Systems, methods, kits, and apparatuses for digital product networks in value chain networks

Abstract

A system may include a product-to-product communication module configured to exchange inter-product communications for a plurality of digitally connected products. A system may include a product-to-user communication module configured to exchange product-to-user communications between the plurality of digitally connected products and their respective users. A system may include a product-to-business communication module configured to exchange product-to-user communications between the plurality of digitally connected products and their associated enterprises. A system may include a data processing module configured to process the inter-product communications, product-to-user communications, and the product-to-business communications to determine time-sensitive alerts related to corresponding one of the plurality of digitally connected products. A system may include a graphical user interface (GUI) module configured to generate one or more user interfaces for displaying a time-sensitive alerts.

Description

SYSTEMS, METHODS, KITS, AND APPARATUSES FOR DIGITAL PRODUCT NETWORKS IN VALUE CHAIN NETWORKSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of:U.S. Provisional Application No. 63 / 464,845, filed on 8 May 2023, U.S. Provisional Application No. 63 / 535,748, filed on 31 August 2023, U.S. Provisional Application No. 63 / 610,894, filed 15 December 2023, U.S. Provisional Application No. 63 / 621,549, filed 16 January 2024, U.S. Provisional Application No. 63 / 625,597, filed 26 January 2024, and U.S. Provisional Application No. 63 / 638,590, filed 25 April 2024.

[0002] All the foregoing applications are hereby incorporated by reference as if fully set forth herein in their entirety.BACKGROUND

[0003] Historically, many of the various categories of goods purchased and used by household consumers, by businesses and by other customers were been supplied mainly through a relatively linear fashion, in which manufacturers and other suppliers of finished goods, components, and other items handed off items to shipping companies, freight forwarders and the like, who delivered them to warehouses for temporary storage, to retailers, where customers purchased them, or directly to customer locations. Manufacturers and retailers undertook various sales and marketing activities to encourage and meet demand by customers, including designing products, positioning them on shelves and in advertising, setting prices, and the like.SUMMARY

[0004] In some aspects, the techniques described herein relate to a digital product network system including: a product-to-product communication module configured to exchange interproduct communications for a plurality of digitally connected products; a product-to-user communication module configured to exchange product-to-user communications between the plurality of digitally connected products and their respective users; a product-to-business communication module configured to exchange product-to-user communications between the plurality of digitally connected products and their associated enterprises; a data processing module configured to process the inter-product communications, product-to-user communications, and the product-to-business communications to determine time-sensitive alerts related to corresponding one of the plurality of digitally connected products; and a graphical user interface (GUI) module configured to generate one or more user interfaces for displaying a timesensitive alerts.

[0005] In some aspects, the techniques described herein relate to a digital product network system, wherein the time-sensitive alerts include at least one of a low battery warning, ahardware failure notification, a software update notification, a connectivity issue notification, an unauthorized access warning, a maintenance reminder, a temperature fluctuation notification, a performance degradation notification, a storage capacity warning, a firmware upgrade alert, a scheduled backup reminder, a data breach notification, a device inactivity alert, a device overactivity or device overheating warning, a compatibility or synchronization issue with other devices, a warranty expiration reminder, a service subscription renewal alert, a sensor malfunction notification, an environmental hazard alert, a device location or movement alert, a user account change, a login notification from an unfamiliar location, a device lifecycle stage transition alert, a predictive alert based on Al analysis, an external power source disconnection warning, an accessory or peripheral malfunction notification, a safety alert, a status alert, a usage alert, a maintenance alert, a promotional alert, a product expiration alert, a health-related alert, a fitness-related alert, an energy-related alert, an inventory alert, a performance degradation alert, a compliance alert, a demand surge alert, a security breach alert, and a user-defined custom alert based on a specific conditions or threshold set by the user.

[0006] In some aspects, the techniques described herein relate to a digital product network system further including a smart contract creation module configured for creation of a smart contract providing terms and conditions that apply to at least one of: the plurality of digitally connected products, a user of at least one of the plurality of digitally connected products, a manufacturer of at least one of the plurality of digitally connected products, a distributer of at least one of the plurality of digitally connected products, an insurance provider for at least one of the plurality of digitally connected products, a retailer of at least one of the plurality of digitally connected products, and a service provider for at least one of the plurality of digitally connected products, wherein the smart contract relates to one or more of a purchase, a return, a lease, a warranty, an extended warranty, a service contract, a maintenance contract, a trade-in, a product add-on, a new product feature, a contract for access to data, a contract for access to one or more models, a software upgrade, a product upgrade, and a contract for access to a product feature.

[0007] In some aspects, the techniques described herein relate to a digital product network system, wherein the smart contract creation module is further configured to automatically create smart contracts based on predefined rules.

[0008] In some aspects, the techniques described herein relate to a digital product network system further including a smart contract template selection module configured to enable selection of a smart contract template, while restricting the selection to one or more smart contract templates that are associated with the at least one of the plurality of digitally connected products.

[0009] In some aspects, the techniques described herein relate to a digital product network system, wherein the smart contract template selection module is further configured to: receive input for parameterizing the selected smart contract template; generate a smart contract by parameterizing one or more variables in the smart contract with the input; and deploy the smart contract by broadcasting the smart contract to the at least one of the plurality of digitally connected products.

[0010] In some aspects, the techniques described herein relate to a digital product network system, wherein the smart contract template selection module is further configured to suggest smart contract templates to a user based on at least one of: a type of the digitally connected product to be subject to the smart contract, one or more past selections of the user.

[0011] In some aspects, the techniques described herein relate to a digital product network system further including a smart contract performance verification module configured to verify the performance of an obligation defined in the smart contract.

[0012] In some aspects, the techniques described herein relate to a digital product network system, wherein the smart contract performance verification module is further configured to automatically trigger an action upon successful verification of the performance of the obligation defined in the smart contract, and wherein the action includes one or more of providing a notification, updating a status of the smart contract, and initiating a new smart contract.

[0013] In some aspects, the techniques described herein relate to a digital product network system further including a record upload module configured for enabling a user to upload records, wherein the records include one or more of photos, videos, audio recordings, device information, usage data, user preferences, troubleshooting records, upgrade records, reviews of product, receipts for product-related costs, receipts for product-related purchases, environmental data, and customer feedback.

[0014] In some aspects, the techniques described herein relate to a digital product network system, wherein the record upload module is further configured to automatically categorize and store the uploaded records based on respective types of associated digitally connected products.

[0015] In some aspects, the techniques described herein relate to a digital product network system further including an application programming interface (API) management module configured to manage one or more APIs, so as to expose the APIs to one or more related applications or third-party systems used by the plurality of digitally connected products.

[0016] In some aspects, the techniques described herein relate to a digital product network system further including a regulatory interface module configured to interact with one or more regulatory systems.

[0017] In some aspects, the techniques described herein relate to a digital product network system further including a data integration module configured to integrate data from the plurality of digitally connected products to create a unified view of data associated with the digitally connected products, by performing one or more operations, wherein the one or more operations include collecting data, aggregating data, normalizing data, standardizing data, cleaning data, enriching data, analyzing data, visualizing data, and integrating with other systems, and wherein the data integrated by the data integration module includes one or more of product usage data, environmental data, product performance data, customer data, supply chain data, demand data, product location data, product customer feedback, product interaction data, product security data, product packaging data, product image data, product video data, product audio data, and product simulation data.

[0018] In some aspects, the techniques described herein relate to a digital product network system, wherein the GUI module is further configured to provide one or more user interfaces displaying related ones of the plurality of digitally connected products associated with a single user by at least one of brand, type, and capability.

[0019] In some aspects, the techniques described herein relate to a method for operating a digital product network system, the method including: exchanging inter-product communications for a plurality of digitally connected products; exchanging product-to-user communications between the plurality of digitally connected products and their respective users; exchanging product-to-business communications between the plurality of digitally connected products and their associated enterprises; processing the inter-product communications, the product-to-user communications, and the product-to-business communications to determine time-sensitive alerts related to corresponding one of the plurality of digitally connected products; and generating one or more user interfaces for displaying the time-sensitive alerts.

[0020] In some aspects, the techniques described herein relate to a method, wherein the timesensitive alerts include at least one of a low battery warning, a hardware failure notification, a software update notification, a connectivity issue notification, an unauthorized access warning, a maintenance reminder, a temperature fluctuation notification, a performance degradation notification, a storage capacity warning, a firmware upgrade alert, a scheduled backup reminder, a data breach notification, a device inactivity alert, a device overactivity or device overheating warning, a compatibility or synchronization issue with other devices, a warranty expiration reminder, a service subscription renewal alert, a sensor malfunction notification, an environmental hazard alert, a device location or movement alert, a user account change, a login notification from an unfamiliar location, a device lifecycle stage transition alert, a predictive alert based on Al analysis, an external power source disconnection warning, an accessory or peripheral malfunction notification, a safety alert, a status alert, a usage alert, a maintenance alert, a promotional alert, a product expiration alert, a health-related alert, a fitness-related alert, an energy-related alert, an inventory alert, a performance degradation alert, a compliance alert, a demand surge alert, a security breach alert, and a user-defined custom alert based on a specific conditions or threshold set by the user.

[0021] In some aspects, the techniques described herein relate to a method further including creation of a smart contract, the smart contract providing terms and conditions that apply to at least one of: the plurality of digitally connected products, a user of at least one of the plurality of digitally connected products, a manufacturer of at least one of the plurality of digitally connected products, a distributer of at least one of the plurality of digitally connected products, an insurance provider for at least one of the plurality of digitally connected products, a retailer of at least one of the plurality of digitally connected products, and a service provider for at least one of the plurality of digitally connected products, wherein the terms and conditions include one or more of a purchase, a return, a lease, a warranty, an extended warranty, a service contract, a maintenance contract, a trade-in, a product add-on, a new product feature, a contract for access todata, a purchase for access to one or more models, a software upgrade, a product upgrade, and unlocking an existing product feature.

[0022] In some aspects, the techniques described herein relate to a method further including automatically creating smart contracts based on predefined rules.

[0023] In some aspects, the techniques described herein relate to a method further including enabling selection of a smart contract template, while restricting the selection to one or more smart contract templates that are associated with the at least one of the plurality of digitally connected products.

[0024] In some aspects, the techniques described herein relate to a method further including: receiving input for parameterizing the selected smart contract template; generating a smart contract by parameterizing one or more variables in the smart contract with the input; and deploying the smart contract by broadcasting the smart contract to the at least one of the plurality of digitally connected products.

[0025] In some aspects, the techniques described herein relate to a method further including suggesting smart contract templates to a user based on at least one of: a type of the digitally connected product to be subject to the smart contract, one or more past selections of the user.

[0026] In some aspects, the techniques described herein relate to a method further including verification of performance of an obligation defined in the smart contract.

[0027] In some aspects, the techniques described herein relate to a method further including automatically triggering an action upon successful verification of the performance of the obligation defined in the smart contract, wherein the action includes one or more of providing a notification, updating status of the smart contract, and initiating a new smart contract.

[0028] In some aspects, the techniques described herein relate to a method further including enabling a user to upload records, wherein the records include one or more of photos, videos, audio recordings, device information, usage data, user preferences, troubleshooting records, upgrade records, reviews of product, receipts for product-related costs or purchases, environmental data, and customer feedback.

[0029] In some aspects, the techniques described herein relate to a method further including automatically categorizing and storing the uploaded records based on respective types and associated digitally connected products.

[0030] In some aspects, the techniques described herein relate to a method further including managing one or more application programming interfaces (APIs), so as to expose the APIs to one or more related applications or third-party systems used by the plurality of digitally connected products.

[0031] In some aspects, the techniques described herein relate to a method further including interacting with one or more regulatory systems.

[0032] In some aspects, the techniques described herein relate to a method further including integrating data from the plurality of digitally connected products to create a unified view of digitally connected products, by performing one or more operations, wherein the one or more operations include collecting data, aggregating data, normalizing data, standardizing data,cleaning data, enriching data, analyzing data, visualizing data, and integrating with other systems, and wherein the data integrated includes one or more of product usage data, environmental data, product performance data, customer data, supply chain data, demand data, product location data, product customer feedback, product interaction data, product security data, product packaging data, product image data, product video data, product audio data, and product simulation data.

[0033] In some aspects, the techniques described herein relate to a method further including providing one or more user interfaces to display related ones of the plurality of digitally connected products associated with a single user by at least one of brand, type, and capability.

[0034] In some aspects, the techniques described herein relate to a digital product network system including: a user interface module configured to: display information associated with a set of digitally connected products; and receive and process user inputs for displaying a set of marketplaces associated with the information; display the set of marketplaces; and receive and process user inputs for selecting a marketplace from the set of marketplaces; and an embedded marketplace module integrated with the user interface module, the embedded marketplace configured to: display the selected marketplace; receive and process user inputs for selecting one or more offerings from a set of offerings from the selected marketplace; receive and process user inputs for completing a set of transactions associated with the one or more selected offerings; and execute the set of transactions.

[0035] In some aspects, the techniques described herein relate to a digital product network system, wherein the embedded marketplace module is configured to execute transactions for product data associated with at least one of the digitally connected products from the set of digitally connected products, wherein the product data includes one or more of product performance data, product pricing data, product usage data, product location data, product customer feedback data, product interaction data, product security data, product image data, product video data, product audio data, product packaging data, product maintenance data, environmental data, and product simulation data.

[0036] In some aspects, the techniques described herein relate to a digital product network system, wherein the embedded marketplace module is configured to execute transactions related to models and / or algorithms associated with at least one digitally connected product from the set of digitally connected products, wherein the models and / or algorithms include one or more of predictive models, machine learning models, artificial intelligence models, classification models, anomaly detection models, clustering algorithms, generative models, large language models, optimization models, recommendation models, neural networks, transformer models, convolutional neural networks, deep learning models, supervised models, semi-supervised models, unsupervised models, reinforcement models, and regression models.

[0037] In some aspects, the techniques described herein relate to a digital product network system, wherein the embedded marketplace module is configured to execute transactions related to one or more of product parts, product upgrades, product insurance, product enhancements, product add-ons, product software, product warranties, extended product warranties, productservice contracts, product maintenance contracts, product trade-ins, and access to product features for at least one digitally connected product from the set of digitally connected products.

[0038] In some aspects, the techniques described herein relate to a digital product network system, wherein the embedded marketplace module is further configured to execute a peer-to- peer transaction associated with one or more of the digitally connected products.

[0039] In some aspects, the techniques described herein relate to a digital product network system, wherein the embedded marketplace module is configured to provide a recommendation for an offering associated with at least one digitally connected product using a set of Artificial Intelligence (Al)-based learning models, wherein at least one of the set of Al-based learning models is trained on a training data set of embedded marketplace transaction data.

[0040] In some aspects, the techniques described herein relate to a digital product network system, wherein the embedded marketplace module is configured to execute transactions between different entities including two or more of users, manufacturers, suppliers, service providers, software providers, data providers, data analytics providers, model providers, digital twin providers, and NFT providers.

[0041] In some aspects, the techniques described herein relate to a digital product network system, wherein the marketplaces provided by the embedded marketplace module are configured to be searchable.

[0042] In some aspects, the techniques described herein relate to a digital product network system, wherein at least one marketplace of the set of marketplaces provides digital twin offerings.

[0043] In some aspects, the techniques described herein relate to a method for executing transactions in a digital product network system, the method including: displaying information associated with a set of digitally connected products, receiving and processing user inputs for displaying a set of marketplaces associated with the information; displaying the set of marketplaces; receiving and processing user inputs for selecting a marketplace from the set of marketplaces; displaying the selected marketplace; receiving and processing user inputs for selecting one or more offerings from a set of offerings from the selected marketplace; receiving and processing user inputs for completing a set of transactions associated with the one or more selected offerings; and executing the set of transactions.

[0044] In some aspects, the techniques described herein relate to a method further including executing transactions related to product data associated with at least one of the digitally connected products from the set of digitally connected products, wherein the product data includes one or more of product performance data, product pricing data, product usage data, product location data, product customer feedback data, product interaction data, product security data, product image data, product video data, product audio data, product packaging data, product maintenance data, environmental data, and product simulation data.

[0045] In some aspects, the techniques described herein relate to a method further including executing transactions related to models and / or algorithms associated with at least one digitally connected product from the set of digitally connected products, wherein the models and / oralgorithms include one or more of predictive models, machine learning models, artificial intelligence models, classification models, anomaly detection models, clustering algorithms, generative models, large language models, optimization models, recommendation models, neural networks, transformer models, convolutional neural networks, deep learning models, supervised models, semi-supervised models, unsupervised models, reinforcement models, and regression models.

[0046] In some aspects, the techniques described herein relate to a method further including executing transactions related to one or more of product parts, product upgrades, product insurance, product enhancements, product add-ons, product software, product warranties, extended product warranties, product service contracts, product maintenance contracts, product trade-ins, and access to new product features for at least one digitally connected product from the set of digitally connected products.

[0047] In some aspects, the techniques described herein relate to a method further including executing a peer-to-peer transaction associated with one or more of the digitally connected products.

[0048] In some aspects, the techniques described herein relate to a method further including providing a recommendation for an offering associated with at least one digitally connected product using a set of Artificial Intelligence (Al)-based learning models, wherein at least one of the set of Al-based learning models is trained on a training data set of embedded marketplace transaction data.

[0049] In some aspects, the techniques described herein relate to a method further including executing transactions between different entities including two or more of users, manufacturers, suppliers, service providers, data providers, data analytics providers, software application providers, model providers, digital twin providers, and NFT providers

[0050] In some aspects, the techniques described herein relate to a method further including providing at least one searchable marketplace.

[0051] In some aspects, the techniques described herein relate to a method, further including providing at least one marketplace that provides digital twin offerings.

[0052] In some aspects, the techniques described herein relate to a digital product network system including one or more processors and one or more memories configured to perform operations including: receiving, by a computing device, information associated with a set of digitally connected products of a digital product network, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; providing the information to a set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to generate a recommendation associated with one or more of the set of digitally connected products; and outputting the recommendation.

[0053] In some aspects, the techniques described herein relate to a digital product network system, wherein the information is related to a date or time of packaging of the set of digitally connected products.

[0054] In some aspects, the techniques described herein relate to a digital product network system, wherein the information is related to a location of the set of digitally connected products.

[0055] In some aspects, the techniques described herein relate to a digital product network system, wherein the information is related to a location of manufacturing for the set of digitally connected products.

[0056] In some aspects, the techniques described herein relate to a digital product network system, wherein the information is related to a location of packaging for the set of digitally connected products.

[0057] In some aspects, the techniques described herein relate to a digital product network system, wherein the information is related to a location of purchase for the set of digitally connected products.

[0058] In some aspects, the techniques described herein relate to a digital product network system, wherein the information is related to a location of opening of packaging or the location of consumption of the set of digitally connected products.

[0059] In some aspects, the techniques described herein relate to a digital product network system, wherein the information is related to a time on a display shelf or time on other display for the set of digitally connected products.

[0060] In some aspects, the techniques described herein relate to a digital product network system, wherein the information is related to customer interaction with a set of displayed digitally connected products of the set of digitally connected products.

[0061] In some aspects, the techniques described herein relate to a digital product network system, wherein the information is related to customer interaction with purchased digitally connected products of the set of digitally connected products.

[0062] In some aspects, the techniques described herein relate to a digital product network system, wherein the wherein the set of Al-based learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model, a supervised model, a semi-supervised model, an unsupervised model, a generative model, a large language model, a recommendation model, or a reinforcement model.

[0063] In some aspects, the techniques described herein relate to a digital product network system, wherein the recommendation is associated with one or more of an operational parameter of one or more of the set of digitally connected products, a provisioning of customer support associated with the one or more of the set of digitally connected products, a price of one or more of the set of digitally connected products, a product design associated with one or more of the set of digitally connected products, product security associated with one or more of the set of digitally connected products, quality control associated with one or more of the set of digitally connected products, marketing or sales strategies associated with one or more of the set of digitally connected products, display strategies associated with one or more of the set of digitallyconnected products, packaging associated with one or more of the set of digitally connected products, software upgrades associated with one or more of the set of digitally connected products, inventory management associated with one or more of the set of digitally connected products, regulatory compliance associated with one or more of the set of digitally connected products, and customer behavior associated with one or more of the set of digitally connected products.

[0064] In some aspects, the techniques described herein relate to a method for generating a recommendation in a digital product network system, the method including: receiving, by a computing device, information associated with a set of digitally connected products of a digital product network, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; providing the information to a set of Artificial Intelligence (AI)- based learning models, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to generate a recommendation associated with one or more of the set of digitally connected products; and outputting the recommendation.

[0065] In some aspects, the techniques described herein relate to a method, wherein the information relates to a date or time of packaging the set of digitally connected products.

[0066] In some aspects, the techniques described herein relate to a method, wherein the information relates to a location of the set of digitally connected products.

[0067] In some aspects, the techniques described herein relate to a method, wherein the information relates to a location of manufacturing of the set of digitally connected products.

[0068] In some aspects, the techniques described herein relate to a method, wherein the information relates to a location of packaging of the set of digitally connected products.

[0069] In some aspects, the techniques described herein relate to a method, wherein the information relates to a location of purchase of the set of digitally connected products.

[0070] In some aspects, the techniques described herein relate to a method, wherein the information relates to a location of opening of packaging or the location of consumption of the set of digitally connected products.

[0071] In some aspects, the techniques described herein relate to a method, wherein the information relates to a time on a display shelf or time on other display for the set of digitally connected products.

[0072] In some aspects, the techniques described herein relate to a method, wherein the information relates to customer interaction with displayed digitally connected products of the set of digitally connected products.

[0073] In some aspects, the techniques described herein relate to a method, wherein the information relates to customer interaction with purchased digitally connected products of the set of digitally connected products.

[0074] In some aspects, the techniques described herein relate to a method, wherein the wherein the set of Al-based learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model, a supervised model, a semi-supervised model, an unsupervised model, a generative model, a large language model, a recommendation model, or a reinforcement model.

[0075] In some aspects, the techniques described herein relate to a method, wherein the recommendation is associated with one or more of an operational parameter of one or more of the set of digitally connected products, a provisioning of customer support associated with the one or more of the set of digitally connected products, a price of one or more of the set of digitally connected products, a product design associated with one or more of the set of digitally connected products, product security associated with one or more of the set of digitally connected products, quality control associated with one or more of the set of digitally connected products, marketing or sales strategies associated with one or more of the set of digitally connected products, display strategies associated with one or more of the set of digitally connected products, packaging associated with one or more of the set of digitally connected products, software upgrades associated with one or more of the set of digitally connected products, inventory management associated with one or more of the set of digitally connected products, regulatory compliance associated with one or more of the set of digitally connected products, and customer behavior associated with one or more of the set of digitally connected products.

[0076] In some aspects, the techniques described herein relate to a digital product network system including one or more processors and one or more memories configured to perform operations including: receiving, by a computing device, information associated with a set of digitally connected products of the digital product network system, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; and providing the information to a digital twin system, wherein the digital twin system is configured to generate a digital twin representing one or more digitally connected products of the set of digitally connected products based on the information, wherein each digital twin provides a substantially real-time representation of the one or more digitally connected products of the set of digitally connected products.

[0077] In some aspects, the techniques described herein relate to a digital product network system, wherein the digital twin system is further configured to generate a digital twin representing one or more of an entity, an environment, and a workflow of the digital product network.

[0078] In some aspects, the techniques described herein relate to a digital product network system, wherein the entity includes one or more of an additional connected product, a physical object, a 3D printer, a manufacturer, a distributor, a consumer, a service provider, a retailer, a payment provider, and a regulator.

[0079] In some aspects, the techniques described herein relate to a digital product network system, wherein the environment includes one or more of a warehouse, a manufacturing facility, a 3D printing facility, a service provider facility, a smart container, a container, a shipping port, a truck, a transportation network, a physical retail store, a digital marketplace, a virtual reality environment, a storage facility, and a digital product network.

[0080] In some aspects, the techniques described herein relate to a digital product network system, wherein the workflow includes one or more of a product design workflow, a product component sourcing workflow, a product manufacturing workflow, a product packaging workflow, a product distribution workflow, a product marketing and sales workflow, a product display workflow, a product interaction workflow, a product usage workflow, a data collection workflow, a product repair workflow, a product recall workflow, a product lifecycle management workflow, a product maintenance workflow, a data processing workflow, a network integration workflow, and a feedback loop workflow.

[0081] In some aspects, the techniques described herein relate to a digital product network system, wherein the information relates to one or more of product design data, product status data, product usage data, product error data, product diagnostic data, product location data, product maintenance data, user interaction data, network data, event data, product performance data, and environmental data.

[0082] In some aspects, the techniques described herein relate to a digital product network system further including providing an embedded marketplace in the digital twin of the digitally connected product for enabling transactions related to of one or more of a model, data, a product update, a product upgrade, a product add-on, a product-related service offering, and a product part related to the one or more digitally connected products.

[0083] In some aspects, the techniques described herein relate to a digital product network system further including simulating at least one of a behavior, a performance, an environment, and an interaction of the digital twin using CAD-based models.

[0084] In some aspects, the techniques described herein relate to a digital product network system, further including generating an alert based on information represented in the digital twin.

[0085] In some aspects, the techniques described herein relate to a digital product network system, further including providing hierarchical digital twins, wherein each hierarchical digital twin represents a hierarchy of the at least one of an entity, an environment, or a workflow in the digital product network.

[0086] In some aspects, the techniques described herein relate to a digital product network system, further including providing context-adaptive digital twins, wherein the context-adaptive digital twin is configured for adapting the representation of the at least one of an entity, an environment, and a workflow based on changes in context.

[0087] In some aspects, the techniques described herein relate to a digital product network system, further including providing a role-based digital twin configured to represent information associated with a role associated with the one or more digitally connected products, wherein the role-based digital twin is configured for a consumer, a supplier, a manufacturer, a logisticsmanager, a retailer, a service provider, a product manager, a marketing and sales manager, an inventory manager, or a quality manager.

[0088] In some aspects, the techniques described herein relate to a method for providing a digital twin in a digital product network system, the method including: receiving, by a computing device, information associated with a set of digitally connected products of the digital product network system, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; and providing the information to a digital twin system, wherein the digital twin system is configured to generate a digital twin of one or more digitally connected products of the set of digitally connected products based on the information, wherein each digital twin provides a substantially real-time representation of the one or more digitally connected products of the set of digitally connected products.

[0089] In some aspects, the techniques described herein relate to a method further including generating a digital twin representing one or more of an entity, an environment, and a workflow of the digital product network.

[0090] In some aspects, the techniques described herein relate to a method, wherein the entity includes one or more of an additional connected product, a physical object, a 3D printer, a manufacturer, a distributor, a consumer, a service provider, a payment provider, and a regulator.

[0091] In some aspects, the techniques described herein relate to a method, wherein the environment includes one or more of a warehouse, a manufacturing facility, a 3D printing facility, a service provider facility, a smart container, a container, a truck, a transportation network, a physical retail store, a digital marketplace, a virtual reality environment, a storage facility, and a connected product network.

[0092] In some aspects, the techniques described herein relate to a method, wherein the workflow includes one or more of a product design workflow, a product component sourcing workflow, a product manufacturing workflow, a product packaging workflow, a product distribution workflow, a product marketing and sales workflow, a product display workflow, a product interaction workflow, a product usage workflow, a data collection workflow, a product repair workflow, a product recall workflow, a product lifecycle management workflow, a product maintenance workflow, a data processing workflow, a network integration workflow, and a feedback loop workflow.

[0093] In some aspects, the techniques described herein relate to a method, wherein the information relates to one or more of product status data, product usage data, product error data, product diagnostic data, product location data, product maintenance data, user interaction data, network data, event data, product performance data, and environmental data.

[0094] In some aspects, the techniques described herein relate to a method further including providing an embedded marketplace in the digital twin of the digitally connected product for purchase, sale, and exchange of one or more of a model, data, a product update, a productupgrade, a product add-on, a product-related service offering, and a product part related to the one or more digitally connected products.

[0095] In some aspects, the techniques described herein relate to a method further including simulating at least one of a behavior, a performance, an environment, and an interaction of the digital twin utilizing CAD-based models.

[0096] In some aspects, the techniques described herein relate to a method further including generating an alert based on data represented in the digital twin.

[0097] In some aspects, the techniques described herein relate to a method further including providing hierarchical digital twins, wherein each hierarchical digital twin represents a hierarchy of the at least one of an entity, an environment, or a workflow in the digital product network.

[0098] In some aspects, the techniques described herein relate to a method further including providing context-adaptive digital twin, wherein each context-adaptive digital twin is configured for adapting the representation of the at least one of an entity, an environment, and a workflow based on changes in context.

[0099] In some aspects, the techniques described herein relate to a method further including providing a role-based digital twin configured to represent a role associated with the digital product network, and wherein the role-based digital twin is configured for a consumer, a supplier, a manufacturer, a logistics manager, a retailer, a service provider, a product manager, a marketing and sales manager, an inventory manager, and a quality manager.

[0100] In some aspects, the techniques described herein relate to a method for providing an Artificial Intelligence (AI)-based supply chain digital twin for digitally connected products, the method including: receiving, by a computing device, information associated with a set of digitally connected products of a digital product network system, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; and providing the information to a digital twin system, wherein the digital twin system is configured to update a digital twin representing a supply chain network for the set of digitally connected products based on the information, wherein the digital twin provides a substantially real-time representation of a set of supply chain entities of the supply chain network; providing the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI- based learning models is trained on a training data set of a set of digital product network data to determine a classification of at least one of: an operating state, a fault condition, an operating flow, or a behavior of the supply chain network and at least one member of the set of Al-based learning models is trained on the training data set to determine, upon receiving the classification of the at least one of the: operating state, the fault condition, the operating flow, or the behavior, a task to be completed for the supply chain network; and providing at least one of an instruction for executing the task in the supply chain network digital twin and a recommendation for executing the task in the supply chain network digital twin.

[0101] In some aspects, the techniques described herein relate to a method, wherein the information includes real-time data about one of: inbound prepaid shipments from suppliers linked to orders; or inventory coming into a network associated with the supply chain network.

[0102] In some aspects, the techniques described herein relate to a method wherein receiving the information associated with the supply chain network includes receiving sensor data indicative of inbound and outbound shipment conditions.

[0103] In some aspects, the techniques described herein relate to a method further including generating simulations for the supply chain network digital twin, wherein the simulations for the supply chain network digital twin are generated with a graphical neural network (GNN).

[0104] In some aspects, the techniques described herein relate to a method, wherein a robotic operating system enables the supply chain network digital twin.

[0105] In some aspects, the techniques described herein relate to a method, wherein the supply chain network digital twin operates within a digital twin system having one or more sets of one or more digital twins, wherein each digital twin of the one or more sets includes an embedded marketplace for digital twin simulations.

[0106] In some aspects, the techniques described herein relate to a method, wherein the supply chain network digital twin operates within a digital twin system having one or more sets of one or more digital twins, wherein each digital twin of the one or more sets includes an embedded marketplace for one of artificial intelligence-based learning models or artificial intelligencebased algorithms.

[0107] In some aspects, the techniques described herein relate to a method, wherein the supply chain network digital twin operates within a digital twin system having one or more sets of one or more digital twins, wherein each digital twin of the one or more sets includes an embedded marketplace for data.

[0108] In some aspects, the techniques described herein relate to a method, wherein the set of the supply chain network entities includes at least one of: digitally connected products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, robotic handling systems, 3D printers, vehicles, autonomous vehicles, hauling facilities, waterways, or port infrastructure facilities.

[0109] In some aspects, the techniques described herein relate to a method, wherein the set of Al-based learning models includes at least one of: a transformer model, a convolutional neuralnetwork, a deep learning model, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.

[0110] In some aspects, the techniques described herein relate to a computing system including one or more processors and one or more memories configured to perform operations including: receiving, by a computing device, information associated with a set of digitally connected products of the digital product network system, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; and providing the information to a digital twin system, wherein the digital twin system is configured to update a digital twin representing a supply chain network for the set of digitally connected products based on the information, wherein the digital twin provides a substantially real-time representation of a set of supply chain entities of the supply chain network; providing the information to a set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the set of Al-based learning models is trained on a training data set of a set of digital product network data to determine a classification of at least one of: an operating state, a fault condition, an operating flow, or a behavior of the supply chain network and at least one member of the set of Al-based learning models is trained on the training data set to determine, upon receiving the classification of the at least one of the: operating state, the fault condition, the operating flow, or the behavior, a task to be completed for the supply chain network; and providing at least one of an instruction for executing the task in the supply chain network digital twin and a recommendation for executing the task in the supply chain network digital twin.[oni] In some aspects, the techniques described herein relate to a computing system, wherein the information includes real-time data about one of: inbound prepaid shipments from suppliers linked to orders; or inventory coming into a network associated with the supply chain network.

[0112] In some aspects, the techniques described herein relate to a computing system, wherein receiving the information associated with the supply chain network includes receiving sensor data indicative of inbound and outbound shipment conditions.

[0113] In some aspects, the techniques described herein relate to a computing system, wherein the operations further include generating simulations for the supply chain network digital twin, wherein the simulations for the supply chain network digital twin are generated with a graphical neural network (GNN).

[0114] In some aspects, the techniques described herein relate to a computing system, wherein a robotic operating system enables the supply chain network digital twin.

[0115] In some aspects, the techniques described herein relate to a computing system, wherein the supply chain network digital twin operates within a digital twin system having one or more sets of one or more digital twins, wherein each digital twin of the one or more sets includes an embedded marketplace for digital twin simulations.

[0116] In some aspects, the techniques described herein relate to a computing system, wherein the supply chain network digital twin operates within a digital twin system having one or moresets of one or more digital twins, wherein each digital twin of the one or more sets includes an embedded marketplace for one of artificial intelligence-based learning models or artificial intelligence-based algorithms.

[0117] In some aspects, the techniques described herein relate to a computing system, wherein the supply chain network digital twin operates within a digital twin system having one or more sets of one or more digital twins, wherein each digital twin of the one or more sets includes an embedded marketplace for data.

[0118] In some aspects, the techniques described herein relate to a computing system, wherein the set of the supply chain network entities includes at least one of: digitally connected products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, robotic handling systems, 3D printers, vehicles, autonomous vehicles, hauling facilities, waterways, or port infrastructure facilities.

[0119] In some aspects, the techniques described herein relate to a computing system, wherein the set of Al-based learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.

[0120] In some aspects, the techniques described herein relate to a digital product network system including one or more processors and one or more memories configured to perform operations including: receiving, by a computing device, information associated with a set of digitally connected products of a digital product network, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; providing the information to a set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to determine a set of demand planning actions providing a computer code instruction set to a machine to execute the set of demand planning actions.

[0121] In some aspects, the techniques described herein relate to a digital product network system, wherein the set of demand planning actions includes at least one of forecasting a measure of demand, inventory management, capacity planning, supply chain coordination, customer prioritization, order quantity limits, dynamic pricing, alternative product promotions, marketing campaigns, product improvements, market expansion, and customer engagement.

[0122] In some aspects, the techniques described herein relate to a digital product network system, wherein the information includes at least one of historical sales data, market data, social media data, customer data, economic sentiment data, economic data, supply chain data, sensor data, market research data, competitive analysis data, economic indicator data, industry trends data, customer feedback data, historical product development data, project management data, resource availability data, supplier data, manufacturing data, quality control data, testing data, regulatory data, compliance data, historical recall data, manufacturing data, supplier data, component data, quality control data, inspection data, failure data, warranty data, service record data, regulatory data, entertainment data, event data, regulatory compliance data, incident data, weather data, and accident report data.

[0123] In some aspects, the techniques described herein relate to a digital product network system, wherein the set of Al-based learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model, a supervised model, a semisupervised model, a generative model, a large language model, a recommendation model, an unsupervised model, a reinforcement model, and a regression model.

[0124] In some aspects, the techniques described herein relate to a digital product network system, wherein the set of demand planning action includes forecasting a measure of demand for each of a plurality of demographic groups at a future date and / or time.

[0125] In some aspects, the techniques described herein relate to a digital product network system, wherein the set of demand planning actions further include generating and distributing a marketing message associated with the set of digitally connected products for a specific demographic group based on the forecasted measure of demand.

[0126] In some aspects, the techniques described herein relate to a digital product network system, wherein the set of demand planning actions includes forecasting a measure of demand for each of a plurality of geographic regions at a future date and / or time.

[0127] In some aspects, the techniques described herein relate to a digital product network system, wherein the set of demand planning actions further include generating and distributing a marketing message associated with the set of digitally connected products for a specific geographic region based on the forecasted measure of demand.

[0128] In some aspects, the techniques described herein relate to a digital product network system, wherein the set of demand planning actions includes forecasting a measure of demand for each of a plurality of channel preference segments at a future date and / or time.

[0129] In some aspects, the techniques described herein relate to a method for demand planning in a digital product network system, the method including: receiving, by a computing device, information associated with a set of digitally connected products of a digital product network, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; providing the information to a set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the set of Al-based learning models is trained on a training dataset of digital product network data to determine a set of demand planning actions for the set of digitally connected products; and providing a computer code instruction set to a machine to execute the set of demand planning actions.

[0130] In some aspects, the techniques described herein relate to a method, wherein the set of demand planning action relates to at least one of forecasting a measure of demand, inventory management, capacity planning, supply chain coordination, customer prioritization, order quantity limits, dynamic pricing, alternative product promotions, marketing campaigns, product improvements, market expansion, and customer engagement.

[0131] In some aspects, the techniques described herein relate to a method, wherein the information includes at least one of historical sales data, market data, social media data, customer data, economic sentiment data, economic data, supply chain data, sensor data, market research data, competitive analysis data, economic indicator data, industry trends data, customer feedback data, historical product development data, project management data, resource availability data, supplier data, manufacturing data, quality control data, testing data, regulatory data, compliance data, historical recall data, manufacturing data, supplier data, component data, quality control data, inspection data, failure data, warranty data, service record data, regulatory data, entertainment data, event data, regulatory compliance data, incident data, weather data, and accident report data.

[0132] In some aspects, the techniques described herein relate to a method, wherein each set of Al-based learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model trained on a set of outcomes of the digital product network, a supervised model, a semi -supervised model, an unsupervised model, a reinforcement model, a generative model, a large language model, a recommendation model, and a regression model.

[0133] In some aspects, the techniques described herein relate to a method, wherein the set of demand planning action includes forecasting a measure of demand for each of a plurality of demographic groups at a future date and / or time.

[0134] In some aspects, the techniques described herein relate to a method, wherein the set of demand planning actions further include generating and distributing a marketing message associated with the set of digitally connected products for a specific demographic group based on the forecasted measure of demand.

[0135] In some aspects, the techniques described herein relate to a method, wherein the set of demand planning actions includes forecasting a measure of demand for each of a plurality of geographic regions at a future date and / or time.

[0136] In some aspects, the techniques described herein relate to a method, wherein the set of demand planning actions further include generating and distributing a marketing message associated with the set of digitally connected products for a specific geographic region based on the forecasted measure of demand.

[0137] In some aspects, the techniques described herein relate to a method, wherein the set of demand planning actions includes forecasting a measure of demand for each of a plurality of channel preference segments at a future date and / or time.

[0138] In some aspects, the techniques described herein relate to a digital product network system including one or more processors and one or more memories configured to perform operations including: receiving, by a computing device, information associated with a set of digitally connected products of a digital product network, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; providing the information to a set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to predict a future inventory level requirement for the set of digitally connected products based on the information and at least one member of the set of Al-based learning models is trained on the training data set to determine an action to meet the predicted inventory level requirement based on the prediction and the information; at least one of outputting the determined action and providing a computer code instruction set to a machine to execute the determined action.

[0139] In some aspects, the techniques described herein relate to a digital product network system, wherein the information includes at least one of historical sales data, market data, social media data, customer data, economic sentiment data, economic data, supply chain data, inventory data, lead time data, sensor data, market research data, competitive analysis data, economic indicator data, industry trends data, customer feedback data, historical product development data, project management data, resource availability data, supplier data, supplier performance data, supplier pricing data, manufacturing data, quality control and testing data, regulatory and compliance data, historical recall data, manufacturing data, supplier data, component data, quality control data, inspection data, failure data, warranty and service records, regulatory data, entertainment data, event data, regulatory compliance data, incident data, weather data, and accident report data.

[0140] In some aspects, the techniques described herein relate to a digital product network system, wherein the set of Al-based learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model trained on a set of outcomes of the digital product network, a supervised model, a semi-supervised model, an unsupervised model, a reinforcement model, a generative model, a large language model, and a regression model.

[0141] In some aspects, the techniques described herein relate to a digital product network system, wherein the determined action relates to at least one of: engaging secondary manufacturers or contract manufacturers to supplement production capacity, implementing overtime shifts at manufacturing facilities to temporarily increase production, redistributing inventory, simplifying product designs, implementing strategic stockpiling, supplementing production capacity with 3D printers, increasing storage and / or warehouse capacity, implementing cross-docking, adjusting production settings, and improving return management.

[0142] In some aspects, the techniques described herein relate to a digital product network system, wherein at least one member of the set of Al-based learning models is trained on atraining data set of digital product network data to generate a prediction of future inventory level requirement for each of a plurality of retail locations.

[0143] In some aspects, the techniques described herein relate to a digital product network system, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to generate a prediction of future inventory level requirement for each of a plurality of geographic regions.

[0144] In some aspects, the techniques described herein relate to a digital product network system, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to generate a prediction of future inventory level requirement for each of a plurality of sales channels.

[0145] In some aspects, the techniques described herein relate to a digital product network system, wherein the operations further include providing at least one of real-time updates on the inventory level and alerts when the inventory level reaches a certain threshold.

[0146] In some aspects, the techniques described herein relate to a digital product network system, wherein the determined action is output to a digital twin user interface.

[0147] In some aspects, the techniques described herein relate to a method for inventory optimization in a digital product network system, the method including: receiving, by a computing device, information associated with a set of digitally connected products of a digital product network, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; providing the information to a set of Artificial Intelligence (AI)- based learning models, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to predict a future inventory level requirement for a set of digitally connected products and at least one member of the set of AI- based learning models is trained on the training data set to determine an action to meet the predicted inventory level requirement; at least one of outputting the determined action and providing a computer code instruction set to a machine to execute the determined action.

[0148] In some aspects, the techniques described herein relate to a method, wherein the information includes at least one of historical sales data, market data, social media data, customer data, economic sentiment data, economic data, supply chain data, inventory data, lead time data, sensor data, market research data, competitive analysis data, economic indicator data, industry trends data, customer feedback data, historical product development data, project management data, resource availability data, supplier data, supplier performance data, supplier pricing data, manufacturing data, quality control and testing data, regulatory and compliance data, historical recall data, manufacturing data, supplier data, component data, quality control data, inspection data, failure data, warranty and service records, regulatory data, entertainment data, event data, regulatory compliance data, incident data, weather data, and accident report data.

[0149] In some aspects, the techniques described herein relate to a method, wherein the set of Al-based learning models includes at least one of: a transformer model, a convolutional neuralnetwork, a deep learning model trained on a set of outcomes of the digital product network, a supervised model, a semi -supervised model, an unsupervised model, a reinforcement model, a generative model, a large language model, and a regression model.

[0150] In some aspects, the techniques described herein relate to a method, wherein the determined action relates to at least one of: engaging secondary manufacturers or contract manufacturers to supplement production capacity, implementing overtime shifts at manufacturing facilities to temporarily increase production, redistributing inventory, simplifying product designs, implementing strategic stockpiling, supplementing production capacity with 3D printers, increasing storage and / or warehouse capacity, implementing cross-docking, improving return management, adjusting production settings, and implementing dynamic pricing strategies.

[0151] In some aspects, the techniques described herein relate to a method, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to generate a prediction of future inventory level requirement for each of a plurality of retail locations.

[0152] In some aspects, the techniques described herein relate to a method, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to generate a prediction of future inventory level requirement for each of a plurality of geographic regions.

[0153] In some aspects, the techniques described herein relate to a method, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to generate a prediction of future inventory level requirement for each of a plurality of sales channels.

[0154] In some aspects, the techniques described herein relate to a method further including providing at least one of real-time updates on the inventory level and alerts when the inventory level reaches a certain threshold.

[0155] In some aspects, the techniques described herein relate to a method, wherein the determined action is output to a digital twin user interface.

[0156] In some aspects, the techniques described herein relate to a digital product network system including one or more processors and one or more memories configured to perform operations including: receiving, by a computing device, information associated with a set of digitally connected products of a digital product network, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; providing the information to a first set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the first set of Al-based learning models is trained on a training data set of digital product network data to determine a classification of at least one of an operating state, a fault condition, an operating flow, and a behavior of at least one of the set of digitally connected products; providing the information to a second set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the second set of Al-based learning models is trained on a training data setof digital product network data to recommend at least one design improvement for the set of digitally connected products based on the classification; at least one of outputting the design improvement recommendation and providing a computer code instruction set to a machine to implement the design improvement associated with the set of digitally connected products based on the output of the Al-based learning models.

[0157] In some aspects, the techniques described herein relate to a digital product network system, wherein the design improvement includes at least one of modifying an existing product features, adding a new product feature, removing a product feature, replacing a product material, modifying a size of a product, modifying a size of a product part, replacing a product part, modifying a product color, modifying a product style, and rearranging a set of components of the product.

[0158] In some aspects, the techniques described herein relate to a digital product network system, wherein the information includes at least one of historical sales data, market data, social media data, customer data, economic sentiment data, economic data, supply chain data, inventory data, influencer data, lead time data, sensor data, product performance data, product feature data, product design data, product component data, market research data, competitive analysis data, economic indicator data, industry trend data, customer feedback data, customer service interaction data, order data, historical product development data, project management data, resource availability data, supplier data, supplier performance data, supplier pricing data, manufacturing data, quality control and testing data, regulatory and compliance data, historical recall data, supplier data, component data, quality control data, inspection data, failure data, warranty and service records, regulatory data, entertainment data, event data, regulatory compliance data, incident data, weather data, and accident report data.

[0159] In some aspects, the techniques described herein relate to a digital product network system, wherein each set of Al-based learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model trained on a set of outcomes of the digital product network, a supervised model, a semi-supervised model, an unsupervised model, a reinforcement model, a generative model, a large language model, and a regression model.

[0160] In some aspects, the techniques described herein relate to a digital product network system, wherein at least one member of a third set of Al-based learning models is trained on a training data set of digital product network data to predict an impact of the at least one design improvement.

[0161] In some aspects, the techniques described herein relate to a digital product network system, wherein the operations further include generating a virtual prototype of the improved product design.

[0162] In some aspects, the techniques described herein relate to a digital product network system, wherein at least one member of a third set of Al-based learning models is trained on a training data set of digital product network data to identify potential risks associated with implementing the at least one design improvement.

[0163] In some aspects, the techniques described herein relate to a digital product network system, wherein at least one member of a third set of Al-based learning models is trained on a training data set of digital product network data to generate a plan for validating the improved product design, including one or more of defining testing parameters, identifying testing resources, and setting up testing environments.

[0164] In some aspects, the techniques described herein relate to a digital product network system, wherein at least one member of a third set of Al-based learning models is trained on a training data set of digital product network data to generate a plan for launching and marketing the improved product design, including one or more of identifying target markets, developing marketing strategies, and planning marketing campaigns.

[0165] In some aspects, the techniques described herein relate to a digital product network system, wherein the operations further include determining a per-unit cost for the at least one design improvement.

[0166] In some aspects, the techniques described herein relate to a method for improving product design in a digital product network system, the method including: receiving, by a computing device, information associated with a set of digitally connected products of a digital product network, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; providing the information to a first set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the first set of Al-based learning models is trained on a training data set of digital product network data to determine a classification of at least one of an operating state, a fault condition, an operating flow, and a behavior of at least one of the set of digitally connected products; providing the information to a second set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the second set of Al-based learning models is trained on a training data set of digital product network data to determine at least one design improvement for the set of digitally connected products based on the classification; at least one of outputting the design improvement recommendation and providing a computer code instruction set to a machine to implement the design improvement associated with the digitally connected products based on the output of the Al-based learning models.

[0167] In some aspects, the techniques described herein relate to a method, wherein the design improvement includes one or more of modifying an existing product feature, adding a new product feature, removing a product feature, replacing a product material, modifying a size of a product, modifying a size of a product part, replacing a product part, modifying a product color, modifying a product style, and rearranging a set of components of the product.

[0168] In some aspects, the techniques described herein relate to a method, wherein the information includes at least one of historical sales data, market data, social media data, customer data, economic sentiment data, economic data, supply chain data, inventory data, influencer data, lead time data, sensor data, product performance data, product feature data, product design data,product component data, market research data, competitive analysis data, economic indicator data, industry trend data, customer feedback data, customer service interaction data, order data, historical product development data, project management data, resource availability data, supplier data, supplier performance data, supplier pricing data, manufacturing data, quality control and testing data, regulatory and compliance data, historical recall data, supplier data, component data, quality control data, inspection data, failure data, warranty and service records, regulatory data, entertainment data, event data, regulatory compliance data, incident data, weather data, and accident report data.

[0169] In some aspects, the techniques described herein relate to a method, wherein the set of Al-based learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model trained on a set of outcomes of the digital product network, a supervised model, a semi -supervised model, an unsupervised model, a reinforcement model, a generative model, a large language model, and a regression model.

[0170] In some aspects, the techniques described herein relate to a method, wherein at least one member of a third set of Al-based learning models is trained on a training data set of digital product network data to predict an impact of the at least one design improvement.

[0171] In some aspects, the techniques described herein relate to a method, wherein the operations further include generating a virtual prototype of the improved product design.

[0172] In some aspects, the techniques described herein relate to a method, wherein at least one member of a third set of Al-based learning models is trained on a training data set of digital product network data to identify potential risks associated with implementing the at least one design improvement.

[0173] In some aspects, the techniques described herein relate to a method, wherein at least one member of a third set of Al-based learning models is trained on a training data set of digital product network data to generate a plan for validating the improved product design, including one or more of defining testing parameters, identifying testing resources, and setting up testing environments.

[0174] In some aspects, the techniques described herein relate to a method, wherein at least one member of a third set of Al-based learning models is trained on a training data set of digital product network data to generate a plan for launching and marketing the improved product design, including one or more of identifying target markets, developing marketing strategies, and planning marketing campaigns.

[0175] In some aspects, the techniques described herein relate to a method, wherein the operations further include determining a per-unit cost for the at least one design improvement.

[0176] In some aspects, the techniques described herein relate to a digital product network system including one or more processors and one or more memories configured to perform operations including: receiving, by a computing device, information associated with a set of digitally connected products of a digital product network, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured topublish data relating to the set of digitally connected products; providing the information to a set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to determine at least one product feature preference for the set of digitally connected products for each location of a set of locations; and outputting the at least one product feature preference for the set of digitally connected products for each location of a set of locations.

[0177] In some aspects, the techniques described herein relate to a digital product network system, wherein the information includes at least one of historical sales data, market data, social media data, customer data, economic sentiment data, economic data, supply chain data, inventory data, influencer data, lead time data, sensor data, product performance data, product feature data, product design data, product component data, market research data, competitive analysis data, economic indicator data, industry trend data, customer feedback data, customer service interaction data, sales data, geographic data, demographic data, customer behavior data, historical product development data, project management data, resource availability data, supplier data, supplier performance data, supplier pricing data, manufacturing data, quality control and testing data, regulatory and compliance data, historical recall data, supplier data, component data, quality control data, inspection data, failure data, warranty and service records, regulatory data, entertainment data, event data, regulatory compliance data, incident data, weather data, and accident report data.

[0178] In some aspects, the techniques described herein relate to a digital product network system, wherein the set of Al-based learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model trained on a set of outcomes of the digital product network, a supervised model, a semi-supervised model, an unsupervised model, a reinforcement model, a generative model, a large language model, and a regression model.

[0179] In some aspects, the techniques described herein relate to a digital product network system, wherein the at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to determine at least one product feature preference for the set of digitally connected products for a plurality of geographic segments.

[0180] In some aspects, the techniques described herein relate to a digital product network system, wherein the operations further include determining a sales measure associated with each of the plurality of geographic segments.

[0181] In some aspects, the techniques described herein relate to a digital product network system, wherein the at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to determine at least one product feature preference for a plurality of demographic segments for the set of digitally connected products.

[0182] In some aspects, the techniques described herein relate to a digital product network system, wherein the operations further include determining a sales measure associated with each of the plurality of demographic segments.

[0183] In some aspects, the techniques described herein relate to a digital product network system, wherein the at least one member of the set of Al-based learning models is trained on atraining data set of digital product network data to determine at least one product feature preference for a plurality of consumer behavioral segments for the set of digitally connected products.

[0184] In some aspects, the techniques described herein relate to a digital product network system, wherein the operations further include determining a sales measure associated with each of the plurality of consumer behavioral segments.

[0185] In some aspects, the techniques described herein relate to a digital product network system, wherein the operations further include providing a recommendation for product development for the set of digitally connected products based on the determined at least one product feature preference, wherein the recommendation includes: modifying one or more of existing product features, adding a new product feature, removing a product feature, replacing a product material, modifying a size of a product, modifying a size of a product part, replacing a product part, modifying a product color, modifying a product style, or rearranging a set of components of the product.

[0186] In some aspects, the techniques described herein relate to a method for determining product feature preferences in a digital product network system, the method including: receiving, by a computing device, information associated with a set of digitally connected products of a digital product network, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; providing the information to a set of Artificial Intelligence (AI)- based learning models, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to determine at least one product feature preference for the set of digitally connected products for each of a set of locations; and outputting the at least one product feature preference for the set of digitally connected products for each location of a set of locations.

[0187] In some aspects, the techniques described herein relate to a method, wherein the information includes at least one of historical sales data, market data, social media data, customer data, economic sentiment data, economic data, supply chain data, inventory data, influencer data, lead time data, sensor data, product performance data, product feature data, product design data, product component data, market research data, competitive analysis data, economic indicator data, industry trend data, customer feedback data, customer service interaction data, sales data, geographic data, demographic data, customer behavior data, historical product development data, project management data, resource availability data, supplier data, supplier performance data, supplier pricing data, manufacturing data, quality control and testing data, regulatory and compliance data, historical recall data, supplier data, component data, quality control data, inspection data, failure data, warranty and service records, regulatory data, entertainment data, event data, regulatory compliance data, incident data, weather data, and accident report data.

[0188] In some aspects, the techniques described herein relate to a method, wherein the set of Al-based learning models includes at least one of: a transformer model, a convolutional neuralnetwork, a deep learning model trained on a set of outcomes of the digital product network, a supervised model, a semi -supervised model, an unsupervised model, a reinforcement model, a generative model, a large language model, and a regression model.

[0189] In some aspects, the techniques described herein relate to a method, wherein the at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to determine at least one product feature preference for a plurality of geographic segments for the set of digitally connected products.

[0190] In some aspects, the techniques described herein relate to the method of 186 further including determining a sales measure associated with each of the plurality of geographic segments.

[0191] In some aspects, the techniques described herein relate to a method, wherein the at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to determine at least one product feature preference for a plurality of demographic segments for the set of digitally connected products.

[0192] In some aspects, the techniques described herein relate to a method further including determining a sales measure associated with each of the plurality of demographic segments.

[0193] In some aspects, the techniques described herein relate to a method, wherein the at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to determine at least one product feature preference for a plurality of behavioral segments for the set of digitally connected products.

[0194] In some aspects, the techniques described herein relate to a method further including determining a sales measure associated with each of the plurality of behavioral segments.

[0195] In some aspects, the techniques described herein relate to a method further including providing a recommendation for product development for the set of digitally connected products based on the at least one product feature preference, wherein the recommendation includes: modifying one or more of existing product features, adding a new product feature, removing a product feature, replacing a product material, modifying a size of a product, modifying a size of a product part, replacing a product part, modifying a product color, modifying a product style, or rearranging a set of components of the product.

[0196] In some aspects, the techniques described herein relate to a generative artificial intelligence system for a digital product network, the system including one or more processors and one or more memories configured to perform operations including: executing, by the generative artificial intelligence system, generative artificial intelligence algorithms trained on digital product network data; receiving input data including at least one of images, video, audio, text, programmatic code, and data; processing the input data using the generative artificial intelligence algorithms to generate output content, wherein the output content includes at least one of structured prose, images, video, audio content, software source code, formatted data, algorithms, definitions, and context-specific structures; generating an internal state of the generative artificial intelligence system, including a set of weights and / or biases as a result ofprior processing; and providing the generated output content to a user interface for presentation to a user.

[0197] In some aspects, the techniques described herein relate to a generative artificial intelligence system, wherein the input data further includes natural -language expressions, single- or multidimensional shapes or models, real-world and / or virtual scene representations, LIDAR point-cloud representations, sensor inputs and / or outputs, vehicle and / or machine telemetry, geographic maps, authentication credentials, financial transactions, smart contracts, processing directives, and device configurations.

[0198] In some aspects, the techniques described herein relate to a generative artificial intelligence system, wherein the generative artificial intelligence system is configured to maintain contextual awareness across interactions to facilitate continuity in dialogue with users.

[0199] In some aspects, the techniques described herein relate to a generative artificial intelligence system, wherein the generative artificial intelligence system is configured to support interpretability and explainability of its outputs by providing a description of a basis of the output.

[0200] In some aspects, the techniques described herein relate to a generative artificial intelligence system, wherein the output includes a recommendation to improve at least one of the digital product network and a digitally connected product within the digital product network.

[0201] In some aspects, the techniques described herein relate to a generative artificial intelligence system, further including a digital twin interface for a digital twin, wherein the digital twin interface is configured to enable access to the generative artificial intelligence system, and wherein the digital twin represents at least one of the digital product network and a digitally connected product within the digital product network.

[0202] In some aspects, the techniques described herein relate to a generative artificial intelligence system, further including a digital twin module configured to generate a digital twin representing at least one of a digital product network and a digitally connected product within the digital product network.

[0203] In some aspects, the techniques described herein relate to a generative artificial intelligence system, wherein the processor is further configured to communicate with robotic systems within the digital product network, and the memory stores instructions that enable the generative artificial intelligence system to translate human commands into robotic actions, thereby facilitating human-to-robot interaction.

[0204] In some aspects, the techniques described herein relate to a generative artificial intelligence system, wherein the output content is further tailored based on an executive role of the user.

[0205] In some aspects, the techniques described herein relate to a generative artificial intelligence system, wherein the generated output includes 3D printing instructions for a set of 3D printers.

[0206] In some aspects, the techniques described herein relate to a generative artificial intelligence system, wherein the system is part of a Dual Process Artificial Neural Network (DP ANN) architecture.

[0207] In some aspects, the techniques described herein relate to a generative artificial intelligence system, further including an augmented reality (AR) interface configured to overlay the generated output content onto a user's real-world view.

[0208] In some aspects, the techniques described herein relate to a method for operating a generative artificial intelligence system within a value chain network, the method including: executing, by the generative artificial intelligence system, generative artificial intelligence algorithms trained on value chain network data; receiving input data including at least one of images, video, audio, text, programmatic code, and data; processing the input data using the generative artificial intelligence system to generate output content, wherein the output content includes at least one of structured prose, images, video, audio content, software source code, formatted data, algorithms, definitions, and context-specific structures; generating an internal state of the generative artificial intelligence system, including a set of weights and / or biases as a result of prior processing; and providing the generated output content to a user interface for presentation to a user.

[0209] In some aspects, the techniques described herein relate to a method, further including receiving additional input data including natural-language expressions, single- or multidimensional shapes or models, real -world and / or virtual scene representations, LIDAR point-cloud representations, sensor inputs and / or outputs, vehicle and / or machine telemetry, geographic maps, authentication credentials, financial transactions, smart contracts, processing directives, and device configurations.

[0210] In some aspects, the techniques described herein relate to a method, further including maintaining contextual awareness across interactions to facilitate continuity in dialogue with users.

[0211] In some aspects, the techniques described herein relate to a method, further including supporting interpretability and explainability of its outputs by providing a description of a basis of the output.

[0212] In some aspects, the techniques described herein relate to a method, wherein the generated output includes a recommendation to improve one or more of a digital product network and a digitally connected product within the digital product network.

[0213] In some aspects, the techniques described herein relate to a method, wherein the method further includes: interfacing with a digital twin via a digital twin interface, wherein the digital twin interface is configured to facilitate access to the generative artificial intelligence system; and representing, by the digital twin, one or more of a digital product network and a digitally connected product within the digital product network.

[0214] In some aspects, the techniques described herein relate to a method, wherein the method further includes: generating, through a digital twin module, a digital twin that represents at leastone or more of a digital product network and a digitally connected product within the digital product network.

[0215] In some aspects, the techniques described herein relate to a method, wherein the method further includes: communicating with one or more robotic systems within the value chain network; translating human commands into robotic actions; and facilitating human-to-robot interaction within the value chain network, thereby enabling the robotic systems to perform tasks in response to the human commands derived from the generated output content.

[0216] In some aspects, the techniques described herein relate to a method, wherein the method further includes: tailoring the output content based on an executive role of the user within an organization.

[0217] In some aspects, the techniques described herein relate to a method, wherein the generated output includes 3D printing instructions for a set of 3D printers.

[0218] In some aspects, the techniques described herein relate to a method, wherein the generative artificial intelligence system operates as part of a Dual Process Artificial Neural Network (DP ANN) architecture.

[0219] In some aspects, the techniques described herein relate to a method, wherein the method further includes: interfacing with an augmented reality (AR) interface; and overlaying, by the AR interface, the generated output content onto a user's real-world view.BRIEF DESCRIPTION OF THE DRAWINGS

[0220] 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:

[0221] FIG. 1 is a block diagram showing prior art relationships of various entities and facilities in a supply chain.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] FIG. 9 is a block diagram showing network relationships of entities in a value chain network in accordance with the present disclosure.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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.

[0254] 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.

[0255] 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.

[0256] 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.

[0257] 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.

[0258] 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.

[0259] 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 the present disclosure.

[0260] 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.

[0261] 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.

[0262] 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.

[0263] 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.

[0264] 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.

[0265] 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.

[0266] 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.

[0267] 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.

[0268] FIG. 48 is a block diagram showing components and relationships in embodiments of a value chain network management platform that uses a microservices architecture.

[0269] 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.

[0270] 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.

[0271] 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.

[0272] 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.

[0273] FIG. 53 is a diagrammatic view that depicts embodiments of a warehouse digital twin kit system in accordance with the present disclosure.

[0274] 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.

[0275] 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.

[0276] 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 the present disclosure.

[0277] 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.

[0278] 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.

[0279] 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

[0280] 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.

[0281] 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.

[0282] 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.

[0283] 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.

[0284] 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.

[0285] 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.

[0286] 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.

[0287] 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.

[0288] FIG. 69 is a schematic illustrating an example set of components of the enterprise control tower and management platform according to some embodiments of the present disclosure.

[0289] FIG. 70 is a schematic illustrating and example of an enterprise data model according to some embodiments of the disclosure.

[0290] 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.

[0291] 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.

[0292] FIG. 73 is a flow chart illustrating an example set of operations for configuring and serving an enterprise digital twin.

[0293] FIG. 74 illustrates an example set of operations of a method for configuring an organizational digital twin.

[0294] FIG. 75 illustrates an example set of operations of a method for generating an executive digital twin

[0295] FIG. 76 is a schematic illustrating an example intelligence services system according to some embodiments of the present disclosure.

[0296] FIG. 77 is a schematic illustrating an example neural network with multiple layers according to some embodiments of the present disclosure.

[0297] FIG. 78 is a schematic illustrating an example convolutional neural network (CNN) according to some embodiments of the present disclosure.

[0298] FIG. 79 is a schematic illustrating an example neural network for implementing natural language processing according to some embodiments of the present disclosure.

[0299] 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.

[0300] FIG. 81 is a schematic illustrating an example environment of a digital product network according to some embodiments of the present disclosure.

[0301] FIG. 82 is a schematic illustrating an example environment of a connected product according to some embodiments of the present disclosure.

[0302] FIG. 83 is a schematic illustrating an example environment of a digital product network according to some embodiments of the present disclosure.

[0303] FIG. 84 is a schematic illustrating an example environment of a digital product network according to some embodiments of the present disclosure.

[0304] FIG. 85 is a flow diagram illustrating a method of using product level data according to some embodiments of the disclosure.

[0305] FIG. 86 is a schematic illustrating an example environment of a digital product network according to some embodiments of the present disclosure.

[0306] FIG. 87 is a block diagram showing a schematic of a dual-process artificial neural network system according to some embodiments of the present disclosure.

[0307] FIG. 88 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.

[0308] FIG. 89 is an example flowchart of one or more VCN processes that may be used with one or more example implementations of the disclosure.

[0309] FIG. 90A is a schematic view of an example control architecture for system facilitation and / or management.

[0310] FIG. 90B is a schematic view of another example control architecture for system facilitation and / or management.

[0311] FIG. 90C is a schematic view of an example control architecture for system facilitation and / or management.

[0312] FIG. 90D is a schematic view of another example control architecture for system facilitation and / or management.

[0313] FIG. 91A is a schematic view of an example management stack that includes a control architecture.

[0314] FIG. 91B is a schematic view of an example management stack capable of implementing a control architecture.

[0315] FIG. 91C is a schematic view of an example management stack that includes a control architecture.

[0316] FIG. 91D is a schematic view of an example management stack capable of implementing a control architecture.

[0317] FIG. 92A is a flow diagram of an example arrangement for a control architecture.

[0318] FIG. 92B is a flow diagram of an example arrangement for a control architecture.

[0319] FIGS. 93 - FIG. 100 are example flowcharts of one or more VCN processes that may be used with one or more example implementations of the disclosure.

[0320] FIG. 101 is a schematic view of an example generative Al system.

[0321] FIG. 102 is a schematic view of an example of a determination of attention by a machine learning model.

[0322] FIG. 103 is a schematic view of an example of a transformer model.

[0323] FIG. 104 is a schematic view of a value chain network (VCN) converging technology stack.

[0324] FIG. 105 is a block diagram representation of the communication system of the digital product network system, according to some embodiments of the present disclosure.

[0325] FIG. 106 is a block diagram representation of the embedded marketplace of the digital product network system, according to some embodiments of the present disclosure.

[0326] FIG. 107 is an example flowchart of one or more DPNX processes that may be used with one or more example implementations of the disclosure.

[0327] FIG. 108 is an example flowchart of one or more DPNX processes that may be used with one or more example implementations of the disclosure.

[0328] FIG. 109 is an example flowchart of one or more DPNX processes that may be used with one or more example implementations of the disclosure.

[0329] FIG. 110 is an example flowchart of one or more DPNX processes that may be used with one or more example implementations of the disclosure.

[0330] FIG. I l l is an example flowchart of one or more DPNX processes that may be used with one or more example implementations of the disclosure.

[0331] FIG. 112 is an example flowchart of one or more DPNX processes that may be used with one or more example implementations of the disclosure.

[0332] FIG. 113 is an example flowchart of one or more DPNX processes that may be used with one or more example implementations of the disclosure.

[0333] Like reference symbols in the various drawings indicate like elementsDETAILED DESCRIPTION

[0334] 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.

[0335] 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.

[0336] 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 behundreds 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.

[0337] 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.

[0338] 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

[0339] 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.

[0340] 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 the 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 geographicdistribution 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.

[0341] Second, agile manufacturing capabilities (such as using 3D printing and robotic assembly techniques, among others), customer profiling technologies, and online ratings and reviews 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.

[0342] 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 fulfillment in 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.

[0343] 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 the 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

[0344] 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 / orother data configurations and transmission modalities received from, for example, digital product networks 21002, directly from customers (e.g., direct connected customer 250), or some other 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.

[0345] 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 intelligence and 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 network.DIGITAL PRODUCT NETWORKS (“DPN”)

[0346] 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

[0347] 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 COM AND PLATFORM

[0348] 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 tower,” 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 custom ers / 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

[0349] 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 / oroperating 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”).

[0350] 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, services, 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.

[0351] 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 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. In embodiments, the data handling layers 608 are configured in a topologythat 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.

[0352] 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 ormore 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, learn, 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 applicable examples 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

[0353] 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.

[0354] 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, homeproducts, 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-based devices, watches, glasses, hearables, head-worn devices, clothing- integrated devices, arm 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 services 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 otherwise 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, ina 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 a value 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.

[0355] 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

[0356] 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 provisionresources 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.

[0357] 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.

[0358] 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 amongvalue 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 A D APPLICATIONS MANAGED BY THE PLATFORM

[0359] 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 service, 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 servicetargeting 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 buying application, 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 pickingsystem, 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 planning 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). Thus, 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 datainfrastructure, 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.

[0360] 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, between 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).

[0361] 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 predictive maintenance application 910 (such as for anticipating, predicting, and undertaking actions to manage faults, failures, shutdowns, damage, required maintenance, required repairs, required service, 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 application912 (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 a smart 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).

[0362] 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 governinteractions 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 service, likelihood of failure, and many other factors).

[0363] 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 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); an opportunity 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, includingwork 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 PPLICATIONS)

[0364] 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, services, 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.

[0365] 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 network entity-oriented data storagesystems 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 that the value chain network management platform 604 may provide management of the 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 cloud-based 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 services (e.g., microservices), 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.

[0366] 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 within the platform 604 by the value chain monitoring systems layer 614. The value chain monitoring systems layer 614 may include, integrate with, and / orcooperate 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 entity in 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.

[0367] 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 ofmachines 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.

[0368] 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.

[0369] 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.) of similar 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.

[0370] 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.

[0371] 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 elsewhere throughout 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, cloudbased 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 anoperator, 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 planning 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 service, 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.

[0372] 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 other capabilities that optimize control, automation, or one or more performance characteristics of one or morevalue 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

[0373] 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.

[0374] 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.

[0375] 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 11 0), distributed storage systems, databases, memory, network-based storage, network-attached storage systems (e.g., part of the network storage architectures 1174such as using NVME, storage attached networks, and other network storage systems), and many others.

[0376] 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) coupledwith 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, or the 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

[0377] 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.

[0378] 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.

[0379] 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.

[0380] 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 and equipment; 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 11 0 (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 andequipment, 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 1172 (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, motiondetection 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-indicating behavior (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.

[0381] 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, Wifi 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 ahandshake among a set of devices, such as to initiate interactions that serve various applications 630 or other capabilities of the platform 604.

[0382] 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.

[0383] 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 network, 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.

[0384] 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, goods design 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.

[0385] 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.

[0386] In embodiments, the 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.

[0387] 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.

[0388] In embodiments, providing coordinated intelligence for the 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 demandaggregation 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.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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 1160may 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.

[0393] 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 be met. 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.

[0394] 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.

[0395] 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.

[0396] 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 aproduct 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.

[0397] 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 other things, 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 deliver two 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 614 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 demandmanagement 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.

[0398] 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.

[0399] 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. In embodiments, 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).

[0400] 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 the like.

[0401] 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.

[0402] 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 of adjustments in supply required to meet demand. Other predictionsinclude 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.

[0403] 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 the machine.

[0404] In embodiments, the set of predictions 3070 may be provided by the management platform 102 directly through a set of adaptive artificial intelligence systems.

[0405] 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.

[0406] 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.

[0407] 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, which may be produced, marketed, sold, resold, rented, leased, given away, 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 adaptiveintelligence 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 adapted intelligence 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.

[0408] 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 marketing application and the like. Such a classification may be useful in determining how to adjust a market expectation for a good that is going to have a lower yield than previously anticipated. The converse may also be true in that the set of classifications 3080 includes at least one classification of a current state of a demand management application and its relationship to a coordinated supply chain application. In embodiments, the set of classifications 3080 is a set of classifications of adjustments in supply required to meet demand, such as adjustments to production worker needs would be classified differently that adjustments in third-party logistics providers. Other classifications may include at least one classification of perceived changes in demand and a resulting potential impact on supply management. Yet other classifications in the set of classifications may include a supply chain application impact on 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 classification in the set of classifications may be as simple as classifying a likelihood that a supply of a good in the category of goods will not meet demand set by a demand setting application.

[0409] In embodiments, the adaptive intelligence systems 614 may provide a set of artificial intelligence capabilities to facilitate providing the set of classifications 3080 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 classify fault conditions or problem states of a demand management application, such as a classification of a lack of sufficient validated feedback. The probabilistic neural network may be used to classify a problem state of 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) as pertaining to at least one of machine operating information and preventive maintenance information for the machine.

[0410] In embodiments, the set of classifications 3080 may be provided by the management platform 102 directly through a set of adaptive artificial intelligence systems. Further, the set of classifications 3080 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.

[0411] In embodiments, the set of classifications 3080 may be classifications 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 outcomes that are classified as desirable (e g., viable candidates for a coordinated set of applications) and outcomes that are classified as undesirable.

[0412] In embodiments, the set of classifications may comprise a set of adaptive intelligence functions, such as a neural network that may be adapted to classify information associated with the category of goods. In an example, the neural network may be a multilayered feed forward neural network.

[0413] In embodiments, performing classifications may include classifying discovered value chain entities as one of demand centric and supply centric.

[0414] In embodiments, the set of classifications 3080 may be achieved through use of artificial intelligence systems 1160 for coordinating the set of coordinated demand management and supply chain applications. Artificial intelligence systems may configure and generate sets of classifications 3080 as a means by which demand management applications and supply chain applications can be coordinated. In an example, classification of information flow throughout a value chain may be classified as being relevant to both a demand management application and a supply chain application; this common relevance may be a point of coordination among the applications. In embodiments, the set of classifications may be artificial intelligence generated classifications of outcomes of operating a supply chain that is dependent on the coordinated demand management applications 824 and supply chain applications 812.

[0415] Referring to FIG. 18, 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 achieving automated control intelligence. 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 automated control signaling 3092 for 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 automated control signals 3092 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 automatically control the sets of supply 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.

[0416] In embodiments, the user interface 3020 may include interfaces for configuring an adaptive intelligence systems 614 to take inputs from selected data sources of the value chain 3094 (such as data sources used by the coordinated set of demand management applications 824 and / or the set of supply chain applications 812) and supply them, such as to a neural network, 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 for producing automated control signals 3092, such as 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 used for determining aspects of the automated control signals, such as for temporal adjustments to control outcomes relating to the value chain at least for the category of goods 3010 and the like.

[0417] In an example, the set of automated control signals may include at least one control signal for automating execution of a supply chain application, such as a production start, an automated material order, an inventory check, a billing application and the like in the coordinated set of demand management applications and supply chain applications. In yet another example of automated control signal generation, the set of automated control signals may include at least one control signal for automating execution of a demand management application, such as a product recall application, an email distribution application and the like in the coordinated set of demand management applications and supply chain applications. In yet other examples, the automate control signals may control timing of demand management applications based on goods supply status.

[0418] In embodiments, the adaptive intelligence systems 614 may apply machine learning to outcomes of supply to automatically adapt a set of demand management application control signals. Similarly, the adaptive intelligence systems 614 may apply machine learning to outcomes of demand management to automatically adapt a set of supply chain application control signals. The adaptive intelligence systems 614 may provide further processing for automated control signal generation, such as by applying artificial intelligence to determine aspects of a value chain that impact automated control of the coordinated set of demand management applications and supply chain applications for a category of goods. The determined aspects could be used in the generation and operation of automated control intelligence / signals, such as by filtering out value chain information for aspects that do not impact the targeted demand management and supply chain applications.

[0419] Automated control of, for example, supply chain applications may be restricted, such as by policy, operational limits, safety constraints and the like. The set of adaptive intelligence systems may determine a range of supply chain application control values within which controlcan be automated. In embodiments, the range may be associated with a supply rate, a supply timing rate, a mix of goods in a category of goods, and the like.

[0420] Embodiments are described herein for using artificial intelligence systems or capabilities to identify, configure and regulate automated control signals. Such embodiments may further include a closed loop of feedback from the coordinated set of demand management and supply chain applications (e.g., state information, output information, outcomes and the like) that is optionally processed with machine learning and used to adapt the automated control signals for at least one of the goods in the category of goods. An automated control signal may be adapted based on, for example, an indication of feedback from a supply chain application that yield of a good suggests a production problem. In this example, the automated control signal may impact production rate and the feedback may cause the signal to automatically self-adjust to a slower production rate until the production problem is resolved.

[0421] Referring to FIG. 19, 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 information routing recommendations. The management platform includes a set of value chain networks 3102 from which network data 3110 is collected from a set of information routing activities, the information including outcomes, parameters, routing activity information and the like. Within the set of value chain networks 3102 is selected a select value chain network 3104 for which at least one information routing recommendation 3130 is provided. An artificial intelligence system 1160 may include a machine learning system and may be trained using a training set derived from the network data 3110 outcomes, parameters and routing activity information for the set of value chain networks 3102. The artificial intelligence system 1160 may further provide an information routing recommendation 3130 based on a current status 3120 of the select value chain network 3104. The artificial intelligence system may use machine learning to train on information transaction types within the set of value chain networks 3102, thereby learning pertinent factors regarding different transaction types (e.g., real-time inventory updates, buyer credit checks, engineering signoff, and the like) and contributing to the information routing recommendation accordingly. The artificial intelligence system may also use machine learning to train on information value for different types and / or classes of information routed in and throughout the set of value chain networks 3102. Information may be valued on a wide range of factors, including timing of information availability and timing of information consumption as well as information content-based value, such as information without which a value chain network element (e.g., a production provider) cannot perform a desired action (e.g., starting volume production without a work order). Therefore information routing recommendations may be based on training on transaction type, information value, and a combination thereof. These are merely exemplary information routing recommendation training and recommendation basis factors and are presented here without limitation on other elements for training and recommendation basis.

[0422] In embodiments, the artificial intelligence system 1160 may provide an information routing recommendation 3130 based on transaction type, transaction type and information type,network type and the like. An information routing recommendation may be based on combinations of factors, such as information type and network type, such as when an information type (streaming) is not compatible with a network type (small transactions).

[0423] In embodiments, the artificial intelligence system 1160 may use machine learning to develop an understanding of networks within the selected value chain network 3104, such as network topology, network loading, network reliability, network latency and the like. This understanding may be combined with, for example, detected or anticipated network conditions to form an information routing recommendation. Aspects such as existence of edge intelligence in a value chain network 3104 can influence one or more information routing recommendations. In an example, a type of information may be incompatible with a network type; however the network may be configured with edge intelligence that can be leveraged by the artificial intelligence system 1160 to adapt the form of the information being routed so that it is compatible with a targeted network type. This is also an example of more general consideration for information routing recommendation - network resources (e.g., presence, availability, and capability), such as edge computing, server access, network-based storage resources and the like. Likewise, value chain network entities may impact information routing recommendations. In embodiments, an information routing recommendation may avoid routing information that is confidential to a first supplier in the value chain through network nodes controlled by competitors of the supplier. In embodiments, an information routing recommendation may include routing information to a first node where it is partially consumed and partially processed for further routing, such as by splitting up the portion partially processed for further routing into destination-specific information sets.

[0424] In embodiments, an artificial intelligence system 1160 may provide an information routing recommendation based on goals, such as goals of a value chain network, goals of information routing, and the like. Goal-based information routing recommendations may include routing goals, such as Quality of Service routing goals, routing reliability goals (which may be measured based on a transmission failure rate and the like). Other goals may include a measure of latency associated with one or more candidate routes. An information routing recommendation may be based on the availability of information in a selected value chain network, such as when information is available and when it needs to be delivered. For information that is available well ahead of when it is needed (e.g., a nightly production report that is available for routing at 2 AM is first needed by 7 AM), routing recommendations may include using resources that are lower cost, may involve short delays in routing and the like. For information that is available just before it is needed (e.g., a result of product testing is needed within a few hundred milliseconds of when the test is finished to maintain a production operation rate, and the like).

[0425] An information routing recommendation may be formed by the artificial intelligence system 1160 based on information persistence factors, such as how long information is available for immediate routing within the value chain network. An information routing recommendation that factors information persistence may select network resources based on availability, cost and the like during a time of information persistence.

[0426] Information value and an impact on information value may factor into an information routing recommendation. As an example, information that is valid for a single shipment (e.g., a production run of a good) may substantively lose value once the shipment has been satisfactorily received. In such an example, an information routing recommendation may indicate routing the relevant information to all of the highest priority consumers of the information while it is still valid. Likewise, routing of information that is consumed by more than one value chain entity may need to be coordinated so that each value chain entity receives the information at a desired time / moment, such as during the same production shift, at their start of day, which may be different if the entities are in different time zones, and the like.

[0427] In embodiments, information routing recommendations may be based on a topology of a value chain, based on location and availability of network storage resources, and the like.

[0428] In embodiments, one or more information routing recommendations may be adapted while the information is routed based on, for example, changes in network resource availability, network resource discovery, network dynamic loading, priority of recommendations that are generated after information for a first recommendation is in-route, and the like.

[0429] Referring to FIG. 20, 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 semi-sentient problem recognitions of pain points in a value chain network. The management platform includes a set of value chain network entities 3152 from which entity -related data 3160 is collected and includes outcomes, parameters, activity information and the like associated with the entities. Within the set of value chain network entities 3152 is selected a set of select value chain network entities 3154 for which at least one pain point problem state 3172 is detected. An artificial intelligence system 1160 may be training on a training set derived from the entity-related data 3160 including training on outcomes associated with value chain entities, parameters associated with, for example, operation of the value chain, value chain activity information and the like. The artificial intelligence system may further employ machine learning to facilitate learning problem state factors 3180 that may characterize problem states input as training data. These factors 3180 may further be used by an instance of artificial intelligence 1160’ that operates on computing resources 3170 that are local to value chain network entities that are experiencing the problem / result of a pain point. A goal of such a configuration of artificial intelligence systems, data sets, and value chain networks is to recognize a problem state in a portion of the selected value chain.

[0430] In embodiments, recognizing problem states may be based on variance analysis, such as variances that occur in value chain measures (e.g., loading, latency, delivery time, cost, and the like), particularly in a specific measure over time. Variances that exceed a variance threshold (e.g., an optionally dynamic range of results of a value chain operation, such as production, shipping, clearing customs, and the like) may be indicative of a pain point.

[0431] In addition to detecting problem states, the platform 102, such as through the methods of semi-sentient problem recognition, predict a pain point based at least in part on a correlation witha detected problem state. The correlation may be derived from the value chain, such as a shipper cannot deliver international goods until they are processed through customs, or a sales forecast cannot be provided with a high degree of confidence without high quality field data and the like. In embodiments, a predicted pain point may be a point of value chain activity further along a supply chain, an activity that occurs in a related activity (e.g., tax planning is related to tax laws), and the like. A predicted pain point may be assigned a risk value based on aspects of the detected problem state and correlations between the predicted pain point activity and the problem state activity. If a production operation can receive materials from two suppliers, a problem state with one of the suppliers may indicate a low risk of a pain point of use of the material. Likewise, if a demand management application indicates high demand for a good and a problem is detected with information on which the demand is based, a risk of excess inventory (pain point) may be high depending on, for example how far along in the value chain the good has progressed.

[0432] In embodiments, semi-sentient problem recognition may involve more than mere linkages of data and operational states of entities engaged in a value chain. Problem recognition may also be based on human factors, such as perceived stress of production supervisors, shippers, and the like. Human factors for use in semi-sentient problem recognition may be collected from sensors that facilitate detection of human stress level and the like (e.g., wearable physiological sensors, and the like).

[0433] In embodiments, semi-sentient problem recognition may also be based on unstructured information, such as digital communication, voice messaging, and the like that may be shared among, originate with, or be received by humans involved in the value chain operations. As an example, natural language processing of email communications among workers in an enterprise may indicate a degree of discomfort with, for example, a supplier to a value chain. While data associated with the supplier (e g., on-time production, quality, and the like) may be within a variance range deemed acceptable, information within this unstructured content may indicate a potential pain point, such as a personal issue with a key participant at the supplier and the like. By employing natural language processing, artificial intelligence, and optionally machine learning, problem state recognition may be enhanced.

[0434] In embodiments, semi-sentient problem recognition may be based on analysis of variances of measures of a value chain operation / entity / application including variance of a given measure over time, variance of two related measures, and the like. In embodiments, variance in outcomes over time may indicate a problem state and / or suggest a pain point. In embodiments, an artificial intelligence-based system may determine an acceptable range of outcome variance and apply that range to measures of a select set of value chain network entities, such as entities that share one or more similarities, to facilitate detection of a problem state. In embodiments, an acceptable range of outcome variance may indicate a problem state trigger threshold that may be used by a local instance of artificial intelligence to signal a problem state. In such a scenario, a problem state may be detected when at least one measure of the value chain activity / entity and the like is greater than the artificial intelligence-determined problem state threshold. Variance analysis for problem state detection may include detecting variances in start / end times ofscheduled value chain network entity activities, variances in at least one of production time, production quality, production rate, production start time, production resource availability or trends thereof, variances in a measure of shipping supply chain entity, variances in a duration of time for transfer from one mode of transport to another (e.g., when the variance is greater than a transport mode problem state threshold), variances in quality testing, and the like.

[0435] In embodiments, a semi-sentient problem recognition system may include a machine learning / artificial intelligence prediction of a correlated pain point further along a supply chain due to a detected pain point, such as a risk and / or need for overtime, expedited shipping, discounting goods prices, and the like.

[0436] In embodiments, a machine learning / artificial intelligence system may process outcomes, parameters, and data collected from a set of data sources relating to a set of value chain entities and activities to detect at least one pain point selected from the list of pain points consisting of late shipment, damaged container, damaged goods, wrong goods, customs delay, unpaid duties, weather event, damaged infrastructure, blocked waterway, incompatible infrastructure, congested port, congested handling infrastructure, congested roadway, congested distribution center, rejected goods, returned goods, waste material, wasted energy, wasted labor force, untrained workforce, poor customer service, empty transport vehicle on return route, excessive fuel prices, excessive tariffs, and the like.

[0437] Referring to FIG. 21, 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 automated coordination of a set of value chain network activities for a set of products of an enterprise. The management platform includes a set of network-connected value chain network entities 3202 that produce activity information 3208 that is used by an artificial intelligence system 1160 to provide automate coordination 3220 of value chain network activities 3212 for a set of products 3210 for an enterprise 3204. In embodiments, value chain monitoring systems 614 may monitor activities of the set of network-connected value chain entities 3202 and work cooperatively with data collection and management systems 640 to gather and store value chain entity monitored information, such as activity information, configuration information, and the like. This gathered information may be configured as activity information 3208 for a set of activities associated with a set of products 3210 of an enterprise 3204. In embodiments, the artificial intelligence systems 1160 may use application programming connectivity facilities 642 for automating access to the monitored activity information 3208.

[0438] A value chain may include a plurality of interconnected entities that each perform several activities for completing the value chain. While humans play a critical role in some activities within a value chain network, greater automated coordination and unified orchestration of supply and demand may be achieved using artificial intelligence-type systems (e.g., machine learning, expert systems, self-organizing systems, and the like including such systems describe herein and in the documents incorporated herein by reference) for coordinating supply chain activities. Use of artificial intelligence may further enrich the emerging nature of self-adaptingsystems, including Internet of Things (loT) devices and intelligent products and the like that not only provide greater capabilities to end users, but can play a critical role in automated coordination of supply chain activities

[0439] 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. The workflow may be configured by an artificial intelligence system 1160 that analyzes the problem with the product 1510, develops an understanding of value chain network activities that produce the product, determines resources required for the workflow, coordinates with inventory and production systems to adapt any existing workflows and the like. Artificial intelligence systems 1160 may further coordinate with demand management applications to address any temporary impact on product availability and the like.

[0440] In embodiments, automated coordination of a set of value chain network activities for a set of products for an enterprise may rely on the methods and systems of coordinated intelligence described herein, such as to facilitate coordinating demand management activities, supply chain activities and the like, optionally using artificial intelligence for providing the coordinated intelligence, coordinating the activities and the like. As an example, artificial intelligence may facilitate determining relationships among value change network activities based on inputs used by the activities and results produced by the activities. Artificial intelligence may be integrated with and / or work cooperatively with activities of the platform, such as value chain network entity activities to continuously monitor activities, identify temporal aspects needing coordination (e.g., when changes in supply temporally impact demand activities), and automate such coordination. Automated coordination of value chain network activities within and across value chain network entity activities may benefit from advanced artificial intelligence systems that may enable use of differing artificial intelligence capabilities for any given value chain set of entities, applications, or conditions. Use of hybrid artificial intelligence systems may provide benefits by applying more than one type of intelligence to a set of conditions to facilitate human and / or computer automated selection thereof. Artificial intelligence can further enhance automated coordination of value chain network entity activities through intelligent operations such as generating sets of predictions, sets of classifications, generation of automate control signals (that may be communicated across value chain network entities and the like). Other exemplary artificial intelligence-based influences on automated coordination of value chain network entity activities include machine learning-based information routing and recommendations thereto, semi-sentient problem recognition based on both structured (e.g., production data) and unstructured (e.g., human emotions) sources, and the like. Artificial intelligence systems may facilitate automated coordination of value chain network entity activities for a set of products or an enterprise based on adaptive intelligence provided by the platform for a category of goods under which the set of products of an enterprise may be grouped. In an example, adaptive intelligence may be providedby the platform for a drapery hanging category of goods and a set of products for an enterprise may include a line of adaptable drapery hangers. Through understanding developed for the overall drapery hanging category, artificial intelligence capabilities may be applied to value chain network activities of the enterprise for automating aspects of the value chain, such as information exchange among activities and the like.DIGITAL TWIN SYSTEM IN VALUE CHAIN ENTITY MANAGEMENT PLATFORM

[0441] Referring to FIG. 22, the adaptive intelligence layer 614 may include a value chain network digital twin system 1700, which may include a set of components, processes, services, interfaces and other elements for development and deployment of digital twin capabilities for visualization of various value chain entities 652, environments, and applications 630, as well as for coordinated intelligence (including artificial intelligence 1160, edge intelligence 1400, analytics and other capabilities) and other value-added services and capabilities that are enabled or facilitated with a digital twin 1700. Without limitation, a digital twin 1700 may be used for and / or applied to each of the processes that are managed, controlled, or mediated by each of the set of applications 614 of the platform application layer.

[0442] In embodiments, the digital twin 1700 may take advantage of the presence of multiple applications 630 within the value chain management platform 604, such that a pair of applications may share data sources (such as in the data storage layer 624) and other inputs (such as from the monitoring layer 614) that are collected with respect to value chain entities 652, as well as sharing outputs, events, state information and outputs, which collectively may provide a much richer environment for enriching content in a digital twin 1700, including through use of artificial intelligence 1160 (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) and through use of content collected by the monitoring layer 614 and data collection systems 640.

[0443] In embodiments, a digital twin 1700 may be used in connection with shared or converged processes among the various pairs of the applications 630 of the application 604, such as, without limitation, of a converged process involving a security application 834 and an inventory management application 820, integrated automation of blockchain-based applications 844 with facility management applications 850, and many others. In embodiments, converged processes may include shared data structures for multiple applications 630 (including ones that track the same transactions on a blockchain but may consume different subsets of available attributes of the data objects maintained in the blockchain or ones that use a set of nodes and links in a common knowledge graph) that may be connected to with the digital twin 1700 such that the digital twin 1700 is updated accordingly. For example, a transaction indicating a change of ownership of an entity 652 may be stored in a blockchain and used by multiple applications 630, such as to enable role-based access control, role-based permissions for remote control, identity-based event reporting, and the like that may be connected to and shared with the digital twin 1700 such that the digital twin 1700 may be updated accordingly. In embodiments,converged processes may include shared process flows across applications 630, including subsets of larger flows that are involved in one or more of a set of applications 614 that may be connected to and shared with the digital twin 1700 such that the digital twin 1700 may be updated accordingly. For example, an inspection flow about a value chain network entity 652 may serve an analytics solution 838, an asset management solution 814, and others.

[0444] In embodiments, a digital twin 1700 may be provided for the wide range of value chain network applications 630 mentioned throughout this disclosure and the documents incorporated herein by reference. An environment for development of a digital twin 1700 may include a set of interfaces for developers in which a developer may configure an artificial intelligence system 1160 to take inputs from selected data sources of the data storage layer 624 and events or other data from the monitoring systems layer 614 and supply them for inclusion in a digital twin 1700. A digital twin 1700 development environment may be configured to take outputs and outcomes from various applications 630.VALUE CHAIN NETWORK DIGITAL TWINS

[0445] Referring to FIG. 23, any of the value chain network entities 652 can be depicted in a set of one or more digital twins 1700, such as by populating the digital twin 1700 with value chain network data object 1004, such as event data 1034, state data 1140, or other data with respect to value chain network entities 652, applications 630, or components or elements of the platform 604 as described throughout this disclosure.

[0446] Thus, the platform 604 may include, integrate, integrate with, manage, control, coordinate with, or otherwise handle any of a wide variety of digital twins 1700, such as distribution twins 1714 (such as representing distribution facilities, assets, objects, workers, or the like); warehousing twins 1712 (such as representing warehouse facilities, assets, objects, workers and the like); port infrastructure twins 1714 (such as representing a seaport, an airport, or other facility, as well as assets, objects, workers and the like); shipping facility twins 1720; operating facility twins 1722; customer twins 1730 (such as representing physical, behavioral, demographic, psychographic, financial, historical, affinity, interest, and other characteristics of groups of customers or individual customers); worker twins 1740 (such as representing physical attributes, physiologic data, status data, psychographic information, emotional states, states of fatigue / energy, states of attention, skills, training, competencies, roles, authority, responsibilities, work status, activities, and other attributes of or involving workers); wearable / portable device twins 1750; process twins 1760; machine twins 21010 (such as for various machines used to support a value chain network 668); product twins 1780; point of origin twins 1560; supplier twins 1630; supply factor twins 1650; maritime facility twins 1572; floating asset twins 1570; shipyard twins 1620; destination twins 1562; fulfillment twins 1600; delivery system twins 1610; demand factor twins 1640; retailer twins 1790; ecommerce and online site and operator twins 1800; waterway twins 1810; roadway twins 1820; railway twins 1830; air facility twins 1840 (such as twins of aircraft, runways, airports, hangars, warehouses, air travel routes, refueling facilities and other assets, objects, workers and the like used in connection with air transport of products 650); autonomous vehicle twins 1850; robotics twins 1860; drone twins 1870; andlogistics factor twins 1880; among others. Each of these may have characteristics of digital twins described throughout this disclosure and the documents incorporated by reference herein, such as mirroring or reflecting changes in states of associated physical objects or other entities, providing capabilities for modeling behavior or interactions of associated physical objects or other entities, enabling simulations, providing indications of status, and many others.

[0447] In example embodiments, a digital twin system may be configured to generate a variety of enterprise digital twins 1700 in connection with a value chain (e.g., specifically value chain network entities 652). For example, an enterprise that produces goods internationally (or at multiple facilities) may configure a set of digital twins 1700, such as supplier twins that depict the enterprise’s supply chain, factory twins of the various production facilities, product twins that represent the products made by the enterprise, distribution twins that represent the enterprise’s distribution chains, and other suitable twins. In doing so, the enterprise may define the structural elements of each respective digital twin as well as any system data that corresponds to the structural elements of the digital twin. For instance, in generating a production facility twin, the enterprise may the layout and spatial definitions of the facility and any processes that are performed in the facility. The enterprise may also define data sources corresponding to the value chain network entities 652, such as sensor systems, smart manufacturing equipment, inventory systems, logistics systems, and the like that provide data relevant to the facility. The enterprise may associate the data sources with elements of the production facility and / or the processes occurring the facility. Similarly, the enterprise may define the structural, process, and layout definitions of its supply chain and its distribution chain and may connect relevant data sources, such as supplier databases, logistics platforms, to generate respective distribution chain and supply chain twins. The enterprise may further associate these digital twins to have a view of its value chain. In embodiments, the digital twin system may perform simulations of the enterprise’s value chain that incorporate real-time data obtained from the various value chain network entities 652 of the enterprise. In some of these embodiments, the digital twin system may recommend decisions to a user interacting with the enterprise digital twins 1700, such as when to order certain parts for manufacturing a certain product given a predicted demand for the manufactured product, when to schedule maintenance on machinery and / or replace machinery (e.g., when digital simulations on the digital twin indicates the demand for certain products may be the lowest or when it would have the least effect on the enterprise’s profits and losses statement), what time of day to ship items, or the like. The foregoing example is a non-limiting example of the manner by which a digital twin may ingest system data and perform simulations in order to further one or more goals.ENTITY DISCOVERY AND INTERACTION MANAGEMENT

[0448] Referring to FIG. 24, the monitoring systems layer 614, including various data collection systems 640 (such as loT data collection systems, data collection systems that search social networks, websites, and other online resources, crowdsourcing systems, and others) may include a set of entity discovery systems 1900, such as for identifying sets of value chain network entities 652, identifying types of value chain network entities 652, identifying specific valuechain network entities 652 and the like, as well as for managing identities of the value chain network entities 652, including for resolving ambiguities (such as where a single entity is identified differently in different systems, where different entities are identified similarly, and the like), for entity identity deduplication, for entity identity resolution, for entity identity enhancement (such as by enriching data objects with additional data that is collected about an entity within the platform), and the like. Entity discovery 1900 may also include discovery of interactions among entities, such as how entities are connected (e.g., by what network connections, data integration systems, and / or interfaces), what data is exchanged among entities (including what types of data objects are exchanged, what common workflows involve entities, what inputs and outputs are exchanged between entities, and the like), what rules or policies govern the entities, and the like. The platform 604 may include a set of entity interaction management systems 1902, which may comprise one or more artificial intelligence systems (including any of the types described throughout this disclosure) for managing a set of interactions among entities that are discovered through entity discovery 1900, including ones that learn on a training set of data to manage interactions among entities based on how entities have been managed by human supervisors or by other systems.

[0449] As an illustrative example among many possible ones, the entity discovery system 1900 may be used to discover a network-connected camera that shows the loading dock of facility that produces a product for an enterprise, as well as to identify what interfaces or protocols are needed to access a feed of video content from the camera. The entity interaction management system 1902 may then be used to interact with the interfaces or protocols to set up access to the feed and to provide the feed to another system for further processing, such as to have an artificial intelligence system 1160 process the feed to discovery content that is relevant to an activity of the enterprise For example, the artificial intelligence system 1160 may process image frames of the video feed to find markings (such as produce labels, SKUs, images, logos, or the like), shapes (such as packages of a particular size or shape), activities (such as loading or unloading activities) or the like that may indicate that a product has moved through the loading dock. This information may substitute for, augment, or be used to validate other information, such as RFID tracking information or the like. Similar discovery and interaction management activities may be undertaken with any of the types of value chain network entities 652 described throughout this disclosure.ROBOTIC PROCESS AUTOMATION IN VALUE CHAIN NETWORK

[0450] Referring to FIG. 25, the adaptive intelligence layer 614 may include a robotic process automation (RPA) system 1442, which may include a set of components, processes, services, interfaces and other elements for development and deployment of automation capabilities for various value chain entities 652, environments, and applications 630. Without limitation, robotic process automation 1442 may be applied to each of the processes that are managed, controlled, or mediated by each of the set of applications 614 of the platform application layer, to functions, components, workflows, processes of the VCNP 604 itself, to processes involving value chain network entities 652 and other processes.

[0451] In embodiments, robotic process automation 1442 may take advantage of the presence of multiple applications 630 within the value chain management platform 604, such that a pair of applications may share data sources (such as in the data storage layer 624) and other inputs (such as from the monitoring layer 614) that are collected with respect to value chain entities 652, as well as sharing outputs, events, state information and outputs, which collectively may provide a much richer environment for process automation, including through use of artificial intelligence 1160 (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). For example, an asset management application 814 may use robotic process automation 1442 for automation of an asset inspection process that is normally performed or supervised by a human (such as by automating a process involving visual inspection using video or still images from a camera or other that displays images of an entity 652, such as where the robotic process automation 1442 system is trained to automate the inspection by observing interactions of a set of human inspectors or supervisors with an interface that is used to identify, diagnose, measure, parameterize, or otherwise characterize possible defects or favorable characteristics of a facility or other asset. In embodiments, interactions of the human inspectors or supervisors may include a labeled data set where labels or tags indicate types of defects, favorable properties, or other characteristics, such that a machine learning system can learn, using the training data set, to identify the same characteristics, which in turn can be used to automate the inspection process such that defects or favorable properties are automatically classified and detected in a set of video or still images, which in turn can be used within the value chain network asset management application 814 to flag items that require further inspection, that should be rejected, that should be disclosed to a prospective buyer, that should be remediated, or the like. In embodiments, robotic process automation 1442 may involve multi -application or cross-application sharing of inputs, data structures, data sources, events, states, outputs or outcomes. For example, the asset management application 814 may receive information from a marketplace application 854 that may enrich the robotic process automation 1442 of the asset management application 814, such as information about the current characteristics of an item from a particular vendor in the supply chain for an asset, which may assist in populating the characteristics about the asset for purposes of facilitating an inspection process, a negotiation process, a delivery process, or the like. These and many other examples of multi-application or cross-application sharing for robotic process automation 1442 across the applications 630 are encompassed by the present disclosure. Robotic...

Claims

CLAIMS1. A digital product network system comprising: a product-to-product communication module configured to exchange inter-product communications for a plurality of digitally connected products; a product-to-user communication module configured to exchange product-to-user communications between the plurality of digitally connected products and their respective users; a product-to-business communication module configured to exchange product-to- user communications between the plurality of digitally connected products and their associated enterprises; a data processing module configured to process the inter-product communications, product-to-user communications, and the product-to-business communications to determine time-sensitive alerts related to corresponding one of the plurality of digitally connected products; and a graphical user interface (GUI) module configured to generate one or more user interfaces for displaying a time-sensitive alerts.

2. The digital product network system of claim 1, wherein the time-sensitive alerts comprise at least one of a low battery warning, a hardware failure notification, a software update notification, a connectivity issue notification, an unauthorized access warning, a maintenance reminder, a temperature fluctuation notification, a performance degradation notification, a storage capacity warning, a firmware upgrade alert, a scheduled backup reminder, a data breach notification, a device inactivity alert, a device overactivity or device overheating warning, a compatibility or synchronization issue with other devices, a warranty expiration reminder, a service subscription renewal alert, a sensor malfunction notification, an environmental hazard alert, a device location or movement alert, a user account change, a login notification from an unfamiliar location, a device lifecycle stage transition alert, a predictive alert based on Al analysis, an external power source disconnection warning, an accessory or peripheral malfunction notification, a safety alert, a status alert, a usage alert, a maintenance alert, a promotional alert, a product expiration alert, a health-related alert, a fitness-related alert, an energy-related alert, an inventory alert, a performance degradation alert, a compliance alert, a demand surge alert, a security breach alert, and a user-defined custom alert based on a specific conditions or threshold set by the user.

3. The digital product network system of claim 1 further comprising a smart contract creation module configured for creation of a smart contract providing terms and conditions that apply to at least one of: the plurality of digitally connected products, a user of at least one of the plurality of digitally connected products, a manufacturer of at least one of the plurality of digitally connected products, a distributer of at least one of the plurality of digitally connected products, an insurance provider for at least one of the plurality of digitally connected products, a retailer of at least one of the plurality ofdigitally connected products, and a service provider for at least one of the plurality of digitally connected products, wherein the smart contract relates to one or more of a purchase, a return, a lease, a warranty, an extended warranty, a service contract, a maintenance contract, a trade-in, a product add-on, a new product feature, a contract for access to data, a contract for access to one or more models, a software upgrade, a product upgrade, and a contract for access to a product feature.

4. The digital product network system of claim 3, wherein the smart contract creation module is further configured to automatically create smart contracts based on predefined rules.

5. The digital product network system of claim 3 further comprising a smart contract template selection module configured to enable selection of a smart contract template, while restricting the selection to one or more smart contract templates that are associated with the at least one of the plurality of digitally connected products.

6. The digital product network system of claim 5, wherein the smart contract template selection module is further configured to: receive input for parameterizing the selected smart contract template; generate a smart contract by parameterizing one or more variables in the smart contract with the input; and deploy the smart contract by broadcasting the smart contract to the at least one of the plurality of digitally connected products.

7. The digital product network system of claim 5, wherein the smart contract template selection module is further configured to suggest smart contract templates to a user based on at least one of: a type of the digitally connected product to be subject to the smart contract, one or more past selections of the user8. The digital product network system of claim 3 further comprising a smart contract performance verification module configured to verify the performance of an obligation defined in the smart contract.

9. The digital product network system of claim 8, wherein the smart contract performance verification module is further configured to automatically trigger an action upon successful verification of the performance of the obligation defined in the smart contract, and wherein the action comprises one or more of providing a notification, updating a status of the smart contract, and initiating a new smart contract.

10. The digital product network system of claim 1 further comprising a record upload module configured for enabling a user to upload records, wherein the records comprise one or more of photos, videos, audio recordings, device information, usage data, user preferences, troubleshooting records, upgrade records, reviews of product, receipts for product-related costs, receipts for product-related purchases, environmental data, and customer feedback.

11. The digital product network system of claim 10, wherein the record upload module is further configured to automatically categorize and store the uploaded records based onrespective types of associated digitally connected products.

12. The digital product network system of claim 1 further comprising an application programming interface (API) management module configured to manage one or more APIs, so as to expose the APIs to one or more related applications or third-party systems used by the plurality of digitally connected products.

13. The digital product network system of claim 1 further comprising a regulatory interface module configured to interact with one or more regulatory systems.

14. The digital product network system of claim 1 further comprising a data integration module configured to integrate data from the plurality of digitally connected products to create a unified view of data associated with the digitally connected products, by performing one or more operations, wherein the one or more operations comprise collecting data, aggregating data, normalizing data, standardizing data, cleaning data, enriching data, analyzing data, visualizing data, and integrating with other systems, and wherein the data integrated by the data integration module comprises one or more of product usage data, environmental data, product performance data, customer data, supply chain data, demand data, product location data, product customer feedback, product interaction data, product security data, product packaging data, product image data, product video data, product audio data, and product simulation data.

15. The digital product network system of claim 1, wherein the GUI module is further configured to provide one or more user interfaces displaying related ones of the plurality of digitally connected products associated with a single user by at least one of brand, type, and capability.

16. A method for operating a digital product network system, the method comprising: exchanging inter-product communications for a plurality of digitally connected products; exchanging product-to-user communications between the plurality of digitally connected products and their respective users; exchanging product-to-business communications between the plurality of digitally connected products and their associated enterprises; processing the inter-product communications, the product-to-user communications, and the product-to-business communications to determine timesensitive alerts related to corresponding one of the plurality of digitally connected products; and generating one or more user interfaces for displaying the time-sensitive alerts.

17. The method of claim 16, wherein the time-sensitive alerts comprise at least one of a low battery warning, a hardware failure notification, a software update notification, a connectivity issue notification, an unauthorized access warning, a maintenance reminder, a temperature fluctuation notification, a performance degradation notification, a storage capacity warning, a firmware upgrade alert, a scheduled backup reminder, a data breach notification, a device inactivity alert, a device overactivity or device overheating warning,a compatibility or synchronization issue with other devices, a warranty expiration reminder, a service subscription renewal alert, a sensor malfunction notification, an environmental hazard alert, a device location or movement alert, a user account change, a login notification from an unfamiliar location, a device lifecycle stage transition alert, a predictive alert based on Al analysis, an external power source disconnection warning, an accessory or peripheral malfunction notification, a safety alert, a status alert, a usage alert, a maintenance alert, a promotional alert, a product expiration alert, a health-related alert, a fitness-related alert, an energy-related alert, an inventory alert, a performance degradation alert, a compliance alert, a demand surge alert, a security breach alert, and a user-defined custom alert based on a specific conditions or threshold set by the user.

18. The method of claim 16 further comprising creation of a smart contract, the smart contract providing terms and conditions that apply to at least one of: the plurality of digitally connected products, a user of at least one of the plurality of digitally connected products, a manufacturer of at least one of the plurality of digitally connected products, a distributer of at least one of the plurality of digitally connected products, an insurance provider for at least one of the plurality of digitally connected products, a retailer of at least one of the plurality of digitally connected products, and a service provider for at least one of the plurality of digitally connected products, wherein the terms and conditions comprise one or more of a purchase, a return, a lease, a warranty, an extended warranty, a service contract, a maintenance contract, a trade-in, a product add-on, a new product feature, a contract for access to data, a purchase for access to one or more models, a software upgrade, a product upgrade, and unlocking an existing product feature.

19. The method of claim 18 further comprising automatically creating smart contracts based on predefined rules.

20. The method of claim 18 further comprising enabling selection of a smart contract template, while restricting the selection to one or more smart contract templates that are associated with the at least one of the plurality of digitally connected products.

21. The method of claim 20 further comprising: receiving input for parameterizing the selected smart contract template; generating a smart contract by parameterizing one or more variables in the smart contract with the input; and deploying the smart contract by broadcasting the smart contract to the at least one of the plurality of digitally connected products.

22. The method of claim 20 further comprising suggesting smart contract templates to a user based on at least one of: a type of the digitally connected product to be subject to the smart contract, one or more past selections of the user.

23. The method of claim 18 further comprising verification of performance of an obligation defined in the smart contract.

24. The method of claim 23 further comprising automatically triggering an action upon successful verification of the performance of the obligation defined in the smart contract,wherein the action comprises one or more of providing a notification, updating status of the smart contract, and initiating a new smart contract.

25. The method of claim 16 further comprising enabling a user to upload records, wherein the records comprise one or more of photos, videos, audio recordings, device information, usage data, user preferences, troubleshooting records, upgrade records, reviews of product, receipts for product-related costs or purchases, environmental data, and customer feedback.

26. The method of claim 25 further comprising automatically categorizing and storing the uploaded records based on respective types and associated digitally connected products.

27. The method of claim 16 further comprising managing one or more application programming interfaces (APIs), so as to expose the APIs to one or more related applications or third-party systems used by the plurality of digitally connected products.

28. The method of claim 16 further comprising interacting with one or more regulatory systems.

29. The method of claim 16 further comprising integrating data from the plurality of digitally connected products to create a unified view of digitally connected products, by performing one or more operations, wherein the one or more operations comprise collecting data, aggregating data, normalizing data, standardizing data, cleaning data, enriching data, analyzing data, visualizing data, and integrating with other systems, and wherein the data integrated comprises one or more of product usage data, environmental data, product performance data, customer data, supply chain data, demand data, product location data, product customer feedback, product interaction data, product security data, product packaging data, product image data, product video data, product audio data, and product simulation data.

30. The method of claim 16 further comprising providing one or more user interfaces to display related ones of the plurality of digitally connected products associated with a single user by at least one of brand, type, and capability.

31. A digital product network system comprising: a user interface module configured to: display information associated with a set of digitally connected products; and receive and process user inputs for displaying a set of marketplaces associated with the information; display the set of marketplaces; and receive and process user inputs for selecting a marketplace from the set of marketplaces; and an embedded marketplace module integrated with the user interface module, the embedded marketplace configured to: display the selected marketplace; receive and process user inputs for selecting one or more offeringsfrom a set of offerings from the selected marketplace; receive and process user inputs for completing a set of transactions associated with the one or more selected offerings, and execute the set of transactions.

32. The digital product network system of claim 31, wherein the embedded marketplace module is configured to execute transactions for product data associated with at least one of the digitally connected products from the set of digitally connected products, wherein the product data comprises one or more of product performance data, product pricing data, product usage data, product location data, product customer feedback data, product interaction data, product security data, product image data, product video data, product audio data, product packaging data, product maintenance data, environmental data, and product simulation data.

33. The digital product network system of claim 31, wherein the embedded marketplace module is configured to execute transactions related to models and / or algorithms associated with at least one digitally connected product from the set of digitally connected products, wherein the models and / or algorithms comprise one or more of predictive models, machine learning models, artificial intelligence models, classification models, anomaly detection models, clustering algorithms, generative models, large language models, optimization models, recommendation models, neural networks, transformer models, convolutional neural networks, deep learning models, supervised models, semisupervised models, unsupervised models, reinforcement models, and regression models.

34. The digital product network system of claim 31, wherein the embedded marketplace module is configured to execute transactions related to one or more of product parts, product upgrades, product insurance, product enhancements, product add-ons, product software, product warranties, extended product warranties, product service contracts, product maintenance contracts, product trade-ins, and access to product features for at least one digitally connected product from the set of digitally connected products.

35. The digital product network system of claim 31, wherein the embedded marketplace module is further configured to execute a peer-to-peer transaction associated with one or more of the digitally connected products.

36. The digital product network system of claim 31, wherein the embedded marketplace module is configured to provide a recommendation for an offering associated with at least one digitally connected product using a set of Artificial Intelligence (AI)-based learning models, wherein at least one of the set of Al-based learning models is trained on a training data set of embedded marketplace transaction data.

37. The digital product network system of claim 31, wherein the embedded marketplace module is configured to execute transactions between different entities comprising two or more of users, manufacturers, suppliers, service providers, software providers, data providers, data analytics providers, model providers, digital twin providers, and NFT providers.

38. The digital product network system of claim 31, wherein the marketplaces provided by the embedded marketplace module are configured to be searchable.

39. The digital product network system of claim 31, wherein at least one marketplace of the set of marketplaces provides digital twin offerings.

40. A method for executing transactions in a digital product network system, the method comprising: displaying information associated with a set of digitally connected products; receiving and processing user inputs for displaying a set of marketplaces associated with the information; displaying the set of marketplaces; receiving and processing user inputs for selecting a marketplace from the set of marketplaces; displaying the selected marketplace; receiving and processing user inputs for selecting one or more offerings from a set of offerings from the selected marketplace; receiving and processing user inputs for completing a set of transactions associated with the one or more selected offerings; and executing the set of transactions.

41. The method of claim 40 further comprising executing transactions related to product data associated with at least one of the digitally connected products from the set of digitally connected products, wherein the product data comprises one or more of product performance data, product pricing data, product usage data, product location data, product customer feedback data, product interaction data, product security data, product image data, product video data, product audio data, product packaging data, product maintenance data, environmental data, and product simulation data.

42. The method of claim 40 further comprising executing transactions related to models and / or algorithms associated with at least one digitally connected product from the set of digitally connected products, wherein the models and / or algorithms comprise one or more of predictive models, machine learning models, artificial intelligence models, classification models, anomaly detection models, clustering algorithms, generative models, large language models, optimization models, recommendation models, neural networks, transformer models, convolutional neural networks, deep learning models, supervised models, semi-supervised models, unsupervised models, reinforcement models, and regression models.

43. The method of claim 40 further comprising executing transactions related to one or more of product parts, product upgrades, product insurance, product enhancements, product add-ons, product software, product warranties, extended product warranties, product service contracts, product maintenance contracts, product trade-ins, and access to new product features for at least one digitally connected product from the set of digitally connected products.

44. The method of claim 40 further comprising executing a peer-to-peer transaction associated with one or more of the digitally connected products.

45. The method of claim 40 further comprising providing a recommendation for an offering associated with at least one digitally connected product using a set of Artificial Intelligence (Al)-based learning models, wherein at least one of the set of Al-based learning models is trained on a training data set of embedded marketplace transaction data.

46. The method of claim 40 further comprising executing transactions between different entities comprising two or more of users, manufacturers, suppliers, service providers, data providers, data analytics providers, software application providers, model providers, digital twin providers, and NFT providers.

47. The method of claim 40 further comprising providing at least one searchable marketplace.

48. The method of claim 40, further comprising providing at least one marketplace that provides digital twin offerings.

49. A digital product network system including one or more processors and one or more memories configured to perform operations comprising: receiving, by a computing device, information associated with a set of digitally connected products of a digital product network, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; providing the information to a set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to generate a recommendation associated with one or more of the set of digitally connected products; and outputting the recommendation.

50. The digital product network system of claim 49, wherein the information is related to a date or time of packaging of the set of digitally connected products.

51. The digital product network system of claim 49, wherein the information is related to a location of the set of digitally connected products.

52. The digital product network system of claim 49, wherein the information is related to a location of manufacturing for the set of digitally connected products.

53. The digital product network system of claim 49, wherein the information is related to a location of packaging for the set of digitally connected products.

54. The digital product network system of claim 49, wherein the information is related to a location of purchase for the set of digitally connected products.

55. The digital product network system of claim 49, wherein the information is related to a location of opening of packaging or the location of consumption of the set of digitally connected products.

56. The digital product network system of claim 49, wherein the information is related to atime on a display shelf or time on other display for the set of digitally connected products.

57. The digital product network system of claim 49, wherein the information is related to customer interaction with a set of displayed digitally connected products of the set of digitally connected products.

58. The digital product network system of claim 49, wherein the information is related to customer interaction with purchased digitally connected products of the set of digitally connected products.

59. The digital product network system of claim 49, wherein the wherein the set of Al-based learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model, a supervised model, a semi-supervised model, an unsupervised model, a generative model, a large language model, a recommendation model, or a reinforcement model.

60. The digital product network system of claim 49, wherein the recommendation is associated with one or more of an operational parameter of one or more of the set of digitally connected products, a provisioning of customer support associated with the one or more of the set of digitally connected products, a price of one or more of the set of digitally connected products, a product design associated with one or more of the set of digitally connected products, product security associated with one or more of the set of digitally connected products, quality control associated with one or more of the set of digitally connected products, marketing or sales strategies associated with one or more of the set of digitally connected products, display strategies associated with one or more of the set of digitally connected products, packaging associated with one or more of the set of digitally connected products, software upgrades associated with one or more of the set of digitally connected products, inventory management associated with one or more of the set of digitally connected products, regulatory compliance associated with one or more of the set of digitally connected products, and customer behavior associated with one or more of the set of digitally connected products.

61. A method for generating a recommendation in a digital product network system, the method comprising: receiving, by a computing device, information associated with a set of digitally connected products of a digital product network, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; providing the information to a set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to generate a recommendation associated with one or more of the set of digitally connected products; and outputting the recommendation.

62. The method of claim 61, wherein the information relates to a date or time of packagingthe set of digitally connected products.

63. The method of claim 61, wherein the information relates to a location of the set of digitally connected products.

64. The method of claim 61, wherein the information relates to a location of manufacturing of the set of digitally connected products.

65. The method of claim 61, wherein the information relates to a location of packaging of the set of digitally connected products.

66. The method of claim 61, wherein the information relates to a location of purchase of the set of digitally connected products.

67. The method of claim 61, wherein the information relates to a location of opening of packaging or the location of consumption of the set of digitally connected products.

68. The method of claim 61, wherein the information relates to a time on a display shelf or time on other display for the set of digitally connected products.

69. The method of claim 61, wherein the information relates to customer interaction with displayed digitally connected products of the set of digitally connected products.

70. The method of claim 61, wherein the information relates to customer interaction with purchased digitally connected products of the set of digitally connected products.

71. The method of claim 61, wherein the wherein the set of Al-based learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model, a supervised model, a semi-supervised model, an unsupervised model, a generative model, a large language model, a recommendation model, or a reinforcement model.

72. The method of claim 61, wherein the recommendation is associated with one or more of an operational parameter of one or more of the set of digitally connected products, a provisioning of customer support associated with the one or more of the set of digitally connected products, a price of one or more of the set of digitally connected products, a product design associated with one or more of the set of digitally connected products, product security associated with one or more of the set of digitally connected products, quality control associated with one or more of the set of digitally connected products, marketing or sales strategies associated with one or more of the set of digitally connected products, display strategies associated with one or more of the set of digitally connected products, packaging associated with one or more of the set of digitally connected products, software upgrades associated with one or more of the set of digitally connected products, inventory management associated with one or more of the set of digitally connected products, regulatory compliance associated with one or more of the set of digitally connected products, and customer behavior associated with one or more of the set of digitally connected products.

73. A digital product network system including one or more processors and one or more memories configured to perform operations comprising: receiving, by a computing device, information associated with a set of digitallyconnected products of the digital product network system, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; and providing the information to a digital twin system, wherein the digital twin system is configured to generate a digital twin representing one or more digitally connected products of the set of digitally connected products based on the information, wherein each digital twin provides a substantially real-time representation of the one or more digitally connected products of the set of digitally connected products.

74. The digital product network system of claim 73, wherein the digital twin system is further configured to generate a digital twin representing one or more of an entity, an environment, and a workflow of the digital product network.

75. The digital product network system of claim 74, wherein the entity comprises one or more of an additional connected product, a physical object, a 3D printer, a manufacturer, a distributor, a consumer, a service provider, a retailer, a payment provider, and a regulator.

76. The digital product network system of claim 74, wherein the environment comprises one or more of a warehouse, a manufacturing facility, a 3D printing facility, a service provider facility, a smart container, a container, a shipping port, a truck, a transportation network, a physical retail store, a digital marketplace, a virtual reality environment, a storage facility, and a digital product network.

77. The digital product network system of claim 74, wherein the workflow comprises one or more of a product design workflow, a product component sourcing workflow, a product manufacturing workflow, a product packaging workflow, a product distribution workflow, a product marketing and sales workflow, a product display workflow, a product interaction workflow, a product usage workflow, a data collection workflow, a product repair workflow, a product recall workflow, a product lifecycle management workflow, a product maintenance workflow, a data processing workflow, a network integration workflow, and a feedback loop workflow.

78. The digital product network system of claim 73, wherein the information relates to one or more of product design data, product status data, product usage data, product error data, product diagnostic data, product location data, product maintenance data, user interaction data, network data, event data, product performance data, and environmental data.

79. The digital product network system of claim 73 further comprising providing an embedded marketplace in the digital twin of the digitally connected product for enabling transactions related to of one or more of a model, data, a product update, a product upgrade, a product add-on, a product-related service offering, and a product part related to the one or more digitally connected products.

80. The digital product network system of claim 73 further comprising simulating at least oneof a behavior, a performance, an environment, and an interaction of the digital twin using CAD-based models.

81. The digital product network system of claim 73, further comprising generating an alert based on information represented in the digital twin.

82. The digital product network system of claim 74, further comprising providing hierarchical digital twins, wherein each hierarchical digital twin represents a hierarchy of the at least one of an entity, an environment, or a workflow in the digital product network.

83. The digital product network system of claim 74, further comprising providing context- adaptive digital twins, wherein the context-adaptive digital twin is configured for adapting the representation of the at least one of an entity, an environment, and a workflow based on changes in context.

84. The digital product network system of claim 73, further comprising providing a rolebased digital twin configured to represent information associated with a role associated with the one or more digitally connected products, wherein the role-based digital twin is configured for a consumer, a supplier, a manufacturer, a logistics manager, a retailer, a service provider, a product manager, a marketing and sales manager, an inventory manager, or a quality manager.

85. A method for providing a digital twin in a digital product network system, the method comprising: receiving, by a computing device, information associated with a set of digitally connected products of the digital product network system, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; and providing the information to a digital twin system, wherein the digital twin system is configured to generate a digital twin of one or more digitally connected products of the set of digitally connected products based on the information, wherein each digital twin provides a substantially real-time representation of the one or more digitally connected products of the set of digitally connected products.

86. The method of claim 85 further comprising generating a digital twin representing one or more of an entity, an environment, and a workflow of the digital product network.

87. The method of claim 86, wherein the entity comprises one or more of an additional connected product, a physical object, a 3D printer, a manufacturer, a distributor, a consumer, a service provider, a payment provider, and a regulator.

88. The method of claim 86, wherein the environment comprises one or more of a warehouse, a manufacturing facility, a 3D printing facility, a service provider facility, a smart container, a container, a truck, a transportation network, a physical retail store, a digital marketplace, a virtual reality environment, a storage facility, and a connected productnetwork.

89. The method of claim 86, wherein the workflow comprises one or more of a product design workflow, a product component sourcing workflow, a product manufacturing workflow, a product packaging workflow, a product distribution workflow, a product marketing and sales workflow, a product display workflow, a product interaction workflow, a product usage workflow, a data collection workflow, a product repair workflow, a product recall workflow, a product lifecycle management workflow, a product maintenance workflow, a data processing workflow, a network integration workflow, and a feedback loop workflow.

90. The method of claim 85, wherein the information relates to one or more of product status data, product usage data, product error data, product diagnostic data, product location data, product maintenance data, user interaction data, network data, event data, product performance data, and environmental data.

91. The method of claim 85 further comprising providing an embedded marketplace in the digital twin of the digitally connected product for purchase, sale, and exchange of one or more of a model, data, a product update, a product upgrade, a product add-on, a product- related service offering, and a product part related to the one or more digitally connected products.

92. The method of claim 85 further comprising simulating at least one of a behavior, a performance, an environment, and an interaction of the digital twin utilizing CAD-based models.

93. The method of claim 85 further comprising generating an alert based on data represented in the digital twin.

94. The method of claim 86 further comprising providing hierarchical digital twins, wherein each hierarchical digital twin represents a hierarchy of the at least one of an entity, an environment, or a workflow in the digital product network.

95. The method of claim 86 further comprising providing context-adaptive digital twin, wherein each context-adaptive digital twin is configured for adapting the representation of the at least one of an entity, an environment, and a workflow based on changes in context.

96. The method of claim 85 further comprising providing a role-based digital twin configured to represent a role associated with the digital product network, and wherein the role-based digital twin is configured for a consumer, a supplier, a manufacturer, a logistics manager, a retailer, a service provider, a product manager, a marketing and sales manager, an inventory manager, and a quality manager.

97. A method for providing an Artificial Intelligence (Al)-based supply chain digital twin for digitally connected products, the method comprising: receiving, by a computing device, information associated with a set of digitally connected products of a digital product network system, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loTdevices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; and providing the information to a digital twin system, wherein the digital twin system is configured to update a digital twin representing a supply chain network for the set of digitally connected products based on the information, wherein the digital twin provides a substantially real-time representation of a set of supply chain entities of the supply chain network; providing the information to a set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the set of Al-based learning models is trained on a training data set of a set of digital product network data to determine a classification of at least one of: an operating state, a fault condition, an operating flow, or a behavior of the supply chain network and at least one member of the set of Al-based learning models is trained on the training data set to determine, upon receiving the classification of the at least one of the: operating state, the fault condition, the operating flow, or the behavior, a task to be completed for the supply chain network; and providing at least one of an instruction for executing the task in the supply chain network digital twin and a recommendation for executing the task in the supply chain network digital twin.

98. The method of claim 97, wherein the information includes real-time data about one of: inbound prepaid shipments from suppliers linked to orders; or inventory coming into a network associated with the supply chain network.

99. The method of claim 97 wherein receiving the information associated with the supply chain network includes receiving sensor data indicative of inbound and outbound shipment conditions.

100. The method of claim 97 further comprising generating simulations for the supply chain network digital twin, wherein the simulations for the supply chain network digital twin are generated with a graphical neural network (GNN).

101. The method of claim 97, wherein a robotic operating system enables the supply chain network digital twin.

102. The method of claim 97, wherein the supply chain network digital twin operates within a digital twin system having one or more sets of one or more digital twins, wherein each digital twin of the one or more sets includes an embedded marketplace for digital twin simulations.

103. The method of claim 97, wherein the supply chain network digital twin operates within a digital twin system having one or more sets of one or more digital twins, wherein each digital twin of the one or more sets includes an embedded marketplace for one of artificial intelligence-based learning models or artificial intelligence-based algorithms.

104. The method of claim 97, wherein the supply chain network digital twin operates within a digital twin system having one or more sets of one or more digital twins, wherein eachdigital twin of the one or more sets includes an embedded marketplace for data.

105. The method of claim 97, wherein the set of the supply chain network entities includes at least one of: digitally connected products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, robotic handling systems, 3D printers, vehicles, autonomous vehicles, hauling facilities, waterways, or port infrastructure facilities.

106. The method of claim 97, wherein the set of Al-based learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.

107. A computing system including one or more processors and one or more memories configured to perform operations comprising: receiving, by a computing device, information associated with a set of digitally connected products of the digital product network system, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; and providing the information to a digital twin system, wherein the digital twin system is configured to update a digital twin representing a supply chain network for the set of digitally connected products based on the information, wherein the digital twin provides a substantially real-time representation of a set of supply chain entities of the supply chain network; providing the information to a set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the set of Al-based learning models is trained on a training data set of a set of digital product network data to determine a classification of at least one of: an operating state, a fault condition, an operating flow, or a behavior of the supply chain network and at least one member of the set of Al-based learning models is trained on the training data set to determine, upon receiving the classification of the at least one of the: operating state, the fault condition, the operating flow, or the behavior, a task to be completed for the supply chain network; andproviding at least one of an instruction for executing the task in the supply chain network digital twin and a recommendation for executing the task in the supply chain network digital twin.

108. The computing system of claim 107, wherein the information includes real-time data about one of: inbound prepaid shipments from suppliers linked to orders; or inventory coming into a network associated with the supply chain network.

109. The computing system of claim 107, wherein receiving the information associated with the supply chain network includes receiving sensor data indicative of inbound and outbound shipment conditions.

110. The computing system of claim 107, wherein the operations further comprise generating simulations for the supply chain network digital twin, wherein the simulations for the supply chain network digital twin are generated with a graphical neural network (GNN).

111. The computing system of claim 107, wherein a robotic operating system enables the supply chain network digital twin.

112. The computing system of claim 107, wherein the supply chain network digital twin operates within a digital twin system having one or more sets of one or more digital twins, wherein each digital twin of the one or more sets includes an embedded marketplace for digital twin simulations.

113. The computing system of claim 107, wherein the supply chain network digital twin operates within a digital twin system having one or more sets of one or more digital twins, wherein each digital twin of the one or more sets includes an embedded marketplace for one of artificial intelligence-based learning models or artificial intelligence-based algorithms.

114. The computing system of claim 107, wherein the supply chain network digital twin operates within a digital twin system having one or more sets of one or more digital twins, wherein each digital twin of the one or more sets includes an embedded marketplace for data.

115. The computing system of claim 107, wherein the set of the supply chain network entities includes at least one of: digitally connected products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, robotic handling systems, 3D printers, vehicles, autonomous vehicles, hauling facilities,waterways, or port infrastructure facilities.

116. The computing system of claim 107, wherein the set of Al-based learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.

117. A digital product network system including one or more processors and one or more memories configured to perform operations comprising: receiving, by a computing device, information associated with a set of digitally connected products of a digital product network, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; providing the information to a set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to determine a set of demand planning actions; and providing a computer code instruction set to a machine to execute the set of demand planning actions.

118. The digital product network system of claim 117, wherein the set of demand planning actions includes at least one of forecasting a measure of demand, inventory management, capacity planning, supply chain coordination, customer prioritization, order quantity limits, dynamic pricing, alternative product promotions, marketing campaigns, product improvements, market expansion, and customer engagement.

119. The digital product network system of claim 117, wherein the information comprises at least one of historical sales data, market data, social media data, customer data, economic sentiment data, economic data, supply chain data, sensor data, market research data, competitive analysis data, economic indicator data, industry trends data, customer feedback data, historical product development data, project management data, resource availability data, supplier data, manufacturing data, quality control data, testing data, regulatory data, compliance data, historical recall data, manufacturing data, supplier data, component data, quality control data, inspection data, failure data, warranty data, service record data, regulatory data, entertainment data, event data, regulatory compliance data, incident data, weather data, and accident report data.

120. The digital product network system of claim 117, wherein the set of Al-based learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model, a supervised model, a semi -supervised model, a generative model, a large language model, a recommendation model, an unsupervised model, a reinforcement model, and a regression model.

121. The digital product network system of claim 117, wherein the set of demand planning action comprises forecasting a measure of demand for each of a plurality of demographicgroups at a future date and / or time.

122. The digital product network system of claim 121, wherein the set of demand planning actions further comprise generating and distributing a marketing message associated with the set of digitally connected products for a specific demographic group based on the forecasted measure of demand.

123. The digital product network system of claim 117, wherein the set of demand planning actions comprises forecasting a measure of demand for each of a plurality of geographic regions at a future date and / or time.

124. The digital product network system of claim 123, wherein the set of demand planning actions further comprise generating and distributing a marketing message associated with the set of digitally connected products for a specific geographic region based on the forecasted measure of demand.

125. The digital product network system of claim 117, wherein the set of demand planning actions comprises forecasting a measure of demand for each of a plurality of channel preference segments at a future date and / or time.

126. A method for demand planning in a digital product network system, the method comprising: receiving, by a computing device, information associated with a set of digitally connected products of a digital product network, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; providing the information to a set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to determine a set of demand planning actions for the set of digitally connected products; and providing a computer code instruction set to a machine to execute the set of demand planning actions.

127. The method of claim 126, wherein the set of demand planning action relates to at least one of forecasting a measure of demand, inventory management, capacity planning, supply chain coordination, customer prioritization, order quantity limits, dynamic pricing, alternative product promotions, marketing campaigns, product improvements, market expansion, and customer engagement.

128. The method of claim 126, wherein the information comprises at least one of historical sales data, market data, social media data, customer data, economic sentiment data, economic data, supply chain data, sensor data, market research data, competitive analysis data, economic indicator data, industry trends data, customer feedback data, historical product development data, project management data, resource availability data, supplierdata, manufacturing data, quality control data, testing data, regulatory data, compliance data, historical recall data, manufacturing data, supplier data, component data, quality control data, inspection data, failure data, warranty data, service record data, regulatory data, entertainment data, event data, regulatory compliance data, incident data, weather data, and accident report data.

129. The method of claim 126, wherein each set of Al-based learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model trained on a set of outcomes of the digital product network, a supervised model, a semisupervised model, an unsupervised model, a reinforcement model, a generative model, a large language model, a recommendation model, and a regression model.

130. The method of claim 126, wherein the set of demand planning action comprises forecasting a measure of demand for each of a plurality of demographic groups at a future date and / or time.

131. The method of claim 130, wherein the set of demand planning actions further comprise generating and distributing a marketing message associated with the set of digitally connected products for a specific demographic group based on the forecasted measure of demand.

132. The method of claim 126, wherein the set of demand planning actions comprises forecasting a measure of demand for each of a plurality of geographic regions at a future date and / or time.

133. The method of claim 132, wherein the set of demand planning actions further comprise generating and distributing a marketing message associated with the set of digitally connected products for a specific geographic region based on the forecasted measure of demand.

134. The method of claim 126, wherein the set of demand planning actions comprises forecasting a measure of demand for each of a plurality of channel preference segments at a future date and / or time.

135. A digital product network system including one or more processors and one or more memories configured to perform operations comprising: receiving, by a computing device, information associated with a set of digitally connected products of a digital product network, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; providing the information to a set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to predict a future inventory level requirement for the set of digitally connected products based on the information and at least one member of the set of Al-based learning models is trained on the training data set to determine an action to meet the predicted inventory level requirement based on theprediction and the information; and at least one of outputting the determined action and providing a computer code instruction set to a machine to execute the determined action136. The digital product network system of claim 135, wherein the information comprises at least one of historical sales data, market data, social media data, customer data, economic sentiment data, economic data, supply chain data, inventory data, lead time data, sensor data, market research data, competitive analysis data, economic indicator data, industry trends data, customer feedback data, historical product development data, project management data, resource availability data, supplier data, supplier performance data, supplier pricing data, manufacturing data, quality control and testing data, regulatory and compliance data, historical recall data, manufacturing data, supplier data, component data, quality control data, inspection data, failure data, warranty and service records, regulatory data, entertainment data, event data, regulatory compliance data, incident data, weather data, and accident report data.

137. The digital product network system of claim 135, wherein the set of Al-based learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model trained on a set of outcomes of the digital product network, a supervised model, a semi-supervised model, an unsupervised model, a reinforcement model, a generative model, a large language model, and a regression model.

138. The digital product network system of claim 135, wherein the determined action relates to at least one of: engaging secondary manufacturers or contract manufacturers to supplement production capacity, implementing overtime shifts at manufacturing facilities to temporarily increase production, redistributing inventory, simplifying product designs, implementing strategic stockpiling, supplementing production capacity with 3D printers, increasing storage and / or warehouse capacity, implementing cross-docking, adjusting production settings, and improving return management.

139. The digital product network system of claim 135, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to generate a prediction of future inventory level requirement for each of a plurality of retail locations.

140. The digital product network system of claim 135, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to generate a prediction of future inventory level requirement for each of a plurality of geographic regions.

141. The digital product network system of claim 135, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to generate a prediction of future inventory level requirement for each of a plurality of sales channels.

142. The digital product network system of claim 135, wherein the operations further comprise providing at least one of real-time updates on the inventory level and alerts when theinventory level reaches a certain threshold.

143. The digital product network system of claim 135, wherein the determined action is output to a digital twin user interface.

144. A method for inventory optimization in a digital product network system, the method comprising: receiving, by a computing device, information associated with a set of digitally connected products of a digital product network, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; providing the information to a set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to predict a future inventory level requirement for a set of digitally connected products and at least one member of the set of Al-based learning models is trained on the training data set to determine an action to meet the predicted inventory level requirement; and at least one of outputting the determined action and providing a computer code instruction set to a machine to execute the determined action.

145. The method of claim 144, wherein the information comprises at least one of historical sales data, market data, social media data, customer data, economic sentiment data, economic data, supply chain data, inventory data, lead time data, sensor data, market research data, competitive analysis data, economic indicator data, industry trends data, customer feedback data, historical product development data, project management data, resource availability data, supplier data, supplier performance data, supplier pricing data, manufacturing data, quality control and testing data, regulatory and compliance data, historical recall data, manufacturing data, supplier data, component data, quality control data, inspection data, failure data, warranty and service records, regulatory data, entertainment data, event data, regulatory compliance data, incident data, weather data, and accident report data.

146. The method of claim 144, wherein the set of Al-based learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model trained on a set of outcomes of the digital product network, a supervised model, a semisupervised model, an unsupervised model, a reinforcement model, a generative model, a large language model, and a regression model.

147. The method of claim 144, wherein the determined action relates to at least one of: engaging secondary manufacturers or contract manufacturers to supplement production capacity, implementing overtime shifts at manufacturing facilities to temporarily increase production, redistributing inventory, simplifying product designs, implementing strategic stockpiling, supplementing production capacity with 3D printers, increasing storage and / or warehouse capacity, implementing cross-docking, improving return management,adjusting production settings, and implementing dynamic pricing strategies.

148. The method of claim 144, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to generate a prediction of future inventory level requirement for each of a plurality of retail locations.

149. The method of claim 144, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to generate a prediction of future inventory level requirement for each of a plurality of geographic regions.

150. The method of claim 144, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to generate a prediction of future inventory level requirement for each of a plurality of sales channels.

151. The method of claim 144 further comprising providing at least one of real-time updates on the inventory level and alerts when the inventory level reaches a certain threshold.

152. The method of claim 144, wherein the determined action is output to a digital twin user interface.

153. A digital product network system including one or more processors and one or more memories configured to perform operations comprising: receiving, by a computing device, information associated with a set of digitally connected products of a digital product network, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; providing the information to a first set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the first set of Al-based learning models is trained on a training data set of digital product network data to determine a classification of at least one of an operating state, a fault condition, an operating flow, and a behavior of at least one of the set of digitally connected products; providing the information to a second set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the second set of Al-based learning models is trained on a training data set of digital product network data to recommend at least one design improvement for the set of digitally connected products based on the classification; and at least one of outputting the design improvement recommendation and providing a computer code instruction set to a machine to implement the design improvement associated with the set of digitally connected products based on the output of the AI- based learning models.

154. The digital product network system of claim 153, wherein the design improvement comprises at least one of modifying an existing product features, adding a new product feature, removing a product feature, replacing a product material, modifying a size of a product, modifying a size of a product part, replacing a product part, modifying a productcolor, modifying a product style, and rearranging a set of components of the product.

155. The digital product network system of claim 153, wherein the information comprises at least one of historical sales data, market data, social media data, customer data, economic sentiment data, economic data, supply chain data, inventory data, influencer data, lead time data, sensor data, product performance data, product feature data, product design data, product component data, market research data, competitive analysis data, economic indicator data, industry trend data, customer feedback data, customer service interaction data, order data, historical product development data, project management data, resource availability data, supplier data, supplier performance data, supplier pricing data, manufacturing data, quality control and testing data, regulatory and compliance data, historical recall data, supplier data, component data, quality control data, inspection data, failure data, warranty and service records, regulatory data, entertainment data, event data, regulatory compliance data, incident data, weather data, and accident report data.

156. The digital product network system of claim 153, wherein each set of Al-based learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model trained on a set of outcomes of the digital product network, a supervised model, a semi-supervised model, an unsupervised model, a reinforcement model, a generative model, a large language model, and a regression model.

157. The digital product network system of claim 153, wherein at least one member of a third set of Al-based learning models is trained on a training data set of digital product network data to predict an impact of the at least one design improvement.

158. The digital product network system of claim 153, wherein the operations further comprise generating a virtual prototype of the improved product design.

159. The digital product network system of claim 153, wherein at least one member of a third set of Al-based learning models is trained on a training data set of digital product network data to identify potential risks associated with implementing the at least one design improvement.

160. The digital product network system of claim 153, wherein at least one member of a third set of Al-based learning models is trained on a training data set of digital product network data to generate a plan for validating the improved product design, including one or more of defining testing parameters, identifying testing resources, and setting up testing environments.

161. The digital product network system of claim 153, wherein at least one member of a third set of Al-based learning models is trained on a training data set of digital product network data to generate a plan for launching and marketing the improved product design, including one or more of identifying target markets, developing marketing strategies, and planning marketing campaigns.

162. The digital product network system of claim 153, wherein the operations further comprise determining a per-unit cost for the at least one design improvement.

163. A method for improving product design in a digital product network system, the methodcomprising: receiving, by a computing device, information associated with a set of digitally connected products of a digital product network, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; providing the information to a first set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the first set of Al-based learning models is trained on a training data set of digital product network data to determine a classification of at least one of an operating state, a fault condition, an operating flow, and a behavior of at least one of the set of digitally connected products; providing the information to a second set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the second set of Al-based learning models is trained on a training data set of digital product network data to determine at least one design improvement for the set of digitally connected products based on the classification; and at least one of outputting the design improvement recommendation and providing a computer code instruction set to a machine to implement the design improvement associated with the digitally connected products based on the output of the Al-based learning models.

164. The method of claim 163, wherein the design improvement comprises one or more of modifying an existing product feature, adding a new product feature, removing a product feature, replacing a product material, modifying a size of a product, modifying a size of a product part, replacing a product part, modifying a product color, modifying a product style, and rearranging a set of components of the product.

165. The method of claim 163, wherein the information comprises at least one of historical sales data, market data, social media data, customer data, economic sentiment data, economic data, supply chain data, inventory data, influencer data, lead time data, sensor data, product performance data, product feature data, product design data, product component data, market research data, competitive analysis data, economic indicator data, industry trend data, customer feedback data, customer service interaction data, order data, historical product development data, project management data, resource availability data, supplier data, supplier performance data, supplier pricing data, manufacturing data, quality control and testing data, regulatory and compliance data, historical recall data, supplier data, component data, quality control data, inspection data, failure data, warranty and service records, regulatory data, entertainment data, event data, regulatory compliance data, incident data, weather data, and accident report data.

166. The method of claim 163, wherein the set of Al-based learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model trained on a set of outcomes of the digital product network, a supervised model, a semi-supervised model, an unsupervised model, a reinforcement model, a generative model, a large language model, and a regression model.

167. The method of claim 163, wherein at least one member of a third set of Al-based learning models is trained on a training data set of digital product network data to predict an impact of the at least one design improvement.

168. The method of claim 163, wherein the operations further comprise generating a virtual prototype of the improved product design.

169. The method of claim 163, wherein at least one member of a third set of Al-based learning models is trained on a training data set of digital product network data to identify potential risks associated with implementing the at least one design improvement.

170. The method of claim 163, wherein at least one member of a third set of Al-based learning models is trained on a training data set of digital product network data to generate a plan for validating the improved product design, including one or more of defining testing parameters, identifying testing resources, and setting up testing environments.

171. The method of claim 163, wherein at least one member of a third set of Al-based learning models is trained on a training data set of digital product network data to generate a plan for launching and marketing the improved product design, including one or more of identifying target markets, developing marketing strategies, and planning marketing campaigns.

172. The method of claim 163, wherein the operations further comprise determining a per-unit cost for the at least one design improvement.

173. A digital product network system including one or more processors and one or more memories configured to perform operations comprising: receiving, by a computing device, information associated with a set of digitally connected products of a digital product network, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; providing the information to a set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to determine at least one product feature preference for the set of digitally connected products for each location of a set of locations; and outputting the at least one product feature preference for the set of digitally connected products for each location of a set of locations.

174. The digital product network system of claim 173, wherein the information comprises at least one of historical sales data, market data, social media data, customer data, economic sentiment data, economic data, supply chain data, inventory data, influencer data, lead time data, sensor data, product performance data, product feature data, product design data, product component data, market research data, competitive analysis data, economicindicator data, industry trend data, customer feedback data, customer service interaction data, sales data, geographic data, demographic data, customer behavior data, historical product development data, project management data, resource availability data, supplier data, supplier performance data, supplier pricing data, manufacturing data, quality control and testing data, regulatory and compliance data, historical recall data, supplier data, component data, quality control data, inspection data, failure data, warranty and service records, regulatory data, entertainment data, event data, regulatory compliance data, incident data, weather data, and accident report data.

175. The digital product network system of claim 173, wherein the set of Al-based learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model trained on a set of outcomes of the digital product network, a supervised model, a semi-supervised model, an unsupervised model, a reinforcement model, a generative model, a large language model, and a regression model.

176. The digital product network system of claim 173, wherein the at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to determine at least one product feature preference for the set of digitally connected products for a plurality of geographic segments.

177. The digital product network system of claim 176, wherein the operations further comprise determining a sales measure associated with each of the plurality of geographic segments.

178. The digital product network system of claim 173, wherein the at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to determine at least one product feature preference for a plurality of demographic segments for the set of digitally connected products.

179. The digital product network system of claim 178, wherein the operations further comprise determining a sales measure associated with each of the plurality of demographic segments.

180. The digital product network system of claim 173, wherein the at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to determine at least one product feature preference for a plurality of consumer behavioral segments for the set of digitally connected products.

181. The digital product network system of claim 180, wherein the operations further comprise determining a sales measure associated with each of the plurality of consumer behavioral segments.

182. The digital product network system of claim 173, wherein the operations further comprise providing a recommendation for product development for the set of digitally connected products based on the determined at least one product feature preference, wherein the recommendation comprises: modifying one or more of existing product features, adding a new product feature, removing a product feature, replacing a product material, modifying a size of a product, modifying a size of a product part, replacing a product part, modifyinga product color, modifying a product style, or rearranging a set of components of the product.

183. A method for determining product feature preferences in a digital product network system, the method comprising: receiving, by a computing device, information associated with a set of digitally connected products of a digital product network, the information generated by at least one of: a set of sensors of the set of digitally connected products, a set of loT devices configured to collect data relating to the set of digitally connected products, or a set of APIs configured to publish data relating to the set of digitally connected products; providing the information to a set of Artificial Intelligence (Al)-based learning models, wherein at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to determine at least one product feature preference for the set of digitally connected products for each of a set of locations; and outputting the at least one product feature preference for the set of digitally connected products for each location of a set of locations.

184. The method of claim 183, wherein the information comprises at least one of historical sales data, market data, social media data, customer data, economic sentiment data, economic data, supply chain data, inventory data, influencer data, lead time data, sensor data, product performance data, product feature data, product design data, product component data, market research data, competitive analysis data, economic indicator data, industry trend data, customer feedback data, customer service interaction data, sales data, geographic data, demographic data, customer behavior data, historical product development data, project management data, resource availability data, supplier data, supplier performance data, supplier pricing data, manufacturing data, quality control and testing data, regulatory and compliance data, historical recall data, supplier data, component data, quality control data, inspection data, failure data, warranty and service records, regulatory data, entertainment data, event data, regulatory compliance data, incident data, weather data, and accident report data.

185. The method of claim 183, wherein the set of Al-based learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model trained on a set of outcomes of the digital product network, a supervised model, a semisupervised model, an unsupervised model, a reinforcement model, a generative model, a large language model, and a regression model.

186. The method of claim 183, wherein the at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to determine at least one product feature preference for a plurality of geographic segments for the set of digitally connected products.

187. The method of 186 further comprising determining a sales measure associated with each of the plurality of geographic segments.

188. The method of claim 183, wherein the at least one member of the set of Al-based learningmodels is trained on a training data set of digital product network data to determine at least one product feature preference for a plurality of demographic segments for the set of digitally connected products.

189. The method of claim 188 further comprising determining a sales measure associated with each of the plurality of demographic segments.

190. The method of claim 183, wherein the at least one member of the set of Al-based learning models is trained on a training data set of digital product network data to determine at least one product feature preference for a plurality of behavioral segments for the set of digitally connected products.

191. The method of claim 190 further comprising determining a sales measure associated with each of the plurality of behavioral segments.

192. The method of claim 183 further comprising providing a recommendation for product development for the set of digitally connected products based on the at least one product feature preference, wherein the recommendation comprises: modifying one or more of existing product features, adding a new product feature, removing a product feature, replacing a product material, modifying a size of a product, modifying a size of a product part, replacing a product part, modifying a product color, modifying a product style, or rearranging a set of components of the product.

193. A generative artificial intelligence system for a digital product network, the system including one or more processors and one or more memories configured to perform operations comprising: executing, by the generative artificial intelligence system, generative artificial intelligence algorithms trained on digital product network data; receiving input data including at least one of images, video, audio, text, programmatic code, and data; processing the input data using the generative artificial intelligence algorithms to generate output content, wherein the output content includes at least one of structured prose, images, video, audio content, software source code, formatted data, algorithms, definitions, and context-specific structures; generating an internal state of the generative artificial intelligence system, including a set of weights and / or biases as a result of prior processing; and providing the generated output content to a user interface for presentation to a user.

194. The generative artificial intelligence system of claim 193, wherein the input data further includes natural-language expressions, single- or multidimensional shapes or models, real-world and / or virtual scene representations, LIDAR point-cloud representations, sensor inputs and / or outputs, vehicle and / or machine telemetry, geographic maps, authentication credentials, financial transactions, smart contracts, processing directives, and device configurations.

195. The generative artificial intelligence system of claim 193, wherein the generative artificial intelligence system is configured to maintain contextual awareness across interactions to facilitate continuity in dialogue with users.

196. The generative artificial intelligence system of claim 193, wherein the generative artificial intelligence system is configured to support interpretability and explainability of its outputs by providing a description of a basis of the output.

197. The generative artificial intelligence system of claim 193, wherein the output includes a recommendation to improve at least one of the digital product network and a digitally connected product within the digital product network.

198. The generative artificial intelligence system of claim 193, further comprising a digital twin interface for a digital twin, wherein the digital twin interface is configured to enable access to the generative artificial intelligence system, and wherein the digital twin represents at least one of the digital product network and a digitally connected product within the digital product network.

199. The generative artificial intelligence system of claim 193, further comprising a digital twin module configured to generate a digital twin representing at least one of a digital product network and a digitally connected product within the digital product network.

200. The generative artificial intelligence system of claim 193, wherein the processor is further configured to communicate with robotic systems within the digital product network, and the memory stores instmctions that enable the generative artificial intelligence system to translate human commands into robotic actions, thereby facilitating human-to-robot interaction.

201. The generative artificial intelligence system of claim 193, wherein the output content is further tailored based on an executive role of the user.

202. The generative artificial intelligence system of claim 193, wherein the generated output includes 3D printing instructions for a set of 3D printers.

203. The generative artificial intelligence system of claim 193, wherein the system is part of a Dual Process Artificial Neural Network (DP ANN) architecture.

204. The generative artificial intelligence system of claim 193, further comprising an augmented reality (AR) interface configured to overlay the generated output content onto a user's real-world view.

205. A method for operating a generative artificial intelligence system within a value chain network, the method comprising: executing, by the generative artificial intelligence system, generative artificial intelligence algorithms trained on value chain network data; receiving input data including at least one of images, video, audio, text, programmatic code, and data; processing the input data using the generative artificial intelligence system to generate output content, wherein the output content includes at least one of structuredprose, images, video, audio content, software source code, formatted data, algorithms, definitions, and context-specific structures; generating an internal state of the generative artificial intelligence system, including a set of weights and / or biases as a result of prior processing; and providing the generated output content to a user interface for presentation to a user.

206. The method of claim 205, further comprising receiving additional input data including natural -language expressions, single- or multidimensional shapes or models, real-world and / or virtual scene representations, LIDAR point-cloud representations, sensor inputs and / or outputs, vehicle and / or machine telemetry, geographic maps, authentication credentials, financial transactions, smart contracts, processing directives, and device configurations.

207. The method of claim 205, further comprising maintaining contextual awareness across interactions to facilitate continuity in dialogue with users.

208. The method of claim 205, further comprising supporting interpretability and explainability of its outputs by providing a description of a basis of the output.

209. The method of claim 205, wherein the generated output includes a recommendation to improve one or more of a digital product network and a digitally connected product within the digital product network.

210. The method of claim 205, further comprising: interfacing with a digital twin via a digital twin interface, wherein the digital twin interface is configured to facilitate access to the generative artificial intelligence system; and representing, by the digital twin, one or more of a digital product network and a digitally connected product within the digital product network.

211. The method of claim 205, further comprising: generating, through a digital twin module, a digital twin that represents at least one or more of a digital product network and a digitally connected product within the digital product network.

212. The method of claim 205, further comprising: communicating with one or more robotic systems within the value chain network; translating human commands into robotic actions; and facilitating human-to-robot interaction within the value chain network, thereby enabling the robotic systems to perform tasks in response to the human commands derived from the generated output content.

213. The method of claim 205, further comprising: tailoring the output content based on an executive role of the user within an organization.

214. The method of claim 205, wherein the generated output includes 3D printing instructions for a set of 3D printers.

215. The method of claim 205, wherein the generative artificial intelligence system operates as part of a Dual Process Artificial Neural Network (DP ANN) architecture.

16. The method of claim 205, further comprising: interfacing with an augmented reality (AR) interface; and overlaying, by the AR interface, the generated output content onto a user's real-world view

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