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

Abstract

The system may include a product-to-product communication module configured to exchange product-to-product communication between multiple digitally connected products. The system may also include a product-to-user communication module configured to exchange product-to-user communication between multiple digitally connected products and their respective users. The system may also include a product-to-enterprise communication module configured to exchange product-to-user communication between multiple digitally connected products and their associated companies. The system may also include a data processing module configured to handle product-to-product, product-to-user, and product-to-enterprise communications and to determine time-dependent alerts related to corresponding products among multiple digitally connected products. The system may also include a graphical user interface (GUI) module configured to generate one or more user interfaces for displaying time-dependent alerts.

Claims

1. A digital product network system, A product-to-product communication module configured to exchange inter-product communications for multiple digitally connected products, A product-to-user communication module configured to exchange product-to-user communication between the aforementioned multiple digitally connected products and their respective users, A product-to-enterprise communication module configured to exchange product-to-enterprise communications between the aforementioned multiple digitally connected products and their associated companies, A data processing module configured to process the inter-product communication, the product-to-user communication, and the product-to-enterprise communication to determine time-dependent alerts associated with each corresponding product among the multiple digitally connected products, A graphical user interface (GUI) module configured to generate one or more user interfaces for displaying the aforementioned time-dependent alerts, A digital product network system characterized by including the following:

2. The digital product network system according to claim 1, characterized in that the time-dependent alerts include at least one of the following: low battery warning, hardware failure notification, software update notification, connectivity problem notification, unauthorized access warning, maintenance reminder, temperature fluctuation notification, performance degradation notification, storage capacity warning, firmware update alert, scheduled backup reminder, data breach notification, device inactivity alert, device overactivity or device overheating warning, compatibility or synchronization issues with other devices, warranty expiration reminder, service contract renewal alert, sensor failure notification, environmental hazard alert, device location or movement alert, user account change, login from suspicious location notification, device lifecycle stage transition alert, predictive alert based on AI analysis, external power disconnection warning, accessory or peripheral failure notification, safety alert, status alert, usage alert, maintenance alert, promotion alert, product expiration alert, health-related alert, fitness-related alert, energy-related alert, inventory alert, performance degradation alert, compliance alert, demand surge alert, security breach alert, and user-defined custom alerts based on specific conditions or thresholds set by the user.

3. Furthermore, it includes a smart contract creation module, which is configured for creating smart contracts that provide conditions applicable to at least one of the following: the plurality of digitally connected products, the user of at least one of the plurality of digitally connected products, the manufacturer of at least one of the plurality of digitally connected products, the distributor of at least one of the plurality of digitally connected products, the insurer of at least one of the plurality of digitally connected products, the retailer of at least one of the plurality of digitally connected products, and the service provider for at least one of the plurality of digitally connected products. The digital product network system according to claim 1, characterized in that the smart contract relates to one or more of the following: purchase, return, lease, warranty, extended warranty, service agreement, maintenance agreement, trade-in, product add-on, new product feature, data access agreement, access agreement for one or more models, software upgrade, product upgrade, and access agreement for product features.

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

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

6. The aforementioned smart contract template selection module further includes: Receiving input for parameterization of the selected smart contract template, The smart contract is generated by parameterizing one or more variables within the smart contract based on the aforementioned input, and Deploying the smart contract by broadcasting it to at least one of the multiple digitally connected products. The digital product network system according to claim 5, characterized in that it is configured to perform the following actions.

7. The digital product network system according to claim 5, further comprising the smart contract template selection module, which is configured to propose a smart contract template to the user based on at least one of the type of digitally connected product to which the smart contract applies, and one or more of the user's past selections.

8. Furthermore, the digital product network system according to claim 3 is characterized by including a smart contract performance verification module configured to verify the performance of obligations defined in the smart contract.

9. The digital product network system according to claim 8, wherein the smart contract performance verification module is further configured to automatically trigger an action when the performance of the obligations defined in the smart contract has been successfully verified, the action comprising one or more of the following: providing a notification, updating the status of the smart contract, and starting a new smart contract.

10. The digital product network system according to claim 1, further comprising a record upload module that enables users to upload records, wherein the records include one or more of the following: photographs, videos, audio recordings, device information, usage data, user settings, troubleshooting records, upgrade records, product reviews, receipts for product-related expenses, receipts for product-related purchases, environmental data, and customer feedback.

11. The digital product network system according to claim 10, further characterized in that the record upload module is configured to automatically classify and store the uploaded records based on each type of associated digitally connected product.

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

13. Furthermore, the digital product network system according to claim 1 is characterized by including a regulatory interface module configured to communicate with one or more regulatory systems.

14. Furthermore, it further includes a data integration module configured to integrate data from the multiple digitally connected products by performing one or more operations, and to create a unified view of the data related to the digitally connected products, The one or more operations described above include data collection, data aggregation, data normalization, data standardization, data cleaning, data enrichment, data analysis, data visualization, and integration with other systems. The digital product network system according to claim 1, characterized in that the data integrated by the data integration module includes one or more of the following: 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 according to claim 1, further characterized in that the GUI module is configured to provide one or more user interfaces for displaying products associated with a single user from among the plurality of digitally connected products, which are associated by at least one of brand, type, and function.

16. A method for operating a digital product network system, Exchanging inter-product communications for multiple digitally connected products, The exchange of product-to-user communications between the aforementioned multiple digitally connected products and their respective users, The exchange of product-to-company communications between the aforementioned multiple digitally connected products and their related companies, Processing the inter-product communications, product-to-user communications, and product-to-enterprise communications to determine time-dependent alerts related to each corresponding product among the multiple digitally connected products, and To generate one or more user interfaces for displaying the aforementioned time-dependent alerts, A method characterized by including the following.

17. The method according to claim 16, wherein the time-dependent alerts include at least one of the following: low battery warning, hardware failure notification, software update notification, connectivity problem notification, unauthorized access warning, maintenance reminder, temperature fluctuation notification, performance degradation notification, storage capacity warning, firmware update alert, scheduled backup reminder, data breach notification, device inactivity alert, device overactivity or device overheating warning, compatibility or synchronization issues with other devices, warranty expiration reminder, service contract renewal alert, sensor failure notification, environmental hazard alert, device location or movement alert, user account change, login from suspicious location notification, device lifecycle stage transition alert, predictive alert based on AI analysis, external power disconnection warning, accessory or peripheral failure notification, safety alert, status alert, usage alert, maintenance alert, promotion alert, product expiration alert, health-related alert, fitness-related alert, energy-related alert, inventory alert, performance degradation alert, compliance alert, demand surge alert, security breach alert, and user-defined custom alerts based on specific conditions or thresholds set by the user.

18. Furthermore, the process includes creating a smart contract, which provides terms applicable to at least one of the following: the plurality of digitally connected products, the user of at least one of the plurality of digitally connected products, the manufacturer of at least one of the plurality of digitally connected products, the distributor of at least one of the plurality of digitally connected products, the insurer of at least one of the plurality of digitally connected products, the retailer of at least one of the plurality of digitally connected products, and the service provider for at least one of the plurality of digitally connected products. The method according to claim 16, characterized in that the conditions include one or more of the following: purchase, return, lease, warranty, extended warranty, service agreement, maintenance agreement, trade-in, product add-on, new product features, data access agreement, purchase of access rights to one or more models, software upgrade, product upgrade, and unlocking of existing product features.

19. The method according to claim 18, further comprising automatically creating a smart contract based on predefined rules.

20. The method according to claim 18, further comprising allowing selection of smart contract templates while restricting such selection to one or more smart contract templates associated with at least one of the plurality of digitally connected products.

21. Receiving input for parameterization of the selected smart contract template, The smart contract is generated by parameterizing one or more variables within the smart contract based on the aforementioned input, and Deploying the smart contract by broadcasting it to at least one of the multiple digitally connected products. The method according to claim 20, further comprising:

22. The method according to claim 20, further comprising suggesting a smart contract template to the user based on at least one of the type of digitally connected product to which the smart contract applies, and one or more of the user's past selections.

23. The method according to claim 18, further comprising verifying the performance of obligations defined in the smart contract.

24. Furthermore, the method according to claim 23, which includes automatically triggering an action when the performance of the obligations defined in the smart contract has been successfully verified, wherein the action includes one or more of the following: providing a notice, updating the status of the smart contract, and initiating a new smart contract.

25. The method according to claim 16, further comprising enabling the user to upload records, wherein the records include one or more of the following: photographs, videos, audio recordings, device information, usage data, user settings, troubleshooting records, upgrade records, product reviews, receipts for product-related expenses or purchases, environmental data, and customer feedback.

26. The method according to claim 25, further comprising automatically classifying and storing the uploaded records based on their respective types and associated digitally connected products.

27. The method according to claim 16 further comprises managing one or more application programming interfaces (APIs), thereby exposing the APIs to one or more associated applications or third-party systems used by the plurality of digitally connected products.

28. Furthermore, the method according to claim 16 is characterized by including interaction with one or more regulatory systems.

29. Furthermore, the process includes integrating data from the multiple digitally connected products by performing one or more operations to create a unified view of the digitally connected products. The one or more operations described above include data collection, data aggregation, data normalization, data standardization, data cleaning, data enrichment, data analysis, data visualization, and integration with other systems. The method according to claim 16, characterized in that the data to be integrated includes one or more of the following: 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 according to claim 16, further comprising providing one or more user interfaces for displaying products associated with a single user, which are associated with at least one of brand, type, and function.

31. A digital product network system, It includes a user interface module, and the user interface module is Displaying information associated with a set of digitally connected products. Receiving and processing user input to display a set of marketplaces associated with the aforementioned information, Displaying the aforementioned set of marketplaces, and Receiving and processing user input for selecting a marketplace from the aforementioned set of marketplaces, It is configured to do the following: Furthermore, it includes an embedded marketplace module integrated with the user interface module, and the embedded marketplace is Display the selected marketplace, Receiving and processing user input for selecting one or more offerings from the selected set of offerings from the aforementioned marketplace, Receiving and processing user input to complete a set of transactions associated with one or more selected offerings, Execute the aforementioned set of transactions, A digital product network system characterized by being configured to perform the following actions.

32. The embedded marketplace module is configured to perform transactions relating to product data associated with at least one digitally connected product from the set of digitally connected products. The digital product network system according to claim 31, characterized in that the data of the product includes one or more of the following: product performance data, product price 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 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. The digital product network system according to claim 31, characterized in that the model and / or algorithm includes one or more of the following: a prediction model, a machine learning model, an artificial intelligence model, a classification model, an anomaly detection model, a clustering algorithm, a generative model, a large-scale language model, an optimization model, a recommendation model, a neural network, a transformer model, a convolutional neural network, a deep learning model, a supervised model, a semi-supervised model, an unsupervised model, a reinforcement learning model, and a regression model.

34. The digital product network system according to claim 31, wherein the embedded marketplace module is configured to perform transactions relating to one or more of the following, relating to at least one digitally connected product from the set of digitally connected products: product parts, product upgrades, product insurance, product enhancements, product add-ons, product software, product warranty, extended product warranty, product service agreement, product maintenance agreement, product trade-in, and access to product features.

35. The digital product network system according to claim 31, further characterized in that the embedded marketplace module is configured to perform peer-to-peer transactions associated with one or more of the digitally connected products.

36. The digital product network system according to claim 31, wherein the embedded marketplace module is configured to provide recommendations regarding offerings associated with at least one digitally connected product using a set of artificial intelligence (AI) based learning models, and at least one of the set of AI based learning models is trained using a training dataset of embedded marketplace transaction data.

37. The digital product network system according to claim 31, characterized in that the embedded marketplace module is configured to perform transactions between different entities, including two or more of the following: 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 according to claim 31, characterized in that the marketplace provided by the embedded marketplace module is configured to be searchable.

39. The digital product network system according to claim 31, characterized in that at least one of the set of marketplaces provides digital twin offerings.

40. A method for executing a transaction in a digital product network system, Displaying information associated with a set of digitally connected products. Receiving and processing user input to display a set of marketplaces associated with the aforementioned information, Displaying the aforementioned set of marketplaces, Receiving and processing user input for selecting a marketplace from the aforementioned set of marketplaces, Display the selected marketplace, Receiving and processing user input for selecting one or more offerings from the selected set of offerings from the aforementioned marketplace, Receiving and processing user input to complete a set of transactions associated with one or more selected offerings, Execute the aforementioned set of transactions, A method characterized by including the following.

41. Furthermore, this includes performing a transaction relating to product data associated with at least one digitally connected product from the set of digitally connected products, The method according to claim 40, characterized in that the data of the product includes one or more of the following: product performance data, product price 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. Furthermore, it includes executing a transaction related to a model and / or algorithm associated with at least one digitally connected product from the set of digitally connected products, The method according to claim 40, characterized in that the model and / or algorithm includes one or more of the following: a prediction model, a machine learning model, an artificial intelligence model, a classification model, an anomaly detection model, a clustering algorithm, a generative model, a large-scale language model, an optimization model, a recommendation model, a neural network, a transformer model, a convolutional neural network, a deep learning model, a supervised model, a semi-supervised model, an unsupervised model, a reinforcement learning model, and a regression model.

43. The method according to claim 40, further comprising performing a transaction relating to one or more of the following, with respect to at least one digitally connected product from the set of digitally connected products: product parts, product upgrades, product insurance, product enhancements, product add-ons, product software, product warranty, extended product warranty, product service agreement, product maintenance agreement, product trade-in, and access to new product features.

44. The method according to claim 40, further comprising performing peer-to-peer transactions associated with one or more of the digitally connected products.

45. The method according to claim 40, further comprising using a set of artificial intelligence (AI)-based learning models to provide recommendations regarding offerings associated with at least one digitally connected product, wherein at least one of the set of AI-based learning models is trained using a training dataset of embedded marketplace transaction data.

46. The method according to claim 40, further comprising performing transactions between different entities, including two or more of the following: users, manufacturers, suppliers, service providers, data providers, data analytics providers, software application providers, model providers, digital twin providers, and NFT providers.

47. The method according to claim 40, further comprising providing at least one searchable marketplace.

48. The method according to claim 40, further comprising providing at least one marketplace for offering digital twin offerings.

49. A digital product network system comprising one or more processors and one or more memories, The one or more processors and the one or more memories are, A computing device receives information associated with a set of digitally connected products in a digital product network, wherein the information is generated by at least one of the following: a set of sensors for the set of digitally connected products, a set of IoT devices configured to collect data related to the set of digitally connected products, or a set of APIs configured to publish data related to the set of digitally connected products. The information is provided to a set of artificial intelligence (AI) based learning models, wherein at least one of the AI-based learning models is trained using a training dataset of digital product network data to generate recommendations associated with one or more of the sets of digitally connected products, and Output the aforementioned recommendation, A digital product network system characterized by being configured to perform operations including those mentioned above.

50. The digital product network system according to claim 49, characterized in that the information relates to the packaging date or packaging time of the set of digitally connected products.

51. The digital product network system according to claim 49, characterized in that the information relates to the location of the set of digitally connected products.

52. The digital product network system according to claim 49, characterized in that the information relates to the manufacturing location of the set of digitally connected products.

53. The digital product network system according to claim 49, characterized in that the information relates to the packaging location of the set of digitally connected products.

54. The digital product network system according to claim 49, characterized in that the information relates to the place of purchase of the set of digitally connected products.

55. The digital product network system according to claim 49, characterized in that the information relates to the location where the packaging of the set of digitally connected products is opened or consumed.

56. The digital product network system according to claim 49, characterized in that the information relates to the display time of the set of digitally connected products on a display shelf or at another display location.

57. The digital product network system according to claim 49, characterized in that the information relates to customer interaction with a displayed set of digitally connected products from the set of digitally connected products.

58. The digital product network system according to claim 49, characterized in that the information relates to customer interactions with the purchased digitally connected product from the set of digitally connected products.

59. The digital product network system according to claim 49, characterized in that the set of AI-based learning models includes at least one of the following: 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-scale language model, a recommendation model, or a reinforcement learning model.

60. The digital product network system according to 49, characterized in that the recommendation is related to one or more of the following: operating parameters of one or more of the set of digitally connected products; provision of customer support related to one or more of the set of digitally connected products; pricing of one or more of the set of digitally connected products; product design related to one or more of the set of digitally connected products; product security related to one or more of the set of digitally connected products; quality control related to one or more of the set of digitally connected products; marketing or sales strategy related to one or more of the set of digitally connected products; display strategy related to one or more of the set of digitally connected products; packaging related to one or more of the set of digitally connected products; software upgrades related to one or more of the set of digitally connected products; inventory management related to one or more of the set of digitally connected products; regulatory compliance related to one or more of the set of digitally connected products; and customer behavior related to one or more of the set of digitally connected products.

61. A method for generating recommendations in a digital product network system, A computing device receives information associated with a set of digitally connected products in a digital product network, wherein the information is generated by at least one of the following: a set of sensors for the set of digitally connected products, a set of IoT devices configured to collect data related to the set of digitally connected products, or a set of APIs configured to publish data related to the set of digitally connected products. The information is provided to a set of artificial intelligence (AI) based learning models, wherein at least one of the AI-based learning models is trained using a training dataset of digital product network data to generate recommendations associated with one or more of the sets of digitally connected products, and Output the aforementioned recommendation, A method characterized by including the following.

62. The method according to claim 61, characterized in that the information relates to the packaging date or packaging time of the set of digitally connected products.

63. The method according to claim 61, characterized in that the information relates to the location of the set of digitally connected products.

64. The method according to claim 61, characterized in that the information relates to the manufacturing location of the set of digitally connected products.

65. The method according to claim 61, characterized in that the information relates to the packaging location of the set of digitally connected products.

66. The method according to claim 61, characterized in that the information relates to the place of purchase of the set of digitally connected products.

67. The method according to claim 61, characterized in that the information relates to the location where the packaging of the set of digitally connected products is opened or consumed.

68. The method according to claim 61, characterized in that the information relates to the display time of the set of digitally connected products on a display shelf or at another display location.

69. The method according to claim 61, characterized in that the information relates to the customer's interaction with the displayed set of digitally connected products from the set of digitally connected products.

70. The method according to claim 61, characterized in that the information relates to the customer's interaction with the purchased digitally connected product from the set of digitally connected products.

71. The method according to claim 61, characterized in that the set of AI-based learning models includes at least one of the following: 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-scale language model, a recommendation model, or a reinforcement learning model.

72. The method according to claim 61, characterized in that the recommendation is related to one or more of the following: operating parameters of one or more of the set of digitally connected products; provision of customer support related to one or more of the set of digitally connected products; pricing of one or more of the set of digitally connected products; product design related to one or more of the set of digitally connected products; product security related to one or more of the set of digitally connected products; quality control related to one or more of the set of digitally connected products; marketing or sales strategy related to one or more of the set of digitally connected products; display strategy related to one or more of the set of digitally connected products; packaging related to one or more of the set of digitally connected products; software upgrades related to one or more of the set of digitally connected products; inventory management related to one or more of the set of digitally connected products; regulatory compliance related to one or more of the set of digitally connected products; and customer behavior related to one or more of the set of digitally connected products.

73. A digital product network system comprising one or more processors and one or more memories, The one or more processors and the one or more memories are, A computing device receives information relating to a set of digitally connected products in the digital product network system, wherein the information is generated by at least one of the following: a set of sensors for the set of digitally connected products, a set of IoT 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; The information is provided to a digital twin system, wherein the digital twin system is configured to generate digital twins representing one or more digitally connected products from the set of digitally connected products based on the information, and each of the digital twins provides a substantially real-time representation of one or more digitally connected products from the set of digitally connected products. A digital product network system characterized by being configured to perform operations including those mentioned above.

74. The digital product network system according to claim 73, further configured to generate digital twins representing one or more entities, environments, and workflows of the digital product network.

75. The digital product network system according to claim 74, characterized in that the entities include one or more additional connected products, physical objects, 3D printers, manufacturers, distributors, consumers, service providers, retailers, payment providers, and regulatory authorities.

76. The digital product network system according to claim 74, characterized in that the environment includes one or more of the following: warehouses, manufacturing facilities, 3D printing facilities, service provider facilities, smart containers, containers, shipping ports, trucks, transportation networks, physical stores, digital marketplaces, virtual reality environments, storage facilities, and digital product networks.

77. The digital product network system according to claim 74, characterized in that the workflow includes one or more of the following: product design workflow, product parts procurement workflow, product manufacturing workflow, product packaging workflow, product distribution workflow, product marketing and sales workflow, product display workflow, product interaction workflow, product usage workflow, data collection workflow, product repair workflow, product recall workflow, product lifecycle management workflow, product maintenance workflow, data processing workflow, network integration workflow, and feedback loop workflow.

78. The digital product network system according to claim 73, characterized in that the aforementioned information relates to one or more of the following: 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 according to claim 73, further comprising providing an embedded marketplace within the digital twin of the digitally connected products to enable transactions relating to one or more of the following: models, data, product updates, product upgrades, product add-ons, product-related service provision, and product parts, relating to the one or more digitally connected products.

80. Furthermore, the digital product network system according to claim 73 is characterized by including the simulation of at least one of the behavior, performance, environment, and interaction of the digital twin using a CAD-based model.

81. Furthermore, the digital product network system according to claim 73 is characterized by comprising generating alerts based on the information represented in the digital twin.

82. The digital product network system according to claim 74, further comprising providing a hierarchical digital twin, each of which represents at least one hierarchical level of entities, environments, or workflows within the digital product network.

83. Furthermore, the digital product network system according to claim 74, comprising providing a context-adaptive digital twin, wherein the context-adaptive digital twin is configured to adapt at least one representation of the entity, the environment, and the workflow based on a change in context.

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

85. A method for providing a digital twin in a digital product network system, A computing device receives information relating to a set of digitally connected products in the digital product network system, wherein the information is generated by at least one of the following: a set of sensors for the set of digitally connected products, a set of IoT 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; The information is provided to a digital twin system, wherein the digital twin system is configured to generate digital twins of one or more digitally connected products from the set of digitally connected products based on the information, and each of the digital twins provides a substantially real-time representation of one or more digitally connected products from the set of digitally connected products. A method characterized by including the following.

86. The method according to claim 85, further comprising generating a digital twin representing one or more entities, environments, and workflows of the digital product network.

87. The method according to claim 86, wherein the entity includes one or more of the following: additional connected products, physical objects, 3D printers, manufacturers, distributors, consumers, service providers, payment providers, and regulatory authorities.

88. The method according to claim 86, characterized in that the environment includes one or more of the following: warehouses, manufacturing facilities, 3D printing facilities, service provider facilities, smart containers, containers, trucks, transportation networks, physical stores, digital marketplaces, virtual reality environments, storage facilities, and connected product networks.

89. The method according to 86, characterized in that the workflow includes one or more of the following: a product design workflow, a product parts procurement 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 according to claim 85, characterized in that the information relates to one or more of the following: 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 according to claim 85, further comprising providing an embedded marketplace within the digital twin of the digitally connected product for the purchase, sale, and exchange of one or more of the following related to the one or more digitally connected products: models, data, product updates, product upgrades, product add-ons, product-related service provision, and product parts.

92. The method according to claim 85, further comprising simulating at least one of the behavior, performance, environment, and interaction of the digital twin using a CAD-based model.

93. The method according to claim 85, further comprising generating alerts based on the data represented in the digital twin.

94. The method according to 86 further comprises providing a hierarchical digital twin, each of which represents at least one hierarchical level of entities, environments, or workflows within the digital product network.

95. The method according to 86, further comprising providing a context-adaptive digital twin, wherein each of the context-adaptive digital twins is configured to adapt at least one representation of the entity, the environment, and the workflow based on a change in context.

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

97. A method for providing an artificial intelligence (AI)-based supply chain digital twin for digitally connected products, A computing device receives information associated with a set of digitally connected products in a digital product network system, wherein the information is generated by at least one of the following: a set of sensors for the set of digitally connected products, a set of IoT devices configured to collect data related to the set of digitally connected products, or a set of APIs configured to publish data related to the set of digitally connected products. Providing the aforementioned information to a digital twin system, wherein the digital twin system is configured to update the digital twin representing the supply chain network of the set of digitally connected products based on the aforementioned information, and the digital twin provides a substantially real-time representation of the set of supply chain entities of the supply chain network. Providing the aforementioned information to a set of artificial intelligence (AI)-based learning models, wherein at least one component of the set of AI-based learning models is trained using a training dataset of digital product network data to determine at least one classification of the supply chain network among operating state, failure state, operation flow, or behavior, and at least one component of the set of AI-based learning models is trained using the training dataset to determine a task to be completed for the supply chain network when it receives the at least one classification of the operating state, failure state, operation flow, or behavior, and To provide at least one of the following: instructions for performing the task in the supply chain network digital twin, or recommendations for performing the task in the supply chain network digital twin. A method characterized by including the following.

98. The method according to claim 97, characterized in that the information includes real-time data on either cash-on-delivery receipts from suppliers associated with an order, or inventory flowing into a network related to the supply chain network.

99. The method according to claim 97, characterized in that receiving the information associated with the supply chain network includes receiving sensor data indicating the status of incoming and outgoing goods.

100. The method according to claim 97, further comprising generating a simulation for the supply chain network digital twin, wherein the simulation for the supply chain network digital twin is generated using a graphical neural network (GNN).

101. The method according to claim 97, characterized in that the supply chain network digital twin is realized by a robot operating system.

102. The method according to 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, and each of the one or more sets of digital twins includes an embedded marketplace for digital twin simulation.

103. The method according to 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, and each digital twin of the one or more sets includes an embedded marketplace for either an artificial intelligence-based learning model or an artificial intelligence-based algorithm.

104. The method according to 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, and each of the one or more sets of digital twins includes an embedded marketplace for data.

105. The method according to claim 97, characterized in that the set of supply chain network entities includes at least one of the following: digitally connected products, suppliers, producers, manufacturers, retailers, companies, 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 forecasting processes, demand management processes, demand aggregation processes, machinery, ships, barges, warehouses, seaports, airports, air routes, waterways, roads, railways, bridges, tunnels, online retailers, e-commerce sites, demand factors, supply factors, delivery systems, floating assets, origin points, destinations, storage points, usage points, 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, transport facilities, waterways, or port infrastructure facilities.

106. The method according to claim 97, characterized in that the set of AI-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 learning model.

107. A computing system comprising one or more processors and one or more memories, The one or more processors and the one or more memories are, A computing device receives information associated with a set of digitally connected products in a digital product network system, wherein the information is generated by at least one of the following: a set of sensors for the set of digitally connected products, a set of IoT devices configured to collect data related to the set of digitally connected products, or a set of APIs configured to publish data related to the set of digitally connected products. Providing the aforementioned information to a digital twin system, wherein the digital twin system is configured to update the digital twin representing the supply chain network of the set of digitally connected products based on the aforementioned information, and the digital twin provides a substantially real-time representation of the set of supply chain entities of the supply chain network. Providing the aforementioned information to a set of artificial intelligence (AI)-based learning models, wherein at least one component of the set of AI-based learning models is trained using a training dataset of digital product network data to determine at least one classification of the supply chain network among operating state, failure state, operation flow, or behavior, and at least one component of the set of AI-based learning models is trained using the training dataset to determine a task to be completed for the supply chain network when it receives the at least one classification of the operating state, failure state, operation flow, or behavior, and To provide at least one of the following: instructions for performing the task in the supply chain network digital twin, or recommendations for performing the task in the supply chain network digital twin. A computing system characterized by being configured to perform operations including those mentioned above.

108. The computing system according to claim 107, characterized in that the information includes real-time data on either cash-on-delivery receipts from suppliers associated with an order, or inventory flowing into a network related to the supply chain network.

109. The computing system according to claim 107, characterized in that receiving the information associated with the supply chain network includes receiving sensor data indicating the status of incoming and outgoing goods.

110. The computing system according to claim 107, wherein the operation further includes generating a simulation for the supply chain network digital twin, the simulation for the supply chain network digital twin is generated using a graphical neural network (GNN).

111. The computing system according to claim 107, characterized in that the supply chain network digital twin is realized by a robot operating system.

112. The computing system according to 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, and each of the one or more sets of digital twins includes an embedded marketplace for digital twin simulation.

113. The computing system according to 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, and each of the one or more sets of digital twins includes an embedded marketplace for either an artificial intelligence-based learning model or an artificial intelligence-based algorithm.

114. The computing system according to 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, and each of the one or more sets of digital twins includes an embedded marketplace for data.

115. The computing system according to 107, characterized in that the set of supply chain network entities includes at least one of the following: digitally connected products, suppliers, producers, manufacturers, retailers, companies, 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 forecasting processes, demand management processes, demand aggregation processes, machinery, ships, barges, warehouses, seaports, airports, air routes, waterways, roads, railways, bridges, tunnels, online retailers, e-commerce sites, demand factors, supply factors, distribution systems, floating assets, origin points, destinations, storage points, usage points, 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, transport facilities, waterways, or port infrastructure facilities.

116. The computing system according to claim 107, characterized in that the set of AI-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 learning model.

117. A digital product network system comprising one or more processors and one or more memories, The one or more processors and the one or more memories are, A computing device receives information associated with a set of digitally connected products in a digital product network, wherein the information is generated by at least one of the following: a set of sensors for the set of digitally connected products, a set of IoT devices configured to collect data related to the set of digitally connected products, or a set of APIs configured to publish data related to the set of digitally connected products. The information is provided to a set of artificial intelligence (AI) based learning models, wherein at least one component of the set of AI based learning models is trained using a training dataset of digital product network data to determine a set of demand planning actions, and To provide a machine with a set of computer code instructions for executing the aforementioned set of demand planning actions, A digital product network system characterized by being configured to perform operations including those mentioned above.

118. The set of demand planning actions is characterized by including at least one of the following: forecasting demand indicators, inventory management, capacity planning, supply chain adjustment, customer prioritization, order quantity limits, dynamic pricing, alternative product promotion, marketing campaigns, product improvement, market expansion, and customer engagement, as described in the digital product network system according to claim 117.

119. The digital product network system according to claim 117, characterized in that the information includes at least one of the following: 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 trend data, customer feedback data, historical product development data, project management data, resource availability data, supplier data, manufacturing data, quality control data, test data, regulatory data, compliance data, historical recall data, manufacturing data, supplier data, parts 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 according to claim 117, characterized in that the set of AI-based learning models includes at least one of the following: transformer models, convolutional neural networks, deep learning models, supervised models, semi-supervised models, generative models, large-scale language models, recommendation models, unsupervised models, reinforcement learning models, and regression models.

121. The digital product network system according to claim 117, characterized in that the set of demand planning actions includes forecasting indicators of demand for each of a plurality of demographic groups at a future date and / or time.

122. The digital product network system according to claim 121, further comprising the set of demand planning actions generating and distributing marketing messages associated with the set of digitally connected products for specific demographic groups based on the indicators of predicted demand.

123. The digital product network system according to claim 117, characterized in that the set of demand planning actions includes forecasting indicators of demand for each of a plurality of geographical regions at a future date and / or time.

124. The digital product network system according to claim 123, further comprising generating and distributing marketing messages associated with the set of digitally connected products for specific geographical areas based on the indicators of predicted demand, as a set of demand planning actions.

125. The digital product network system according to claim 117, characterized in that the set of demand planning actions includes forecasting indicators 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, A computing device receives information associated with a set of digitally connected products in a digital product network, wherein the information is generated by at least one of the following: a set of sensors for the set of digitally connected products, a set of IoT devices configured to collect data related to the set of digitally connected products, or a set of APIs configured to publish data related to the set of digitally connected products. The information is provided to a set of artificial intelligence (AI)-based learning models, wherein at least one component of the set of AI-based learning models is trained using a training dataset of digital product network data to determine a set of demand planning actions for the set of digitally connected products, and To provide a machine with a set of computer code instructions for executing the aforementioned set of demand planning actions, A method characterized by including the following.

127. The method according to claim 126, characterized in that the set of demand planning actions relates to at least one of the following: forecasting demand indicators, inventory management, capacity planning, supply chain adjustment, customer prioritization, order quantity limits, dynamic pricing, alternative product promotion, marketing campaigns, product improvement, market expansion, and customer engagement.

128. The method according to claim 126, characterized in that the information includes at least one of the following: 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 trend data, customer feedback data, historical product development data, project management data, resource availability data, supplier data, manufacturing data, quality control data, test data, regulatory data, compliance data, historical recall data, manufacturing data, supplier data, parts 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 according to claim 126, characterized in that each of the set of AI-based learning models includes at least one of the following: a transformer model, a convolutional neural network, a deep learning model trained on a set of results from the digital product network, a supervised model, a semi-supervised model, an unsupervised model, a reinforcement learning model, a generative model, a large language model, a recommendation model, and a regression model.

130. The method according to claim 126, characterized in that the set of demand planning actions includes forecasting indicators of demand for each of a plurality of demographic groups at a future date and / or time.

131. The method according to claim 130, further comprising generating and distributing marketing messages associated with the set of digitally connected products for specific demographic groups based on the indicators of predicted demand.

132. The method according to claim 126, characterized in that the set of demand planning actions includes forecasting indicators of demand for each of a plurality of geographical regions at a future date and / or time.

133. The method according to claim 132, further comprising generating and distributing marketing messages associated with the set of digitally connected products for specific geographical areas based on the indicators of predicted demand.

134. The method according to claim 126, characterized in that the set of demand planning actions includes forecasting indicators of demand for each of a plurality of channel preference segments at a future date and / or time.

135. A digital product network system comprising one or more processors and one or more memories, The one or more processors and the one or more memories are, A computing device receives information associated with a set of digitally connected products in a digital product network, wherein the information is generated by at least one of the following: a set of sensors for the set of digitally connected products, a set of IoT devices configured to collect data related to the set of digitally connected products, or a set of APIs configured to publish data related to the set of digitally connected products. Providing the aforementioned information to a set of artificial intelligence (AI)-based learning models, wherein at least one component of the set of AI-based learning models is trained using a training dataset of digital product network data to predict future inventory level requirements for the set of digitally connected products based on the aforementioned information, and at least one component of the set of AI-based learning models is trained using the training dataset to determine actions to satisfy the predicted inventory level requirements based on the predictions and the aforementioned information, and To output the determined action, and to provide the machine with a set of computer code instructions for performing the determined action, at least one of the above. A digital product network system characterized by being configured to perform operations including those mentioned above.

136. The digital product network system according to claim 135, characterized in that the information includes at least one of the following: 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 trend 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, parts 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 reporting data.

137. The digital product network system according to claim 135, characterized in that the set of AI-based learning models includes at least one of the following: transformer models, convolutional neural networks, deep learning models trained on a set of digital product network results, supervised models, semi-supervised models, unsupervised models, reinforcement learning models, generative models, large-scale language models, and regression models.

138. The digital product network system according to 135, characterized in that the decided action relates to at least one of the following: involving secondary manufacturers or contract manufacturers to supplement production capacity; implementing overtime work at manufacturing facilities to temporarily increase production; reallocating inventory; simplifying product design; 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 according to claim 135, characterized in that at least one component of the set of AI-based learning models is trained using a training dataset of digital product network data to generate predictions of future inventory level requirements for each of a plurality of retail stores.

140. The digital product network system according to claim 135, characterized in that at least one component of the set of AI-based learning models is trained using a training dataset of digital product network data to generate predictions of future inventory level requirements for each of a plurality of geographical regions.

141. The digital product network system according to claim 135, characterized in that at least one component of the set of AI-based learning models is trained using a training dataset of digital product network data to generate predictions of future inventory level requirements for each of a plurality of sales channels.

142. The digital product network system according to claim 135, further comprising providing at least one of the following operations: real-time updates of inventory levels and alerts when inventory levels reach a predetermined threshold.

143. The digital product network system according to claim 135, characterized in that the determined action is output to a digital twin user interface.

144. A method for optimizing inventory in a digital product network system, A computing device receives information associated with a set of digitally connected products in a digital product network, wherein the information is generated by at least one of the following: a set of sensors for the set of digitally connected products, a set of IoT devices configured to collect data related to the set of digitally connected products, or a set of APIs configured to publish data related to the set of digitally connected products. Providing the aforementioned information to a set of artificial intelligence (AI)-based learning models, wherein at least one component of the set of AI-based learning models is trained using a training dataset of digital product network data to predict future inventory level requirements for the set of digitally connected products, and at least one component of the set of AI-based learning models is trained using the training dataset to determine actions to satisfy the predicted inventory level requirements, and To output the determined action, and to provide the machine with a set of computer code instructions for performing the determined action, at least one of the above. A method characterized by including the following.

145. The method according to claim 144, characterized in that the information includes at least one of the following: 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 trend 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, parts 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 reporting data.

146. The method according to claim 144, characterized in that the set of AI-based learning models includes at least one of the following: a deep learning model trained on a set of results from a transformer model, a convolutional neural network, a digital product network, a supervised model, a semi-supervised model, an unsupervised model, a reinforcement learning model, a generative model, a large-scale language model, and a regression model.

147. The method according to claim 144, characterized in that the decided action relates to at least one of the following: involving secondary manufacturers or contract manufacturers to supplement production capacity; implementing overtime work at manufacturing facilities to temporarily increase production; reallocating inventory; simplifying product design; implementing strategic stockpiling; supplementing production capacity with 3D printers; increasing storage and / or warehouse capacity; implementing cross-docking; improving returns management; adjusting production settings; and implementing a dynamic pricing strategy.

148. The method according to claim 144, characterized in that at least one component of the set of AI-based learning models is trained using a training dataset of digital product network data to generate predictions of future inventory level requirements for each of a plurality of retail stores.

149. The method according to claim 144, characterized in that at least one component of the set of AI-based learning models is trained using a training dataset of digital product network data to generate predictions of future inventory level requirements for each of a plurality of geographical regions.

150. The method according to claim 144, characterized in that at least one component of the set of AI-based learning models is trained using a training dataset of digital product network data to generate predictions of future inventory level requirements for each of a plurality of sales channels.

151. Furthermore, the method according to claim 144 is characterized by providing at least one of the following: real-time updates of inventory levels and alerts when inventory levels reach a predetermined threshold.

152. The method according to claim 144, characterized in that the determined action is output to a digital twin user interface.

153. A digital product network system comprising one or more processors and one or more memories, The one or more processors and the one or more memories are, A computing device receives information associated with a set of digitally connected products in a digital product network, wherein the information is generated by at least one of the following: a set of sensors for the set of digitally connected products, a set of IoT devices configured to collect data related to the set of digitally connected products, or a set of APIs configured to publish data related to the set of digitally connected products. The information is provided to a first set of artificial intelligence (AI)-based learning models, wherein at least one component of the first set of AI-based learning models is trained using a training dataset of digital product network data to determine at least one classification of operating state, failure state, operation flow, and behavior of at least one of the set of digitally connected products. The information is provided to a second set of artificial intelligence (AI)-based learning models, wherein at least one component of the second set of AI-based learning models is trained using a training dataset of digital product network data to recommend at least one design improvement for the set of digitally connected products based on the classification, and Based on the output of the AI-based learning model, outputting recommendations for design improvements related to the set of digitally connected products, and providing a set of computer code instructions to a machine to implement the design improvements related to the set of digitally connected products, at least one of these: A digital product network system characterized by being configured to perform operations including those mentioned above.

154. The digital product network system according to claim 153, characterized in that the design improvements include at least one of the following: changing existing product functions, adding new product functions, deleting product functions, replacing product materials, changing product size, changing the size of product components, replacing product components, changing product color, changing product style, and rearranging a set of product components.

155. The digital product network system according to claim 153, characterized in that the information includes at least one of the following: 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 function 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 reporting data.

156. The digital product network system according to claim 153, characterized in that each set of AI-based learning models includes at least one of the following: transformer models, convolutional neural networks, deep learning models trained on a set of results from the digital product network, supervised models, semi-supervised models, unsupervised models, reinforcement learning models, generative models, large-scale language models, and regression models.

157. The digital product network system according to claim 153, characterized in that at least one component of a third set of AI-based learning models is trained using a training dataset of digital product network data to predict the impact of the at least one design improvement.

158. The digital product network system according to claim 153, wherein the operation further comprises generating a virtual prototype of the improved product design.

159. The digital product network system according to claim 153, characterized in that at least one component of a third set of AI-based learning models is trained using a training dataset of digital product network data to identify potential risks associated with implementing the at least one design improvement.

160. The digital product network system according to claim 153, wherein at least one component of a third set of AI-based learning models is trained using a training dataset of digital product network data to generate a plan for verifying the improved product design, the plan comprising one or more of the following: defining test parameters, identifying test resources, and setting up a test environment.

161. The digital product network system according to 153, wherein at least one component of a third set of AI-based learning models is trained using a training dataset of digital product network data to generate a launch and marketing plan for the improved product design, the plan comprising one or more of the following: identifying a target market, developing a marketing strategy, and planning a marketing campaign.

162. The digital product network system according to claim 153, wherein the operation further comprises determining the cost per unit for the at least one design improvement.

163. A method for improving product design in a digital product network system, A computing device receives information associated with a set of digitally connected products in a digital product network, wherein the information is generated by at least one of the following: a set of sensors for the set of digitally connected products, a set of IoT devices configured to collect data related to the set of digitally connected products, or a set of APIs configured to publish data related to the set of digitally connected products. The information is provided to a first set of artificial intelligence (AI)-based learning models, wherein at least one component of the first set of AI-based learning models is trained using a training dataset of digital product network data to determine at least one classification of operating state, failure state, operation flow, and behavior of at least one of the set of digitally connected products. The information is provided to a second set of artificial intelligence (AI)-based learning models, wherein at least one component of the second set of AI-based learning models is trained using a training dataset of digital product network data to determine at least one design improvement for the set of digitally connected products based on the classification, and Based on the output of the AI-based learning model, outputting recommendations for design improvements related to the digitally connected product, and providing a set of computer code instructions to a machine to implement the design improvements related to the digitally connected product, at least one of these: A method characterized by including the following.

164. The method according to claim 163, characterized in that the design improvement includes one or more of the following: changing an existing product function, adding a new product function, deleting a product function, replacing a product material, changing the size of a product, changing the size of a product component, replacing a product component, changing the color of a product, changing the style of a product, and rearranging a set of components of a product.

165. The method according to claim 163, characterized in that the information includes at least one of the following: 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 function 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 reporting data.

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

167. The method according to claim 163, characterized in that at least one component of a third set of AI-based learning models is trained using a training dataset of digital product network data to predict the impact of the at least one design improvement.

168. The method according to claim 163, further comprising generating a virtual prototype of the improved product design.

169. The method according to claim 163, characterized in that at least one component of a third set of AI-based learning models is trained using a training dataset of digital product network data to identify potential risks associated with implementing the at least one design improvement.

170. The method according to claim 163, wherein at least one component of a third set of AI-based learning models is trained using a training dataset of digital product network data to generate a plan for verifying the improved product design, the plan comprising one or more of the following: defining test parameters, identifying test resources, and setting up a test environment.

171. The method according to claim 163, wherein at least one component of a third set of AI-based learning models is trained using a training dataset of digital product network data to generate a launch and marketing plan for the improved product design, the plan comprising one or more of the following: identifying a target market, developing a marketing strategy, and planning a marketing campaign.

172. The method according to claim 163, wherein the operation further comprises determining the cost per unit for the at least one design improvement.

173. A digital product network system comprising one or more processors and one or more memories, The one or more processors and the one or more memories are, A computing device receives information associated with a set of digitally connected products in a digital product network, wherein the information is generated by at least one of the following: a set of sensors for the set of digitally connected products, a set of IoT devices configured to collect data related to the set of digitally connected products, or a set of APIs configured to publish data related to the set of digitally connected products. The information is provided to a set of artificial intelligence (AI) based learning models, wherein at least one component of the set of AI based learning models is trained using a training dataset of digital product network data to determine at least one product feature preference for the set of digitally connected products at each location in the set of locations, and Outputting the at least one product feature preference for the set of digitally connected products at each position in the set of positions. A digital product network system characterized by being configured to perform operations including those mentioned above.

174. The digital product network system according to claim 173, characterized in that the information includes at least one of the following: 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 function 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 reporting data.

175. The digital product network system according to claim 173, characterized in that the set of AI-based learning models includes at least one of the following: transformer models, convolutional neural networks, deep learning models trained on a set of digital product network results, supervised models, semi-supervised models, unsupervised models, reinforcement learning models, generative models, large-scale language models, and regression models.

176. The digital product network system according to claim 173, characterized in that at least one component of the set of AI-based learning models is trained using a training dataset of digital product network data to determine at least one product feature preference for the set of digitally connected products in a plurality of geographic segments.

177. The digital product network system according to claim 176, further comprising determining sales metrics associated with each of the plurality of geographic segments.

178. The digital product network system according to claim 173, characterized in that at least one component of the set of AI-based learning models is trained using a training dataset of digital product network data to determine at least one product feature preference for a set of digitally connected products for a plurality of demographic segments.

179. The digital product network system according to claim 178, further comprising determining sales metrics associated with each of the plurality of demographic segments.

180. The digital product network system according to claim 173, characterized in that at least one component of the set of AI-based learning models is trained using a training dataset of digital product network data to determine at least one product feature preference for a set of digitally connected products with respect to a plurality of consumer behavior segments.

181. The digital product network system according to claim 180, further comprising determining a sales metric associated with each of the plurality of consumer behavior segments.

182. The operation further comprises providing product development recommendations for the set of digitally connected products based on the determined at least one product feature preference, wherein the recommendations include modifying one or more existing product features, adding new product features, deleting product features, substituting product materials, changing the size of a product, changing the size of a product component, substituting a product component, changing the color of a product, changing the style of a product, or rearranging a set of product components, as described in 173.

183. A method for determining product feature preferences in a digital product network system, A computing device receives information associated with a set of digitally connected products in a digital product network, wherein the information is generated by at least one of the following: a set of sensors for the set of digitally connected products, a set of IoT devices configured to collect data related to the set of digitally connected products, or a set of APIs configured to publish data related to the set of digitally connected products. The information is provided to a set of artificial intelligence (AI)-based learning models, wherein at least one component of the set of AI-based learning models is trained using a training dataset of digital product network data to determine at least one product feature preference for the set of digitally connected products in each of the set of locations, and Outputting the at least one product feature preference for the set of digitally connected products at each position in the set of positions. A method characterized by including the following.

184. The method according to claim 183, characterized in that the information includes at least one of the following: 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 function 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, parts 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 reporting data.

185. The method according to claim 183, wherein the set of AI-based learning models includes at least one of the following: a deep learning model trained on a set of results from a transformer model, a convolutional neural network, a digital product network, a supervised model, a semi-supervised model, an unsupervised model, a reinforcement learning model, a generative model, a large-scale language model, and a regression model.

186. The method according to claim 183, wherein at least one component of the set of AI-based learning models is trained using a training dataset of digital product network data to determine at least one product feature preference for a set of digitally connected products with respect to a plurality of geographical segments.

187. The method according to claim 186, further comprising determining a sales indicator associated with each of the aforementioned geographical segments.

188. The method according to claim 183, characterized in that at least one component of the set of AI-based learning models is trained using a training dataset of digital product network data to determine at least one product feature preference for a set of digitally connected products for a plurality of demographic segments.

189. The method according to claim 188, further comprising determining a sales indicator associated with each of the plurality of demographic segments.

190. The method according to claim 183, characterized in that at least one component of the set of AI-based learning models is trained using a training dataset 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 according to claim 190, further comprising determining a sales indicator associated with each of the aforementioned multiple behavioral segments.

192. The method according to 183, further comprising providing product development recommendations for the set of digitally connected products based on the at least one product feature preference, wherein the recommendations include modifying one or more existing product features, adding new product features, deleting product features, substituting product materials, changing the size of a product, changing the size of a product component, substituting a product component, changing the color of a product, changing the style of a product, or rearranging a set of product components.

193. A generative artificial intelligence system for digital product networks, It includes one or more processors and one or more memories, and the one or more processors and the one or more memories are The generative artificial intelligence system executes a generative artificial intelligence algorithm trained on digital product network data. Receiving input data that includes at least one of the following: images, videos, audio, text, program code, and data. The process involves using the generative artificial intelligence algorithm to process the input data and generate output content, wherein the output content includes at least one of the following: structured text, images, videos, audio content, software source code, formatted data, algorithms, definitions, and context-specific structures. To generate the internal state of the generative artificial intelligence system, which includes a set of weights and / or biases as a result of prior processing, The generated output content is provided to the user interface for presentation to the user. A generative artificial intelligence system characterized by being configured to perform operations including those mentioned above.

194. The generative artificial intelligence system according to claim 193, wherein the input data further includes natural language representations, 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 instructions, and device configurations.

195. The generative artificial intelligence system according to claim 193, characterized in that it is configured to maintain contextual awareness throughout the interaction in order to facilitate the continuity of the interaction with the user.

196. The generative artificial intelligence system according to claim 193, characterized in that the generative artificial intelligence system is configured to support the interpretability and explainability of its output by providing an explanation of the basis for the output.

197. The generative artificial intelligence system according to claim 193, characterized in that the output includes recommendations for improving at least one of the digital product network and the digitally connected products within the digital product network.

198. Furthermore, the generative artificial intelligence system according to 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 the digital twin represents at least one of the digital product network and the digitally connected products within the digital product network.

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

200. The generative artificial intelligence system according to claim 193, wherein the processor is further configured to communicate with a robotic system in 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.

201. The generative artificial intelligence system according to claim 193, further characterized in that the output content is also adjusted based on the user's role as an executive.

202. The generative artificial intelligence system according to claim 193, characterized in that the generated output includes 3D printing instructions for a set of 3D printers.

203. The generative artificial intelligence system according to claim 193, characterized in that the system is part of a dual-process artificial neural network (DPANN) architecture.

204. Furthermore, the generative artificial intelligence system according to claim 193 further includes an augmented reality (AR) interface configured to superimpose the generated output content onto the user's field of view in the real world.

205. A method for operating a generative artificial intelligence system within a value chain network, The aforementioned generative artificial intelligence system executes a generative artificial intelligence algorithm trained on value chain network data. Receiving input data that includes at least one of the following: images, videos, audio, text, program code, and data. The process involves using the generative artificial intelligence system to process the input data and generate output content, wherein the output content includes at least one of the following: structured text, images, videos, audio content, software source code, formatted data, algorithms, definitions, and context-specific structures. To generate the internal state of the generative artificial intelligence system, which includes a set of weights and / or biases as a result of prior processing, The generated output content is provided to the user interface for presentation to the user. A method characterized by including the following.

206. The method according to 205 is further characterized by receiving additional input data, including natural language representations, 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 instructions, and device configurations.

207. Furthermore, the method according to claim 205 is characterized by including maintaining contextual awareness throughout the interaction in order to facilitate the continuity of the interaction with the user.

208. The method according to claim 205, further comprising supporting the interpretability and explainability of the output by providing an explanation of the basis for the output.

209. The method according to 205, characterized in that the generated output includes recommendations for improving a digital product network and one or more digitally connected products within the digital product network.

210. Interface 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 The aforementioned digital twin represents a digital product network and one or more digitally connected products within the digital product network. The method according to claim 205, further comprising:

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

212. To communicate with one or more robot systems within the aforementioned value chain network, Converting human commands into robot actions, and, To facilitate human-to-robot interaction within the value chain network, thereby enabling the robot system to perform tasks in response to human commands derived from the generated output content. The method according to claim 205, further comprising:

213. The method according to claim 205, further comprising adjusting the output content based on the user's role as an executive within the organization.

214. The method according to claim 205, characterized in that the generated output includes 3D printing instructions for a plurality of 3D printers.

215. The method according to claim 205, characterized in that the generative artificial intelligence system operates as part of a dual-process artificial neural network (DPANN) architecture.

216. To integrate with an augmented reality (AR) interface, and, The AR interface overlays the generated output content onto the user's field of view in the real world. The method according to claim 205, further comprising:

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