disclosure.

[0362] FIG. 25 is a block diagram showing components and relationships of a robotic process automation system in embodiments of a value chain network management platform in accordance with the present disclosure.

[0363] FIG. 26 is a block diagram showing components and relationships of a set of opportunity miners in an embodiment of a value chain network management platform in accordance with the present disclosure.

[0364] FIG. 27 is a block diagram showing components and relationships of a set of edge intelligence systems in embodiments of a value chain network management platform in accordance with the present disclosure.

[0365] FIG. 28 is a block diagram showing components and relationships in an embodiment of a value chain network management platform in accordance with the present disclosure

[0366] FIG. 29 is a block diagram showing additional details of components and relationships in embodiments of a value chain network management platform in accordance with the present disclosure.

[0367] FIG. 30 is a block diagram showing components and relationships in an embodiment of a value chain network management platform that enables centralized orchestration of value chain network entities in accordance with the present disclosure.

[0368] FIG. 31 is a block diagram showing components and relationships of a unified database in an embodiment of a value chain network management platform in accordance with the present disclosure.

[0369] FIG. 32 is a block diagram showing components and relationships of a set of unified data collection systems in embodiments of a value chain network management platform in accordance with the present disclosure.

[0370] FIG. 33 is a block diagram showing components and relationships of a set of Internet of Things monitoring systems in embodiments of a value chain network management platform in accordance with the present disclosure.

[0371] FIG. 34 is a block diagram showing components and relationships of a machine vision system and a digital twin in embodiments of a value chain network management platform in accordance with the present disclosure

[0372] FIG. 35 is a block diagram showing components and relationships of a set of adaptive edge intelligence systems in embodiments of a value chain network management platform in accordance with the present disclosure

[0373] FIG 36 is a block diagram showing additional details of components and relationships of a set of adaptive edge intelligence systems in embodiments of a value chain network management platform in accordance with the present disclosure.

[0374] FIG. 37 is a block diagram showing components and relationships of a set of unified adaptive intelligence systems in embodiments of a value chain network management platform in accordance with the present disclosure.

[0375] FIG. 38 is a schematic of a system configured to train an artificial system that is leveraged by a value chain system using real world outcome data and a digital twin system according to some embodiments of the present disclosure.

[0376] FIG. 39 is a schematic of a system configured to train an artificial system that is leveraged by a container fleet management system using real world outcome data and a digital twin system according to some embodiments of tire present disclosure.

[0377] FIG. 40 is a schematic of a system configured to train an artificial system that is leveraged by a logistics design system using real world outcome data and a digital twin system according to some embodiments of the present disclosure.

[0378] FIG. 41 is a schematic of a system configured to train an artificial system that is leveraged by a packaging design system using real world outcome data and a digital twin system according to some embodiments of the present disclosure.

[0379] FIG. 42 is a schematic of a system configured to train an artificial system that is leveraged by a waste mitigation system using real world outcome data and a digital twin system according to some embodiments of the present disclosure.

[0380] FIG. 43 is a schematic illustrating an example of a portion of an information technology system for value chain artificial intelligence leveraging digital twins according to some embodiments of the present disclosure.

[0381] FIG. 44 is a block diagram showing components and relationships of a set of intelligent project management facilities in embodiments of a value chain network management platform in accordance with the present disclosure

[0382] FIG. 45 is a block diagram showing components and relationships of an intelligent task recommendation system in embodiments of a value chain network management platform in accordance with the present disclosure.

[0383] FIG. 46 is a block diagram showing components and relationships of a routing system among nodes of a value chain network in embodiments of a value chain network management platform in accordance with the present disclosure.

[0384] FIG. 47 is a block diagram showing components and relationships of a dashboard for managing a set of digital twins in embodiments of a value chain network management platform.

[0385] FIG. 48 is a block diagram showing components and relationships in embodiments of a value chain network management platform that uses a microservices architecture.

[0386] FIG. 49 is a block diagram showing components and relationships of an Internet of Things data collection architecture and sensor recommendation system in embodiments of a value chain network management platform.

[0387] FIG. 50 is a block diagram showing components and relationships of a social data collection architecture in embodiments of a value chain network management platform.

[0388] FIG. 51 is a block diagram showing components and relationships of a crowdsourcing data collection architecture in embodiments of a value chain network management platform

[0389] FIG 52 is a diagrammatic view that depicts embodiments of a set of value chain network digital twins representing virtual models of a set of value chain network entities in accordance with the present disclosure.

[0390] FIG. 53 is a diagrammatic viewthat depicts embodiments of a warehouse digital twin kit system in accordance with the present disclosure.

[0391] FIG. 54 is a diagrammatic view that depicts embodiments of a stress test performed on a value chain network in accordance with the present disclosure

[0392] FIG. 55 is a diagrammatic view that depicts embodiments of methods used by a machine for detecting faults and predicting any future failures of the machine in accordance with the present disclosure.

[0393] FIG. 56 is a diagrammatic view that depicts embodiments of deployment of machine twins to perform predictive maintenance on a set of machines in accordance with tire present disclosure.

[0394] FIG. 57 is a schematic illustrating an example of a portion of a system for value chain customer digital twins and customer profile digital twins according to some embodiments of the present disclosure.

[0395] FIG. 58 is a schematic illustrating an example of an advertising application that interfaces with the adaptive intelligent systems layer in accordance with the present disclosure.

[0396] FIG. 59 is a schematic illustrating an example of an e-commerce application integrated with the adaptive intelligent systems layer in accordance with the present disclosure

[0397] FIG. 60 is a schematic illustrating an example of a demand management application integrated with the adaptive intelligent systems layer in accordance with the present disclosure.

[0398] FIG. 61 is a schematic illustrating an example of a portion of a system for value chain smart supply component digital twins according to some embodiments of the present disclosure.

[0399] FIG. 62 is a schematic illustrating an example of a risk management application that interfaces with the adaptive intelligent systems layer in accordance with the present disclosure.

[0400] FIG. 63 is a diagrammatic view- of maritime assets associated with a value chain network management platform including components of a port infrastructure in accordance with the present disclosure

[0401] FIGS. 64 and 65 are diagrammatic views of maritime assets associated with a value chain network management platform including components of a ship in accordance with the present disclosure.

[0402] FIG. 66 is a diagrammatic view of maritime assets associated with a value chain network management platform including components of a barge in accordance with the present disclosure.

[0403] FIG. 67 is a diagrammatic view of maritime assets associated with a value chain network management platform including those involved in maritime events, legal proceedings and making use of geofenced parameters in accordance with the present disclosure.

[0404] FIG. 68 is a schematic illustrating an example environment of the enterprise and executive control tower and management platform, including data sources in communication therewith, according to some embodiments of the present disclosure.

[0405] FIG. 69 is a schematic illustrating an example set of components of the enterprise control to 'er and management platform according to some embodiments of the present disclosure.

[0406] FIG. 70 is a schematic illustrating and example of an enterprise data model according to some embodiments of the disclosure.

[0407] FIG 71 is a schematic illustrating examples of different types of enterprise digital twins, including executive digital twins, in relation to the data layer, processing layer, and application layer of the enterprise digital twin framework according to some embodiments of the present disclosure

[0408] FIG. 72 is a schematic illustrating an example implementation of the enterprise and executive control tower and management platform according to some embodiments of the present disclosure.

[0409] FIG. 73 is a flow chart illustrating an example set of operations for configuring and serving an enterprise digital twin.

[0410] FIG. 74 illustrates an example set of operations of a method for configuring an organizational digital twin.

[0411] FIG. 75 illustrates an example set of operations of a method for generating an executive digital twin.

[0412] FIG. 76 is a schematic illustrating an example intelligence services system according to some embodiments of the present disclosure.

[0413] FIG. 77 is a schematic illustrating an example neural network with multiple layers according to some embodiments of the present disclosure.

[0414] FIG. 78 is a schematic illustrating an example convolutional neural network (CNN) according to some embodiments of the present disclosure.

[0415] FIG. 79 is a schematic illustrating an example neural network for implementing natural language processing according to some embodiments of the present disclosure

[0416] FIG. 80 is a schematic illustrating an example reinforcement learning-based approach for executing one or more tasks by a mobile system according to some embodiments of the present disclosure.

[0417] FIG. 81 is a schematic illustrating an example physical orientation determination chip according to some embodiments of the present disclosure.

[0418] FIG. 82 is a schematic illustrating an example network enhancement chip according to some embodiments of the present disclosure.

[0419] FIG. 83 is a schematic illustrating an example diagnostic chip according to some embodiments of the present disclosure

[0420] FIG. 84 is a schematic illustrating an example governance chip according to some embodiments of the present disclosure.

[0421] FIG. 85 is a schematic illustrating an example prediction, classification, and recommendation chip according to some embodiments of the present disclosure.

[0422] FIG. 86 is a diagrammatic view illustrating an example environment of an autonomous additive manufacturing platform according to some embodiments of the present disclosure

[0423] FIG. 87 is a schematic illustrating an example implementation of an autonomous additive manufacturing platform for automating and optimizing the digital production workflow for metal additive manufacturing according to some embodiments of the present disclosure.

[0424] FIG. 88 is a flow diagram illustrating the optimization of different parameters of an additive manufacture process according to some embodiments of the present disclosure.

[0425] FIG. 89 is a schematic view illustrating a system for learning on data from an autonomous additive manufacturing platform to train an artificial learning system to use digital twins for classification, predictions and decision making according to some embodiments of the present disclosure.

[0426] FIG 90 is a schematic illustrating an example implementation of an autonomous additive manufacturing platform including various components along with other entities of a distributed manufacturing network according to some embodiments of the present disclosure.

[0427] FIG. 91 is a schematic illustrating an example implementation of an autonomous additive manufacturing platform for automating and managing manufacturing functions and sub-processes including process and material selection, hybrid part workflows, feedstock formulation, part design optimization, risk prediction and management, marketing and customer service according to some embodiments of the present disclosure

[0428] FIG. 92 is a diagrammatic view of a distributed manufacturing network enabled by an autonomous additive manufacturing platform and built on a distributed ledger system according to some embodiments of the present disclosure.

[0429] FIG. 93 is a schematic illustrating an example implementation of a distributed manufacturing network where the digital thread data is tokenized and stored in a distributed ledger so as to ensure traceability of parts printed at one or more manufacturing nodes in the distributed manufacturing network according to some embodiments of the present disclosure.

[0430] FIG. 94 is a diagrammatic view illustrating an example implementation of a conventional computer vision system for creating an image of an object of interest.

[0431] FIG 95 is a schematic illustrating an example implementation of a dynamic vision system for dynamically learning an object concept about an object of interest according to some embodiments of the present disclosure.

[0432] FIG. 96 is a schematic illustrating an example architecture of a dynamic vision system according to some embodiments of the present disclosure.

[0433] FIG. 97 is a flow diagram illustrating a method for object recognition by a dynamic vision system according to some embodiments of the present disclosure.

[0434] FIG. 98 is a schematic illustrating an example implementation of a dynamic vision system for modelling, simulating and optimizing various optical, mechanical, design and lighting parameters of the dynamic vision system according to some embodiments of the present disclosure

[0435] FIG 99 is a schematic view illustrating an example implementation of a dynamic vision system depicting detailed view of various components along with integration of the dynamic vision system with one or more third party systems according to some embodiments of the present disclosure.

[0436] FIG. 100 is a schematic illustrating an example environment of a fleet management platform according to some embodiments of the present disclosure.

[0437] FIG. 101 is a schematic illustrating example configurations of a multi-purpose robot and a special purpose robot according to some embodiments of the present disclosure.

[0438] FIG. 102 is a schematic illustrating an example platform-level intelligence layer of a fleet management platform according to some embodiments of the present disclosure

[0439] FIG. 103 is a schematic illustrating an example configuration of an intelligence layer according to some embodiments of the present disclosure.

[0440] FIG. 104 is a schematic illustrating an example security framework according to some embodiments of the present disclosure.

[0441] FIG. 105 is a schematic illustrating an example environment of a fleet management platform according to some embodiments of the present disclosure.

[0442] FIG 106 is a schematic illustrating an example data flow of a job configuration system according to some embodiments of the present disclosure

[0443] FIG. 107 is a schematic illustrating an example data flow of a fleet operations system according to some embodiments of the present disclosure.

[0444] FIG. 108 is a schematic illustrating an example job parsing system and task definition system and an example data flow thereof according to some embodiments of the present disclosure.

[0445] FIG. 109 is a schematic illustrating an example fleet configuration system and an example data flow thereof according to some embodiments of the present disclosure

[0446] FIG. 110 is a schematic illustrating an example workflow definition system and an example data flow thereof according to some embodiments of the present disclosure

[0447] FIG. I l l is a schematic illustrating example configurations of a multi-purpose robot and components thereof according to some embodiments of the present disclosure

[0448] FIG. 112 is a schematic illustrating an example architecture of the robot control system according to some embodiments of the present disclosure.

[0449] FIG. 113 is a schematic illustrating an example architecture of the robot control system 12150 that utilizes data from multiple sensors in the vision and sensing system according to some embodiments of the present disclosure.

[0450] FIG 114 is a schematic illustrating an example vision and sensing system of a robot according to some embodiments of the present disclosure.

[0451] FIG. 115 is a schematic illustrating an example process that is executed by a multipurpose robot to harvest crops according to some embodiments of the present disclosure.

[0452] FIG. 116 is a schematic illustrating an example environment of the intermodal smart container system according to some embodiments of the present disclosure

[0453] FIG. 117 is a schematic illustrating example configurations of a smart container according to some embodiments of the present disclosure.

[0454] FIG. 118 is a schematic illustrating an intelligence service adapted to provide intelligence services to the smart intermodal container system according to some embodiments of the present disclosure

[0455] FIG. 119 is a schematic illustrating a digital twin module according to some embodiments of the present disclosure according to some embodiments of the present disclosure.

[0456] FIG. 120 illustrates an example embodiment of a method of receiving requests to update one or more properties of digital twins of shipping entities and / or environments.

[0457] FIG. 121 illustrates an example embodiment of a method for updating a set of cost of downtime values in the digital twin of a smart container according to some embodiments of the present disclosure

[0458] FIG. 122 is a schematic illustrating an example environment of a digital product network according to some embodiments of the present disclosure.

[0459] FIG. 123 is a schematic illustrating an example environment of a connected product according to some embodiments of the present disclosure.

[0460] FIG. 124 is a schematic illustrating an example environment of a digital product network according to some embodiments of the present disclosure.

[0461] FIG. 125 is a schematic illustrating an example environment of a digital product network according to some embodiments of the present disclosure.

[0462] FIG 126 is a flow diagram illustrating a method of using product level data according to some embodiments of the disclosure

[0463] FIG. 127 is a schematic illustrating an example environment of a digital product network according to some embodiments of the present disclosure.

[0464] FIG. 128 is a schematic illustrating an example of a smart futures contract system according to some embodiments of the present disclosure.

[0465] FIG. 129 is a schematic illustrating an example environment of an edge networking system according to some embodiments of the present disclosure.

[0466] FIG. 130 is a schematic illustrating an example environment of an edge networking system including a VCN bus according to some embodiments of the present disclosure.

[0467] FIG. 131 is a schematic illustrating an example environment of an edge networking system according to some embodiments of the present disclosure including a configured device EDNW system.

[0468] FIG. 132 is a block diagram showing a schematic of a dual-process artificial neural network system according to some embodiments of the present disclosure.

[0469] FIG. 133 is a schematic view of an example control tower dashboard for one or more VCN processes that may be used with one or more example implementations of the disclosure.

[0470] FIG 134 is an example flowchart of one or more VCN processes that may be used with one or more example implementations of the disclosure.

[0471] FIG. 135A is a schematic view of an example control architecture for system facilitation and / or management.

[0472] FIG. 135B is a schematic view of another example control architecture for system facilitation and / or management.

[0473] FIG. 135C is a schematic view of an example control architecture for system facilitation and / or management.

[0474] FIG. 135D is a schematic view of another example control architecture for system facilitation and / or management.

[0475] FIG. 136A is a schematic view of an example management stack that includes a control architecture similar to FIGS 135A and 135B

[0476] FIG. 136B is a schematic view of an example management stack capable of implementing a control architecture similar to that of FIGS. 135A and 135B for a value chain network

[0477] FIG. 136C is a schematic view of an example management stack that includes a control architecture similar to FIGS. 135C and l35D.

[0478] FIG. 136D is a schematic view of an example management stack capable of implementing a control architecture similar to that of FIGS. 135C and 135D for a value chain network

[0479] FIG. 137A is a flow diagram of an example arrangement for a control architecture similar to that of FIGS. 135A and 135B.

[0480] FIG. 137B is a flow diagram of an example arrangement for a control architecture similar to that of FIGS. 135C and 135D.

[0481] FIG. 138 is an example flowchart of one or more VCN processes that may be used with one or more example implementations of the disclosure.

[0482] FIGS. 139-145 are example flowcharts of one or more VCN processes that may be used with one or more example implementations of the disclosure.

[0483] FIG 146 is a schematic view of an example generative Al system

[0484] FIG 147 is a schematic view of an example of a determination of attention by a machine learning model

[0485] FIG. 148 is a schematic view of an example of a transformer model.

[0486] FIG. 149 is a schematic view of a value chain network (VCN) converging technology stack.

[0487] FIG. 150 is a schematic view of an example robotic system.

[0488] FIG. 151 is a schematic view of an example robotic fleet operations platform.

[0489] FIG. 152 is a schematic view of an example specialized integrated chipset.

[0490] FIG. 153 is a schematic view of an example robot and / or robot fleet management platform.

[0491] FIG. 154 is a schematic view of an example environment including a fleet management platform and / or robot.

[0492] FIG. 155 is a schematic view of an example environment including a fleet management platform and / or robot.

[0493] FIG. 156 is a schematic view of an example Al convergence system of systems.

[0494] FIG. 157 is a schematic view of an example offering layer.

[0495] FIG. 158 is a schematic view of an example transactions layer.

[0496] FIG. 159 is a schematic view of an example operations layer.

[0497] FIG. 160 is a schematic view of an example network layer.

[0498] FIG. 161 is a schematic view of an example data layer.

[0499] FIG 162 is a schematic view of an example data layer

[0500] FIG. 163 is a schematic view of an example intelligent data layer architecture.

[0501] FIG. 164 is a schematic view of an example network layer.

[0502] FIG. 165 is a schematic view of an example Al subsystem integrator system.

[0503] FIG. 166 is a schematic view of an example multiplatform attention management system.

[0504] FIG. 167 is a schematic view of an example product ecosystem digital twin orchestration system

[0505] FIG. 168 is a schematic view of an example configured artificial intelligence system.

[0506] FIG. 169 is a schematic view of an example KYX system.

[0507] FIG. 170 is an illustration of a matrix for organizing and interconnecting various features of Al agent understanding

[0508] FIG. 171 is a is a diagram that illustrates an exemplary embodiment of a system of models architecture within the intelligence system of the configured artificial intelligence system.

[0509] FIG. 172 is a is a diagram that illustrates an exemplary embodiment of chipset architectures for systems of models.

[0510] FIG. 173 is a schematic diagram detailing an example artificial neural network with multiple layers.

[0511] FIG. 174 is a schematic diagram detailing an example of training and inference of an example artificial neural network.

[0512] FIG. 175 is a schematic diagram detailing an example of a determination of attention by a machine learning model

[0513] FIG. 176 is a schematic diagram of a first transformer model.

[0514] FIG. 177 is a schematic diagram of a second transformer model.

[0515] FIG. 178 is a schematic diagram detailing an example system in which a large language model includes a retrieval component that provides a RAG capability.

[0516] FIG. 179 is a schematic diagram detailing an example of tool use by an example Al agent

[0517] FIG 180 is a schematic diagram detailing an example Al agent featuring an agent loop

[0518] FIG 181 is a schematic diagram detailing a development of an artificial neural network by reinforcement learning.

[0519] Like reference symbols in the various drawings indicate like elements.DETAILED DESCRIPTION

[0520] In example embodiments, systems and processes of this disclosure may include information technology processes and systems for management of value chain network entities, including supply chain and demand management entities. In example embodiments, enterprise management platforms, more particularly involving an edge-distributed database and query language for storing and retrieving value chain data may also be used.

[0521] Orders for products were fulfilled by manufacturers through a supply chain, such as depicted in FIG. 1, where suppliers 122 in various supply environments 160, operating production facilities 134 or acting as resellers or distributors for others, made a product 130 available at a point of origin 102 in response to an order. The product 130 was passed through the supply chain, being conveyed and stored via various hauling facilities 138 and distribution facilities 134, such as warehouses 132, fulfillment centers 112 and delivery' systems 114, such as trucks and other vehicles, trains, and the like. In many cases, maritime facilities and infrastructure, such as ships, barges, docks and ports provided transport over waterways between the points of origin 102 and one or more destinations 104.

[0522] Organizations have access to an almost unlimited amount of data With the advent of smart connected devices, wearable technologies, the Internet of Things (loT), and the like, the amount of data available to an organization that is planning, overseeing, managing and operating a value chain network has increased dramatically and will likely continue to do so. For example, in a manufacturing facility, warehouse, campus, or other operating environment, there may be hundreds to thousands of loT sensors that provide metrics such as vibration data that measure the vibration signatures of important machinery, temperatures throughout the facility, motion sensors that can track throughput,asset tracking sensors and beacons to locate items, cameras and optical sensors, chemical and biological sensors, and many others. Additionally, as wearable technologies become more prevalent, wearables may provide insight into the movement, health indicators, physiological states, activity states, movements, and other characteristics of workers. Furthermore, as organizations implement CRM systems, ERP systems, operations systems, information technology systems, advanced analytics and other systems that leverage information and information technology, organizations have access to an increasingly wide array of other large data sets, such as marketing data, sales data, operational data, information technology data, performance data, customer data, financial data, market data, pricing data, supply chain data, and the like, including data sets generated by or for the organization and third-party data sets.

[0523] The presence of more data and data of new types offers many opportunities for organizations to achieve competitive advantages; however, it also presents problems, such as of complexity and volume, such that users can be overwhelmed, missing opportunities for insight A need exists for methods and systems that allow enterprises not only to obtain data, but to convert the data into insights and to translate the insights into well-informed decisions and timely execution of efficient operations.

[0524] Acquiring large data sets from thousands, or potentially millions of devices (containing large numbers of sensors) distributed across multiple organizations in a value chain network has become more typical. For example, there is a proliferation of Radio Frequency Identification (RFID) Tags to individual goods in retail stores. In this situation and other similar situations, a vast number of data streams can overwhelm the ability to transmit the data across networks and / or the ability to create effective automated centralized decisions.

[0525] The proliferation of data generators (e g , sensors) has created an opportunity to manage networks such as value chain networks with input from massive numbers of distributed points of semi-intelligent control However current approaches often rely on limited centralized data collection due to bandwidth, storage, processing, and / or other limitations.

[0526] Over time, companies have increasingly used technology solutions to improve outcomes related to a traditional supply chain like the one depicted in FIG. 1 , such as software systems for predicting and managing customer demand, RFID and asset tracking systems for tracking goods as they move through the supply chain, navigation and routing systems to improve the efficiency of route selection, and the like. However, some large trends have placed manufacturers, retailers and other businesses under increasing pressure to improve supply chain performance. First, online and ecommerce operators, in particular Amazon™ have become tire largest retail channels for many categories of goods and have introduced distribution and fulfillment centers 112 throughout some geographies like the United States that house hundreds of thousands, and sometimes more, product categories (SKUs), so that customers can receive items the day after they are ordered, and in some cases on the same day (and in some cases delivered to the door by a drone, robot, and / or autonomous vehicle. For retailers that do not have extensive geographic distribution of fulfillment centers or warehouses, customer expectations for speed of delivery place increased pressure on supply chain efficiency and optimization. Accordingly, a need still exists for improved supply chain methods and systems.

[0527] Second, agile manufacturing capabilities (such as using 3D printing and robotic assembly techniques, among others), customer profiling technologies, and online ratings and rev iews have led to increased customer expectations for customization and personalization of products. Accordingly, in order to compete, manufacturers and retailers need improved methods and systems for understanding, predicting, and satisfying customer demand.

[0528] Historically, supply chain management and demand planning and management have been largely separate activities, unified primarily when demand is converted to an order, which is passed to the supply side for fulfillmentin a supply chain. As expectations for speed and personalization increase, a need exists for methods and systems that can provide unified orchestration of supply and demand.

[0529] In parallel with these other large trends has been the emergence of the Internet of Things, in which some categories of products, particularly smart home products like thermostats, lighting systems, and speakers, are increasingly enabled with onboard network connectivity and processing capability, often including a voice controlled intelligent agent like Alexa™ or Siri™ that allows device control and triggering of certain application features, such as playing music, or even ordering a product. In some cases, smart products 650 even initiate orders, such as printers that order refill cartridges. Intelligent products 650 are in some cases involved in a coordinated system, such as where an Amazon™ Echo™ product controls a television, or where a sensor-enabled thermostat or security camera connects to a mobile device, but most intelligent products are still involved in sets of largely isolated, application-specific interactions. As artificial intelligence capabilities increase, and as more and more computing and networking power is moved to network-enabled edge devices and systems that reside in supply environments 670, in demand environments 672, and in all of tire locations, systems, and facilities that populate the path of a product 1510 from the loading dock of a manufacturer to the point of destination 612 of a customer 662 or retailers 664, a need and opportunity exists for dramatically improved intelligence, control, and automation of all of the factors involved in demand and supply.VALUE CHAIN NETWORKS

[0530] Referring to FIG. 2, a block diagram is presented at 200 showing components and interrelationships of systems and processes of a value chain network In example embodiments, “value chain network,” as used herein, refers to elements and interconnections of historically segregated demand management systems and processes and supply chain management systems and processes, enabled by the development and convergence of numerous diverse technologies. In example embodiments a value chain control tower 260 (e.g., referred to herein in some cases as a ‘Value chain network management platform”, a “VCNP”, or simply as “the system”, or “the platform”) may be connected to, in communication with, or otherwise operatively coupled with data processing facilities including, but not limited to, big data centers (e.g., big data processing 230) and related processing functionalities that receive data flow, data pools, data streams and / or other data configurations and transmission modalities received from, for example, digital product networks 21002, directly from customers (e.g., direct connected customer 250), or some oilier third party 220. Communications related to market orchestration activities and communications 210, analytics 232, or some other type of input may also be utilized by the value chain control tower for demand enhancement 262, synchronized planning 234, intelligent procurement 238, dynamic fulfillment 240 or some other smart operation informed by coordinated and adaptive intelligence, as described herein.

[0531] Referring to FIG. 3, another block diagram is presented showing components and interrelationships of systems and processes of a value chain network and related uses cases, data handling, and associated entities. In example embodiments, the value chain control tower 360 may coordinate market orchestration activities 310 including, but not limited to, demand curve management 352, synchronization of an ecosystem 348, intelligent procurement 344, dynamic fulfillment 350, value chain analytics 340, and / or smart supply chain operations 342. In example embodiments, the value chain control tower 360 may be connected to, in communication with, or otherwise operatively coupled with adaptive data pipelines 302 and processing facilities that may be further connected to, in communication with, or otherwise operationally coupled with external data sources 320 and a data handling stack 330 (e.g., value chain network technology) that may include intelligent, user-adaptive interfaces, adaptive intelligenceand control 332, and / or adaptive data monitoring and storage 334, as described herein The value chain control tower 302 may also be further connected to, in communication with, or otherwise operatively coupled with additional value chain entities including, but not limited to, digital product networks 21002, customers (e.g., directed connected customers 362), and / or other connected operations 364 and entities of a value chain networkDIGITAL PRODUCT NETWORKS (“DPN ”)

[0532] Referring to FIG. 4, a block diagram is presented showing components and interrelationships of systems and processes of the digital products networks at 400. In example embodiments, products (including goods and services) may create and transmit data, such as product level data, to a communication layer within the value chain network technology stack and / or to an edge data processing facility. This data may produce enhanced product level data and may be combined with third party data for further processing, modeling or other adaptive or coordinated intelligence activity, as described herein. This may include, but is not limited to, producing and / or simulating product and value chain use cases, the data for which may be utilized by products, product development processes, product design, and the like.STACK VIEW EXAMPLES

[0533] Referring to FIG. 5, a block diagram is presented at 500 showing components and interrelationships of systems and processes of a value chain network technology stack, which may include, but is not limited to a presentation layer, an intelligence layer, and serverless functionalities such as platforms (e.g., development and hosting platforms), data facilities (e.g., relating to data with loT and Big Data), and data aggregation facilities. In example embodiments, the presentation layer may include, but is not limited to, a user interface, and modules for investigation and discovery and tracking users’ experience and engagements In example embodiments, the intelligence layer may include, but is not limited to, a statistical and computation methods, semantic models, an analytics library, a development environment for analytics, algorithms, logic and rules, and machine learning. In example embodiments, the platforms or the value chain network technology stack may include a development environment, APIs for connectivity, cloud and / or hosting applications, and device discovery. In example embodiments, the data aggregation facilities or layer may include, but is not limited to, modules for data normalization for common transmission and heterogeneous data collection from disparate devices. In example embodiments, the data facilities or layer may include, but is not limited to, loT and big data access, control, and collection and alternatives. In example embodiments, the value chain network technology stack may be further associated with additional data sources and / or technology enablers.VALUE CHAIN ORCHESTRATION FROM A COMMAND PLATFORM

[0534] FIG. 6 illustrates a connected value chain network 668 in which a value chain network management platform 604 (referred to herein in some cases as a “value chain control tow'er,” the “VCNP,” or simply as “the system,’’ or “the platform”) orchestrates a variety of factors involved in planning, monitoring, controlling, and optimizing various entities and activities involved in the value chain network 668, such as supply and production factors, demand factors, logistics and distribution factors, and the like. By virtue of a unified platform 604 for monitoring and managing supply factors and demand factors as well as status information (e g , quality and status, plan, order and confirm, and / or track and trace) can be shared about and between various entities (e.g., including customers / consumers, suppliers, distribution such as distributors, suppliers, and production such as producers or production facilities) as demand factors are understood and accounted for, as orders are generated and fulfilled, and as products are created and moved through a supply chain. The value chain network 668 may include not only an intelligent product 1510, but all of the equipment, infrastructure, personnel and other entities involved in planning and satisfying demand for it.VALUE CHAIN NETWORK AND VALUE CHAIN NETWORK MANAGEMENT PLATFORM

[0535] Referring to FIG. 7, the value chain network 668 managed by a value chain management platform 604 may include a set of value chain network entities 652, such as, without limitation: a product 1510, which may be an intelligent product 1510; a set of production facilities 674 involved in producing finished goods, components, systems, sub-systems, materials used in goods, or the like; various entities, activities and other supply factors 648 involved in supply environments 670, such as suppliers 642, points of origin 610, and the like; various entities, activities and other demand factors 644 involved in demand environments 672, such as customers 662 (including consumers, businesses, and intermediate customers such as value added resellers and distributors'), retailers 664 (including online retailers, mobile retailers, conventional bricks and mortar retailers, pop-up shops and the like) and the like located and / or operating at various destinations 612; various distribution environments 678 and distribution facilities 658, such as warehousing facilities 654, fulfillment facilities 628, and delivery systems 632, and the like, as well as maritime facilities 622, such as port infrastructure facilities 660, floating assets 620, and shipyards 638, among others. In embodiments, the value chain network management platform 604 monitors, controls, and otherwise enables management (and in some cases autonomous or semi-autonomous behavior) of a wide range of value chain network 668 processes, workflows, activities, events and applications 630 (collectively referred to in some cases simply as “applications 630”).

[0536] Referring still to FIG 7, a high-level schematic of the value chain network management platform 604 is illustrated. The value chain network management platform 604 may include a set of systems, applications, processes, modules, sendees, layers, devices, components, machines, products, sub-systems, interfaces, connections, and other elements working in coordination to enable intelligent management of a set of value chain entities 652 that may occur, operate, transact or the like within, or own, operate, support or enable, one or more value chain network processes, workflows, activities, events and / or applications 630 or that may otherwise be part of, integrated with, linked to, or operated on by the VCNP 604 in connection with a product 1510 (which may be any category of product, such as a finished good, software product, hardware product, component product, material, item of equipment, item of consumer packaged goods, consumer product, food product, beverage product, home product, business supply product, consumable product, pharmaceutical product, medical device product, technology product, entertainment product, or any other type of product and / or set of related services, and which may, in embodiments, encompass an intelligent product 1510 that is enabled with a set of capabilities such as, without limitation data processing, networking, sensing, autonomous operation, intelligent agent, natural language processing, speech recognition, voice recognition, touch interfaces, remote control, self-organization, self-healing, process automation, computation, artificial intelligence, analog or digital sensors, cameras, sound processing systems, data storage, data integration, and / or various Internet of Things capabilities, among others.

[0537] In embodiments, the management platform 604 may include a set of data handling layers 608 each of which is configured to provide a set of capabilities that facilitate development and deployment of intelligence, such as for facilitating automation, machine learning, applications of artificial intelligence, intelligent transactions, state management, event management, process management, and many others, for a wide variety of value chain network applications and end uses. In embodiments, the data handling layers 608 are configured in a topology that facilitates shared data collection and distribution across multiple applications and uses within the platform 604 by a value chain monitoring systems layer 614. The value chain monitoring systems layer 614 may include, integrate with, and / or cooperate with various data collection and management systems 640, referred to for convenience in some cases asdata collection systems 640, for collecting and organizing data collected from or about value chain entities 652, as well as data collected from or about the various data layers 624 or services or components thereof. In embodiments, the data handling layers 608 are configured in a topology that facilitates shared or common data storage across multiple applications and uses of the platform 604 by a value chain network-oriented data storage systems layer 624, referred to herein for convenience in some cases simply as a data storage layer 624 or storage layer 624. As shown in FIG. 7, the data handling layers 608 may also include an adaptive intelligent systems layer 614. The adaptive intelligence systems layer 614 may include a set of data processing, artificial intelligence and computational systems 634 that are described in more detail elsewhere throughout this disclosure. The data processing, artificial intelligence and computational systems 634 may relate to artificial intelligence (e.g., expert systems, artificial intelligence, neural, supervised, machine learning, deep learning, model-based systems, and the like). Specifically, the data processing, artificial intelligence and computational systems 634 may relate to various examples, in some embodiments, such as use of a recurrent network as adaptive intelligence system operating on a blockchain of transactions in a supply chain to determine a pattern, use with biological systems, opportunity mining (e.g., where artificial intelligence system may be used to monitor for new data sources as opportunities for automatically deploying intelligence), robotic process automation (e.g., automation of intelligent agents for various workflows), edge and network intelligence (e.g., implicated on monitoring systems such as adaptively using available RF spectrum, adaptively using available fixed network spectrum, adaptively storing data based on available storage conditions, adaptively sensing based on a kind of contextual sensing), and the like.

[0538] In embodiments, the data handling layers 608 may be depicted in vertical stacks or ribbons in the figures and may represent many functionalities available to the platform 604 including storage, monitoring, and processing applications and resources and combinations thereof. In embodiments, the set of capabilities of the data handling layers 608 may include a shared microservices architecture. By way of these examples, the set of capabilities may be deployed to provide multiple distinct services or applications, which can be configured as one or more services, workflows, or combinations thereof. In some examples, the set of capabilities may be deployed within or be resident to certain applications or processes. In some examples, the set of capabilities can include one or more activities marshaled for the benefit of the platform In some examples, the set of capabilities may include one or more events organized for the benefit of the platform. In embodiments, one of the sets of capabilities of the platform may be deployed within at least a portion of a common architecture such as common architecture that supports a common data schema. In embodiments, one of the sets of capabilities of the platform may be deployed within at least a portion of a common architecture that can support a common storage. In embodiments, one of the sets of capabilities of the platform may be deployed within at least a portion of a common architecture that can support common monitoring systems. In embodiments, one or more sets of capabilities of the platform may be deployed within at least a portion of a common architecture that can support one or more common processing frameworks. In embodiments, the set of capabilities of the data handling layers 608 can include examples where the storage functionality supports scalable processing capabilities, scalable monitoring systems, digital twin systems, payments interface systems, and the like By way of these examples, one or more software development kits can be provided by the platform along with deployment interfaces to facilitate connections and use of the capabilities of the data handling layers 608. In further examples, adaptive intelligence systems may analyze, leam, configure, and reconfigure one or more of the capabilities of the data handling layers 608. In embodiments, the platform 604 may, for example, include a common data storage schema serving a shipyard entity related service and a warehousing entity service. There are many other applicableexamples and combinations applicable to the foregoing example including the many value chain entities disclosed herein By way of these examples, the platform 604 may be shown to create connectivity (e.g., supply of capabilities and information) across many value chain entities. In many examples, there are pairings (doubles, triples, quadruplets, etc ) of similar kinds of value chain entities using one or more smaller sets of capabilities of the data handling layers 608 to deploy (interact with, rely on, etc.) a common data schema, a common architecture, a common interface, and the like. While services and capabilities can be provided to single value chain entities, the platform can be shown to provide myriad benefits to value chains and consumers by supporting connectivity across value chain entities and applications used by the entities.VALUE CHAIN NETWORK ENTITIES MANAGED BY THE PLATFORM

[0539] Referring to FIG. 8, the value chain network management platform 604 is illustrated in connection with a set of value chain entities 652 that may be subject to management by the platform 604, may integrate with or into the platform 604, and / or may supply inputs to and / or take outputs from the platform 604, such as ones involved in or for a wide range of value chain activities (such as supply chain activities, logistics activities, demand management and planning activities, delivery activities, shipping activities, warehousing activities, distribution and fulfillment activities, inventory aggregation, storage and management activities, marketing activities, and many others, as involved in various value chain network processes, workflows, activities, events and applications 630 (collectively “applications 630” or simply “activities”)). Connections with the value chain entities 652 may be facilitated by a set of connectivity facilities 642 and interfaces 702, including a wide range of components and systems described throughout this disclosure and in greater detail below This may include connectivity and interface capabilities for individual services of the platform, for the data handling layers, for the platform as a whole, and / or among value chain entities 652, among others.

[0540] These value chain entities 652 may include any of the wide variety of assets, systems, devices, machines, components, equipment, facilities, individuals or other entities mentioned throughout this disclosure or in the documents incorporated herein by reference, such as, without limitation: machines 724 and their components (e g., delivery vehicles, forklifts, conveyors, loading machines, cranes, lifts, haulers, trucks, loading machines, unloading machines, packing machines, picking machines, and many others, including robotic systems, e.g., physical robots, collaborative robots (e.g , “cobots”), drones, autonomous vehicles, software bots and many others); products 650 (which may be any category of products, such as a finished goods, software products, hardware products, component products, material, items of equipment, items of consumer packaged goods, consumer products, food products, beverage products, home products, business supply products, consumable products, pharmaceutical products, medical device products, technology products, entertainment products, or any other type of products and / or set of related services); value chain processes 722 (such as shipping processes, hauling processes, maritime processes, inspection processes, hauling processes, loading / unloading processes, packing / unpacking processes, configuration processes, assembly processes, installation processes, quality control processes, environmental control processes (e g., temperature control, humidity control, pressure control, vibration control, and others), border control processes, port- related processes, software processes (including applications, programs, services, and others), packing and loading processes, financial processes (e.g., insurance processes, reporting processes, transactional processes, and many others), testing and diagnostic processes, security processes, safety processes, reporting processes, asset tracking processes, and many others); wearable and portable devices 720 (such as mobile phones, tablets, dedicated portable devices for value chain applications and processes, data collectors (including mobile data collectors), sensor-baseddevices, watches, glasses, hearables, head-worn devices, clothing-integrated devices, ami bands, bracelets, neck- worn devices, AR / VR devices, headphones, and many others); workers 718 (such as delivery' workers, shipping workers, barge workers, port workers, dock workers, train workers, ship workers, distribution of fulfillment center workers, warehouse workers, vehicle drivers, business managers, engineers, floor managers, demand managers, marketing managers, inventory managers, supply chain managers, cargo handling workers, inspectors, delivery personnel, environmental control managers, financial asset managers, process supervisors and workers (for any of the processes mentioned herein), security personnel, safety personnel and many others); suppliers 642 (such as suppliers of goods and related sendees of all types, component suppliers, ingredient suppliers, materials suppliers, manufacturers, and many others); customers 662 (including consumers, licensees, businesses, enterprises, value added and other resellers, retailers, end users, distributors, and others who may purchase, license, or othenvise use a category of goods and / or related services); a wide range of operating facilities 712 (such as loading and unloading docks, storage and warehousing facilities 654, vaults, distribution facilities 658 and fulfillment centers 628, air travel facilities 740 (including aircraft, airports, hangars, runways, refueling depots, and the like), maritime facilities 622 (such as port infrastructure facilities 622 (such as docks, yards, cranes, roll-on / roll-off facilities, ramps, containers, container handling systems, waterways 732, locks, and many others), shipyard facilities 638, floating assets 620 (such as ships, barges, boats and others), facilities and other items at points of origin 610 and / or points of destination 628, hauling facilities 710 (such as container ships, barges, and other floating assets 620, as well as land-based vehicles and other delivery systems 632 used for conveying goods, such as trucks, trains, and the like); items or elements factoring in demand (i e , demand factors 644) (including market factors, events, and many others); items or elements factoring in supply (i e , supply factors 648)(including market factors, weather, availability of components and materials, and many others); logistics factors 750 (such as availability of travel routes, weather, fuel prices, regulatory factors, availability of space (such as on a vehicle, in a container, in a package, in a warehouse, in a fulfillment center, on a shelf, or the like), and many others); retailers 664 (including online retailers 730 and others such as in the form of eCommerce sites 730); pathways for conveyance (such as waterways 732, roadways 734, air travel routes, railways 738 and the like); robotic systems 744 (including mobile robots, cobots, robotic systems for assisting human workers, robotic delivery systems, and others): drones 748 (including for package delivery, site mapping, monitoring or inspection, and the like); autonomous vehicles 742 (such as for package delivery): software platforms 752 (such as enterprise resource planning platforms, customer relationship management platforms, sales and marketing platforms, asset management platforms, Internet of Things platforms, supply chain management platforms, platform as a service platforms, infrastructure as a service platforms, software-based data storage platforms, analytic platforms, artificial intelligence platforms, and others); and many others. In some example embodiments, the product 1510 may be encompassed as an intelligent product 1510 or the VCNP 604 may include the intelligent product 1510. The intelligent product 1510 may be enabled with a set of capabilities such as, without limitation data processing, networking, sensing, autonomous operation, intelligent agent, natural language processing, speech recognition, voice recognition, touch interfaces, remote control, self-organization, self-healing, process automation, computation, artificial intelligence, analog or digital sensors, cameras, sound processing systems, data storage, data integration, and / or various Internet of Things capabilities, among others. The intelligent product 1510 may include a form of information technology. The intelligent product 1510 may have a processor, computer random access memory, and a communication module. The intelligent product 1510 may be a passive intelligent product that is similar to a RFID type of data structure where the intelligent product may be pinged or read. The product 1510 may be considered avalue chain network entity (e.g., under control of platform) and may be rendered intelligent by surrounding infrastructure and adding an RFID such that data may be read from the intelligent product 1510. The intelligent product 1510 may fit in a value chain network in a connected way such that connectivity was built around the intelligent product 1510 through a sensor, an loT device a tag, or another component

[0541] In embodiments, the monitoring systems layer 614 may monitor any or all of the value chain entities 652 in a value chain network 668, may exchange data with the value chain entities 652, may provide control instructions to or take instructions from any of the value chain entities 652, or the like, such as through the various capabilities of the data handling layers 608 described throughout this disclosure.NETWORK CHARACTERISTICS OF THE VALUE CHAIN NETWORK ENTITIES

[0542] Referring to FIG. 9, orchestration of a set of deeply interconnected value chain network entities 652 in a value chain network 668 by the value chain network management platform 604 is illustrated. Each of the value chain network entities 652 may have a connection to the VCNP 604, to a set of other value chain network entities 652 (which may be a local network connection, a peer-to-peer connection, a mobile network connection, a connection via a cloud, or other connection), and / or through the VCNP 604 to other value chain network entities 652. The value chain network management platform 604 may manage the connections, configure or provision resources to enable connectivity, and / or manage applications 630 that take advantage of the connections, such as by using information from one set of entities 652 to inform applications 630 involving another set of entities 652, by coordinating activities of a set of entities 652, by providing input to an artificial intelligence system of the VCNP 604 or of or about a set of entities 652, by interacting with edge computation systems deployed on or in entities 652 and their environments, and the like

[0543] The entities 652 may be external such that the VCNP 604 may interact with these entities 652. When the VCNP 604 functions as the control tower to establish monitoring (e.g., establish monitoring such as common monitoring across several entities 652). In one unified platform, there may be an interface where a user may view various items such as user’s destinations, ports, air and rail assets, as well as orders, etc. Then, the next step may be to establish a common data schema that enables services that work on or in any one of these applications. This may involve taking any of the data that is flowing through or about any of these entities 652 and pull the data into a framework where other applications across supply and demand may interact with the entities 652. This may be a shared data pipeline coming from an loT system and other external data sources, feeding into the monitoring layer, being stored in a common data schema in the storage layer, and then various intelligence may be trained to identify implications across these entities 652. In an example embodiment, a supplier may be bankrupt, or a determination is made that the supplier is bankrupt, and then the VCNP 604 may automatically trigger a substitute smart contract to be sent to a secondary supplier with altered terms. There may be management of different aspects of the supply chain. For example, changing pricing instantly and automatically on the demand side in response to one more supplier’s being identified as bankrupt (e.g., from bankruptcy announcement). Other similar examples may be used based on what occurs in that automation layer which may be enabled by the VCNP 604 Then, at the interface layer of this VCNP 604, a digital twin may be used by user to view all these entities 652 that are not typically shown together and monitor what is going on with each of these entities 652 including identification of problem states For example, after viewing three quarters of bad financial reports on a supplier, a report may be flagged to watch it closely for potential future bankruptcy, etc.

[0544] For example, an loT system deployed in a fulfillment center 628 may coordinate with an intelligent product 1510 that takes customer feedback about the product 1510, and an application 630 for the fulfillment center 628 may, upon receiving customer feedback via a connection path to the intelligent product 1510 about a problem with the product 1510, initiate a workflow to perform corrective actions on similar products 650 before the products 650 are sent out from the fulfillment center 628. Similarly, a port infrastructure facility 660, such as a yard for holding shipping containers, may inform a fleet of floating assets 620 via connections to the floating assets 620 (such as ships, barges, or the like) that the port is near capacity, thereby kicking off a negotiation process (which may include an automated negotiation based on a set of rules and governed by a smart contract) for the remaining capacity and enabling some assets 620 to be redirected to alternative ports or holding facilities These and many other connections among value chain network entities 652, whether one-to-one connections, one-to-many connections, many-to-many connections, or connections among defined groups of entities 652 (such as ones controlled by the same owner or operator), are encompassed herein as applications 630 managed by the VCNP 604.VALUE CHAIN NETWORK ACTIVITIES AND APPLICATIONS MANAGED BY THE PLATFORM

[0545] Referring to FIG. 10, the set of applications 614 provided on the VCNP 604, integrated with the VCNP 604 and / or managed by or for the VCNP 604 and / or involving a set of value chain network entities 652 may include, without limitation, one or more of any of a wide range of types of applications, such as: a supply chain management applications 21004 (such as, without limitation, for management of timing, quantities, logistics, shipping, delivery, and other details of orders for goods, components, and other items); an asset management application 814 (such as, without limitation, for managing value chain assets, such as floating assets (such as ships, boats, barges, and floating platforms), real property (such as used for location of warehouses, ports, shipyards, distribution centers and other buildings), equipment, machines and fixtures (such as used for handling containers, cargo, packages, goods, and other items), vehicles (such as forklifts, delivery' trucks, autonomous vehicles, and other systems used to move items), human resources (such as workers), software, information technology resources, data processing resources, data storage resources, power generation and / or storage resources, computational resources and other assets): a finance application 822 (such as, without limitation, for handling finance matters relating to value chain entities and assets, such as involving payments, security, collateral, bonds, customs, duties, imposts, taxes and others); a 6 (such as, without limitation, for managing risk or liability with respect to a shipment, goods, a product, an asset, a person, a floating asset, a vehicle, an item of equipment, a component, an information technology system, a security system, a security event, a cybersecurity system, an item of property, a health condition, mortality, fire, flood, weather, disability, negligence, business interruption, injury, damage to property, damage to a business, breach of a contract, and others); a demand management application 824 (such as, without limitation, an application for analyzing, planning, or promoting interest by customers of a category of goods that can be supplied by or with facilities of a value chain product or sendee, such as a demand planning application, a demand prediction application, a sales application, a future demand aggregation application, a marketing application, an advertising application, an e- commerce application, a marketing analytics application, a customer relationship management application, a search engine optimization application, a sales management application, an advertising network application, a behavioral tracking application, a marketing analytics application, a location-based product or service-targeting application, a collaborative filtering application, a recommendation engine for a product or service, and others, including ones that use or are enabled by one or more features of an intelligent product 1510 or that are executed using intelligence capabilities on an intelligent product 1510); a trading application 858 (such as, without limitation, a buyingapplication, a selling application, a bidding application, an auction application, a reverse auction application, a bid / ask matching application, an analytic application for analyzing value chain performance, yield, return on investment, or other metrics, or others); a tax application 850 (such as, without limitation, for managing, calculating, reporting, optimizing, or otherwise handling data, events, workflows, or other factors relating to a tax, a tariff, an impost, a levy, a tariff, a duty, a credit, a fee or other government-imposed charge, such as, without limitation, customs duties, value added tax, sales tax, income tax, property tax, municipal fees, pollution tax, renewal energy credit, pollution abatement credit, import duties, export duties, and others); an identity management application 830 (such as for managing one or more identities of entities 652 involved in a value chain, such as, without limitation, one or more of an identity verification application, a biometric identify validation application, a pattern-based identity verification application, a location-based identity verification application, a user behavior-based application, a fraud detection application, a network address-based fraud detection application, a black list application, a white list application, a content inspection-based fraud detection application, or other fraud detection application; an inventory management application 820 (such as, without limitation, for managing inventory in a fulfillment center, distribution center, warehouse, storage facility, store, port, ship or other floating asset, or other location); a security application, solution or service 834 (referred to herein as a security application, such as, without limitation, any of the identity management applications 830 noted above, as well as a physical security system (such as for an access control system (such as using biometric access controls, fingerprinting, retinal scanning, passwords, and other access controls), a safe, a vault, a cage, a safe room, a secure storage facility, or the like), a monitoring system (such as using cameras, motion sensors, infrared sensors and other sensors), a perimeter security system, a floating security system for a floating asset, a cyber security system (such as for virus detection and remediation, intrusion detection and remediation, spam detection and remediation, phishing detection and remediation, social engineering detection and remediation, cyber-attack detection and remediation, packet inspection, traffic inspection, DNS attack remediation and detection, and others) or other security application); a safety application 840 (such as, without limitation, for improving safety of workers, for reducing the likelihood of damage to property, for reducing accident risk, for reducing the likelihood of damage to goods (such as cargo), for risk management with respected to insured items, collateral for loans, or the like, including any application for detecting, characterizing or predicting the likelihood and / or scope of an accident or other damaging event, including safety management based on any of the data sources, events or entities noted throughout this disclosure or the documents incorporated herein by reference); a blockchain application 844 (such as, without limitation, a distributed ledger capturing a series of transactions, such as debits or credits, purchases or sales, exchanges of in kind consideration, smart contract events, or the like, or other blockchain-based application); a facility management application 850 (such as, without limitation, for managing infrastructure, buildings, systems, real property, personal property, and other property involved in supporting a value chain, such as a shipyard, a port, a distribution center, a warehouse, a dock, a store, a fulfillment center, a storage facility, or others, as well as for design, management or control of systems and facilities in or around a property, such as an information technology system, a robotic / autonomous vehicle system, a packaging system, a packing system, a picking system, an inventory tracking system, an inspection system, a routing system for mobile robots, a workflow system for human assets, or the like); a regulatory application 852 (such as, without limitation, an application for regulating any of the applications, services, transactions, activities, workflows, events, entities, or other items noted herein and in the documents incorporated by reference herein, such as regulation of permitted routes, permitted cargo and goods, permitted parties to transactions, required disclosures, privacy, pricing, marketing, offering of goods and services, use of data(including data privacy regulations, regulations relating to storage of data and others), banking, marketing, sales, financial planning, and many others); a commerce application, solution or service 854 (such as, without limitation an e-commerce site marketplace, an online site, an auction site or marketplace, a physical goods marketplace, an advertising marketplace, a reverse-auction marketplace, an advertising network, or other marketplace); a vendor management application 832 (such as, without limitation, an application for managing a set of vendors or prospective vendors and / or for managing procurement of a set of goods, components or materials that may be supplied in a value chain, such as involving features such as vendor qualification, vendor rating, requests for proposal, requests for information, bonds or other assurances of performance, contract management, and others); an analytics application 838 (such as, without limitation, an analytic application with respect to any of the data types, applications, events, workflows, or entities mentioned throughout this disclosure or the documents incorporated by reference herein, such as a big data application, a user behavior application, a prediction application, a classification application, a dashboard, a pattern recognition application, an econometric application, a financial yield application, a return on investment application, a scenario planning application, a decision support application, a demand prediction application, a demand plaiming application, a route planning application, a weather prediction application, and many others); a pricing application 842 (such as, without limitation, for pricing of goods, services (including any mentioned throughout this disclosure and the documents incorporated by reference herein; and a smart contract application, solution, or service (referred to collectively herein as a smart contract application 848, such as, without limitation, any of the smart contract types referred to in this disclosure or in the documents incorporated herein by reference, such as a smart contract for sale of goods, a smart contract for an order for goods, a smart contract for a shipping resource, a smart contract for a worker, a smart contract for delivery of goods, a smart contract for installation of goods, a smart contract using a token or cryptocurrency for consideration, a smart contract that vests a right, an option, a future, or an interest based on a future condition, a smart contract for a security, commodity, future, option, derivative, or the like, a smart contract for current or future resources, a smart contract that is configured to account for or accommodate a tax, regulatory or compliance parameter, a smart contract that is configured to execute an arbitrage transaction, or many others). T hus, the value chain management platform 604 may host an enable interaction among a wide range of disparate applications 630 (such term including the above-referenced and other value chain applications, services, solutions, and the like), such that by virtue of shared microservices, shared data infrastructure, and shared intelligence, any pair or larger combination or permutation of such services may be improved relative to an isolated application of the same type.

[0546] Referring still to FIG. 10, the set of applications 614 provided on the VCNP 604, integrated with the VCNP 604 and / or managed by or for the VCNP 604 and / or involving a set of value chain network entities 652 may further include, without limitation: a payments application 860 (such as for calculating payments (including based on situational factors such as applicable taxes, duties and the like for the geography of an entity 652), transferring funds, resolving payments to parties, and the like, for any of the applications 630 noted herein); a process management application 862 (such as for managing any of the processes or workflows described throughout this disclosure, including supply processes, demand processes, logistics processes, delivery processes, fulfillment processes, distribution processes, ordering processes, navigation processes, and many others); a compatibility testing application 864, such as for assessing compatibility among value chain network entities 652 or activities involved in any of the processes, workflows, activities, or other applications 630 described herein (such as for determining compatibility of a container or package with a product 1510, the compatibility of a product 1510 with a set of customer requirements,the compatibility of a product 1510 with another product 1510 (such as where one is a refill, resupply, replacement part, or the like for the other), the compatibility of a infrastructure and equipment entities 652 (such as between a container ship or barge and a port or waterway, between a container and a storage facility, between a truck and a roadway , between a drone or robot and a package, betw een a drone, AV or robot and a delivery destination, and many others): an infrastructure testing application 802 (such as for testing the capabilities of infrastructure elements to support a product 1510 or an application 630 (such as, without limitation, storage capabilities, lifting capabilities, moving capabilities, storage capacity, network capabilities, environmental control capabilities, software capabilities, security capabilities, and many others)); and / or an incident management application 910 (such as for managing events, accidents, and other incidents that may occur in one or more environments involving value chain network entities 652, such as, without limitation, vehicle accidents, worker injuries, shutdown incidents, property damage incidents, product damage incidents, product liability incidents, regulatory non-compliance incidents, health and / or safety incidents, traffic congestion and / or delay incidents (including network traffic, data traffic, vehicle traffic, maritime traffic, human worker traffic, and others, as well as combinations among them), product failure incidents, system failure incidents, system performance incidents, fraud incidents, misuse incidents, unauthorized use incidents, and many others)

[0547] Referring still to FIG. 10, the set of applications 614 provided on the VCNP 604, integrated with the VCNP 604 and / or managed by or for tire VCNP 604 and / or involving a set of value chain network entities 652 may further include, without limitation: a predictive maintenance application 910 (such as for anticipating, predicting, and undertaking actions to manage faults, failures, shutdow ns, damage, required maintenance, required repairs, required sendee, required support, or the like for a set of value chain network entities 652, such as products 650, equipment, infrastructure, buildings, vehicles, and others); a logistics application 912 (such as for managing logistics for pickups, deliveries, transfer of goods onto hauling facilities, loading, unloading, packing, picking, shipping, driving, and other activities involving in the scheduling and management of the movement of products 650 and other items between points of origin and points of destination through various intermediate locations; a reverse logistic application 914 (such as for handling logistics for returned products 650, waste products, damaged goods, or other items that can be transferred on a return logistics path); a waste reduction application 920 (such as for reducing packaging waste, solid waste, waste of energy, liquid waste, pollution, contaminants, waste of computing resources, waste of human resources, or other waste involving a value chain network entity 652 or activity); an augmented reality, mixed reality and / or virtual reality application 930 (such as for visualizing one or more value chain network entities 652 or activities involved in one or more of the applications 630, such as, without limitation, movement of a product 1510, the interior of a facility, the status or condition of an item of goods, one or more environmental conditions, a weather condition, a packing configuration for a container or a set of containers, or many others); a demand prediction application 940 (such as for predicting demand for a product 1510, a category of products, a potential product, and / or a factor involved in demand, such as a market factor, a wealth factor, a demographic factor, a weather factor, an economic factor, or the like); a demand aggregation application 942 (such as for aggregating information, orders and / or commitments (optionally embodied in one or more contracts, which may be smart contracts) for one or more products 650, categories, or the like, including current demand for existing products and future demand for products that are not yet available): a customer profiling application 944 (such as for profiling one or more demographic, psychographic, behavioral, economic, geographic, or other attributes of a set of customers, including based on historical purchasing data, loyalty program data, behavioral tracking data (including data captured in interactions by a customer with asmart product 1510), online clickstream data, interactions with intelligent agents, and other data sources); and / or a component supply application 948 (such as for managing a supply chain of components for a set of products 650).

[0548] Referring still to FIG. 10, the set of applications 614 provided on the VCNP 604, integrated with the VCNP 604 and / or managed by or for the VCNP 604 and / or involving a set of value chain network entities 652 may further include, without limitation: a policy management application 868 (such as for deploying one or more policies, rules, or the like for governance of one or more value chain network entities 652 or applications 630, such as to govern execution of one or more workflows (which may involve configuring polices in the platform 604 on a per- workflow basis), to govern compliance with regulations (including maritime, food and drug, medical, environmental, health, safety, tax, financial reporting, commercial, and other regulations as described throughout this disclosure or as would be understood in the art), to govern provisioning of resources (such as connectivity, computing, human, energy, and other resources), to govern compliance with corporate policies, to govern compliance with contracts (including smart contracts, wherein the platform 604 may automatically deploy governance features to relevant entities 652 and applications 630, such as via connectivity facilities 642), to govern interactions with other entities (such as involving policies for sharing of information and access to resources), to govern data access (including privacy data, operational data, status data, and many other data types), to govern security access to infrastructure, products, equipment, locations, or the like, and many others; a product configuration application 870 (such as for allowing a product manager and / or automated product configuration process (optionally using robotic process automation) to determine a configuration for a product 1510, including configuration on-the-fly, such as during agile manufacturing, or involving configuration or customization in route (such as by 3D printing one or more features or elements), or involving configuration or customization remotely, such as by downloading firmware, configuring field programmable gate arrays, installing software, or the like; a warehousing and fulfillment application 872 (such as for managing a warehouse, distribution center, fulfillment center, or the like, such as involving selection of products, configuring storage locations for products, determining routes by which personnel, mobile robots, and the like move products around a facility, determining picking and packing schedules, routes and workflows, managing operations of robots, drones, conveyors, and other facilities, determining schedules for moving products out to loading docks or the like, and many other functions); a kit configuration and deployment application 874 (such as for enabling a user of the VCNP to configure a kit, box, or otherwise pre-integrated, pre-provisioned, and / or pre- configured system to allow a customer or worker to rapidly deploy a subset of capabilities of the VCNP 604 for a specific value chain network entity 652 and / or application 630); and / or a product testing application 878 for testing a product 1510 (including testing for performance, activation of capabilities and features, safety, compliance with policy or regulations, quality, quality of sendee, likelihood of failure, and many other factors).

[0549] Referring still to FIG. 10, the set of applications 614 provided on the VCNP 604, integrated with the VCNP 604 and / or managed by or for tire VCNP 604 and / or involving a set of value chain network entities 652 may further include, without limitation a maritime fleet management application 880 (for managing a set of maritime assets, such as container ships, barges, boats, and the like, as well as related infrastructure facilities such as docks, cranes, ports, and others, such as to determine optimal routes for fleet assets based on weather, market, traffic, and other conditions, to ensure compliance with policies and regulations, to ensure safety, to improve environmental factors, to improve financial metrics, and many others); a shipping management application 882 (such as for managing a set of shipping assets, such as trucks, trains, airplanes, and the like, such as to optimize financial yield, to improve safety, to reduce energy consumption, to reduce delays, to mitigate environmental impact, and for many other purposes); anopportunity matching application 884 (such as for matching one or more demand factors with one or more supply factors, for matching needs and capabilities of value chain network entities 652, for identifying reverse logistics opportunities, for identifying opportunities for inputs to enrich analytics, artificial intelligence and / or automation, for identifying cost-saving opportunities, for identifying profit and / or arbitrage opportunities, and many others); a workforce management application 888 (such as for managing workers in various work forces, including work forces in, on or for fulfillment centers, ships, ports, warehouses, distribution centers, enterprise management locations, retail stores, online / ecommerce site management facilities, ports, ships, boats, barges, trains, depots, and other facilities mentioned throughout this disclosure); a distribution and delivery application 890 (such as for planning, scheduling, routing, and otherwise managing distribution and delivery of products 650 and other items); and / or an enterprise resource planning (ERP) application 892 (such as for planning utilization of enterprise resources, including workforce resources, financial resources, energy resources, physical assets, digital assets, and other resources)CORE CAPABILITIES AND INTERACTIONS OF THE DATA HANDLING LAYERS (ADAPTIVE INTELLIGENCE, MONITORING, DATA STORAGE AND APPLICATIONS)

[0550] Referring to FIG 11, a high-level schematic of an embodiment of the value chain network management platform 604 is illustrated, including a set of systems, applications, processes, modules, sendees, layers, devices, components, machines, products, sub-systems, interfaces, connections, and other elements working in coordination to enable intelligent management of sets of the value chain entities 652 that may occur, operate, transact or the like within, or own, operate, support or enable, one or more value chain network processes, workflows, activities, events and / or applications 630 or that may otherwise be part of, integrated with, linked to, or operated on by the platform 604 in connection with a product 1510 (which may be a finished good, software product, hardware product, component product, material, item of equipment, consumer packaged good, consumer product, food product, beverage product, home product, business supply product, consumable product, pharmaceutical product, medical device product, technology product, entertainment product, or any other type of product or related service, which may, in embodiments, encompass an intelligent product that is enabled with processing, networking, sensing, computation, and / or other Internet of Things capabilities). Value chain entities 652, such as involved in or for a wide range of value chain activities (such as supply chain activities, logistics activities, demand management and planning activities, delivery activities, shipping activities, warehousing activities, distribution and fulfillment activities, inventory aggregation, storage and management activities, marketing activities, and many others, as involved in various value chain network processes, workflows, activities, events and applications 630 may include any of the wide variety of assets, systems, devices, machines, components, equipment, facilities, individuals or other entities mentioned throughout this disclosure or in the documents incorporated herein by reference.

[0551] In embodiments, the value chain network management platform 604 may include the set of data handling layers 608, each of which is configured to provide a set of capabilities that facilitate development and deployment of intelligence, such as for facilitating automation, machine learning, applications of artificial intelligence, intelligent transactions, intelligent operations, remote control, analytics, monitoring, reporting, state management, event management, process management, and many others, for a wide variety of value chain network applications and end uses. In embodiments, the data handling layers 608 may include a value chain network monitoring systems layer 614, a value chain netw ork entity-oriented data storage systems layer 624 (referred to in some cases herein for convenience simply as a data storage layer 624), an adaptive intelligent systems layer 614 and a value chain network management platform 604. The value chain network management platform 604 may include the data handling layers 608 such thatthe value chain network management platform 604 may provide management of tire value chain network management platform 604 and / or management of the other layers such as the value chain network monitoring systems layer 614, the value chain network entity-oriented data storage systems layer 624 (e.g., data storage layer 624), and the adaptive intelligent systems layer 614 Each of the data handling layers 608 may include a variety of services, programs, applications, workflows, systems, components and modules, as further described herein and in the documents incorporated herein by reference. In embodiments, each of the data handling layers 608 (and optionally the platform 604 as a whole) is configured such that one or more of its elements can be accessed as a service by other layers 624 or by other systems (e.g., being configured as a platform-as-a-service deployed on a set of cloud infrastructure components in a microservices architecture). For example, the platform 604 may have (or may configure and / or provision), and a data handling layer 608 may use, a set of connectivity facilities 642, such as network connections (including various configurations, types and protocols), interfaces, ports, application programming interfaces (APIs), brokers, services, connectors, wired or wireless communication links, human-accessible interfaces, software interfaces, micro-services, SaaS interfaces, PaaS interfaces, laaS interfaces, cloud capabilities, or the like by which data or information may be exchanged between a data handling layer 608 and other layers, systems or sub-systems of the platform 604, as well as with other systems, such as value chain entities 652 or external systems, such as cloudbased or on-premises enterprise systems (e.g , accounting systems, resource management systems, CRM systems, supply chain management systems and many others). Each of the data handling layers 608 may include a set of sen ices (e.g., microsendees), for data handling, including facilities for data extraction, transformation and loading; data cleansing and deduplication facilities; data normalization facilities; data synchronization facilities; data security facilities; computational facilities (e g , for performing pre-defined calculation operations on data streams and providing an output stream); compression and de-compression facilities; analytic facilities (such as providing automated production of data visualizations) and others.

[0552] In embodiments, each data handling layer 608 has a set of application programming connectivity facilities 642 for automating data exchange with each of the other data handling layers 608. These may include data integration capabilities, such as for extracting, transforming, loading, normalizing, compression, decompressing, encoding, decoding, and otherwise processing data packets, signals, and other information as it exchanged among the layers and / or the applications 630, such as transforming data from one format or protocol to another as needed in order for one layer to consume output from another. In embodiments, the data handling layers 608 are configured in a topology that facilitates shared data collection and distribution across multiple applications and uses w ithin the platform 604 by the value chain monitoring systems layer 614. The value chain monitoring systems layer 614 may include, integrate with, and / or cooperate with various data collection and management systems 640, referred to for convenience in some cases as data collection systems 640, for collecting and organizing data collected from or about value chain entities 652, as well as data collected from or about the various data layers 624 or services or components thereof. For example, a stream of physiological data from a wearable device worn by a worker undertaking a task or a consumer engaged in an activity can be distributed via the monitoring systems layer 614 to multiple distinct applications in the value chain management platform 604, such as one that facilitates monitoring the physiological, psychological, performance level, attention, or other state of a worker and another that facilitates operational efficiency and / or effectiveness. In embodiments, the monitoring systems layer 614 facilitates alignment, such as time-synchronization, normalization, or the like of data that is collected with respect to one or more value chain network entities 652. For example, one or more video streams or other sensor data collected of or with respect to a worker 718 or other entityin a value chain network facility or environment, such as from a set of camera-enabled loT devices, may be aligned with a common clock, so that the relative timing of a set of videos or other data can be understood by systems that may process the videos, such as machine learning systems that operate on images in the videos, on changes between images in different frames of the video, or the like In such an example, the monitoring systems layer 614 may further align a set of videos, camera images, sensor data, or the like, with other data, such as a stream of data from wearable devices, a stream of data produced by value chain network systems (such as ships, lifts, vehicles, containers, cargo handling systems, packing systems, delivery systems, drones / robots, and the like), a stream of data collected by mobile data collectors, and the like. Configuration of the monitoring systems layer 614 as a common platform, or set of microservices, that are accessed across many applications, may dramatically reduce the number of interconnections required by an owner or other operator within a value chain network in order to have a growing set of applications monitoring a growing set of loT devices and other systems and devices that are under its control.

[0553] In embodiments, the data handling layers 608 are configured in a topology that facilitates shared or common data storage across multiple applications and uses of the platform 604 by the value chain network-oriented data storage systems layer 624, referred to herein for convenience in some cases simply as the data storage layer 624 or storage layer 624. For example, various data collected about the value chain entities 652, as well as data produced by the other data handling layers 608, may be stored in the data storage layer 624, such that any of the services, applications, programs, or the like of the various data handling layers 608 can access a common data source (which may comprise a single logical data source that is distributed across disparate physical and / or virtual storage locations). This may facilitate a dramatic reduction in the amount of data storage required to handle the enormous amount of data produced by or about value chain network entities 652 as applications 630 and uses of value chain networks grow and proliferate. For example, a supply chain or inventory management application in the value chain management platform 604, such as one for ordering replacement parts for a machine or item of equipment, may access the same data set about what parts have been replaced for a set of machines as a predictive maintenance application that is used to predict whether a component of a ship, or facility of a port is likely to require replacement parts. Similarly, prediction may be used with respect to the resupply of items.

[0554] In embodiments, value chain network data objects 1004 may be provided according to an object-oriented data model that defines classes, objects, attributes, parameters and other features of the set of data objects (such as associated with value chain network entities 652 and applications 630) that are handled by the platform 604.

[0555] In embodiments, the data storage systems layer 624 may provide an extremely rich environment for collection of data that can be used for extraction of features or inputs for intelligence systems, such as expert systems, analytic systems, artificial intelligence systems, robotic process automation systems, machine learning systems, deep learning systems, supervised learning systems, or other intelligent systems as disclosed throughout this disclosure and the documents incorporated herein by reference. As a result, each application 630 in the platform 604 and each adaptive intelligent system in the adaptive intelligent systems layer 614 can benefit from the data collected or produced by or for each of the others In embodiments, the data storage systems layer 624 may facilitate collection of data that can be used for extraction of features or inputs for intelligence systems such as a development framework from artificial intelligence. In examples, the collections of data may pull in and / or house event logs (naturally stored or ad-hoc, as needed), perform periodic checks on onboard diagnostic data, or the like. In examples, pre calculation of features may be deployed using AWS Lambda, for example, or various other cloud-based on-demand compute capabilities, such as pre-calculations, multiplexing signals. In many examples, there are pairings (doubles, triples, quadruplets, etc.) ofsimilar kinds of value chain entities that may use one or more sets of capabilities of the data handling layers 608 to deploy connectivity and services across value chain entities and across applications used by the entities even when amassing hundreds and hundreds of data types from relatively disparate entities. In these examples, various pairings of similar types of value chain entities using, at least in part, the connectivity and services across value chain entities and applications, may direct the information from the pairings of connected data to artificial intelligence services including the various neural networks disclosed herein and hybrid combinations thereof. In these examples, genetic programming techniques may be deployed to prune some of the input features in the information from the pairings of connected data. In these examples, genetic programming techniques may also be deployed to add to and augment the input features in the information from the pairings. These genetic programming techniques may be shown to increase the efficacy of the determinations established by the artificial intelligence services. In these examples, the information from the pairings of connected data may be migrated to other layers on the platform including to support or deploy robotic process automation, prediction, forecasting, and other resources such that the shared data schema may facilitate as capabilities and resources for the platform 604.

[0556] A wide range of data types may be stored in the storage layer 624 using various storage media and data storage types, data architectures 1002, and formats, including, without limitation: asset and facility data 1030, state data 1140 (such as indicating a state, condition status, or other indicator with respect to any of the value chain network entities 652, any of the applications 630 or components or workflows thereof, or any of the components or elements of the platform 604, among others), worker data 1032 (including identity data, role data, task data, workflow data, health data, attention data, mood data, stress data, physiological data, performance data, quality data and many other types); event data 1034 ((such as with respect to any of a wide range of events, including operational data, transactional data, workflow data, maintenance data, and many other types of data that includes or relates to events that occur within a value chain network 668 or with respect to one or more applications 630, including process events, financial events, transaction events, output events, input events, state-change events, operating events, workflow events, repair events, maintenance events, service events, damage events, injury events, replacement events, refueling events, recharging events, shipping events, warehousing events, transfers of goods, crossing of borders, moving of cargo, inspection events, supply events, and many others); claims data 664 (such as relating to insurance claims, such as for business interruption insurance, product liability insurance, insurance on goods, facilities, or equipment, flood insurance, insurance for contract-related risks, and many others, as well as claims data relating to product liability, general liability, workers compensation, injury and other liability claims and claims data relating to contracts, such as supply contract performance claims, product delivery requirements, warranty claims, indemnification claims, delivery requirements, timing requirements, milestones, key performance indicators and others); accounting data 730 (such as data relating to completion of contract requirements, satisfaction of bonds, payment of duties and tariffs, and others); and risk management data 732 (such as relating to items supplied, amounts, pricing, delivery, sources, routes, customs information and many others), among many other data types associated with value chain network entities 652 and applications 630

[0557] In embodiments, the data handling layers 608 are configured in a topology that facilitates shared adaptation capabilities, which may be provided, managed, mediated and the like by one or more of a set of services, components, programs, systems, or capabilities of the adaptive intelligent systems layer 614, referred to in some cases herein for convenience as the adaptive intelligence layer 614 The adaptive intelligence systems layer 614 may include a set of data processing, artificial intelligence and computational systems 634 that are described in more detail elsewherethroughout this disclosure. Thus, use of various resources, such as computing resources (such as available processing cores, available servers, available edge computing resources, available on-device resources (for single devices or peered networks), and available cloud infrastructure, among others), data storage resources (including local storage on devices, storage resources in or on value chain entities or environments (including on-device storage, storage on asset tags, local area network storage and the like), network storage resources, cloud-based storage resources, database resources and others), networking resources (including cellular network spectrum, wireless network resources, fixed network resources and others), energy resources (such as available battery power, available renewable energy, fuel, grid-based power, and many others) and others may be optimized in a coordinated or shared way on behalf of an operator, enterprise, or the like, such as for the benefit of multiple applications, programs, workflows, or the like. For example, the adaptive intelligence layer 614 may manage and provision available network resources for both a supply chain management application and for a demand planning application (among many other possibilities), such that low latency resources are used for supply chain management application (where rapid decisions may be important) and longer latency resources are used for the demand plaiming application. As described in more detail throughout this disclosure and the documents incorporated herein by reference, a wide variety of adaptations may be provided on behalf of the various services and capabilities across the various layers 624, including ones based on application requirements, quality of sendee, on-time delivery', service objectives, budgets, costs, pricing, risk factors, operational objectives, efficiency objectives, optimization parameters, returns on investment, profitability, uptime / downtime, worker utilization, and many others.

[0558] The value chain management platform 604, referred to in some cases herein for convenience as the platform 604, may include, integrate with, and enable the various value chain network processes, workflows, activities, events and applications 630 described throughout this disclosure that enable an operator to manage more than one aspect of a value chain network environment or entity 652 in a common application environment (e.g., shared, pooled, similarly licenses whether shared data for one person, multiple people, or anonymized), such as one that takes advantage of common data storage in the data storage layer 624, common data collection or monitoring in the monitoring systems layer 614 and / or common adaptive intelligence of the adaptive intelligence layer 614. Outputs from the applications 630 in the platform 604 may be provided to the other data handing layers 624. These may include, without limitation, state and status information for various objects, entities, processes, flows and the like: object information, such as identity, attribute and parameter information for various classes of objects of various data types; event and change information, such as for workflows, dynamic systems, processes, procedures, protocols, algorithms, and other flows, including timing information; outcome information, such as indications of success and failure, indications of process or milestone completion, indications of correct or incorrect predictions, indications of correct or incorrect labeling or classification, and success metrics (including relating to yield, engagement, return on investment, profitability, efficiency, timeliness, quality of service, quality of product, customer satisfaction, and others) among others. Outputs from each application 630 can be stored in the data storage layer 624, distributed for processing by the data collection layer 614, and used by the adaptive intelligence layer 614 The cross-application nature of the platform 604 thus facilitates convenient organization of all of the necessary infrastructure elements for adding intelligence to any given application, such as by supplying machine learning on outcomes across applications, providing enrichment of automation of a given application via machine learning based on outcomes from other applications or other elements of the platform 604, and allowing application developers to focus on application-native processes while benefiting from other capabilities of the platform 604. In examples, there may be systems, components, services and othercapabilities that optimize control, automation, or one or more performance characteristics of one or more value chain network entities 652; or ones that may generally improve any of process and application outputs and outcomes 1040 pursued by use of the platform 604. In some examples, outputs and outcomes 1040 from various applications 630 may be used to facilitate automated learning and improvement of classification, prediction, or the like that is involved in a step of a process that is intended to be automated.SOME DATA STORAGE LAYER DETAILS - ALTERNATIVE DATA ARCHITECTURES

[0559] Referring to FIG. 12, additional details, components, sub-systems, and other elements of an optional embodiment of the data storage layer 624 of the platform 604 are illustrated. Various data architectures may be used, including conventional relational and object-oriented data architectures, blockchain architectures 1180, asset tag data storage architectures 1178, local storage architectures 1190, network storage architectures 1174, multi-tenant architectures 1132, distributed data architectures 1002, value chain network (VCN) data object architectures 1004, cluster-based architectures 1128, event data-based architectures 1034, state data-based architectures 1140, graph database architectures 1124, self-organizing architectures 1134, and other data architectures 1002.

[0560] The adaptive intelligent systems layer 614 of the platform 604 may include one or more protocol adaptors 1110 for facilitating data storage, retrieval access, query' management, loading, extraction, normalization, and / or transformation to enable use of the various other data storage architectures 1002, such as allowing extraction from one form of database and loading to a data system that uses a different protocol or data structure.

[0561] In embodiments, the value chain network-oriented data storage systems layer 624 may include, without limitation, physical storage systems, virtual storage systems, local storage systems (e g , part of the local storage architectures 1 190), distributed storage systems, databases, memory, network-based storage, network-attached storage systems (e.g., part of the network storage architectures 1174 such as using NVME, storage attached networks, and other network storage systems), and many others.

[0562] In embodiments, the storage layer 624 may store data in one or more knowledge graphs (such as a directed acyclic graph, a data map, a data hierarchy, a data cluster including links and nodes, a self-organizing map, or the like) in the graph database architectures 1124. In example embodiments, the knowledge graph may be a prevalent example of when a graph database and graph database architecture may be used. In some examples, the knowledge graph may be used to graph a workflow. For a linear workflow, a directed acyclic graph may be used. For a contingent workflow, a cyclic graph may be used. The graph database (e.g., graph database architectures 1124) may include the knowledge graph or the knowledge graph may be an example of the graph database. In example embodiments, the knowledge graph may include ontology and connections (e.g., relationships) between the ontology of the knowledge graph. In an example, the knowledge graph may be used to capture an articulation of knowledge domains of a human expert such that there may be an identification of opportunities to design and build robotic process automation or other intelligence that may replicate this knowledge set. The platform may be used to recognize that a type of expert is using this factual knowledge base (from the knowledge graph) coupled with competencies that may be replicable by artificial intelligence that may be different depending on type of expertise involved For example, artificial intelligence such as a convolutional neural network may be used with spatiotemporal aspects that may be used to diagnose issues or packing up a box in a warehouse. Whereas the platform may use a different type of knowledge graph for a self-organizing map of an expert whose main job is to segment customers into customer segmentation groups. In some examples, the knowledge graph may be built from various data such as job credentials, job listings, parsing output deliverables In embodiments, the data storage layer 624 may store data in a digital thread, ledger, orthe like, such as for maintaining a serial or other records of an entities 652 over time, including any of the entities described herein. In embodiments, the data storage layer 624 may use and enable an asset tag 1178, which may include a data structure that is associated with an asset and accessible and managed, such as by use of access controls, so that storage and retrieval of data is optionally linked to local processes, but also optionally open to remote retrieval and storage options. In embodiments, the storage layer 624 may include one or more blockchains 1180, such as ones that store identity data, transaction data, historical interaction data, and the like, such as with access control that may be role-based or may be based on credentials associated with a value chain entity 652, a service, or one or more applications 630. Data stored by the data storage systems 624 may include accounting and other financial data 730, access data 734, asset and facility data 1030 (such as for any of the value chain assets and facilities described herein), asset tag data 1178, worker data 1032, event data 1034, risk management data 732, pricing data 738, safety data 664 and many other types of data that may be associated with, produced by, or produced about any of the value chain entities and activities described herein and in the documents incorporated by reference.ADAPTIVE INTELLIGENT SYSTEMS AND MONITORING LAYERS

[0563] Referring to FIG. 13, additional details, components, sub-systems, and other elements of an optional embodiment of the platform 604 are illustrated. The management platform 604 may, in various optional embodiments, include the set of applications 614, by which an operator or owner of a value chain network entity, or other users, may manage, monitor, control, analyze, or otherwise interact with one or more elements of a value chain network entity 652, such as any of the elements noted in connection above and throughout this disclosure.

[0564] In embodiments, the adaptive intelligent systems layer 614 may include a set of systems, components, services and other capabilities that collectively facilitate the coordinated development and deployment of intelligent systems, such as ones that can enhance one or more of the applications 630 at the application platform 604; ones that can improve the performance of one or more of the components, or the overall performance (e.g., speed / latency, reliability, quality of service, cost reduction, or other factors) of the connectivity facilities 642; ones that can improve other capabilities within the adaptive intelligent systems layer 614; ones that improve the performance (e g., speed / latency, energy utilization, storage capacity, storage efficiency, reliability, security, or the like) of one or more of the components, or the overall performance, of the value chain network-oriented data storage systems 624; ones that optimize control, automation, or one or more performance characteristics of one or more value chain network entities 652; or ones that generally improve any of the process and application outputs and outcomes 1040 pursued by use of the platform 604.

[0565] These adaptive intelligent systems 614 may include a robotic process automation system 1442, a set of protocol adaptors 1110, a packet acceleration system 1410, an edge intelligence system 1420 (which may be a self- adaptive system), an adaptive networking system 1430, a set of state and event managers 1450, a set of opportunity miners 1460, a set of artificial intelligence systems 1160, a set of digital twin systems 1700, a set of entity interaction systems 1920 (such as for setting up, provisioning, configuring and otherwise managing sets of interactions between and among sets of value chain network entities 652 in the value chain network 668), and other systems

[0566] In embodiments, the value chain monitoring systems layer 614 and its data collection systems 640 may include a wide range of systems for the collection of data. This layer may include, without limitation, real time monitoring systems 1520 (such as onboard monitoring systems like event and status reporting systems on ships and other floating assets, on delivery vehicles, on trucks and other hauling assets, and in shipyards, ports, warehouses, distribution centers and other locations; on-board diagnostic (OBD) and telematics systems on floating assets, vehicles andequipment; systems providing diagnostic codes and events via an event bus, communication port, or other communication system; monitoring infrastructure (such as cameras, motion sensors, beacons, RFID systems, smart lighting systems, asset tracking systems, person tracking systems, and ambient sensing systems located in various environments where value chain activities and other events take place), as well as removable and replaceable monitoring systems, such as portable and mobile data collectors, RFID and other tag readers, smart phones, tablets and other mobile devices that are capable of data collection and the like); software interaction observation systems 1500 (such as for logging and tracking events involved in interactions of users with software user interfaces, such as mouse movements, touchpad interactions, mouse clicks, cursor movements, keyboard interactions, navigation actions, eye movements, finger movements, gestures, menu selections, and many others, as well as software interactions that occur as a result of other programs, such as over APIs, among many others); mobile data collectors 1 170 (such as described extensively herein and in documents incorporated by reference), visual monitoring systems 1930 (such as using video and still imaging systems, LIDAR, IR and other systems that allow visualization of items, people, materials, components, machines, equipment, personnel, gestures, expressions, positions, locations, configurations, and other factors or parameters of entities 652, as well as inspection systems that monitor processes, activities of workers and the like); point of interaction systems 1530 (such as dashboards, user interfaces, and control systems for value chain entities); physical process observation systems 1510 (such as for tracking physical activities of operators, workers, customers, or the like, physical activities of individuals (such as shippers, delivery' workers, packers, pickers, assembly personnel, customers, merchants, vendors, distributors and others), physical interactions of workers with other workers, interactions of workers with physical entities like machines and equipment, and interactions of physical entities with other physical entities, including, without limitation, by use of video and still image cameras, motion sensing systems (such as including optical sensors, LIDAR, IR and other sensor sets), robotic motion tracking systems (such as tracking movements of systems attached to a human or a physical entity) and many others; machine state monitoring systems 1940 (including onboard monitors and external monitors of conditions, states, operating parameters, or other measures of the condition of any value chain entity, such as a machine or component thereof, such as a machine, such as a client, a server, a cloud resource, a control system, a display screen, a sensor, a camera, a vehicle, a robot, or other machine); sensors and cameras 1950 and other loT data collection systems 1 172 (including onboard sensors, sensors or other data collectors (including click tracking sensors) in or about a value chain environment (such as, without limitation, a point of origin, a loading or unloading dock, a vehicle or floating asset used to convey goods, a container, a port, a distribution center, a storage facility, a warehouse, a delivery vehicle, and a point of destination), cameras for monitoring an entire environment, dedicated cameras for a particular machine, process, worker, or the like, wearable cameras, portable cameras, cameras disposed on mobile robots, cameras of portable devices like smart phones and tablets, and many others, including any of the many sensor types disclosed throughout this disclosure or in the documents incorporated herein by reference); indoor location monitoring systems 1532 (including cameras, IR systems, motion-detection systems, beacons, RFID readers, smart lighting systems, triangulation systems, RF and other spectrum detection systems, time-of-flight systems, chemical noses and other chemical sensor sets, as well as other sensors); user feedback systems 1534 (including survey systems, touch pads, voice-based feedback systems, rating systems, expression monitoring systems, affect monitoring systems, gesture monitoring systems, and others); behavioral monitoring systems 1538 (such as for monitoring movements, shopping behavior, buying behavior, clicking behavior, behavior indicating fraud or deception, user interface interactions, product return behavior, behavior indicative of interest, attention, boredom or the like, mood-indicatingbehavior (such as fidgeting, staying still, moving closer, or changing posture) and many others); and any of a wide variety' of Internet of Things (loT) data collectors 1172, such as those described throughout this disclosure and in the documents incorporated by' reference herein.

[0567] In embodiments, the value chain monitoring systems layer 614 and its data collection systems 640 may include an entity' discovery system 1900 for discovering one or more value chain network entities 652, such as any of the entities described throughout this disclosure. This may include components or sub-systems for searching for entities within the value chain network 668, such as by device identifier, by network location, by geolocation (such as by geofence), by indoor location (such as by proximity to known resources, such as loT-enabled devices and infrastructure, Wi-Fi routers, switches, or the like), by cellular location (such as by proximity to cellular towers), by identity management systems (such as where an entity 652 is associated with another entity 652, such as an owner, operator, user, or enterprise by an identifier that is assigned by and / or managed by the platform 604), and the like. Entity discovery 1900 may initiate a handshake among a set of devices, such as to initiate interactions that serve various applications 630 or other capabilities of the platform 604.

[0568] Referring to FIG. 14, a management platform of an information technology system, such as a management platform for a value chain of goods and / or services is depicted as a block diagram of functional elements and representative interconnections. The management platform includes a user interface 3020 that provides, among other things, a set of adaptive intelligence systems 614. The adaptive intelligence systems 614 provide coordinated intelligence (including artificial intelligence 1160, expert systems 3002, machine learning 3004, and the like) for a set of demand management applications 824 and for a set of supply chain applications 812 for a category of goods 3010, which may be produced and sold through the value chain The adaptive intelligence systems 614 may deliver artificial intelligence 1160 through a set of data processing, artificial intelligence and computational systems 634. In embodiments, the adaptive intelligence systems 614 are selectable and / or configurable through the user interface 3020 so that one or more of the adaptive intelligence systems 614 can operate on or in cooperation with the sets of value chain applications (e.g., demand management applications 824 and supply chain applications 812) The adaptive intelligence systems 614 may include artificial intelligence, including any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference.

[0569] In embodiments, user interface may include interfaces for configuring an artificial intelligence system 1160 to take inputs from selected data sources of the value chain (such as data sources used by the set of demand management applications 824 and / or the set of supply chain applications 812) and supply them, such as to a neural net 'ork, artificial intelligence system 1160 or any of the other adaptive intelligence systems 614 described throughout this disclosure and in the documents incorporated herein by reference to enhance, control, improve, optimize, configure, adapt or have another impact on a value chain for the category of goods 3010. In embodiments, the selected data sources of the value chain may be applied either as inputs for classification or prediction, or as outcomes relating to the value chain, the category of goods 3010 and the like

[0570] In embodiments, providing coordinated intelligence may include providing artificial intelligence capabilities, such as artificial intelligence systems 1160 and the like. Artificial intelligence systems may facilitate coordinated intelligence for the set of demand management applications 824 or the set of supply chain applications 812 or both, such as for a category of goods, such as by processing data that is available in any of the data sources of the value chain, such as value chain processes, bills of materials, manifests, delivery schedules, weather data, traffic data, goodsdesign specifications, customer complaint logs, customer reviews, Enterprise Resource Planning (ERP) System, Customer Relationship Management (CRM) System, Customer Experience Management (CEM) System, Service Lifecycle Management (SLM) System, Product Lifecycle Management (PLM) System, and the like.

[0571] In embodiments, the user interface 3020 may provide access to, among other things artificial intelligence capabilities, applications, systems and the like for coordinating intelligence for applications of the value chain and particularly for value chain applications for the category of goods 3010. The user interface 3020 may be adapted to receive information descriptive of the category of goods 3010 and configure user access to the artificial intelligence capabilities responsive thereto, so that the user, through the user interface is guided to artificial intelligence capabilities that are suitable for use with value chain applications (e.g., the set of demand management applications 824 and supply chain applications 812) that contribute to goods / services in the category of goods 3010. The user interface 3020 may facilitate providing coordinated intelligence that comprises artificial intelligence capabilities that provide coordinated intelligence for a specific operator and / or enterprise that participates in the supply chain for the category of goods

[0572] In embodiments, tire user interface 3020 may be configured to facilitate the user selecting and / or configuring multiple artificial intelligence systems 1160 for use with the value chain. The user interface may present the set of demand management applications 824 and supply chain applications 812 as connected entities that receive, process, and produce outputs each of which may be shared among the applications. Types of artificial intelligence systems 1160 may be indicated in the user interface 3020 responsive to sets of connected applications or their data elements being indicated in the user interface, such as by the user placing a pointer proximal to a connected set of applications and the like In embodiments, the user interface 3020 may facilitate access to the set of adaptive intelligence systems provides a set of capabilities that facilitate development and deployment of intelligence for at least one function selected from a list of functions consisting of supply chain application automation, demand management application automation, machine learning, artificial intelligence, intelligent transactions, intelligent operations, remote control, analytics, monitoring, reporting, state management, event management, and process management.

[0573] The adaptive intelligence systems 614 may be configured with data processing, artificial intelligence and computational systems 634 that may operate cooperatively to provide coordinated intelligence, such as when an artificial intelligence system 1160 operates on or responds to data collected by or produced by other systems of the adaptive intelligence systems 614, such as a data processing system and the like. In embodiments, providing coordinated intelligence may include operating a portion of a set of artificial intelligence systems 1160 that employs one or more types of neural network that is described herein and in the documents incorporated herein by reference and that processes any of the demand management application outputs and supply chain application outputs to provide the coordinated intelligence.

[0574] In embodiments, providing coordinated intelligence for tire set of demand management applications 824 may include configuring at least one of the adaptive intelligence systems 614 (e.g., through the user interface 3020 and the like) for at least one or more demand management applications selected from a list of demand management applications including a demand planning application, a demand prediction application, a sales application, a future demand aggregation application, a marketing application, an advertising application, an e-commerce application, a marketing analytics application, a customer relationship management application, a search engine optimization application, a sales management application, an advertising network application, a behavioral tracking application, amarketing analytics application, a location-based product or service-targeting application, a collaborative filtering application, a recommendation engine for a product or sendee, and the like.

[0575] Similarly, providing coordinated intelligence for the set of supply chain applications 812 may include configuring at least one of the adaptive intelligence systems 614 for at least one or more supply chain applications selected from a list of supply chain applications including a goods timing management application, a goods quantity management application, a logistics management application, a shipping application, a delivery application, an order for goods management application, an order for components management application, and the like.

[0576] In embodiments, the management platform 102 may, such as through the user interface 3020 facilitate access to the set of adaptive intelligence systems 614 that provide coordinated intelligence for a set of demand management applications 824 and supply chain applications 812 through the application of artificial intelligence. In such embodiments, the user may seek to align supply with demand while ensuring profitability and the like of a value chain for a category of goods 3010. By providing access to artificial intelligence capabilities 1160, the management platform allows the user to focus on the applications of demand and supply while gaining advantages of techniques such as expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and the like.

[0577] In embodiments, the management platform 102 may, through the user interface 3020 and the like provide a set of adaptive intelligence systems 614 that provide coordinated artificial intelligence 1160 for the sets of demand management applications 824 and supply chain applications 812 for the category of goods 3020 by, for example, determining (automatically) relationships among demand management and supply chain applications based on inputs used by the applications, results produced by the applications, and value chain outcomes The artificial intelligence 1160 may be coordinated by, for example, the set of data processing, artificial intelligence and computational systems 634 available through the adaptive intelligence systems 614

[0578] In embodiments, the management platform 102 may be configured with a set of artificial intelligence systems 1160 as part of a set of adaptive intelligence systems 614 that provide the coordinated intelligence for the sets of demand management applications 824 and supply chain applications 812 for a category of goods 3010. The set of artificial intelligence systems 1160 may provide the coordinated intelligence so that at least one supply chain application of the set of supply chain applications 812 produces results that address at least one aspect of supply for at least one of the goods in the category of goods as determined by at least one demand management application of the set of demand management applications 824. In examples, a behavioral tracking demand management application may generate results for behavior of uses of a good in the category of goods 3010. The artificial intelligence systems 1160 may process the behavior data and conclude that there is a perceived need for greater consumer access to a second product in the category' of goods 3010. This coordinated intelligence may be, optionally automatically, applied to the set of supply chain applications 812 so that, for example, production resources or other resources in the value chain for the category of goods are allocated to the second product. In examples, a distributor who handles stocking retailer shelves may receive a new stocking plan that allocates more retail shelf space for the second product, such as by taking away space from a lower margin product and the like.

[0579] In embodiments, the set of artificial intelligence systems 1160 and the like may provide coordinated intelligence for the sets of supply chain and demand management applications by, for example, determining an optionally temporal prioritization of demand management application outputs that impact control of supply chain applications so that an optionally temporal demand for at least one of the goods in the category of goods 3010 can bemet. Seasonal adjustments in prioritization of demand application results are one example of a temporal change. Adjustments in prioritization may also be localized, such as when a large college football team is playing at their home stadium and local supply of tailgating supplies may temporally be adjusted even though demand management application results suggest that small propane stoves are not currently in demand in a wider region

[0580] A set of adaptive intelligence systems 614 that provide coordinated intelligence , such as by providing artificial intelligence capabilities 1160 and the like may also facilitate development and deployment of intelligence for at least one function selected from a list of functions consisting of supply chain application automation, demand management application automation, machine learning, artificial intelligence, intelligent transactions, intelligent operations, remote control, analytics, monitoring, reporting, state management, event management, and process management. The set of adaptive intelligence systems 614 may be configured as a layer in the platform and an artificial intelligence system therein may operate on or be responsive to data collected by and / or produced by other systems (e.g., data processing systems, expert systems, machine learning systems and the like) of the adaptive intelligence systems layer.

[0581] In addition to providing coordinated intelligence configured for specific categories of goods, the coordinated intelligence may be provided for a specific value chain entity 652, such as a supply chain operator, business, enterprise, and the like that participates in the supply chain for the category’ of goods.

[0582] Providing coordinated intelligence may include employing a neural network to process at least one of the inputs and outputs of the sets of demand management and supply chain applications. Neural networks may be used with demand applications, such as a demand planning application, a demand prediction application, a sales application, a future demand aggregation application, a marketing application, an advertising application, an e- commerce application, a marketing analytics application, a customer relationship management application, a search engine optimization application, a sales management application, an advertising network application, a behavioral tracking application, a marketing analytics application, a location-based product or service-targeting application, a collaborative filtering application, a recommendation engine for a product or service, and the like. Neural networks may also be used with supply chain applications such as a goods timing management application, a goods quantity management application, a logistics management application, a shipping application, a delivery application, an order for goods management application, an order for components management application, and the like. Neural networks may provide coordinated intelligence by processing data that is available in any of a plurality of value chain data sources for the category of goods including without limitation processes, bill of materials, weather, traffic, design specification, customer complaint logs, customer reviews, Enterprise Resource Planning (ERP) System, Customer Relationship Management (CRM) System, Customer Experience Management (CEM) System, Service Lifecycle Management (SLM) System, Product Lifecycle Management (PLM) System, and the like. Neural networks configured for providing coordinated intelligence may share adaptation capabilities with other adaptive intelligence systems 614, such as when these systems are configured in a topology that facilitates such shared adaptation. In embodiments, neural networks may facilitate provisioning available value chain / supply chain network resources for both the set of demand management applications and for the set of supply chain applications In embodiments, neural networks may provide coordinated intelligence to improve at least one of the list of outputs consisting of a process output, an application output, a process outcome, an application outcome, and the like.

[0583] Referring to FIG. 15, a management platform of an information technology system, such as a management platform for a value chain of goods and / or services is depicted as a block diagram of functional elements and representative interconnections. The management platform includes a user interface 3020 that provides, among otherthings, a hybrid set of adaptive intelligence systems 614. The hybrid set of adaptive intelligence systems 614 provide coordinated intelligence through the application of artificial intelligence, such as through application of a hybrid artificial intelligence system 3060, and optionally through one or more expert systems, machine learning systems, and the like for use with a set of demand management applications 824 and for a set of supply chain applications 812 for a category of goods 3010, which may be produced and sold through the value chain. The hybrid adaptive intelligence systems 614 may delivertwo types of artificial intelligence systems, type A 3052 and type B 3054 through a set of data processing, artificial intelligence and computational systems 634. In embodiments, the hybrid adaptive intelligence systems 614 are selectable and / or configurable through the user interface 3020 so that one or more of the hybrid adaptive intelligence systems 614 can operate on or in cooperation with the sets of supply chain applications (e.g , demand management applications 824 and supply chain applications 812). The hybrid adaptive intelligence systems 61 may include a hybrid artificial intelligence system 3060 that may include at least two types of artificial intelligence capabilities including any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference. The hybrid adaptive intelligence systems 614 may facilitate applying a first type of artificial intelligence system 1160 to the set of demand management applications 824 and a second type of artificial intelligence system 1160 to the set of supply chain applications 812, wherein each of the first type and second type of artificial intelligence system 1160 can operate independently, cooperatively, and optionally coordinate operation to provide coordinated intelligence for operation of the value chain that produces at least one of the goods in the category of goods 3010

[0584] In embodiments, the user interface 3020 may include interfaces for configuring a hybrid artificial intelligence system 3060 to take inputs from selected data sources of the value chain (such as data sources used by the set of demand management applications 824 and / or the set of supply chain applications 812) and supply them, such as to at least one of the two types of artificial intelligence systems in the hybrid artificial intelligence system 3060, types of which are described throughout this disclosure and in the documents incorporated herein by reference to enhance, control, improve, optimize, configure, adapt or have another impact on a value chain for the category of goods 3010. In embodiments, the selected data sources of the value chain may be applied either as inputs for classification or prediction, or as outcomes relating to the value chain, the category of goods 3010 and the like.

[0585] In embodiments, the hybrid adaptive intelligence systems 614 provides a plurality of distinct artificial intelligence systems 1160, a hybrid artificial intelligence system 3060, and combinations thereof. In embodiments, any of the plurality of distinct artificial intelligence systems 1160 and the hybrid artificial intelligence system 3060 may be configured as a plurality of neural network-based systems, such as a classification-adapted neural network, a prediction-adapted neural network and the like. As an example of hybrid adaptive intelligence systems 614, a machine learning-based artificial intelligence system may be provided for the set of demand management applications 824 and a neural network-based artificial intelligence system may be provided for the set of supply chain applications 812. As an example of a hybrid artificial intelligence system 3060, the hybrid adaptive intelligence systems 614 may provide the hybrid artificial intelligence system 3060 that may include a first type of artificial intelligence that is applied to the demand management applications 824 and which is distinct from a second type of artificial intelligence that is applied to the supply chain applications 812. A hybrid artificial intelligence system 3060 may include any combination of types of artificial intelligence systems including a plurality of a first type of artificial intelligence (e.g , neural networks) and at least one second type of artificial intelligence (e.g., an expert system) and the like. Inembodiments, a hybrid artificial intelligence system may comprise a hybrid neural network that applies a first type of neural network with respect to the demand management applications 824 and a second type of neural network with respect to the supply chain applications 812. Yet further, a hybrid artificial intelligence system 3060 may provide two types of artificial intelligence to different applications, such as different demand management applications 824 (e g , a sales management application and a demand prediction application) or different supply chain applications 812 (e g., a logistics control application and a production quality control application).

[0586] In embodiments, hybrid adaptive intelligence systems 614 may be applied as distinct artificial intelligence capabilities to distinct demand management applications 824. As examples, coordinated intelligence through a hybrid artificial intelligence capabilities may be provided to a demand planning application by a feed-forward neural network, to a demand prediction application by a machine learning system, to a sales application by a self-organizing neural network, to a future demand aggregation application by a radial basis function neural network, to a marketing application by a convolutional neural network, to an advertising application by a recurrent neural network, to an e- commerce application by a hierarchical neural network, to a marketing analytics application by a stochastic neural network, to a customer relationship management application by an associative neural network and tire like.

[0587] Referring to FIG. 16, a management platform of an information technology system, such as a management platform for a value chain of goods and / or services is depicted as a block diagram of functional elements and representative interconnections for providing a set of predictions 3070. The management platform includes a user interface 3020 that provides, among other things, a set of adaptive intelligence systems 614. The adaptive intelligence systems 614 provide a set of predictions 3070 through the application of artificial intelligence, such as through application of an artificial intelligence system 1160, and optionally through one or more expert systems, machine learning systems, and the like for use with a coordinated set of demand management applications 824 and supply chain applications 812 for a category' of goods 3010, which may be produced and sold through the value chain. The adaptive intelligence systems 614 may deliver the set of prediction 3070 through a set of data processing, artificial intelligence and computational systems 634. In embodiments, the adaptive intelligence systems 614 are selectable and / or configurable through the user interface 3020 so that one or more of the adaptive intelligence systems 614 can operate on or in cooperation with the coordinated sets of value chain applications. The adaptive intelligence systems 614 may include an artificial intelligence system that provides artificial intelligence capabilities known to be associated with artificial intelligence including any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference. The adaptive intelligence systems 614 may facilitate applying adapted intelligence capabilities to the coordinated set of demand management applications 824 and supply chain applications 812 such as by producing a set of predictions 3070 that may facilitate coordinating the two sets of value chain applications, or at least facilitate coordinating at least one demand management application and at least one supply chain application from their respective sets.

[0588] In embodiments, the set of predictions 3070 includes a least one prediction of an impact on a supply chain application based on a current state of a coordinated demand management application, such as a prediction that a demand for a good will decrease earlier than previously anticipated. The converse may also be true in that the set of predictions 3070 includes at least one prediction of an impact on a demand management application based on a current state of a coordinated supply chain application, such as a prediction that a lack of supply of a good will likely impact a measure of demand of related goods. In embodiments, the set of predictions 3070 is a set of predictions ofadjustments in supply required to meet demand. Other predictions include at least one prediction of change in demand that impacts supply. Yet other predictions in the set of predictions predict a change in supply that impacts at least one of the set of demand management applications, such as a promotion application for at least one good in the category of goods A prediction in the set of predictions may be as simple as setting a likelihood that a supply of a good in the category of goods will not meet demand set by a demand setting application.

[0589] In embodiments, the adaptive intelligence systems 614 may provide a set of artificial intelligence capabilities to facilitate providing the set of predictions for the coordinated set of demand management applications and supply chain applications. In one non-limiting example, the set of artificial intelligence capabilities may include a probabilistic neural network that may be used to predict a fault condition or a problem state of a demand management application such as a lack of sufficient validated feedback. The probabilistic neural network may be used to predict a problem state with a machine performing a value chain operation (e.g., a production machine, an automated handling machine, a packaging machine, a shipping machine and the like) based on a collection of machine operating information and preventive maintenance information for tire machine.

[0590] In embodiments, the set of predictions 3070 may be provided by the management platform 102 directly through a set of adaptive artificial intelligence systems.

[0591] In embodiments, the set of predictions 3070 may be provided for the coordinated set of demand management applications and supply chain applications for a category of goods by applying artificial intelligence capabilities for coordinating the set of demand management applications and supply chain applications.

[0592] In embodiments, the set of predictions 3070 may be predictions of outcomes for operating a value chain with the coordinated set demand management applications and supply chain applications for the category of goods, so that a user may conduct test cases of coordinated sets of demand management applications and supply chain applications to determine which sets may produce desirable outcomes (viable candidates for a coordinated set of applications) and which may produce undesirable outcomes.

[0593] Referring to FIG. 17, a management platform of an information technology system, such as a management platform for a value chain of goods and / or services is depicted as a block diagram of functional elements and representative interconnections for providing a set of classifications 3080. The management platform includes a user interface 3020 that provides, among other things, a set of adaptive intelligence systems 614. The adaptive intelligence systems 614 provide a set of classifications 3080 through, for example, the application of artificial intelligence, such as through application of an artificial intelligence system 1160, and optionally through one or more expert systems, machine learning systems, and the like for use with a coordinated set of demand management applications 824 and supply chain applications 812 for a category' of goods 3010, 'hich may be produced, marketed, sold, resold, rented, leased, given a 'ay, serviced, recycled, renewed, enhanced, and the like through the value chain. The adaptive intelligence systems 614 may deliver the set of classifications 3080 through a set of data processing, artificial intelligence and computational systems 634. In embodiments, the adaptive intelligence systems 614 are selectable and / or configurable through the user interface 3020 so that one or more of the adaptive intelligence systems 614 can operate on or in cooperation with the coordinated sets of value chain applications. The adaptive intelligence systems 614 may include an artificial intelligence system that provides, among other things classification capabilities through any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference. The adaptive intelligence systems 614 may facilitate applying adaptedintelligence capabilities to the coordinated set of demand management applications 824 and supply chain applications 812 such as by producing a set of classifications 3080 that may facilitate coordinating the two sets of value chain applications, or at least facilitate coordinating at least one demand management application and at least one supply chain application from their respective sets

[0594] In embodiments, the set of classifications 3080 includes at least one classification of a current state of a supply chain application for use by a coordinated demand management application, such as a classification of a problem state that may impact operation of a demand management application, such as a mar...

Claims

CLAIMSWhat is claimed is:VCN Control Tower System and AI-Based Know Your Model System1 A value chain network control tower system, comprising: a processor and memory configured to execute a first set of know your model Al -based learning models, wherein the first set of know your model Al-based learning models is configured to: perform at least one model intake and registration action associated with a second set of Al-based learning models; perform at least one model evaluation and risk assessment action associated with the second set of Al -based learning models; and perform at least one model deployment action associated with the second set of Al-based learning models.

2. The value chain network control tower system of claim 1 , wherein the first set of know your model AI- based learning models is further configured to perform at least one model monitoring and observability action associated with the second set of Al-based learning models.

3. The value chain network control tower system of claim 1 , wherein the first set of know your model AI- based learning models is further configured to perform at least one model updating and retraining action associated with the second set of Al-based learning models.

4. The value chain network control tower system of claim 1 , wherein the first set of know your model AI- based learning models includes at least one of: a linear classification model, a regression model, a decision treebased model, a kernel machine, a support vector machine, an instance-based learning model, a nearest neighbor model, a Bayesian model, a clustering model, a convolutional neural network, a deep convolutional neural network, a feed forward neural network, a recurrent neural network, a long / short term memory neural network, a transformer model, a large language model, or a self-attention model.

5. The value chain network control tower system of claim 1 , wherein the second set of Al-based learning models includes at least one of: a linear classification model, a regression model, a decision tree-based model, a kernel machine, a support vector machine, an instance-based learning model, a nearest neighbor model, a Bayesian model, a clustering model, a convolutional neural network, a deep convolutional neural network, a feed forward neural network, a recurrent neural network, a long / short term memory neural network, a transformer model, a large language model, or a self-attention model.

6. The value chain network control tower system of claim 1 , wherein the second set of Al-based learning models is associated with at least one of: a demand prediction, a product lifecycle management action, an inventory management action, a robotic fleet management action, a shipping container fleet management action, a logistics action, a demand shaping action, or a procurement action.

7. The value chain network control tower system of claim 1, wherein the at least one model intake and registration action includes at least one of: model documentation collection, model registration procedures, model metadata collection, input and output interface standardization, legal and licensing validation checks, model card collection and management, cataloging and tagging operations, API schema conformance and endpoint definition, model interface WTapping, endpoint configuration testing in sandbox or staging environments, telemetry hook integration, invocation method registration including assigning resource identifiers and configuring API endpoints, or security and privacy validation.

8. The value chain network control tower system of claim 1, wherein the at least one model evaluation and risk assessment action includes at least one of: evaluation of foundational properties, evaluation of task performance, evaluation of safety and risk management, evaluation of alignment and compliance, evaluation of operational metrics, or evaluation of tooling and transparency9. The value chain network control tower system of claim 1 , wherein the at least one model deployment action includes at least one of: production integration, API disclosure, scaling, selection of usage caps and quotas, cost and latency budgeting, selecting region-specific deployment strategies for data sovereignty, model endpoint wrapping, containerized environment hosting, standardized API access configuration, authentication via identity and access management systems or API keys, throttling policy application, metrics logging for monitoring and observability services, automatic scaling of model inference based on demand, elastic inference backend provisioning with scalable GPU-backed infrastructure clusters, horizontal scaling tuned to provider service level agreements, budget control implementation with alert triggering and usage stopping at thresholds, region-specific model deployment for data residency and compliance requirements, or geo-fencing enforcement by location-aware routing layers.

10. The value chain network control tower system of claim 2, wherein the at least one model monitoring and observability action includes at least one of: logging of API calls, capturing payload sizes, capturing error responses, capturing latency buckets, inference latency tracking, token use per invocation tracking, model-specific usage metric tracking, model throughput tracking, model GPU utilization tracking, request rate tracking, data drift detection, monitoring prompt types, comparing model output distributions over time, implementing audit trails, implementing real-time feedback loops, abuse and misuse detection, or detection of anomalous usage11. The value chain network control tower system of claim 3, wherein the at least one model updating and retraining action includes at least one of: data pipeline refresh actions, fine-tuning and customization actions, model iteration and versioning actions, model retraining actions, model comparison with stored responses and metrics comparison, updating a retrieval augmented generation knowledge base, or conducting A / B testing between model versions.

12. The value chain network control tower system of claim 1, further comprising a digital twin system, wherein the digital twin system is configured to generate a digital twin of the second set of Al-based learning models.

13. The value chain network control tower system of claim 12, wherein the at least one model evaluation and risk assessment action includes performing digital twin simulation using the digital twin and a simulation system.

14. The value chain network control tower system of claim 1 , wherein the first set of know your model AI- based learning models is configured to perform at least one of ensemble model management including dynamic weighting or selection optimization for the second set of Al -based learning models.

15. The value chain network control tower system of claim 1 , wherein the first set of know your model AI- based learning models is configured to integrate with at least one external regulatory compliance framework to ensure adherence to industry-specific Al governance standards for the second set of Al-based learning models.

16. The value chain network control tower system of claim 1, wherein the first set of know your model AI- based learning models is configured to generate at least one of: a model performance metric or a compliance report for regulatory- auditing of the second set of Al-based learning models.

17. A method for operating a value chain network control tower, comprising: executing, via a processor and memory, a first set of know your model Al-based learning models, wherein the first set of know your model Al-based learning models are configured to: perform at least one model intake and registration action associated with a second set of Al-based learning models; perform at least one model evaluation and risk assessment action associated with the second set of Al -based learning models; and perform at least one model deployment action associated with the second set of Al-based learning models.

18. The method of claim 17 wherein the first set of know your model Al-based learning models is further configured to perform at least one model monitoring and observability action associated with the second set of AI- based learning models.

19. The method of claim 17 wherein the first set of know your model Al-based learning models is further configured to perform at least one model updating and retraining action associated with the second set of Al-based learning models.

20. The method of claim 17, wherein the first set of know your model Al-based learning models includes at least one of: a linear classification model, a regression model, a decision tree-based model, a kernel machine, a support vector machine, an instance-based learning model, a nearest neighbor model, a Bayesian model, a clustering model, a convolutional neural network, a deep convolutional neural network, a feed forward neural network, a recurrent neural network, a long / short term memory neural network, a transformer model, a large language model, or a self-attention model21 The method of claim 17, wherein the second set of Al-based learning models includes at least one of: a linear classification model, a regression model, a decision tree-based model, a kernel machine, a support vector machine, an instance-based learning model, a nearest neighbor model, a Bayesian model, a clustering model, a convolutional neural network, a deep convolutional neural network, a feed forward neural network, a recurrent neural network, a long / short term memory neural network, a transformer model, a large language model, or a selfattention model.

22. The method of claim 17, wherein the second set of Al-based learning models is associated with at least one of: a demand prediction, a product lifecycle management action, an inventory management action, a robotic fleet management action, a shipping container fleet management action, a logistics action, a demand shaping action, or a procurement action.

23. The method of claim 17, wherein the at least one model intake and registration action includes at least one of: model documentation collection, model registration procedures, model metadata collection, input and output interface standardization, legal and licensing validation checks, model card collection and management, cataloging and tagging operations, API schema conformance and endpoint definition, model interface wrapping, endpoint configuration testing in sandbox or staging environments, telemetry hook integration, invocation method registration including assigning resource identifiers and configuring API endpoints, or security and privacy validation.

24. The method of claim 17, wherein the at least one model evaluation and risk assessment action includes at least one of: evaluation of foundational properties, evaluation of task performance, evaluation of safety and risk management, evaluation of alignment and compliance, evaluation of operational metrics, or evaluation of tooling and transparency.

25. The method of claim 17, wherein the at least one model deployment action includes at least one of: production integration, API disclosure, scaling, selection of usage caps and quotas, cost and latency budgeting, selecting region-specific deployment strategies for data sovereignty, model endpoint wrapping, containerized environment hosting, standardized API access configuration, or authentication via identity and access management systems or API keys, throttling policy application, metrics logging for monitoring and observability sendees, automatic scaling of model inference based on demand, elastic inference backend provisioning with scalable GPU- backed infrastructure clusters, horizontal scaling tuned to provider service level agreements, budget control implementation with alert triggering and usage stopping at thresholds, region-specific model deployment for data residency and compliance requirements, or geo-fencing enforcement by location-aware routing layers.

26. The method of claim 18, wherein the at least one model monitoring and observability action includes at least one of: logging of API calls, capturing payload sizes, capturing error responses, capturing latency buckets, inference latency tracking, token use per invocation tracking, model-specific usage metric tracking, model throughput tracking, model GPU utilization tracking, request rate tracking, data drift detection, monitoring prompt types, comparing model output distributions over time, implementing audit trails, implementing real-time feedback loops, abuse and misuse detection, or detection of anomalous usage.

27. The method of claim 19, wherein the at least one model updating and retraining action includes at least one of: data pipeline refresh actions, fine-tuning and customization actions, model iteration and versioning actions, model retraining actions, model comparison with stored responses and metrics comparison, updating a retrieval augmented generation knowledge base, or conducting A / B testing between model versions28 The method of claim 17, further comprising generating a digital twin of the second set of AT-based learning models using a digital twin system29. The method of claim 28, wherein the at least one model evaluation and risk assessment action includes performing digital twin simulation using the digital twin and a simulation system.

30. The method of claim 17 wherein the first set of know your model Al-based learning models is further configured to perform ensemble model management including dynamic weighting and selection optimization for the second set of Al-based learning models.Robotic Fleet Management System and Know Your Robot System31. A robotic fleet management system, comprising: a processor and memory configured to execute a know your robot system, wherein tire know your robot system is configured to: establish an initial connection with a candidate robotic system; perform at least one discovery' and authentication action for the candidate robotic system; receive and process at least one capability declaration from the candidate robotic system; execute at least one compliance validation action for the candidate robotic system; perform at least one contextual configuration and operational parameterization action associated with the candidate robotic system; and perform at least one deployment action for the candidate robotic system.

32. The robotic fleet management system of claim 31 , wherein the at least one discovery and authentication action comprises at least one of: receiving discovery packets comprising cryptographic identity credentials, device serial numbers, or pre-provisioned public key infrastructure certificates via secure networking protocols; or performing hardware attestation using trusted platform modules or secure enclave cryptographic signatures.

33. The robotic fleet management system of claim 31, wherein the capability declaration comprises a machine- readable manifest describing at least one of a supported robotic tasks, a hardware specification, an operational constraint, an energy management detail, a capability profile, or a certified Al model signature34. The robotic fleet management system of claim 31, wherein the at least one compliance validation action comprises at least one of: comparing a received capability profile against a stored organizational governance policy or external regulatory' frameworks, verifying that an Al model hash matches an approved production model recorded in a compliance ledger, or confirming geolocation constraints for a restricted area.

35. The robotic fleet management system of claim 31 , wherein tire at least one contextual configuration and operational parameterization action comprises at least one of: transmitting contextual data comprising environment maps, dynamic scheduling parameters, or region-specific task priority lists; and deploying operational restrictions including geofencing boundaries, maximum acceleration limits, or time-constrained task permissions.

36. The robotic fleet management system of claim 31 , wherein the know your robot system is further configured to: integrate the candidate robotic system into a centralized orchestration and control plane that exposes at least one of: a dynamic task assignment API, a real-time telemetry collection sendee, or a health status subscription channel37 The robotic fleet management system of claim 31 , wherein the know your robot system is further configured to: perform continuous monitoring of the candidate robotic system post-deployment by streaming real-time health telemetry.

38. The robotic fleet management system of claim 31 , wherein the know your robot system is further configured to: integrate with a digital twin system to generate a digital twin of the candidate robotic system for simulation-based testing or performance validation.

39. The robotic fleet management system of claim 31 , wherein tire know your robot system is further configured to integrate with a know your model system to perform at least one of: a model intake and registration action, a model evaluation and risk assessment action, or a model deployment action for a set of models associated with the candidate robotic system.

40. The robotic fleet management system of claim 31 , wherein the know your robot system is further configured to integrate with a know your sensor system to perform at least one of: a sensor validation and authentication action, a sensor monitoring and anomaly detection action, or a sensor calibration and optimization action for a set of sensors associated with the candidate robotic system41. A method for executing a know your robot system by a robotic fleet management system, comprising: establishing an initial connection with a candidate robotic system; performing at least one discovery and authentication action for the candidate robotic system; receiving and processing at least one capability declaration from the candidate robotic system; executing at least one compliance validation action for the candidate robotic system;performing at least one contextual configuration and operational parameterization action associated with the candidate robotic system; and performing at least one deployment action for the candidate robotic system.42 The method of claim 41 , wherein the at least one discovery and authentication action comprises at least one of: receiving discovery packets comprising cryptographic identity credentials, device serial numbers, or preprovisioned public key infrastructure certificates via secure networking protocols; or performing hardware attestation using trusted platform modules or secure enclave cryptographic signatures.

43. The method of claim 41 , wherein the at least one capability declaration comprises a machine-readable manifest describing at least one of: supported robotic tasks, a hardware specification, an operational constraint, an energy management detail, a capability profile, or a certified Al model signature.

44. The method of claim 41 , wherein the at least one compliance validation action comprises at least one of: comparing a received capability profile against a stored organizational governance policy or external regulatory frameworks, verifying that an Al model hash matches an approved production model recorded in a compliance ledger, or confirming geolocation constraints for a restricted area.

45. The method of claim 41 , wherein the at least one contextual configuration and operational parameterization action comprises at least one of: transmitting contextual data comprising environment maps, dynamic scheduling parameters, or region-specific task priority lists; and deploying operational restrictions including geofencing boundaries, maximum acceleration limits, or time-constrained task permissions46. The method of claim 41 , further comprising integrating the candidate robotic system into a centralized orchestration and control plane that exposes at least one of: a dynamic task assignment API, a real-time telemetry collection service, or a health status subscription channel.

47. The method of claim 41 , further comprising: performing continuous monitoring of the candidate robotic system post-deployment by streaming real-time health telemetry.

48. The method of claim 41 , further comprising: integrating with a digital twin system to generate a digital twin of the candidate robotic system for simulation-based testing or performance validation.

49. The method of claim 41 , further comprising: integrating with a know your model system to perform at least one of: a model intake and registration action, a model evaluation and risk assessment action, or a model deployment action for a set of models associated with the candidate robotic system50. The method of claim 41 , further comprising: integrating with a know your sensor system to perform at least one of a sensor validation and authentication action, a sensor monitoring and anomaly detection action, or a sensor calibration and optimization action for a set of sensors associated with the candidate robotic system.Shipping Container Fleet Management System and Know Your Shipping Container System51. A shipping container fleet management system, comprising: a processor and memory configured to execute a know your shipping container system, wherein the know your shipping container system is configured to: establish an initial connection with a candidate shipping container; perform at least one discovery' and authentication action for the candidate shipping container; receive and process at least one capability declaration from the candidate shipping container; execute at least one compliance validation action for the candidate shipping container; perform at least one contextual configuration and operational parameterization action associated with the candidate shipping container; and perform at least one deployment action for the candidate shipping container.

52. The shipping container fleet management system of claim 51 , wherein the at least one discovery and authentication action comprises at least one of: receiving discovery packets comprising cryptographic identity credentials, container identification numbers, geolocation data, or pre-provisioned public key infrastructure certificates via secure networking protocols; or performing hardware attestation using trusted platform modules or secure enclave cryptographic signatures.

53. The shipping container fleet management system of claim 51 , wherein the at least one capability declaration comprises a machine-readable manifest comprising at least one of: supported cargo types, environmental sensors, security features, tracking capabilities, operational constraints, weight limits, permissible routes, hazardous materials handling requirements, a capability profile, or certified sensor calibration data54 The shipping container fleet management system of claim 51 , wherein the at least one compliance validation action comprises at least one of: comparing a received capability profile against stored organizational governance policies or external regulatory frameworks, verifying that declared cargo types comply with customs regulations, confirming route restrictions based on hazardous materials classifications, or enforcing security protocols including access control and tamper detection.

55. The shipping container fleet management system of claim 51, wherein the at least one contextual configuration and operational parameterization action comprises at least one of: transmitting contextual data comprising route plans, weather forecasts, port schedules, or destination facility requirements; and deploying operational restrictions including speed limits, geofencing boundaries for restricted areas, or temperature setpoints for sensitive cargo.

56. The shipping container fleet management system of claim 51 , wherein the know your shipping container system is further configured to: integrate the candidate shipping container into a centralized orchestration and control plane that exposes at least one of: a dynamic routing API, a real-time telemetry collection service, or a health status subscription channel.57 The shipping container fleet management system of claim 51, wherein the know your shipping container system is further configured to: perform continuous monitoring of the candidate shipping container post-deployment by streaming real-time telemetry.

58. The shipping container fleet management system of claim 51 , wherein the know your shipping container system is further configured to:integrate with a digital twin system to generate a digital twin of the candidate shipping container for simulationbased testing or performance validation.

59. The shipping container fleet management system of claim 51, wherein the know your shipping container system is further configured to: integrate with a know your model system to perform at least one of: a model intake and registration action, a model evaluation and risk assessment action, or a model deployment action for a set of Al models associated with the candidate shipping container.

60. The shipping container fleet management system of claim 51 , wherein the know your shipping container system is further configured to: integrate with a know your sensor system to perform at least one of a sensor validation and authentication action, a sensor monitoring and anomaly detection action, or a sensor calibration and optimization action for a set of sensors associated with the candidate shipping container.

61. A method for executing a know your shipping container system by a shipping container fleet management system, comprising: establishing an initial connection with a candidate shipping container; performing at least one discovery and authentication action for the candidate shipping container; receiving and processing at least one capability declaration from the candidate shipping container; executing at least one compliance validation action for the candidate shipping container; performing at least one contextual configuration and operational parameterization action associated with the candidate shipping container; and performing at least one deployment action for the candidate shipping container.

62. The method of claim 61 , wherein the at least one discovery and authentication action comprises at least one of: receiving discovery packets comprising cryptographic identity credentials, container identification numbers, geolocation data, or pre-provisioned public key infrastructure certificates via secure networking protocols; or performing hardware attestation using trusted platform modules or secure enclave cryptographic signatures.

63. The method of claim 61 , wherein the at least one capability declaration comprises a machine-readable manifest describing at least one of: supported cargo types, environmental sensors, security features, tracking capabilities, operational constraints, weight limits, permissible routes, hazardous materials handling requirements, a capability profile, or certified sensor calibration data.

64. The method of claim 61 , wherein the at least one compliance validation action comprises at least one of: comparing a received capability profile against stored organizational governance policies or external regulatory frameworks, verifying that declared cargo types comply with customs regulations, confirming route restrictions based on hazardous materials classifications, or enforcing security protocols including access control and tamper detection65. The method of claim 61 , wherein the at least one contextual configuration and operational parameterization action comprises at least one of: transmitting contextual data comprising route plans, weather forecasts, port schedules, or destination facility requirements; and deploying operational restrictions including speed limits, geofencing boundaries for restricted areas, or temperature setpoints for sensitive cargo.

66. The method of claim 61 , further comprising: integrating the candidate shipping container into a centralized orchestration and control plane that exposes at least one of: a dynamic routing API, a real-time telemetry collection service, and a health status subscription channel.67 The method of claim 61 , further comprising: performing continuous monitoring of the candidate shipping container post-deployment by streaming real-time telemetry.

68. The method of claim 61 , further comprising: integrating with a digital twin system to generate a digital Avin of the candidate shipping container for simulationbased testing or performance validation.

69. The method of claim 61 , further comprising: integrating with a know your model system to perform at least one of: a model intake and registration action, a model evaluation and risk assessment action, or a model deployment action for a set of Al models associated with the candidate shipping container.

70. The method of claim 61 , further comprising: integrating with a know your sensor system to perform at least one of a sensor validation and authentication action, a sensor monitoring and anomaly detection action, or a sensor calibration and optimization action for a set of sensors associated with the candidate shipping container.VCN Control Tower System and Know Your Digital Tw in System71 A value chain network control tower system, comprising: a processor and memory configured to execute a know your digital twin system, wherein the know your digital twin system is configured to: perform at least one digital twin generation action for a physical value chain network asset; perform at least one evaluation and risk assessment action for the digital twin; and perform at least one deployment action for the digital twin.

72. The value chain network control tower system of claim 71 , wherein the at least one digital twin generation action comprises at least one of: generating an initial 3D representation of a physical asset using photogrammetry, LiDAR scanning, or CAD model integration; comparing initial geometry against real-time sensor data via a data reconciliation module; or performing model calibration using least-squares optimization to fine-tune a behavior model of the digital twin73. The value chain network control tower system of claim 71, wherein the at least one evaluation and risk assessment action comprises at least one of: executing anomaly detection using at least one recurrent neural network trained on historical operational data; performing a risk assessment by simulating at least one operational scenario; or generating a digital twin trust score.74 The value chain network control tower system of claim 71 , wherein the at least one deployment action comprises at least one of: provisioning and scaling of the digital twin through containerized architecture; or implementing a federated learning framework for distributed training of digital twin behavioral models.

75. The value chain network control tower system of claim 71 , wherein the know your digital twin system supports value chain network digital twin types including at least one of:a value chain network digital twin representing a value chain network infrastructure; a manufacturing plant digital twin; a distribution center digital twin; a transportation network digital twin; a retail point of sale digital twin; a warehouse digital twin; or a logistics system digital twin.

76. The value chain network control tower system of claim 71 , wherein the know your digital twin system is further configured to: create a network of interconnected digital twins representing related assets or components within the value chain network.

77. The value chain network control tower system of claim 71 , wherein the know your digital twin system is further configured to: integrate with a standardized data ingestion pipeline compatible with at least one industrial protocol; facilitate a data exchange with at least one existing control system, SC ADA system, or enterprise resource planning platform; or provide a configuration management module to centrally manage and deploy changes to the digital twin78. The value chain network control tower system of claim 71 , wherein the know your digital twin system is further configured to: provide a pre-built library of digital twin templates representing physical asset types79. The value chain network control tower system of claim 71 , wherein the know your digital twin system is further configured to: perform at least one monitoring and observability action for the digital twin.

80. The value chain network control tower system of claim 71 , wherein the know your digital twin system is further configured to: perform at least one updating and retraining action for the digital twin.

81. The value chain network control tower system of claim 79, wherein the at least one monitoring and observability action comprises at least one of: continuously updating digital twin with at least one real-time data stream from the physical asset; detecting a deviation from a predicted behavior and automatically retraining a simulation engine; or providing at least one tool for version control to track changes to the digital twin.

82. The value chain network control tower system of claim 80, wherein the at least one updating and retraining action comprises at least one of: ingestion of new operational data; incorporating an improvement in sensor technology; or recalibrating a behavioral model against an updated physical asset specification.

83. The value chain network control tower system of claim 71 , wherein the know your digital twin system is further configured to: integrate with a know your model system to perform at least one of: a model intake and registration action, a model evaluation and risk assessment action, or a model deployment action for a set of models associated with the digital twin system.

84. The value chain network control tower system of claim 71 , wherein the know your digital twin system is further configured to: integrate with a know your sensor system to perform at least one of: a sensor validation and authentication action, a sensor monitoring and anomaly detection action, or a sensor calibration and optimization action for a set of sensors associated with the physical asset represented by the digital twin85. A method for executing a know your digital twin system by a value chain network control tower system, comprising: performing at least one digital twin generation action for a physical value chain network asset: performing at least one evaluation and risk assessment action for the digital twin; or performing at least one deployment action for the digital twin.

86. The method of claim 85, wherein the at least one digital twin generation action comprises at least one of: generating an initial 3D representation of a physical asset using photogrammetry, TiDAR scanning, or CAD model integration; comparing the initial geometry against real-time sensor data via a data reconciliation module; or performing model calibration using least-squares optimization to fine-tune a behavior model of the digital twin87. The method of claim 85, wherein the at least one evaluation and risk assessment action comprises at least one of: executing anomaly detection using at least one recurrent neural network trained on historical operational data; performing a risk assessment by simulating at least one operational scenario; or generating a digital twin trust score88 The method of claim 85, wherein the at least one deployment action comprises at least one of: provisioning and scaling of the digital twin through containerized architecture; or implementing a federated learning framework for distributed training of digital twin behavioral models.

89. The method of claim 85, wherein the know your digital twin system supports value chain network digital twin types including at least one of: a value chain network digital twin representing a value chain network infrastructure; a manufacturing plant digital twin; a distribution center digital twin; a transportation network digital twin; a retail point of sale digital twin; a warehouse digital twin; or a logistics system digital twin.

90. The method of claim 85, further comprising: creating a network of interconnected digital twins representing related assets or components within the value chain network.91 The method of claim 85, further comprising: integrating with a standardized data ingestion pipeline compatible with at least one industrial protocol; facilitating a data exchange with an existing control system, a SCADA system, or an enterprise resource planning platform; or providing a configuration management module to centrally manage and deploy changes to the digital twin.

92. The method of claim 85, further comprising:providing a pre-built library of digital twin templates representing physical asset types.

93. The method of claim 85, further comprising: performing at least one monitoring and observability action for the digital twin.94 The method of claim 85, further comprising: performing at least one updating and retraining action for the digital twin.

95. The method of claim 93, wherein the at least one monitoring and observability action comprises at least one of: continuously updating the digital twin with at least one real-time data stream from a physical asset; detecting a deviation from a predicted behavior and automatically retraining a simulation engine: or providing at least one tool for version control to track changes to the digital twin.

96. The method of claim 94, wherein the at least one updating and retraining action comprises at least one of: ingestion of new operational data; incorporating an improvement in a sensor technology; or recalibrating a behavioral model against an updated physical asset specification.

97. The method of claim 85, further comprising: integrating with a know your model system to perform at least one of: a model intake and registration action, a model evaluation and risk assessment action, or a model deployment action for a set of models associated with the digital twin system.98 The method of claim 85, further comprising: integrating with a know your sensor system to perform at least one of a sensor validation and authentication action, a sensor monitoring and anomaly detection action, or a sensor calibration and an optimization action for a set of sensors associated with the physical asset represented by the digital twin.VCN Control Tower System and Know Your Physical Al System99. A value chain network control tower system, comprising: a processor and memory configured to execute a know your physical Al system, wherein the know your physical Al system is configured to: establish an initial connection with a candidate physical Al system; perform at least one discovery and authentication action for the candidate physical Al system; receive and process at least one capability declaration from tire candidate physical Al system; execute at least one compliance validation action for the candidate physical Al system; perform at least one contextual configuration and operational parameterization action associated with the candidate physical Al system; and perform at least one deployment action for the candidate physical Al system.

100. The system of claim 99, wherein the discovery and authentication action includes receiving from the candidate physical Al system at least one of: a discovery packet including cryptographic identity credentials, a device serial number, or a pre-provisioned public key infrastructure (PKI) certificate via a secure networking protocol.

101. The system of claim 99, wherein the know your physical Al system is further configured to perform at least one of hardware attestation using trusted platform modules or secure enclave cryptographic signatures to verify device integrity or authenticity of the candidate physical Al system102. The system of claim 99, wherein the at least one capability declaration includes a machine-readable manifest describing at least one of: supported Al tasks, hardware specifications, operational constraints, or certified Al model signatures.103 The system of claim 99, wherein the at least one compliance validation action includes comparing a received capability profile against at least one of: a stored organizational governance policy or an external regulatory framework.

104. The system of claim 99, wherein the contextual configuration and operational parameterization action includes securely transmitting via encrypted over-the-air (OTA) update channels at least one of: dynamic scheduling parameters or an interaction policy.

105. The system of claim 99, wherein the know your physical Al system integrates the candidate physical Al system into a centralized orchestration and control plane that exposes at least one of: a dynamic task assignment API, a real-time telemetry collection service, or a health status subscription channel.

106. The system of claim 99, wherein the know your physical Al system performs at least one of an initial sandbox validation or a simulation-based testing of the candidate physical Al system prior to deployment.

107. The system of claim 99, wherein the know your physical Al system incorporates a distributed trust ledger component to immutably record onboarding steps, policy validations, operational events, or update transactions using blockchain or tamper-evident cryptographic data structures.

108. The system of claim 99, wherein the know your physical Al system is further configured to: integrate with a digital twin system to generate a digital twin of the candidate physical Al system for at least one of: simulation-based testing or performance validation109. The system of claim 99, wherein the know your physical Al system is further configured to: integrate with a know your model system to perform at least one of: a model intake and registration action, a model evaluation and risk assessment action, or a model deployment action for a set of models associated with the candidate physical Al system.

110. The system of claim 99, wherein the know your physical Al system is further configured to: integrate with a know your sensor system to perform at least one of action for a set of sensors associated with the candidate physical Al system wherein the action comprises at least one of: a sensor validation and authentication action, a sensor monitoring and anomaly detection action, or a sensor calibration and optimization action.

111. A method for executing a know your physical Al system by a value chain network control tower, comprising: establishing an initial connection with a candidate physical Al system; performing at least one discovery and authentication action for the candidate physical Al system; receiving and processing at least one capability declaration from the candidate physical Al system; executing at least one compliance validation action for the candidate physical Al system; performing at least one contextual configuration and operational parameterization action associated with the candidate physical Al system; and performing at least one deployment action for the candidate physical Al system.

112. The method of claim 111 , wherein the discovery and authentication action includes receiving from the candidate physical Al system at least one of: a discovery packet including cryptographic identity credentials, adevice serial number, or a pre-provisioned public key infrastructure (PKI) certificate via a secure networking protocol.

113. The method of claim 1 11 further comprising: performing hardware attestation using trusted platform modules or secure enclave cryptographic signatures to verily device integrity or authenticity of the candidate physical Al system.

114. The method of claim 1 11 , wherein the at least one capability declaration includes a machine-readable manifest describing at least one of: supported Al tasks, hardware specifications, operational constraints, or certified Al model signatures.

115. The method of claim 111, wherein the at least one compliance validation action includes comparing a received capability profile against at least one of: a stored organizational governance policy or an external regulatory framework.

116. The method of claim 111, wherein the contextual configuration and operational parameterization action includes securely transmitting via encrypted over-the-air (OTA) update channels at least one of: dynamic scheduling parameters or an interaction policy.

117. The method of claim 111, further comprising: integrating the candidate physical Al system into a centralized orchestration and control plane that exposes at least one of: a dynamic task assignment API, a real-time telemetry collection service, or a health status subscription channel.118 The method of claim 1 11, further comprising: performing initial sandbox validation and simulation-based testing of the candidate physical Al system prior to deployment.

119. The method of claim 1 11, further comprising: incorporating a distributed trust ledger component to immutably record onboarding steps, policy validations, operational events, or update transactions using blockchain or tamper-evident cryptographic data structures.

120. The method of claim 111, further comprising: integrating with a digital twin system to generate a digital twin of the candidate physical Al system for at least one of: simulation-based testing or performance validation.

121. The method of claim 111, furtlier comprising: integrating with a know your model system to perform at least one of: a model intake and registration action, a model evaluation and risk assessment action, or a model deployment action for a set of models associated with the candidate physical Al system.

122. The method of claim 111, further comprising: integrating with a know your sensor system to perform at least one action for a set of sensors associated with the candidate physical Al system wherein the action comprises: a sensor validation and authentication action, a sensor monitoring and anomaly detection action, or a sensor calibration and optimization actionVCN Control Tower System and Know Your Model System123. A value chain network control tower system, comprising: a processor and memory configured to execute a know your model system, wherein the know your model system is configured to: perform at least one model intake and registration action associated with a set of candidate models;perform at least one model evaluation and risk assessment action associated with the set of candidate models; and perform at least one model deployment action associated with the set of candidate models.

124. The value chain network control tower system of claim 123, wherein the know your model system is further configured to perform at least one model monitoring and observability action associated with the set of candidate models.

125. The value chain network control tower system of claim 123, wherein the know your model system is further configured to perform at least one model updating and retraining action associated with the set of candidate models.

126. The value chain network control tower system of claim 123, wherein the set of candidate models is associated with at least one of a demand prediction action, a product lifecycle management action, an inventory management action, a robotic fleet management action, a shipping container fleet management action, a logistics action, a demand shaping action, or a procurement action.

127. The value chain network control tower system of claim 123, wherein the at least one model intake and registration action includes at least one of: model documentation collection, model registration procedures, model metadata collection, input and output interface standardization, legal and licensing validation checks, model card collection and management, cataloging and tagging operations, API schema conformance and endpoint definition, model interface wrapping, endpoint configuration testing in sandbox or staging environments, telemetry hook integration, invocation method registration including assigning resource identifiers and configuring API endpoints, or security and privacy validation128 The value chain network control low er system of claim 123, wherein the at least one model evaluation and risk assessment action includes at least one of: evaluation of foundational properties, evaluation of task performance, evaluation of safety and risk management, evaluation of alignment and compliance, evaluation of operational metrics, or evaluation of tooling and transparency.

129. The value chain network control tower system of claim 123, wherein the at least one model deployment action includes at least one of: production integration, API disclosure, scaling, selection of usage caps and quotas, cost and latency budgeting, selecting region-specific deployment strategies for data sovereignty, model endpoint wrapping, containerized environment hosting, standardized API access configuration, authentication via identity and access management systems or API keys, throttling policy application, metrics logging for monitoring and observability services, automatic scaling of model inference based on demand, elastic inference backend provisioning with scalable GPU-backed infrastructure clusters, horizontal scaling tuned to provider service level agreements, budget control implementation with alert triggering and usage stopping at thresholds, region-specific model deployment for data residency and compliance requirements, or geo-fencing enforcement by location-aware routing layers.

130. The value chain network control tow er system of claim 124, w herein the at least one model monitoring and observability action includes at least one of: logging of API calls, capturing payload sizes, capturing error responses, capturing latency buckets, inference latency tracking, token use per invocation tracking, model-specific usage metric tracking, model throughput tracking, model GPU utilization tracking, request rate tracking, data drift detection, monitoring prompt types, comparing model output distributions over time, implementing audit trails, implementing real-time feedback loops, abuse and misuse detection, or detection of anomalous usage.

131. The value chain network control tower system of claim 125, wherein the at least one model updating and retraining action includes at least one of: data pipeline refresh actions, fine-tuning and customization actions, model iteration and versioning actions, model retraining actions, model comparison with stored responses and metrics comparison, updating a retrieval augmented generation knowledge base, or conducting A / B testing between model versions.

132. The value chain network control tower system of claim 123, further comprising a digital twin system, wherein the digital twin system is configured to generate a digital twin of the set of candidate models.

133. The value chain network control tower system of claim 132, wherein the at least one model evaluation and risk assessment action includes performing digital twin simulation using the digital twin and a simulation system.

134. A method for executing a know your model system by a value chain network control tower, comprising: performing at least one model intake and registration action associated with a set of candidate models; performing at least one model evaluation and risk assessment action associated with the set of candidate models; and performing at least one model deployment action associated with the set of candidate models.

135. The method of claim 134, further comprising performing at least one model monitoring and observability action associated with the set of candidate models.

136. The method of claim 134, further comprising performing at least one model updating and retraining action associated with the set of candidate models.137 The method of claim 134, wherein the set of candidate models is associated with at least one of a demand prediction, a product lifecycle management action, an inventory management action, a robotic fleet management action, a shipping container fleet management action, a logistics action, a demand shaping action, or a procurement action138. The method of claim 134, wherein the at least one model intake and registration action includes at least one of: model documentation collection, model registration procedures, model metadata collection, input and output interface standardization, legal and licensing validation checks, model card collection and management, cataloging and tagging operations, API schema conformance and endpoint definition, model interface wrapping, endpoint configuration testing in sandbox or staging environments, telemetry hook integration, invocation method registration including assigning resource identifiers and configuring API endpoints, or security and privacy validation.

139. The method of claim 134, wherein the at least one model evaluation and risk assessment action includes at least one of: evaluation of foundational properties, evaluation of task performance, evaluation of safety and risk management, evaluation of alignment and compliance, evaluation of operational metrics, or evaluation of tooling and transparency.

140. The method of claim 134, wherein the at least one model deployment action includes at least one of: production integration, API disclosure, scaling, selection of usage caps and quotas, cost and latency budgeting, selecting region-specific deployment strategies for data sovereignty, model endpoint wrapping, containerized environment hosting, standardized API access configuration, authentication via identity and access management systems or API keys, throttling policy application, metrics logging for monitoring and observability sendees, automatic scaling of model inference based on demand, elastic inference backend provisioning with scalable GPU- backed infrastructure clusters, horizontal scaling tuned to provider service level agreements, budget controlimplementation with alert triggering and usage stopping at thresholds, region-specific model deployment for data residency and compliance requirements, or geo-fencing enforcement by location-aware routing layers.

141. The method of claim 135, wherein the at least one model monitoring and observability action includes at least one of: logging of API calls, capturing payload sizes, capturing error responses, capturing latency buckets, inference latency tracking, token use per invocation tracking, model-specific usage metric tracking, model throughput tracking, model GPU utilization tracking, request rate tracking, data drift detection, monitoring prompt types, comparing model output distributions over time, implementing audit trails, implementing real-time feedback loops, abuse and misuse detection, or detection of anomalous usage.

142. The method of claim 136, wherein the at least one model updating and retraining action includes at least one of: data pipeline refresh actions, fine-tuning and customization actions, model iteration and versioning actions, model retraining actions, model comparison with stored responses and metrics comparison, updating a retrieval augmented generation knowledge base, or conducting A / B testing between model versions.

143. The method of claim 134, further comprising generating a digital twin of tire set of candidate models using a digital twin system.

144. The method of claim 143, wherein the at least one model evaluation and risk assessment action includes performing digital twin simulation using the digital twin and a simulation system.VCN Control Tower System and Know Your Al Agent System145. A value chain network control tower system, comprising: a processor and memory configured to execute a know your Al agent system, wherein the know your Al agent system is configured to: observe configuration, input, internal processing, reasoning, choices, actions, and output of an Al agent; rationalize associations and relationships between internal and external features of the Al agent; verify accuracy and consistency of a conceptual model of one or more features of the Al agent; alter parameters, training, deployment, and interactions of the Al agent based on understanding derived from observation, rationalization, and verification; and regulate reasoning, actions, and outputs of the Al agent through enforcement of governance policies.

146. The system of claim 145, wherein the know your Al agent system is configured to apply understanding functions during at least one of: a conception stage involving design and planning of the Al agent, a development stage involving training and testing of the Al agent, a deployment stage involving storage and provisioning of the Al agent for specific tasks, or an interaction stage involving communication and data exchange between the Al agent and other entities.

147. The system of claim 145, wherein the know your Al agent system is configured to understand at least one of: performance of the Al agent including accuracy, efficiency, and reliability metrics, alignment of the Al agent with organizational objectives and ethical standards, context of the Al agent including operational environment and resource dependencies, or security of the Al agent including operational integrity and safeguarding measures148. The system of claim 145, wherein the know your Al agent system implements architectural and organizational techniques including trust zones that segregate Al agents based on information sensitivity or processing priority.

149. The system of claim 145, wherein the know your Al agent system implements supervision techniques including deployment of supervising Al agents configured to monitor at least one of reasoning, actions, interactions, or outputs of subject Al agents and generate assessments of compliance with governance requirements.150 The system of claim 145, wherein the know' your Al agent system implements investigative techniques including passive preserv ation of logs and records documenting stages of at least one of conceptualization, development, deployment, or interactions of the Al agent for regulatory' compliance and retrospective analysis.

151. The system of claim 145, wherein the know your Al agent system implements investigative techniques including at least one of active monitoring or analysis involving real-time observation of Al agent behavior, automated testing of Al agent responses, or proactive identification of potential issues or anomalies.

152. The system of claim 145, wherein the know your Al agent system implements investigative techniques including at least one of review and reflection on a history of behavior of the Al agent over time to detect changes, trends, or inconsistencies in behavior including instances of drift.

153. The system of claim 145, wherein tire know your Al agent system is configured to generate records including at least one of logs, reports, summaries, or annotations that preserve, describe, analyze, or document reasoning of Al agents in specific instances or general patterns of analysis.

154. The system of claim 145, wherein the know your Al agent system is configured to take actions based on understanding of the Al agent including at least one of generating notifications and alerts in response to discovering performance errors, misalignment, misbehavior, adversarial attacks, or security vulnerabilities, or intervening in current processing of the Al agent to override, supersede, correct, cancel, rollback, or terminate roles or actions155 A method for executing a know your Al agent system by a v alue chain network control tower system, comprising: observing configuration, input, internal processing, reasoning, choices, actions, and output of an Al agent: rationalizing associations and relationships between internal and external features of the Al agent; verifying accuracy and consistency of a conceptual model of one or more features of the Al agent; altering parameters, training, deployment, and interactions of the Al agent based on understanding derived from observation, rationalization, and verification; and regulating reasoning, actions, and outputs of the Al agent through enforcement of governance policies.

156. The method of claim 155, further comprising applying understanding functions during at least one of: a conception stage involving design and planning of the Al agent, a development stage involving training and testing of the Al agent, a deployment stage involving storage and provisioning of the Al agent for specific tasks, or an interaction stage involving communication and data exchange between the Al agent and other entities157. The method of claim 155, further comprising understanding at least one of: performance of the Al agent including at least one of accuracy, efficiency, and reliability metrics, alignment of the Al agent with organizational objectives and ethical standards, context of the Al agent including operational environment and resource dependencies, or security of the Al agent including operational integrity or safeguarding measures158. The method of claim 155, further comprising implementing architectural and organizational techniques including trust zones that segregate Al agents based on information sensitivity or processing priority.

159. The method of claim 155, further comprising implementing supervision techniques including deploying supervising Al agents configured to monitor reasoning, actions, interactions, or outputs of subject Al agents and generate assessments of compliance with governance requirements.

160. The method of claim 155, further comprising implementing investigative techniques including passive preservation of logs and records documenting stages of conceptualization, development, deployment, and interactions of the Al agent for regulatory compliance and retrospective analysis.161 The method of claim 155, further comprising implementing investigative techniques including active monitoring and analysis involving real-time observation of Al agent behavior, automated testing of Al agent responses, and proactive identification of potential issues or anomalies.

162. The method of claim 155, further comprising implementing investigative techniques including review and reflection on a history of behavior of the Al agent over time to detect changes, trends, and inconsistencies in behavior including instances of drift.

163. The method of claim 155, further comprising generating records including logs, reports, summaries, or annotations that preserve, describe, analyze, or document reasoning of Al agents in specific instances or general patterns of analysis.

164. The method of claim 155, further comprising taking actions based on understanding of tire Al agent including generating notifications and alerts in response to discovering performance errors, misalignment, misbehavior, adversarial attacks, or security vulnerabilities, and intervening in current processing of the Al agent to override, supersede, correct, cancel, rollback, or terminate roles or actions.VCN Control Tower System and Bias Detection System165. A value chain network control tower system, comprising: a processor and memory configured to execute a bias detection system, wherein the bias detection system is configured to: analyze a machine learning model across multiple dimensions of bias using dynamically adaptable evaluation frameworks; generate interactive bias profiles that map detected issues to specific model components and operational impact vectors.

166. The system of claim 165, wherein the bias detection system includes a multi-source bias definition repository that stores parameterized definitions of biases.

167. The system of claim 165, wherein the bias detection system includes an adaptive metric generator that synthesizes custom evaluation metrics by combining at least one base statistical measure with at least one context- aware adjustment factor.

168. The system of claim 165, wherein the bias detection system includes a cross-domain evaluator engine that executes at least one bias detection protocol.

169. The system of claim 165, wherein the bias detection system includes a dynamic reporting module that generates interactive bias profiles mapping detected issues to a specific model component.

170. The system of claim 165, wherein the bias detection system implements artificial intelligence systems configured to detect bias in the machine learning model through machine learning algorithms that identify bias patterns.

171. The system of claim 165, wherein the bias detection system implements artificial intelligence systems configured to detect at least one specific category of bias in the machine learning model, wherein the specific category of bias includes at least one of: demographic bias, geographic bias, temporal bias, or domain-specific bias patterns.

172. The system of claim 165, wherein the bias detection system implements multi-dimensional data bias checking that provides evaluation of potential biases across at least one of a demographic dimension or a geographic dimension.173 The system of claim 165, wherein the bias detection system implements automated alerting mechanisms174. The system of claim 165, wherein the bias detection system integrates with at least one regulatory compliance framework.

175. A method for detecting bias in a value chain network, comprising: analyzing a machine learning model across multiple dimensions of bias using dynamically adaptable evaluation frameworks; and generating interactive bias profiles that map detected issues to specific model components and operational impact vectors.

176. The method of claim 175, lurtlier comprising storing parameterized definitions of biases in a multi-source bias definition repository.

177. The method of claim 175, further comprising synthesizing custom evaluation metrics by combining at least one base statistical measure with at least one context-aware adjustment factor using an adaptive metric generator.

178. The method of claim 175, further comprising executing at least one bias detection protocol using a crossdomain evaluator engine.

179. The method of claim 175, further comprising generating interactive bias profiles mapping detected issues to a specific model component using a dynamic reporting module180 The method of claim 175, further comprising detecting bias in the machine learning model through machine learning algorithms that identify bias patterns using artificial intelligence systems.

181. The method of claim 175, further comprising detecting at least one specific category of bias in the machine learning model using artificial intelligence systems, wherein the specific category of bias includes at least one of: demographic bias, geographic bias, temporal bias, or domain-specific bias patterns.

182. The method of claim 175, further comprising implementing multi-dimensional data bias checking that provides evaluation of potential biases across at least one of a demographic dimension or a geographic dimensions.

183. The method of claim 175, further comprising implementing automated alerting mechanisms.

184. The method of claim 175, lurtlier comprising integrating with at least one regulatory compliance framework.VCN Control Tower System and Know Your Data System185. A value chain network control tower system, comprising: a processor and memory configured to execute a know your data system, wherein the know your data system is configured to: receive data instances from external data sources; extract metadata from the data instances to assess data provenance; evaluate trustworthiness of the data instances; detect anomalies in the data instances; validate integrity of the data instances by analyzing deviations across at least one historical dataset and at least one simulated environment;compare the data instances against historical data repositories to enable baseline comparisons for identifying statistical outliers; and generate data source scores for weighting the data instances186 The system of claim 185, wherein the know your data system is further configured to maintain a threat intelligence database that stores indicators of detected anomalies in the data instances.

187. The system of claim 185, wherein the know your data system is further configured to take mitigating actions upon detecting adversarial activity in data instances including at least one of data quarantine protocols or adaptive model reconfiguration routines.

188. The system of claim 185, wherein the external data sources include at least one of loT sensor networks transmitting environmental measurements, user devices submitting real-time reports via API endpoints, or distributed ledger nodes broadcasting transaction records.

189. The system of claim 185, wherein extracting metadata includes at least one of validating geolocation tags against GPS timestamps embedded in device-generated telemetry packets or verifying cryptographic signatures attached to industrial sensor readings using public keys associated with the sensors.

190. The system of claim 185, wherein detecting anomalies includes applying statistical analysis or pattern recognition algorithms to identify deviations from expected input characteristics of data using at least one of data normalization pipelines or feature extraction modules.

191. The system of claim 185, wherein the know your data system employs ensemble techniques that combine unsupervised and supervised models to flag discrepancies across comparison sources for anomaly detection192 The system of claim 185, wherein the know your data system integrates with device registries that store at least one of cryptographic attestation keys or hardware profiles used during source verification of the data instances.

193. The system of claim 185, wherein the data source scores are used to weight the data instances before transmission to Al decision-making models, and data instances scoring below a dynamically adjusted confidence threshold are disregarded.

194. The system of claim 185, wherein the know your data system is configured to detect fake data injection attempts or sockpuppeting behavior in the data instances using pattern recognition and anomaly detection algorithms.

195. A method for managing data in a value chain network, comprising: receiving data instances from external data sources; extracting metadata from the data instances to assess data provenance; evaluating trustworthiness of the data instances; detecting anomalies in the data instances; validating integrity of the data instances by analyzing deviations across at least one historical dataset and at least one simulated environment; comparing the data instances against historical data repositories to enable baseline comparisons for identifying statistical outliers; and generating data source scores for weighting the data instances.

196. The method of claim 195, further comprising maintaining a threat intelligence database that stores indicators of detected anomalies in the data instances.

197. The method of claim 195, further comprising taking mitigating actions upon detecting adversarial activity in data instances, including at least one of data quarantine protocols or adaptive model reconfiguration routines.

198. The method of claim 195, wherein the external data sources include at least one of loT sensor networks transmitting environmental measurements, user devices submitting real-time reports via API endpoints, or distributed ledger nodes broadcasting transaction records.

199. The method of claim 195, wherein extracting metadata includes at least one of validating geolocation tags against GPS timestamps embedded in device-generated telemetry packets or verifying cryptographic signatures attached to industrial sensor readings using public keys associated with the sensors.

200. The method of claim 195, wherein detecting anomalies includes applying statistical analysis or pattern recognition algorithms to identify deviations from expected input characteristics of data using at least one of data normalization pipelines or feature extraction modules.

201. The method of claim 195, further comprising employing ensemble techniques that combine unsupervised and supervised models to flag discrepancies across comparison sources for anomaly detection.

202. The method of claim 195, further comprising integrating with device registries that store cryptographic attestation keys and hardware profiles used during source verification of the data instances.

203. The method of claim 195, wherein the data source scores are used to weight the data instances before transmission to Al decision-making models, and data instances scoring below a dynamically adjusted confidence threshold are disregarded.204 The method of claim 195, further comprising detecting fake data injection attempts or sockpuppeting behavior in the data instances using at least one of pattern recognition or anomaly detection algorithms Procurement System and Know Your Model System205. A procurement system, comprising: a processor and memory configured to execute a know your model system, wherein the know your model system is configured to: perform at least one model intake and registration action associated with a set of candidate models; perform at least one model evaluation and risk assessment action associated with the set of candidate models; and perform at least one model deployment action associated with the set of candidate models.

206. The procurement system of claim 205, wherein tire know your model system is further configured to perform at least one model monitoring and observability action associated with the set of candidate models.

207. The procurement system of claim 205, wherein the know your model system is further configured to perform at least one model updating and retraining action associated with the set of candidate models.

208. The procurement system of claim 205, wherein the at least one model intake and registration action includes at least one of: model documentation collection, model registration procedures, model metadata collection, input and output interface standardization, legal and licensing validation checks, model card collection and management, cataloging and tagging operations, API schema conformance and endpoint definition, model interface wrapping, endpoint configuration testing in sandbox or staging environments, telemetry hook integration, invocation method registration including assigning resource identifiers and configuring API endpoints, or security and privacy validation.

209. The procurement system of claim 205, wherein the at least one model evaluation and risk assessment action includes at least one of: evaluation of foundational properties, evaluation of task performance, evaluation ofsafety and risk management, evaluation of alignment and compliance, evaluation of operational metrics, or evaluation of tooling and transparency.

210. The procurement system of claim 205, wherein the at least one model deployment action includes at least one of: production integration, API disclosure, scaling, selection of usage caps and quotas, cost and latency budgeting, selecting region-specific deployment strategies for data sovereignty, model endpoint wrapping, containerized environment hosting, standardized API access configuration, authentication via identity and access management systems or API keys, throttling policy application, metrics logging for monitoring and observability services, automatic scaling of model inference based on demand, elastic inference backend provisioning with scalable GPU-backed infrastructure clusters, horizontal scaling tuned to provider service level agreements, budget control implementation with alert triggering and usage stopping at thresholds, region- specific model deployment for data residency and compliance requirements, or geo-fencing enforcement by location-aware routing layers.

211. The procurement system of claim 206, wherein the at least one model monitoring and observability action includes at least one of: logging of API calls, capturing pay load sizes, capturing error responses, capturing latency buckets, inference latency tracking, token use per invocation tracking, model-specific usage metric tracking, model throughput tracking, model GPU utilization tracking, request rate tracking, data drift detection, monitoring prompt types, comparing model output distributions over time, implementing audit trails, implementing real-time feedback loops, abuse and misuse detection, or detection of anomalous usage.

212. The procurement system of claim 207, wherein the at least one model updating and retraining action includes at least one of: data pipeline refresh actions, fine-tuning and customization actions, model iteration and versioning actions, model retraining actions, model comparison with stored responses and metrics comparison, updating a retrieval augmented generation knowledge base, or conducting A / B testing between model versions213. The procurement system of claim 205, further comprising a digital twin system, wherein the digital twin system is configured to generate a digital twin of the set of candidate models.

214. The procurement system of claim 213, wherein the at least one model evaluation and risk assessment action includes performing a simulation using the digital twin.

215. A method for executing a know your model system by a procurement system, comprising: performing at least one model intake and registration action associated with a set of candidate models; performing at least one model evaluation and risk assessment action associated with the set of candidate models; and performing at least one model deployment action associated with the set of candidate models.

216. The method of claim 215, further comprising performing at least one model monitoring and observability action associated with the set of candidate models.

217. The method of claim 215, further comprising performing at least one model updating and retraining action associated with the set of candidate models.218 The method of claim 215, wherein the at least one model intake and registration action includes at least one of: model documentation collection, model registration procedures, model metadata collection, input and output interface standardization, legal and licensing validation checks, model card collection and management, cataloging and tagging operations, API schema conformance and endpoint definition, model interface wrapping, endpoint configuration testing in sandbox or staging environments, telemetry hook integration, invocation methodregistration including assigning resource identifiers and configuring API endpoints, or security and privacy validation.

219. The method of claim 215, wherein the at least one model evaluation and risk assessment action includes at least one of: evaluation of foundational properties, evaluation of task performance, evaluation of safety and risk management, evaluation of alignment and compliance, evaluation of operational metrics, or evaluation of tooling and transparency.

220. The method of claim 215, wherein the at least one model deployment action includes at least one of: production integration, API disclosure, scaling, selection of usage caps and quotas, cost and latency budgeting, selecting region-specific deployment strategies for data sovereignty, model endpoint wrapping, containerized environment hosting, standardized API access configuration, authentication via identity and access management systems or API keys, throttling policy application, metrics logging for monitoring and observability services, automatic scaling of model inference based on demand, elastic inference backend provisioning with scalable GPU- backed infrastructure clusters, horizontal scaling tuned to provider service level agreements, budget control implementation with alert triggering and usage stopping at thresholds, region-specific model deployment for data residency and compliance requirements, or geo-fencing enforcement by location-aware routing layers.

221. The method of claim 216, wherein the at least one model monitoring and observability action includes at least one of: logging of API calls, capturing payload sizes, capturing error responses, capturing latency buckets, inference latency tracking, token use per invocation tracking, model-specific usage metric tracking, model throughput tracking, model GPU utilization tracking, request rate tracking, data drift detection, monitoring prompt types, comparing model output distributions over time, implementing audit trails, implementing real-time feedback loops, abuse and misuse detection, or detection of anomalous usage.

222. The method of claim 217, wherein the at least one model updating and retraining action includes at least one of: data pipeline refresh actions, fine-tuning and customization actions, model iteration and versioning actions, model retraining actions, model comparison with stored responses and metrics comparison, updating a retrieval augmented generation knowledge base, or conducting A / B testing between model versions.

223. The method of claim 215, further comprising generating a digital twin of tire set of candidate models by a digital twin system.

224. The method of claim 223, wherein the at least one model evaluation and risk assessment action includes performing a simulation using the digital twin.Logistics System and Know Your Model System225. A logistics system, comprising: a processor and memory configured to execute a know your model system, wherein tire know your model system is configured to: perform at least one model intake and registration action associated with a set of candidate models; perform at least one model evaluation and risk assessment action associated with the set of candidate models; and perform at least one model deployment action associated with the set of candidate models.

226. The logistics system of claim 225, wherein the know your model system is further configured to perform at least one model monitoring and observability action associated with the set of candidate models.

227. The logistics system of claim 225, wherein the know your model system is further configured to perform at least one model updating and retraining action associated with the set of candidate models.

228. The logistics system of claim 225, wherein the at least one model intake and registration action includes at least one of: model documentation collection, model registration procedures, model metadata collection, input and output interface standardization, legal and licensing validation checks, model card collection and management, cataloging and tagging operations, API schema conformance and endpoint definition, model interface wrapping, endpoint configuration testing in sandbox or staging environments, telemetry hook integration, invocation method registration including assigning resource identifiers and configuring API endpoints, or security and privacy validation.

229. The logistics system of claim 225, wherein the at least one model evaluation and risk assessment action includes at least one of: evaluation of foundational properties, evaluation of task performance, evaluation of safety and risk management, evaluation of alignment and compliance, evaluation of operational metrics, or evaluation of tooling and transparency.

230. The logistics system of claim 225, wherein the at least one model deployment action includes at least one of: production integration, API disclosure, scaling, selection of usage caps and quotas, cost and latency budgeting, selecting region-specific deployment strategies for data sovereignty, model endpoint wrapping, containerized environment hosting, standardized API access configuration, authentication via identity and access management systems or API keys, throttling policy application, metrics logging for monitoring and observability services, automatic scaling of model inference based on demand, elastic inference backend provisioning with scalable GPU- backed infrastructure clusters, horizontal scaling tuned to provider service level agreements, budget control implementation w ith alert triggering and usage stopping at thresholds, region-specific model deployment for data residency and compliance requirements, or geo-fencing enforcement by location-aware routing layers231 . The logistics system of claim 226, wherein the at least one model monitoring and observability action includes at least one of: logging of API calls, capturing payload sizes, capturing error responses, capturing latency buckets, inference latency tracking, token use per invocation tracking, model-specific usage metric tracking, model throughput tracking, model GPU utilization tracking, request rate tracking, data drift detection, monitoring prompt types, comparing model output distributions over time, implementing audit trails, implementing real-time feedback loops, abuse and misuse detection, or detection of anomalous usage.

232. The logistics system of claim 227, wherein the at least one model updating and retraining action includes at least one of: data pipeline refresh actions, fine-tuning and customization actions, model iteration and versioning actions, model retraining actions, model comparison with stored responses and metrics comparison, updating a retrieval augmented generation knowledge base, or conducting A / I3 testing between model versions.

233. The logistics system of claim 225, further comprising a digital twin system, wherein the digital twin system is configured to generate a digital twin of the set of candidate models.

234. The logistics system of claim 233, wherein the at least one model evaluation and risk assessment action includes performing a simulation using the digital twin.235 A method for executing a know your model system by a logistics system, comprising: performing at least one model intake and registration action associated with a set of candidate models; performing at least one model evaluation and risk assessment action associated with the set of candidate models: and performing at least one model deployment action associated with the set of candidate models.

236. The method of claim 235, further comprising performing at least one model monitoring and observability action associated with the set of candidate models.

237. The method of claim 235, further comprising performing at least one model updating and retraining action associated with the set of candidate models238. The method of claim 235, wherein the at least one model intake and registration action includes at least one of: model documentation collection, model registration procedures, model metadata collection, input and output interface standardization, legal and licensing validation checks, model card collection and management, cataloging and tagging operations, API schema conformance and endpoint definition, model interface wrapping, endpoint configuration testing in sandbox or staging environments, telemetry hook integration, invocation method registration including assigning resource identifiers and configuring API endpoints, or security and privacy validation.

239. The method of claim 235, wherein the at least one model evaluation and risk assessment action includes at least one of: evaluation of foundational properties, evaluation of task performance, evaluation of safety and risk management, evaluation of alignment and compliance, evaluation of operational metrics, or evaluation of tooling and transparency.

240. The method of claim 235, wherein the at least one model deployment action includes at least one of: production integration, API disclosure, scaling, selection of usage caps and quotas, cost and latency budgeting, selecting region-specific deployment strategies for data sovereignty, model endpoint wrapping, containerized environment hosting, standardized API access configuration, authentication via identity and access management systems or API keys, throttling policy application, metrics logging for monitoring and observability sendees, automatic scaling of model inference based on demand, elastic inference backend provisioning with scalable GPU- backed infrastructure clusters, horizontal scaling tuned to provider service level agreements, budget control implementation with alert triggering and usage stopping at thresholds, region-specific model deployment for data residency and compliance requirements, or geo-fencing enforcement by location-aware routing layers.

241. The method of claim 236, wherein the at least one model monitoring and observability action includes at least one of: logging of API calls, capturing payload sizes, capturing error responses, capturing latency buckets, inference latency tracking, token use per invocation tracking, model-specific usage metric tracking, model throughput tracking, model GPU utilization tracking, request rate tracking, data drift detection, monitoring prompt types, comparing model output distributions over time, implementing audit trails, implementing real-time feedback loops, abuse and misuse detection, or detection of anomalous usage.

242. The method of claim 237, wherein the at least one model updating and retraining action includes at least one of: data pipeline refresh actions, fine-tuning and customization actions, model iteration and versioning actions, model retraining actions, model comparison with stored responses and metrics comparison, updating a retrieval augmented generation knowledge base, or conducting A / B testing between model versions.243 The method of claim 235, further comprising generating a digital twin of the set of candidate models using a digital twin system.

244. The method of claim 243, wherein the at least one model evaluation and risk assessment action includes performing a simulation using the digital twin.Inventory Management System and Know Your Model System245. An inventory management system, comprising: a processor and memory configured to execute a know your model system, wherein tire know your model system is configured to: perform at least one model intake and registration action associated with a set of candidate models; perform at least one model evaluation and risk assessment action associated with the set of candidate models; and perform at least one model deployment action associated with the set of candidate models.

246. The inventory' management system of claim 245, wherein the know your model system is further configured to perform at least one model monitoring and observability action associated with the set of candidate models.

247. The inventory management system of claim 245, wherein the know your model system is further configured to perform at least one model updating and retraining action associated with the set of candidate models.

248. The inventory management system of claim 245, wherein the at least one model intake and registration action includes at least one of: model documentation collection, model registration procedures, model metadata collection, input and output interface standardization, legal and licensing validation checks, model card collection and management, cataloging and tagging operations, API schema conformance and endpoint definition, model interface wrapping, endpoint configuration testing in sandbox or staging environments, telemetry hook integration, invocation method registration including assigning resource identifiers and configuring API endpoints, or security and privacy validation.249 The inventory management system of claim 245, wherein the at least one model evaluation and risk assessment action includes at least one of: evaluation of foundational properties, evaluation of task performance, evaluation of safety and risk management, evaluation of alignment and compliance, evaluation of operational metrics, or evaluation of tooling and transparency.

250. The inventory' management system of claim 245, w herein the at least one model deployment action includes at least one of: production integration, API disclosure, scaling, selection of usage caps and quotas, cost and latency budgeting, selecting region-specific deployment strategies for data sovereignty, model endpoint wrapping, containerized environment hosting, standardized API access configuration, authentication via identity and access management systems or API keys, throttling policy application, metrics logging for monitoring and observability services, automatic scaling of model inference based on demand, elastic inference backend provisioning with scalable GPU-backed infrastructure clusters, horizontal scaling tuned to provider service level agreements, budget control implementation with alert triggering and usage stopping at thresholds, region- specific model deployment for data residency and compliance requirements, or geo-fencing enforcement by location-aware routing layers.

251. The inventory management system of claim 246, wherein the at least one model monitoring and observability' action includes at least one of: logging of API calls, capturing payload sizes, capturing error responses, capturing latency buckets, inference latency tracking, token use per invocation tracking, model-specific usage metric tracking, model throughput tracking, model GPU utilization tracking, request rate tracking, data drift detection, monitoring prompt types, comparing model output distributions over time, implementing audit trails, implementing real-time feedback loops, abuse and misuse detection, or detection of anomalous usage.

252. The inventory' management system of claim 247, w herein the at least one model updating and retraining action includes at least one of: data pipeline refresh actions, fine-tuning and customization actions, model iterationand versioning actions, model retraining actions, model comparison with stored responses and metrics comparison, updating a retrieval augmented generation knowledge base, or conducting A / B testing between model versions253. The inventor;' management system of claim 245, further comprising a digital twin system, wherein the digital twin system is configured to generate a digital twin of the set of candidate models254. The inventory management system of claim 253, wherein the at least one model evaluation and risk assessment action includes performing a simulation using the digital twin.

255. A method for executing a know your model system by an inventory management system, comprising: performing at least one model intake and registration action associated with a set of candidate models; performing at least one model evaluation and risk assessment action associated with the set of candrdate models; and performing at least one model deployment action associated with the set of candidate models.

256. The method of claim 255, further comprising performing at least one model monitoring and observability action associated with the set of candidate models.

257. The method of claim 255, further comprising performing at least one model updating and retraining action associated with the set of candidate models.

258. The method of claim 255, wherein the at least one model intake and registration action includes at least one of: model documentation collection, model registration procedures, model metadata collection, input and output interface standardization, legal and licensing validation checks, model card collection and management, cataloging and tagging operations, API schema conformance and endpoint definition, model interface wrapping, endpoint configuration testing in sandbox or staging environments, telemetry hook integration, invocation method registration including assigning resource identifiers and configuring API endpoints, or security and privacy validation.

259. The method of claim 255, wherein the at least one model evaluation and risk assessment action includes at least one of: evaluation of foundational properties, evaluation of task performance, evaluation of safety and risk management, evaluation of alignment and compliance, evaluation of operational metrics, or evaluation of tooling and transparency.

260. The method of claim 255, wherein the at least one model deployment action includes at least one of: production integration, API disclosure, scaling, selection of usage caps and quotas, cost and latency budgeting, selecting region-specific deployment strategies for data sovereignty, model endpoint wrapping, containerized environment hosting, standardized API access configuration, authentication via identity and access management systems or API keys, throttling policy application, metrics logging for monitoring and observability services, automatic scaling of model inference based on demand, elastic inference backend provisioning with scalable GPU- backed infrastructure clusters, horizontal scaling tuned to provider service level agreements, budget control implementation with alert triggering and usage stopping at thresholds, region-specific model deployment for data residency and compliance requirements, or geo-fencing enforcement by location-aware routing layers261 . The method of claim 256, wherein the at least one model monitoring and observability action includes at least one of: logging of API calls, capturing payload sizes, capturing error responses, capturing latency buckets, inference latency tracking, token use per invocation tracking, model-specific usage metric tracking, model throughput tracking, model GPU utilization tracking, request rate tracking, data drift detection, monitoring prompttypes, comparing model output distributions over time, implementing audit trails, implementing real-time feedback loops, abuse and misuse detection, or detection of anomalous usage.

262. The method of claim 257, wherein the at least one model updating and retraining action includes at least one of: data pipeline refresh actions, fine-tuning and customization actions, model iteration and versioning actions, model retraining actions, model comparison with stored responses and metrics comparison, updating a retrieval augmented generation knowledge base, or conducting A / B testing between model versions.

263. The method of claim 255, further comprising generating a digital twin of the set of candidate models by a digital twin system.

264. The method of claim 263, wherein the at least one model evaluation and risk assessment action includes performing a simulation using the digital twin.Digital Product Network System and Know Your Physical Al System265. A digital product network system, comprising: a processor and memory configured to execute a know your physical Al system, wherein tire know your physical Al sy stem is configured to: establish an initial connection with a candidate physical Al system; perform at least one discovery' and authentication action for the candidate physical Al system; receive and process at least one capability declaration from the candidate physical Al system; execute at least one compliance validation action for the candidate physical Al system; perform at least one contextual configuration and operational parameterization action associated with the candidate physical Al system; and perform at least one deployment action for the candidate physical Al system.

266. The digital product network system of claim 265, wherein the discovery' and authentication action includes receiving by the candidate physical Al system a discovery' packet including cryptographic identity credentials, device serial number, or pre-provisioned public key infrastructure (PKI) certificate via a secure networking protocol.

267. The digital product network system of claim 265, wherein the know your physical Al system performs hardware attestation using trusted platform modules or secure enclave cryptographic signatures to verify device integrity or authenticity of tire candidate physical Al system.

268. The digital product network system of claim 265, wherein the at least one capability declaration includes a machine-readable manifest describing at least one of: supported Al tasks, hardware specifications, operational constraints, or certified Al model signatures.

269. The digital product network system of claim 265, wherein tire at least one compliance validation action includes comparing a received capability profile against at least one of a stored organizational governance policy or an external regulatory framework.270 The digital product network system of claim 265, wherein the at least one contextual configuration and operational parameterization action includes securely transmitting via encrypted over-the-air (OTA) update channels at least one of dynamic scheduling parameters or interaction policies.

271. The digital product network system of claim 265, wherein the know your physical Al system integrates the candidate physical Al system into a centralized orchestration and control plane that exposes at least one of: a dynamic task assignment API, a real-time telemetry collection service, or a health status subscription channel.

272. The digital product network system of claim 265, wherein tire know your physical Al system performs initial sandbox validation and simulation-based testing of the candidate physical Al system prior to deployment.

273. The digital product network system of claim 265, wherein the know your physical Al system incorporates a distributed trust ledger component to immutably record onboarding steps, policy validations, operational events, or update transactions using block chain or tamper-evident cryptographic data structures274. The digital product network system of claim 265, wherein the know your physical Al system is further configured to integrate with a digital twin system to generate a digital twin of the candidate physical Al system for simulation-based testing or performance validation.

275. The digital product network system of claim 265, wherein the know your physical Al system is further configured to integrate with a know your model system to perform at least one of: a model intake and registration action, a model evaluation and risk assessment action, or a model deployment action for a set of models associated with the candidate physical Al system.

276. The digital product network system of claim 265, wherein the know your physical Al system is further configured to integrate with a know your sensor system to perform at least one of a sensor validation and authentication action, a sensor monitoring and anomaly detection action, or a sensor calibration and optimization action for a set of sensors associated with the candidate physical Al system.

277. A method for executing a know your physical Al system by a digital product network, comprising: establishing an initial connection with a candidate physical Al system; performing at least one discovery and authentication action for the candidate physical Al system; receiving and processing at least one capability declaration from the candidate physical Al system; executing at least one compliance validation action for the candidate physical Al system; performing at least one contextual configuration and operational parameterization action associated with the candidate physical Al system; and performing at least one deployment action for the candidate physical Al system.

278. The method of claim 277, wherein the at least one discovery and authentication action includes the candidate physical Al system broadcasting a discovery packet including cryptographic identity credentials, device serial number, or pre-provisioned public key infrastructure (PKI) certificate via a secure networking protocol.

279. The method of claim 277, wherein performing the at least one discovery and authentication action includes performing hardware attestation using trusted platform modules or secure enclave cryptographic signatures to verify device integrity or authenticity of the candidate physical Al system.

280. The method of claim 277, wherein the at least one capability declaration includes a machine-readable manifest describing at least one of: supported Al tasks, hardware specifications, operational constraints, or certified Al model signatures.

281. The method of claim 277, wherein the at least one compliance validation action includes comparing a received capability profile against at least one of a stored organizational governance policy or an external regulator;' framework.

282. The method of claim 277, wherein the at least one contextual configuration and operational parameterization action includes at least one of: securely transmitting dynamic scheduling parameters or interaction policies via encrypted over-the-air (OTA) update channels.

283. The method of claim 277, further comprising integrating the candidate physical Al system into a centralized orchestration and control plane that exposes at least one of: a dynamic task assignment API, a real-time telemetry collection service, or a health status subscription channel.284 The method of claim 277, further comprising performing initial sandbox validation and simulation-based testing of the candidate physical Al system prior to deployment.

285. The method of claim 277, further comprising immutably recording onboarding steps, policy validations, operational events, or update transactions using blockchain or tamper-evident cryptographic data structures via a distributed trust ledger component.

286. The method of claim 277, further comprising integrating with a digital twin system to generate a digital twin of the candidate physical Al system for simulation-based testing or performance validation.

287. The method of claim 277, further comprising integrating with a know your model system to perform at least one of: a model intake and registration action, a model evaluation and risk assessment action, or a model deployment action for a set of models associated with the candidate physical Al system.

288. The method of claim 277, further comprising integrating with a know your sensor system to perform at least one of a sensor validation and authentication action, a sensor monitoring and anomaly detection action, or a sensor calibration and optimization action, for a set of sensors associated with the candidate physical Al system. VCN System and Al Agent with User Prompt289. A value chain network system comprising: at least one processor; at least one memory storing instructions that, when executed by the at least one processor, cause the system to: receive a user prompt that includes a request involving at least one value chain network action; generate, based on the user prompt, an action plan using an Al agent that includes a large language model and a tool set; and automatically execute the at least one action in accordance with the action plan, wherein the Al agent interfaces with applications, devices, and resources to perform the at least one action.

290. The system of claim 289, wherein the at least one action includes: raw material sourcing, supplier selection and qualification, supplier performance monitoring, contract negotiation and procurement, logistics and inbound transportation, supplier risk management, inventory replenishment planning, component and part quality inspections, just-in-time delivery coordination, supply chain visibility and tracking, demand forecasting and sales planning, production scheduling, manufacturing and assembly operations, equipment maintenance and reliability, quality control and testing, process optimization and continuous improvement, waste reduction and lean practices, digital twin or simulation-driven planning, energy and resource management, finished goods inventory' management, packaging and labeling, order fulfillment and warehouse operations, distribution network planning, transportation and logistics optimization, last-mile delivery management, returns and reverse logistics, export compliance and documentation, customer needs analysis, customization and configuration sendees, marketing and demand generation, channel partner management, sales enablement, customer relationship management, after-sales support and warranty services, field service and maintenance, customer feedback collection, loyalty program and retention strategies, enterprise resource planning integration, data governance and data quality management, cybersecurity and network protection, legal and compliance monitoring, financial planning and cost control, sustainability and ESG compliance, workforce training and talent development, risk assessment and businesscontinuity planning, cross-organization collaboration platforms, Al-driven insights and predictive analytics, supplier and partner ecosystem innovation programs, real-time KPI and performance monitoring, scenario simulation and impact analysis, dynamic reconfiguration of resources, autonomous negotiation and contract execution, multi-party data exchange and trusted ledger operations, Al -based anomaly detection and mitigation, automated regulatory reporting and audit trails, procurement, fleet management, additive manufacturing, or continuous value stream mapping and optimization.

291. The system of claim 289, wherein the Al agent includes an Al agent process that includes an iterative agent loop that enables processing of workflows with automated task decomposition and planning.

292. The system of claim 289, wherein the large language model is configured to determine actions and generate responses according to a system prompt that describes capabilities and use of the tools in the tool set.

293. The system of claim 289, wherein the Al agent incorporates self-critique capabilities to detect and correct hallucinations or fabricated facts during processing of user prompts or generation of outputs.

294. The system of claim 289, wherein the system is integrated with a digital twin system configured to generate at least one digital twin of at least one value chain network asset or process.

295. The system of claim 294, wherein the digital twin system is configured to execute at least one simulation on the at least one digital twin to perform scenario simulation or impact analysis for the value chain network.

296. The system of claim 289, wherein the tool set comprises at least one of: executable programs, APIs, or communication interfaces for performing value chain network tasks.297 The system of claim 289, wherein the Al agent is configured to invoke a sequence of tools from the tool set to fulfill the request indicated in the user prompt298. The system of claim 289, wherein the system comprises a governance and analysis system configured to monitor and regulate the Al agent's performance of value chain network tasks to ensure compliance with organizational policies and regulatory frameworks299. A method for executing value chain network tasks by an Al agent, comprising: receiving a user prompt that includes a request involving at least one value chain network action: generating, based on the user prompt, an action plan using an Al agent that includes a large language model and a tool set; and automatically executing at least one action in accordance with the action plan, wherein tire Al agent interfaces with at least one of applications, devices, and resources to perform the at least one action.

300. The method of claim 299, wherein the at least one action includes: raw material sourcing, supplier selection and qualification, supplier performance monitoring, contract negotiation and procurement, logistics and inbound transportation, supplier risk management, inventory replenishment planning, component and part quality inspections, just-in-time delivery coordination, supply chain visibility and tracking, demand forecasting and sales planning, production scheduling, manufacturing and assembly operations, equipment maintenance and reliability, quality control and testing, process optimization and continuous improvement, waste reduction and lean practices, digital twin or simulation-driven planning, energy and resource management, finished goods inventory management, packaging and labeling, order fulfillment and warehouse operations, distribution network planning, transportation and logistics optimization, last-mile delivery management, returns and reverse logistics, export compliance and documentation, customer needs analysis, customization and configuration services, marketing and demand generation, channel partner management, sales enablement, customer relationship management, after-salessupport and warranty sendees, field sendee and maintenance, customer feedback collection, loyalty program and retention strategies, enterprise resource planning integration, data governance and data quality management, cybersecurity' and network protection, legal and compliance monitoring, financial planning and cost control, sustainability and ESG compliance, workforce training and talent development, risk assessment and business continuity planning, cross-organization collaboration platforms, Al-driven insights and predictive analytics, supplier and partner ecosystem innovation programs, real-time KPI and performance monitoring, scenario simulation and impact analysis, dynamic reconfiguration of resources, autonomous negotiation and contract execution, multi-party data exchange and trusted ledger operations, Al -based anomaly detection and mitigation, automated regulatory reporting and audit trails, procurement, fleet management, additive manufacturing, or continuous value stream mapping and optimization.

301. The method of claim 299, wherein the Al agent includes an iterative agent loop that enables processing of workflows with automated task decomposition or plaiming.

302. The method of claim 299, wherein the large language model is configured to determine actions and generate responses according to a system prompt that describes capabilities and use of the tools in the tool set.

303. The method of claim 299, wherein the Al agent incorporates self-critique capabilities to detect and correct hallucinations or fabricated facts during processing of user prompts or generation of outputs.

304. The method of claim 299, further comprising integrating with a digital twin system configured to generate at least one digital twin of at least one value chain network asset or process.305 The method of claim 304, further comprising executing at least one simulation on the at least one digital twin to perform scenario simulation and impact analysis for the value chain network306. The method of claim 299, wherein the tool set comprises at least one of: executable programs, APIs, and communication interfaces for performing value chain network tasks.

307. The method of claim 299, wherein the Al agent is configured to invoke a sequence of tools from the tool set to fulfill the request indicated in the user prompt.

308. The method of claim 299, further comprising monitoring and regulating the Al agent's performance of value chain network tasks using a governance and analysis system to ensure compliance with organizational policies and regulatory frameworks.VCN System and Al Agent with Input Data309. A value chain network system comprising: at least one processor; at least one memory storing instructions that, when executed by the at least one processor, cause the system to: receive input data from one or more data sources; generate, based on the input data, an action plan using an Al agent that includes a large language model and a tool set; and automatically execute at least one action in accordance with the action plan, wherein the Al agent interfaces with at least one of applications, devices, and resources to perform the at least one action.

310. The system of claim 309, wherein the at least one action includes: raw material sourcing, supplier selection and qualification, supplier performance monitoring, contract negotiation and procurement, logistics and inbound transportation, supplier risk management, inventory replenishment planning, component and part quality inspections, just-in-time delivery coordination, supply chain visibility and tracking, demand forecasting and salesplanning, production scheduling, manufacturing and assembly operations, equipment maintenance and reliability, quality control and testing, process optimization and continuous improvement, waste reduction and lean practices, digital twin or simulation-driven planning, energy and resource management, finished goods inventory management, packaging and labeling, order fulfillment and warehouse operations, distribution network planning, transportation and logistics optimization, last-mile delivery management, returns and reverse logistics, export compliance and documentation, customer needs analysis, customization and configuration services, marketing and demand generation, channel partner management, sales enablement, customer relationship management, after-sales support and warranty services, field service and maintenance, customer feedback collection, loyalty program and retention strategies, enterprise resource planning integration, data governance and data quality management, cybersecurity and network protection, legal and compliance monitoring, financial planning and cost control, sustainability and ESG compliance, workforce training and talent development, risk assessment and business continuity planning, cross-organization collaboration platforms, Al-driven insights and predictive analytics, supplier and partner ecosystem innovation programs, real-time KPI and performance monitoring, scenario simulation and impact analysis, dynamic reconfiguration of resources, autonomous negotiation and contract execution, multi-party data exchange and trusted ledger operations, Al-based anomaly detection and mitigation, automated regulatory' reporting and audit trails, procurement, fleet management, additive manufacturing, or continuous value stream mapping and optimization.

311. The system of claim 309, wherein the Al agent includes an iterative agent loop that enables processing of workflows with automated task decomposition or planning312 The system of claim 309, wherein the large language model is configured to determine actions and generate responses according to a system prompt that describes capabilities and use of the tools in the tool set.

313. The system of claim 309, wherein the Al agent incorporates self-critique capabilities to detect and correct hallucinations or fabricated facts during processing of input data or generation of outputs.

314. The system of claim 309, wherein the system is integrated with a digital twin system configured to generate at least one digital twin of at least one value chain network asset or process.

315. The system of claim 314, wherein the digital twin system is configured to execute at least one simulation on the at least one digital twin to perform scenario simulation and impact analysis for the value chain network.

316. The system of claim 309, wherein the tool set comprises at least one of: executable programs, APIs, or communication interfaces for performing value chain network tasks.

317. The system of claim 309, wherein the Al agent is configured to invoke a sequence of tools from the tool set to fulfill a request indicated in the input data.

318. The system of claim 309, wherein the system comprises a governance and analysis system configured to monitor and regulate the Al agent's performance to ensure compliance with organizational policies or regulatory frameworks.319 A method for executing v alue chain network tasks, comprising : receiving input data from one or more data sources; generating, based on the input data, an action plan using an Al agent that includes a large language model and a tool set; and automatically executing at least one action in accordance with the action plan, wherein the Al agent interfaces with at least one of applications, devices, and resources to perform the at least one action.

320. The method of claim 319, wherein the at least one action includes: raw material sourcing, supplier selection and qualification, supplier performance monitoring, contract negotiation and procurement, logistics and inbound transportation, supplier risk management, inventory replenishment planning, component and part quality inspections, just-in-time delivery coordination, supply chain visibility and tracking, demand forecasting and sales planning, production scheduling, manufacturing and assembly operations, equipment maintenance and reliability, quality control and testing, process optimization and continuous improvement, waste reduction and lean practices, digital twin or simulation-driven planning, energy and resource management, finished goods inventory management, packaging and labeling, order fulfillment and warehouse operations, distribution network planning, transportation and logistics optimization, last-mile delivery management, returns and reverse logistics, export compliance and documentation, customer needs analysis, customization and configuration services, marketing and demand generation, channel partner management, sales enablement, customer relationship management, after-sales support and warranty services, field service and maintenance, customer feedback collection, loyalty program and retention strategies, enterprise resource planning integration, data governance and data quality management, cybersecurity and network protection, legal and compliance monitoring, financial planning and cost control, sustainability and ESG compliance, workforce training and talent development, risk assessment and business continuity planning, cross-organization collaboration platforms, Al-driven insights and predictive analytics, supplier and partner ecosystem innovation programs, real-time KPI and performance monitoring, scenario simulation and impact analysis, dynamic reconfiguration of resources, autonomous negotiation and contract execution, multi-party data exchange and trusted ledger operations, Al -based anomaly detection and mitigation, automated regulatory reporting and audit trails, procurement, fleet management, additive manufacturing, or continuous value stream mapping and optimization.

321. The method of claim 319, wherein the Al agent includes an iterative agent loop that enables processing of workflows with automated task decomposition or planning.

322. The method of claim 319, wherein the large language model is configured to determine actions and generate responses according to a system prompt that describes capabilities and use of the tools in the tool set.

323. The method of claim 319, wherein the Al agent incorporates self-critique capabilities to detect and correct hallucinations or fabricated facts during processing of input data or generation of outputs.

324. The method of claim 319, further comprising integrating with a digital twin system configured to generate at least one digital twin of at least one value chain network asset or process.

325. The method of claim 324, further comprising executing at least one simulation on the at least one digital twin to perform scenario simulation and impact analysis for the value chain network.

326. The method of claim 319, wherein the tool set comprises at least one of: executable programs, APIs, or communication interfaces for performing value chain network tasks.

327. The method of claim 319, wherein the Al agent is configured to invoke a sequence of tools from the tool set to fulfill a request indicated in the input data328. The method of claim 319, further comprising monitoring and regulating the Al agent's performance using a governance and analysis system to ensure compliance with organizational policies or regulatory frameworks.

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