Conceptio › Archive › Google Patents

Google Patentsopen access

Value chain network planning using machine learning and digital twin simulation — Strong Force Vcn Portfolio 2019, Llc (US12314060B2)

Strong Force Vcn Portfolio 2019, Llc · Google Patents

Google Patents · Patents · License: Open Access

strongforcevcnportfolio2019

patent, google patents, intellectual property, US12314060B2, Strong Force Vcn Portfolio 2019, Llc, Taylor CHARON, en, 2025

ABSTRACT

Abstract

A VCN process may receive, by a value chain network digital twin, information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained to determine a task to be completed for the value chain network. A VCN process may provide at least one of an instruction for executing the task in the value chain network digital twin and a recommendation for executing the task in the value chain network digital twin.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Application Number PCT/US23/36158, filed on 27 Oct. 2023, which claims the benefit of U.S. Provisional Application No. 63/381,545, filed on 28 Oct. 2022. All of the foregoing applications are hereby incorporated by reference as if fully set forth herein in their entirety.

BACKGROUND

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

SUMMARY

In one example implementation, a method, performed by one or more computing devices, may include but is not limited to configuring a set of secondary computing devices of a set of value chain network entities for communication with a primary computing device of an enterprise operator, wherein the primary computing device may manage the set of secondary computing devices. At least one member of the set of secondary computing devices may receive a set of primary commands from the primary computing device, wherein each of the set of commands may be at least one of a task or a request. At least a portion of one or more computing devices capable of fulfilling the primary command may be assigned as a set of one or more computing devices to be managed by the set of secondary computing devices. The set of secondary computing devices may configure the set of one or more computing devices to fulfill one or more secondary commands based upon, at least in part, the primary command. The set of secondary computing devices may fulfill the primary command based upon, at least in part, the set of one or more computing devices fulfilling the one or more secondary commands A generated system output may be sent to the primary computing device responding to the primary command based upon, at least in part, the set of one or more computing devices fulfilling the one or more secondary commands.

One or more of the following example features may be included. Configuring the set of one or more computing devices to fulfill the one or more secondary commands may include generating a set of at least one configured system service (CSS). Generating the set of at least one CSS may include utilizing an output of the primary computing device as an input to generate one or more control parameters. Configuring the set of one or more computing devices to fulfill the one or more secondary commands may include providing intelligence received from the at least one CSS to the set of one or more computing devices. Assigning at least the portion of one or more computing devices capable of fulfilling the primary command as the set of one or more computing devices to be managed by the secondary computing device may include executing an enrollment process between the set of one or more computing devices and the secondary computing device. Executing the enrollment process may include conducting an inventory of communication protocols and data formats used by the set of one or more computing devices. The secondary computing device may obtain one or more application programming interfaces (APIs) to enable communication and data format translation between each computing device of the set of one or more computing devices and the secondary computing device. The primary computing device may be bypassed to receive external data at the secondary computing device when the external data is unused by the primary computing device. Generating the set of at least one CSS may include generating at least one CSS for each interface layer in a management stack. Intelligent decision-making regarding strategy for the primary command may be performed based upon, at least in part, a configured intelligence service (CIS). The set of the value chain network entities may include at least one of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, robotic handling systems, 3D printers, vehicles, autonomous vehicles, hauling facilities, waterways, or port infrastructure facilities.

In another example implementation, a computing system may include one or more processors and one or more memories configured to perform operations that may include but are not limited to configuring a set of secondary computing devices of a set of value chain network entities for communication with a primary computing device of an enterprise operator, wherein the primary computing device may manage the set of secondary computing devices. At least one member of the set of secondary computing devices may receive a set of primary commands from the primary computing device, wherein each of the set of commands may be at least one of a task or a request. At least a portion of one or more computing devices capable of fulfilling the primary command may be assigned as a set of one or more computing devices to be managed by the set of secondary computing devices. The set of secondary computing devices may configure the set of one or more computing devices to fulfill one or more secondary commands based upon, at least in part, the primary command. The set of secondary computing devices may fulfill the primary command based upon, at least in part, the set of one or more computing devices fulfilling the one or more secondary commands. A generated system output may be sent to the primary computing device responding to the primary command based upon, at least in part, the set of one or more computing devices fulfilling the one or more secondary commands.

In another example implementation, a computer program product may reside on a computer readable storage medium having a plurality of instructions stored thereon which, when executed across one or more processors, may cause at least a portion of the one or more processors to perform operations that may include but are not limited to configuring a set of secondary computing devices of a set of value chain network entities for communication with a primary computing device of an enterprise operator, wherein the primary computing device may manage the set of secondary computing devices. At least one member of the set of secondary computing devices may receive a set of primary commands from the primary computing device, wherein each of the set of commands may be at least one of a task or a request. At least a portion of one or more computing devices capable of fulfilling the primary command may be assigned as a set of one or more computing devices to be managed by the set of secondary computing devices. The set of secondary computing devices may configure the set of one or more computing devices to fulfill one or more secondary commands based upon, at least in part, the primary command. The set of secondary computing devices may fulfill the primary command based upon, at least in part, the set of one or more computing devices fulfilling the one or more secondary commands. A generated system output may be sent to the primary computing device responding to the primary command based upon, at least in part, the set of one or more computing devices fulfilling the one or more secondary commands.

In one example implementation, a method, performed by one or more computing devices, may include but is not limited to configuring a set of sub-level computing devices for communication with a primary computing device, wherein the primary computing device may manage the set of sub-level computing devices to orchestrate performance of a set of value chain network entities. The set of sub-level computing devices may receive a primary command from the primary computing device, wherein the command may be one of a task or a request associated with the value chain network. At least a portion of one or more computing devices capable of fulfilling the primary command may be assigned as a set of one or more computing devices to be managed by the sub-level computing device, wherein the sub-level computing device may be a computing device that manages or executes performance of a particular entity or relationship of the value chain network. The sub-level computing device may configure the set of one or more computing devices to fulfill one or more secondary commands based upon, at least in part, the primary command. The sub-level computing device may fulfill the primary command based upon, at least in part, the set of one or more computing devices fulfilling the one or more secondary commands.

One or more of the following example features may be included. Configuring the set of one or more computing devices to fulfill the one or more secondary commands may include generating a set of at least one configured system service (CSS). Generating the set of at least one CSS may include utilizing an output of a plurality of sources as an input to generate one or more control parameters. Configuring the set of one or more computing devices to fulfill the one or more secondary commands may include providing intelligence received from the at least one CSS to the set of one or more computing devices. Assigning at least the portion of one or more computing devices capable of fulfilling the primary command as the set of one or more computing devices to be managed by the sub-level computing device may include executing an enrollment process between the set of one or more computing devices and the sub-level computing device. Executing the enrollment process may include conducting an inventory of communication protocols and data formats used by the set of one or more computing devices. The sub-level computing device may obtain one or more application programming interfaces (APIs) to enable communication and data format translation between each computing device of the set of one or more computing devices and the sub-level computing device. The primary computing device may be bypassed to receive external data at the sub-level computing device when the external data is unused by the primary computing device. Generating the set of at least one CSS may include generating at least one CSS for each interface layer in a management stack. Intelligent decision-making may be performed regarding strategy for the primary command based upon, at least in part, a configured intelligence service (CIS). The set of the value chain network entities may include at least one of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges

CROSS-REFERENCE TO RELATED APPLICATIONS

BACKGROUND

SUMMARY

One or more of the following example features may be included. Configuring the set of one or more computing devices to fulfill the one or more secondary commands may include generating a set of at least one configured system service (CSS). Generating the set of at least one CSS may include utilizing an output of a plurality of sources as an input to generate one or more control parameters. Configuring the set of one or more computing devices to fulfill the one or more secondary commands may include providing intelligence received from the at least one CSS to the set of one or more computing devices. Assigning at least the portion of one or more computing devices capable of fulfilling the primary command as the set of one or more computing devices to be managed by the sub-level computing device may include executing an enrollment process between the set of one or more computing devices and the sub-level computing device. Executing the enrollment process may include conducting an inventory of communication protocols and data formats used by the set of one or more computing devices. The sub-level computing device may obtain one or more application programming interfaces (APIs) to enable communication and data format translation between each computing device of the set of one or more computing devices and the sub-level computing device. The primary computing device may be bypassed to receive external data at the sub-level computing device when the external data is unused by the primary computing device. Generating the set of at least one CSS may include generating at least one CSS for each interface layer in a management stack. Intelligent decision-making may be performed regarding strategy for the primary command based upon, at least in part, a configured intelligence service (CIS). The set of the value chain network entities may include at least one of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, robotic handling systems, 3D printers, vehicles, autonomous vehicles, hauling facilities, waterways, or port infrastructure facilities. A generated system output may be sent to the primary computing device responding to the primary command based upon, at least in part, the set of one or more computing devices fulfilling the one or more secondary commands.

In another example implementation, a computing system may include one or more processors and one or more memories configured to perform operations that may include but are not limited to configuring a set of sub-level computing devices for communication with a primary computing device, wherein the primary computing device may manage the set of sub-level computing devices to orchestrate performance of a set of value chain network entities. The set of sub-level computing devices may receive a primary command from the primary computing device, wherein the command may be one of a task or a request associated with the value chain network. At least a portion of one or more computing devices capable of fulfilling the primary command may be assigned as a set of one or more computing devices to be managed by the sub-level computing device, wherein the sub-level computing device may be a computing device that manages or executes performance of a particular entity or relationship of the value chain network. The sub-level computing device may configure the set of one or more computing devices to fulfill one or more secondary commands based upon, at least in part, the primary command. The sub-level computing device may fulfill the primary command based upon, at least in part, the set of one or more computing devices fulfilling the one or more secondary commands.

One or more of the following example features may be included. Configuring the set of one or more computing devices to fulfill the one or more secondary commands may include generating a set of at least one configured system service (CSS). Generating the set of at least one CSS may include utilizing an output of a plurality of sources as an input to generate one or more control parameters. Configuring the set of one or more computing devices to fulfill the one or more secondary commands may include providing intelligence received from the at least one CSS to the set of one or more computing devices. Assigning at least the portion of one or more computing devices capable of fulfilling the primary command as the set of one or more computing devices to be managed by the sub-level computing device may include executing an enrollment process between the set of one or more computing devices and the sub-level computing device. Executing the enrollment process may include conducting an inventory of communication protocols and data formats used by the set of one or more computing devices. The sub-level computing device may obtain one or more application programming interfaces (APIs) to enable communication and data format translation between each computing device of the set of one or more computing devices and the sub-level computing device. The primary computing device may be bypassed to receive external data at the sub-level computing device when the external data is unused by the primary computing device. Generating the set of at least one CSS may include generating at least one CSS for each interface layer in a management stack. Intelligent decision-making may be performed regarding strategy for the primary command based upon, at least in part, a configured intelligence service (CIS). The set of the value chain network entities may include at least one of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, robotic handling systems, 3D printers, vehicles, autonomous vehicles, hauling facilities, waterways, or port infrastructure facilities. A generated system output may be sent to the primary computing device responding to the primary command based upon, at least in part, the set of one or more computing devices fulfilling the one or more secondary commands.

In another example implementation, a computer program product may reside on a computer readable storage medium having a plurality of instructions stored thereon which, when executed across one or more processors, may cause at least a portion of the one or more processors to perform operations that may include but are not limited to configuring a set of sub-level computing devices for communication with a primary computing device, wherein the primary computing device may manage the set of sub-level computing devices to orchestrate performance of a set of value chain network entities. The set of sub-level computing devices may receive a primary command from the primary computing device, wherein the command may be one of a task or a request associated with the value chain network. At least a portion of one or more computing devices capable of fulfilling the primary command may be assigned as a set of one or more computing devices to be managed by the sub-level computing device, wherein the sub-level computing device may be a computing device that manages or executes performance of a particular entity or relationship of the value chain network. The sub-level computing device may configure the set of one or more computing devices to fulfill one or more secondary commands based upon, at least in part, the primary command. The sub-level computing device may fulfill the primary command based upon, at least in part, the set of one or more computing devices fulfilling the one or more secondary commands.

One or more of the following example features may be included. Configuring the set of one or more computing devices to fulfill the one or more secondary commands may include generating a set of at least one configured system service (CSS). Generating the set of at least one CSS may include utilizing an output of a plurality of sources as an input to generate one or more control parameters. Configuring the set of one or more computing devices to fulfill the one or more secondary commands may include providing intelligence received from the at least one CSS to the set of one or more computing devices. Assigning at least the portion of one or more computing devices capable of fulfilling the primary command as the set of one or more computing devices to be managed by the sub-level computing device may include executing an enrollment process between the set of one or more computing devices and the sub-level computing device. Executing the enrollment process may include conducting an inventory of communication protocols and data formats used by the set of one or more computing devices. The sub-level computing device may obtain one or more application programming interfaces (APIs) to enable communication and data format translation between each computing device of the set of one or more computing devices and the sub-level computing device. The primary computing device may be bypassed to receive external data at the sub-level computing device when the external data is unused by the primary computing device. Generating the set of at least one CSS may include generating at least one CSS for each interface layer in a management stack. Intelligent decision-making may be performed regarding strategy for the primary command based upon, at least in part, a configured intelligence service (CIS). The set of the value chain network entities may include at least one of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, robotic handling systems, 3D printers, vehicles, autonomous vehicles, hauling facilities, waterways, or port infrastructure facilities. A generated system output may be sent to the primary computing device responding to the primary command based upon, at least in part, the set of one or more computing devices fulfilling the one or more secondary commands.

In one example implementation, a method, performed by one or more computing devices, may include but is not limited to receiving, by a computing device, information associated with a set of value chain network entities of a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data relating to the set of value chain network entities, or a set of APIs configured to publish data relating to the set of value chain network entities. The information may be provided to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models may be trained on a training data set of a set of value chain network entities operating data to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. A potential risk in the value chain may be determined based upon, at least in part, an output of the AI-based learning classification. An action may be executed to mitigate the potential risk in the value chain network.

One or more of the following example features may be included. Executing the action to mitigate the potential risk in the value chain network may include flagging the potential risk in the value chain network. Executing the action to mitigate the potential risk in the value chain network may include responding to the potential risk in the value chain network. Data associated with warehouse management, inventory management, order management, analytics may be unified to optimize an omnichannel fulfillment. Executing the action to mitigate the potential risk in the value chain network may include resolving an out-of-stock situation. Executing the action to mitigate the potential risk in the value chain network may include predicting when to place an order based upon, at least in part, upstream data. Executing the action to mitigate the potential risk in the value chain network may include supply planning Executing the action to mitigate the potential risk in the value chain network may include optimizing an inventory mix. External data may be received, and a strategy to reduce transportation costs may be determined based upon, at least in part, the external data. A platform may be provided with a plurality of AI-based learning models for download. The set of the value chain network entities may include at least one of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, robotic handling systems, 3D printers, vehicles, autonomous vehicles, hauling facilities, waterways, or port infrastructure facilities. The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on a set of outcomes of the value chain network entity, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model. The training data set for the set of AI-based learning models may include one of a set of objects or events that are labeled to classify the set of objects or events according to a classification taxonomy that may include at least one of the operating state, the fault condition, the operating flow, or the behavior.

In another example implementation, a computing system may include one or more processors and one or more memories configured to perform operations that may include but are not limited to receiving, by a computing device, information associated with a set of value chain network entities of a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data relating to the set of value chain network entities, or a set of APIs configured to publish data relating to the set of value chain network entities. The information may be provided to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models may be trained on a training data set of a set of value chain network entities operating data to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. A potential risk in the value chain may be determined based upon, at least in part, an output of the AI-based learning classification. An action may be executed to mitigate the potential risk in the value chain network.

In another example implementation, a computer program product may reside on a computer readable storage medium having a plurality of instructions stored thereon which, when executed across one or more processors, may cause at least a portion of the one or more processors to perform operations that may include but are not limited to receiving, by a computing device, information associated with a set of value chain network entities of a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data relating to the set of value chain network entities, or a set of APIs configured to publish data relating to the set of value chain network entities. The information may be provided to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models may be trained on a training data set of a set of value chain network entities operating data to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. A potential risk in the value chain may be determined based upon, at least in part, an output of the AI-based learning classification. An action may be executed to mitigate the potential risk in the value chain network.

In one example implementation, a method, performed by one or more computing devices, may include but is not limited to receiving, by a computing device, information associated with a set of value chain network entities of a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data relating to the set of value chain network entities, or a set of APIs configured to publish data relating to the set of value chain network entities. The information may be provided to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models may be trained on a training data set of a set of value chain network entities operating data to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. A procurement action to be taken in the value chain network may be determined based upon, at least in part, an output of the set of AI-based learning models. The procurement action may be executed to facilitate an improvement of at least one of the operating state, the fault condition, the operating flow, or the behavior of at least the one entity of the set of value chain network entities.

One or more of the following example features may be included. An alert describing the procurement action that was executed may be provided. The information may include past behavior over time, historical data, and current data. Real-time information on supplier performance may be provided. Compliance of suppliers and procurement teams may be monitored. Purchase orders associated with the procurement action may be automatically generated. Invoice processing associated with the procurement action may be automatically processed. Data associated with warehouse management, inventory management, order management, analytics may be unified to optimize an omnichannel fulfillment. Executing the procurement action may include resolving an out-of-stock situation. Executing the procurement action may include predicting when to place an order based upon, at least in part, upstream data. The set of the value chain network entities may include at least one of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, robotic handling systems, 3D printers, vehicles, autonomous vehicles, hauling facilities, waterways, or port infrastructure facilities. The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on a set of outcomes of the value chain network entity, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model. The training data set for the set of AI-based learning models may include one of a set of objects or events that are labeled to classify the set of objects or events according to a classification taxonomy that may include at least one of the operating state, the fault condition, the operating flow, or the behavior. The procurement action may be executed by a value chain network digital twin.

In another example implementation, a computing system may include one or more processors and one or more memories configured to perform operations that may include but are not limited to receiving, by a computing device, information associated with a set of value chain network entities of a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data relating to the set of value chain network entities, or a set of APIs configured to publish data relating to the set of value chain network entities. The information may be provided to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models may be trained on a training data set of a set of value chain network entities operating data to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. A procurement action to be taken in the value chain network may be determined based upon, at least in part, an output of the set of AI-based learning models. The procurement action may be executed to facilitate an improvement of at least one of the operating state, the fault condition, the operating flow, or the behavior of at least the one entity of the set of value chain network entities.

In another example implementation, a computer program product may reside on a computer readable storage medium having a plurality of instructions stored thereon which, when executed across one or more processors, may cause at least a portion of the one or more processors to perform operations that may include but are not limited to receiving, by a computing device, information associated with a set of value chain network entities of a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data relating to the set of value chain network entities, or a set of APIs configured to publish data relating to the set of value chain network entities. The information may be provided to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models may be trained on a training data set of a set of value chain network entities operating data to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. A procurement action to be taken in the value chain network may be determined based upon, at least in part, an output of the set of AI-based learning models. The procurement action may be executed to facilitate an improvement of at least one of the operating state, the fault condition, the operating flow, or the behavior of at least the one entity of the set of value chain network entities.

In one example implementation, a method, performed by one or more computing devices, may include but is not limited to receiving, by a computing device, information associated with a set of value chain network entities of a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data relating to the set of value chain network entities, or a set of APIs configured to publish data relating to the set of value chain network entities. The information may be provided to a first set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the first set of AI-based learning models may be trained on a training data set of value chain network data to generate a prediction of future demand for an item in the value chain network. The information may be provided to a second set of AI-based learning models, wherein at least one member of the second set of AI-based learning models may be trained on a training data set of a set of value chain network entities operating data to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. A potential risk in the value chain network associated with the at least one value chain network entity may be determined based upon, at least in part, an output of the AI-based learning models. A recommendation may be output to mitigate the potential risk in the value chain network or an action to mitigate the potential risk in the value chain network may be automatically executed.

One or more of the following example features may be included. Executing the action to mitigate the potential risk in the value chain network associated with the item may include flagging the potential risk in the value chain network associated with the item. Executing the action to mitigate the potential risk in the value chain network associated with the item may include responding to the potential risk in the value chain network associated with the item. Data associated with warehouse management, inventory management, order management, analytics may be unified to optimize an omnichannel fulfillment. Executing the action to mitigate the potential risk in the value chain network associated with the item may include resolving an out-of-stock situation. Executing the action to mitigate the potential risk in the value chain network associated with the item may include predicting when to place an order based upon, at least in part, upstream data. An alert may be provided describing the action to mitigate the potential risk in the value chain network associated with the item that was executed. The information may include past behavior over time, historical data, and current data. The potential risk may be a potential disruption in the value chain network associated with the item. A visualization associated with at least one of inbound or outbound shipments associated with the item may be rendered. The set of the value chain network entities may include at least one of products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, robotic handling systems, 3D printers, vehicles, autonomous vehicles, hauling facilities, waterways, or port infrastructure facilities. The set of AI-based learning models may include at least one of a transformer model, a convolutional neural network, a deep learning model trained on a set of outcomes of the value chain network entity, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model. The training data set for the set of AI-based learning model

CLAIMS

Claims ( 28 )

What is claimed is:

1. A computer-implemented method comprising:

receiving, by a computing device, information associated with a set of products moving through a value chain network,

wherein the information includes real-time data associated with (i) at least one of inbound shipments or outbound shipments of the set of products from or to a value chain network entity of the value chain network, and (ii) an inventory level of the set of products at the value chain network entity,

wherein the value chain network entity includes at least one of: a supplier, a manufacturer, a retailer, or distribution center, and

wherein the information is generated by at least one of: a set of sensors of the value chain network entity, a set of Internet of Things (IoT) devices configured to collect data relating to the value chain network entity, or a set of application programming interfaces (APIs) configured to publish data relating to the value chain network entity;

providing, by the computing device, the information to a set of machine learning models and a value chain network digital twin;

training, by the computing device, at least one machine learning model of the set of machine learning models using an output generated from execution of at least one simulation by the value chain network digital twin, wherein the at least one simulation simulates demand for the set of products;

generating, by the computing device, a prediction associated with demand for the set of products, wherein the computing device uses the at least one trained machine learning model to generate the prediction;

in response to the prediction indicating the demand for the set of products is greater than a threshold, automatically generating, by the at least one trained machine learning model, a task to be completed by a smart machine of the value chain network,

wherein the threshold is based on the inventory level, and

wherein the smart machine is a device embedded with artificial intelligence machine-to-machine and cognitive computing technologies that is used to at least one of: reason, problem-solve, make decisions, or take actions;

providing, by the computing device, an instruction for executing the task to the smart machine;

executing, by the smart machine, the task including:

transporting, by the smart machine, an additional set of products to or from the value chain network entity to prevent a disruption in the value chain network;

gathering additional real-time data associated with the execution of the task; and

providing the additional real-time data to the computing device;

updating, by the computing device, the value chain network digital twin based on the additional real-time data; and

refining, by the computing device, at least one machine learning model of the set of machine learning models based on the additional real-time data.

2. The computer-implemented method of claim 1 , further comprising rendering at least one of: a virtual reality (VR) environment, an augmented reality (AR) environment, a mixed reality (MR) environment, or a diminished reality environment (DR) for a user to interact with the value chain network digital twin.

3. The computer-implemented method of claim 1 , wherein the information includes real-time data about at least one of:

inbound prepaid shipments from suppliers linked to orders; or

inventory coming into a network associated with the value chain network.

4. The computer-implemented method of claim 1 , wherein the receiving the information includes receiving sensor data indicative of at least one of inbound or outbound shipment conditions.

5. The computer-implemented method of claim 1 , further comprising:

executing simulations with the value chain network digital twin to determine at least one of the disruption or a risk in the value chain network,

wherein the simulations are executed with a graph neural network (GNN).

6. The computer-implemented method of claim 5 , further comprising:

in response to determining the disruption, automatically generating, via the at least one trained machine learning model of the set of machine learning models, an additional task to be completed to rectify or avoid the disruption; and

executing, via a robotic operating system, the additional task by:

monitoring an inventory level for an item in the value chain network, and

automatically generating one or more purchase orders for the item in response to the inventory level being less than a threshold.

7. The computer-implemented method of claim 1 , wherein a robotic operating system enables the value chain network digital twin.

8. The computer-implemented method of claim 1 ,

wherein the value chain network digital twin operates within a digital twin system having one or more sets of one or more digital twins, and

wherein each digital twin of the one or more sets includes an embedded marketplace for digital twin simulations.

9. The computer-implemented method of claim 1 ,

wherein the value chain network digital twin operates within a digital twin system having one or more sets of one or more digital twins, and

wherein each digital twin of the one or more sets includes an embedded marketplace for at least one of artificial intelligence-based learning models or artificial intelligence-based algorithms.

10. The computer-implemented method of claim 1 ,

wherein the value chain network digital twin operates within a digital twin system having one or more sets of one or more digital twins, and

wherein each digital twin of the one or more sets includes an embedded marketplace for data.

11. The computer-implemented method of claim 1 , wherein the value chain network entity includes at least one of: products, producers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, robotic handling systems, 3D printers, vehicles, autonomous vehicles, hauling facilities, waterways, or port infrastructure facilities.

12. The computer-implemented method of claim 1 , wherein the set of machine learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model trained on a set of outcomes of the value chain network entity, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.

13. The computer-implemented method of claim 1 , wherein the set of machine learning models is trained using a training dataset including at least one of a set of objects or events that are labeled to classify a set of objects or events according to a classification taxonomy that includes at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network.

14. The computer-implemented method of claim 1 , further comprising:

generating, by the at least one trained machine learning model of the set of machine learning models, an additional task,

wherein the additional task includes at least one of:

diversifying suppliers in the value chain network digital twin,

building inventory buffers in the value chain network digital twin,

identifying suppliers in the value chain network digital twin that use alternative transportation methods,

renegotiating contracts in the value chain network digital twin,

using technology in the value chain network digital twin, or

using predictive sourcing in the value chain network digital twin.

15. The computer-implemented method of claim 1 , further comprising:

rendering, by the computing device, a mixed reality (MR) environment for a user to interact with the value chain network digital twin,

wherein rendering the MR environment includes generating MR visualizations, and

wherein the MR visualizations are used to overlay inventory levels onto a shelf of the value chain network entity.

16. The computer-implemented method of claim 15 , wherein the MR environment is configured to be viewable to the user via augmented reality glasses.

17. The computer-implemented method of claim 1 , wherein the smart machine includes at least one of: a physical robot, an automated guided vehicle, a smart container, or a drone.

18. A computing system comprising one or more processors and one or more memories configured to perform operations including:

receiving, by a computing device, information associated with a set of products moving through a value chain network,

wherein the value chain network entity includes at least one of: a supplier, a manufacturer, a retailer, or distribution center, and

providing, by the computing device, the information to a set of machine learning models and a value chain network digital twin;

wherein the threshold is based on the inventory level, and

providing, by the computing device, an instruction for executing the task to the smart machine;

executing, by the smart machine, the task including:

transporting, by the smart machine, an additional set of products to or from the value chain network entity to prevent a disruption in the value chain network;

gathering additional real-time data associated with the execution of the task; and

providing the additional real-time data to the computing device;

updating, by the computing device, the value chain network digital twin based on the additional real-time data; and

refining, by the computing device, at least one machine learning model of the set of machine learning models based on the additional real-time data.

19. The computing system of claim 18 , wherein the operations further include rendering at least one of: a virtual reality (VR) environment, an augmented reality (AR) environment, a mixed reality (MR) environment, or a diminished reality environment (DR) for a user to interact with the value chain network digital twin.

20. The computing system of claim 18 , wherein the information includes real-time data about at least one of:

inbound prepaid shipments from suppliers linked to orders; or

inventory coming into a network associated with the value chain network.

21. The computing system of claim 18 , wherein the receiving the information includes receiving sensor data indicative of at least one of inbound or outbound shipment conditions.

22. The computing system of claim 18 ,

wherein the operations further include executing simulations with the value chain network digital twin to determine at least one of the disruption or a risk in the value chain network, and

wherein the simulations are executed with a graph neural network (GNN).

23. The computing system of claim 18 , further comprising a robotic operating system that enables the value chain network digital twin.

24. The computing system of claim 18 ,

wherein the value chain network digital twin operates within a digital twin system having one or more sets of one or more digital twins, and

wherein each digital twin of the one or more sets includes an embedded marketplace for digital twin simulations.

25. The computing system of claim 18 ,

wherein the value chain network digital twin operates within a digital twin system having one or more sets of one or more digital twins, and

wherein each digital twin of the one or more sets includes an embedded marketplace for at least one of artificial intelligence-based learning models or artificial intelligence-based algorithms.

26. The computing system of claim 18 ,

wherein the value chain network digital twin operates within a digital twin system having one or more sets of one or more digital twins, and

wherein each digital twin of the one or more sets includes an embedded marketplace for data.

27. The computing system of claim 18 , wherein the set of machine learning models includes at least one of: a transformer model, a convolutional neural network, a deep learning model trained on a set of outcomes of the value chain network entity, a supervised model, a semi-supervised model, an unsupervised model, or a reinforcement model.

28. The computing system of claim 18 , wherein the set of machine learning models is trained using a training dataset including at least one of a set of objects or events that are labeled to classify the set of objects or events according to a classification taxonomy that includes at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network.

US18/525,831

2019-11-05

2023-11-30

Value chain network planning using machine learning and digital twin simulation

Active

US12314060B2

( en )

Priority Applications (1)

Application Number

Priority Date

Filing Date

Title

US18/525,831

US12314060B2

( en )

2019-11-05

2023-11-30

Value chain network planning using machine learning and digital twin simulation

Applications Claiming Priority (15)

Application Number

Priority Date

Filing Date

Title

US201962931193P

2019-11-05

US202062969153P

2020-02-03

US202063016976P

2020-04-28

US202063054606P

2020-07-21

US202063069533P

2020-08-24

US202063087292P

2020-10-04

PCT/US2020/059227

WO2021092263A1

( en )

2019-11-05

2020-11-05

Control tower and enterprise management platform for value chain networks

US202063127983P

2020-12-18

US202163185348P

2021-05-06

IN202111029964

2021-07-03

IN202111036187

2021-08-10

PCT/US2021/064233

WO2022133330A1

( en )

2020-12-18

2021-12-17

Robot fleet management and additive manufacturing for value chain networks

US202263381545P

2022-10-28

PCT/US2023/036158

WO2024091687A2

( en )

2022-10-28

2023-10-27

Systems, methods, kits, and apparatuses for managing value chain networks in system of systems

US18/525,831

US12314060B2

( en )

2019-11-05

2023-11-30

Value chain network planning using machine learning and digital twin simulation

Related Parent Applications (1)

Application Number

Title

Priority Date

Filing Date

PCT/US2023/036158

Continuation

WO2024091687A2

( en )

2019-11-05

2023-10-27

Systems, methods, kits, and apparatuses for managing value chain networks in system of systems

Publications (2)

Publication Number

Publication Date

US20240144103A1

US20240144103A1 ( en )

2024-05-02

US12314060B2

true

US12314060B2 ( en )

2025-05-27

Family

ID=99636378

Family Applications (9)

Application Number

Title

Priority Date

Filing Date

US17/683,082

Active

2041-10-29

US12572150B2

( en )

2019-11-05

2022-02-28

Robot fleet management for value chain networks

US18/525,816

Active

US12379729B2

( en )

2019-11-05

2023-11-30

Machine-learning-driven supply chain out-of-stock inventory resolution and contract negotiation

US18/525,813

Abandoned

US20240144138A1

( en )

2019-11-05

2023-11-30

Systems, methods, kits, and apparatuses for using artifical intelligence to flag and respond to disruptions in a value chain network

US18/525,821

Abandoned

US20240144140A1

( en )

2019-11-05

2023-11-30

Systems, methods, kits, and apparatuses for predictive sourcing in value chain networks

US18/525,826

Active

US12585282B2

( en )

2019-11-05

2023-11-30

Training inventory management robots using digital twins, trained machine learning models, and human feedback

US18/525,843

Pending

US20240118702A1

( en )

2019-11-05

2023-11-30

Systems, methods, kits, and apparatuses for managing control towers in value chain networks

US18/525,824

Pending

US20240144011A1

( en )

2019-11-05

2023-11-30

Systems, methods, kits, and apparatuses for using artificial intelligence for instructing smart machines in value chain networks

US18/525,831

Active

US12314060B2

( en )

2019-11-05

2023-11-30

Value chain network planning using machine learning and digital twin simulation

US18/525,809

Abandoned

US20240144115A1

( en )

2019-11-05

2023-11-30

Systems, methods, kits, and apparatuses for managing mini control towers in value chain networks

Family Applications Before (7)

Application Number

Title

Priority Date

Filing Date

US17/683,082

Active

2041-10-29

US12572150B2

( en )

2019-11-05

2022-02-28

Robot fleet management for value chain networks

US18/525,816

Active

US12379729B2

( en )

2019-11-05

2023-11-30

Machine-learning-driven supply chain out-of-stock inventory resolution and contract negotiation

US18/525,813

Abandoned

US20240144138A1

( en )

2019-11-05

2023-11-30

Systems, methods, kits, and apparatuses for using artifical intelligence to flag and respond to disruptions in a value chain network

US18/525,821

Abandoned

US20240144140A1

( en )

2019-11-05

2023-11-30

Systems, methods, kits, and apparatuses for predictive sourcing in value chain networks

US18/525,826

Active

US12585282B2

( en )

2019-11-05

2023-11-30

Training inventory management robots using digital twins, trained machine learning models, and human feedback

US18/525,843

Pending

US20240118702A1

( en )

2019-11-05

2023-11-30

Systems, methods, kits, and apparatuses for managing control towers in value chain networks

US18/525,824

Pending

US20240144011A1

( en )

2019-11-05

2023-11-30

Systems, methods, kits, and apparatuses for using artificial intelligence for instructing smart machines in value chain networks

Family Applications After (1)

Application Number

Title

Priority Date

Filing Date

US18/525,809

Abandoned

US20240144115A1

( en )

2019-11-05

2023-11-30

Systems, methods, kits, and apparatuses for managing mini control towers in value chain networks

Country Status (1)

Country

Link

( 9 )

US12572150B2

( en )

Cited By (4)

* Cited by examiner, â Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US20240402006A1

( en )

2023-06-02

2024-12-05

International Business Machines Corporation

Flexible ultraviolet sensor

US20250148414A1

( en )

2023-11-07

2025-05-08

King Fahd University Of Petroleum And Minerals

Middleware-based uav-ugb logistics system

US20250375888A1

( en )

2024-06-06

2025-12-11

Nvidia Corporation

Techniques for vision-based robot control

US12561647B1

( en )

2025-07-28

2026-02-24

U.S. Bancorp, National Association

Machine learning models for generation of execution action data structures based on device delivery latency

Families Citing this family (237)

* Cited by examiner, â Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US20120278179A1

( en )

2011-04-28

2012-11-01

Ray Campbell

Systems and methods for deducing user information from input device behavior

US9558555B2

( en )

2013-02-22

2017-01-31

Leap Motion, Inc.

Adjusting motion capture based on the distance between tracked objects

US9733715B2

( en )

2013-03-15

2017-08-15

Leap Motion, Inc.

Resource-responsive motion capture

US10641508B2

( en )

2016-02-12

2020-05-05

Goodman Manufacturing Company LP

Systems and methods for air temperature control using a target time based control plan

US12549519B2

( en )

2016-10-24

2026-02-10

Servicenow, Inc.

Security system, device, and method for protecting control systems

US11153277B2

( en )

2016-10-24

2021-10-19

Mission Secure, Inc.

Security system, device, and method for internet of things networks

US11615285B2

( en )

2017-01-06

2023-03-28

Ecole Polytechnique Federale De Lausanne (Epfl)

Generating and identifying functional subnetworks within structural networks

US20210334736A1

( en )

2017-08-31

2021-10-28

Crc R&D, Llc

Management of vehicular traffic at a facility having allocable space resources

US11861511B2

( en )

2017-10-04

2024-01-02

Trustees Of Tufts College

Systems and methods for ensuring safe, norm-conforming and ethical behavior of intelligent systems

US12412072B2

( en )

2018-06-11

2025-09-09

Inait Sa

Characterizing activity in a recurrent artificial neural network

US11663478B2

( en )

2018-06-11

2023-05-30

Inait Sa

Characterizing activity in a recurrent artificial neural network

US11893471B2

( en )

2018-06-11

2024-02-06

Inait Sa

Encoding and decoding information and artificial neural networks

US11972343B2

( en )

2018-06-11

2024-04-30

Inait Sa

Encoding and decoding information

US11341451B2

( en )

2018-10-10

2022-05-24

Questaweb Holdings Inc.

Hierarchical blockchain architecture for global trade management

US12330646B2

( en )

2018-10-18

2025-06-17

Autobrains Technologies Ltd

Off road assistance

US10748038B1

( en )

2019-03-31

2020-08-18

Cortica Ltd.

Efficient calculation of a robust signature of a media unit

US11420777B1

( en )

2019-02-15

2022-08-23

Lockheed Martin Corporation

Spherical mobility system

US11694088B2

( en )

2019-03-13

2023-07-04

Cortica Ltd.

Method for object detection using knowledge distillation

US11652603B2

( en )

2019-03-18

2023-05-16

Inait Sa

Homomorphic encryption

US11569978B2

( en )

2019-03-18

2023-01-31

Inait Sa

Encrypting and decrypting information

US12047715B2

( en )

2019-04-24

2024-07-23

Crc R&D, Llc

Systems and methods for automatic recognition of vehicle information

US11579608B2

( en )

2019-08-22

2023-02-14

Walmart Apollo, Llc

System and method for removing debris from a storage facility

US11900244B1

( en )

2019-09-30

2024-02-13

Amazon Technologies, Inc.

Attention-based deep reinforcement learning for autonomous agents

AU2020379834A1

( en )

2019-11-05

2022-06-09

Strong Force Vcn Portfolio 2019, Llc

Control tower and enterprise management platform for value chain networks

WO2021092263A1

( en )

2019-11-05

2021-05-14

Strong Force Vcn Portfolio 2019, Llc

Control tower and enterprise management platform for value chain networks

US20210133669A1

( en )

2019-11-05

2021-05-06

Strong Force Vcn Portfolio 2019, Llc

Control tower and enterprise management platform with robotic process automation layer to automate actions for subset of applications benefitting value chain network entities

US12572150B2

( en )

2019-11-05

2026-03-10

Strong Force Vcn Portfolio 2019, Llc

Robot fleet management for value chain networks

JP7451946B2

( en )

2019-11-07

2024-03-19

æ ªå¼ä¼ç¤¾ã¢ã¤ã·ã³

Control device

US20210174275A1

( en )

2019-11-13

2021-06-10

Erinn Valencich

System and method for the design industry that enables creative teams to design, collaborate and connect with clients and track and manage progress of each client project providing client status to all interested parties

US12354045B2

( en )

2019-11-25

2025-07-08

Oii, Inc.

Orchestrated intelligent supply chain optimizer

US11797827B2

( en )

2019-12-11

2023-10-24

Inait Sa

Input into a neural network

US11580401B2

( en )

2019-12-11

2023-02-14

Inait Sa

Distance metrics and clustering in recurrent neural networks

US11816553B2

( en )

2019-12-11

2023-11-14

Inait Sa

Output from a recurrent neural network

US11651210B2

( en )

2019-12-11

2023-05-16

Inait Sa

Interpreting and improving the processing results of recurrent neural networks

EP3839835A1

( en )

2019-12-20

2021-06-23

Robert Bosch GmbH

Method and apparatus for performing learning from demonstrations, particularly imitation learning

US12282875B2

( en )

2019-12-30

2025-04-22

Nb Ventures, Inc.

Linkedchain, control tower and blockchain for enterprise applications

US11647047B2

( en )

2020-01-07

2023-05-09

Avanan Inc.

Intercepting reports of harmful e-mails that are reported to an authority

US12475513B1

( en )

2020-02-19

2025-11-18

Allstate Insurance Company

Systems and methods for predicted total loss based on image analysis and point of view determinations

US12469083B1

( en )

2020-02-19

2025-11-11

Allstate Insurance Company

Systems and methods for predicted total loss determinations based on image analysis

US12536484B2

( en )

2020-03-31

2026-01-27

Thrv, Llc

Systems and methods for job segmentation and corresponding need identification

KR102430559B1

( en )

2020-04-09

2022-08-09

ì¬íí

Apparatus and method for providing ict-based driver-specific evaluation analysis and reward plaform for two-wheeled vehicle driving

WO2021203441A1

( en )

2020-04-10

2021-10-14

åä¸ºææ¯æéå¬å¸

Vehicle moving method and vehicle

US12583715B1

( en )

2020-04-20

2026-03-24

Everguard, Inc.

Adaptive multimodal safety systems and methods for bridge crane safety

US12598271B1

( en )

2020-04-20

2026-04-07

Everguard, Inc.

Adaptive multimodal safety systems with incident localization and visualization

JP7600263B2

( en )

2020-04-24

2024-12-16

ãªã«ãã»ã¤ããã¼ã·ã§ã³ã»ãªãããã

Energy storage systems for load handling equipment.

US12411212B1

( en )

2020-05-11

2025-09-09

Everguard, Inc.

Mobile safety systems

US11685047B2

( en )

2020-05-21

2023-06-27

Intrinsic Innovation Llc

Skill template distribution for robotic demonstration learning

US11986958B2

( en )

2020-05-21

2024-05-21

Intrinsic Innovation Llc

Skill templates for robotic demonstration learning

US20230214727A1

( en )

2020-06-05

2023-07-06

Nippon Telegraph And Telephone Corporation

Degradation Estimation Device and Degradation Estimation Method

KR102192534B1

( en )

2020-07-21

2020-12-17

ì´ì°ì¢

Business platform for network marketing

WO2022046073A1

( en )

2020-08-28

2022-03-03

Hewlett-Packard Development Company, L.P.

Real-time anomaly detection in three dimensional printers

US11562467B2

( en )

2020-08-31

2023-01-24

Servicenow Canada Inc.

Method and system for designing an optical filter

US12049116B2

( en )

2020-09-30

2024-07-30

Autobrains Technologies Ltd

Configuring an active suspension

AT524243A1

( en )

2020-10-07

2022-04-15

Tgw Logistics Group Gmbh

Method for computer-assisted processing of a change in the status of a goods carrier in a storage and order-picking system and storage and order-picking system

CN120498642A

( en )

2020-10-09

2025-08-15

å´äºè

Authentication chain using public key infrastructure

CN114415163A

( en )

2020-10-13

2022-04-29

å¥¥ç¹è´ç¿ææ¯æéå¬å¸

Camera-based distance measurement

US20220126451A1

( en )

2020-10-26

2022-04-28

Realtime Robotics, Inc.

Safety systems and methods employed in robot operations

TWI744057B

( en )

2020-10-27

2021-10-21

åç«æåå¤§å¸

Deep forged film detection system and method

TWI744068B

( en )

2020-11-03

2021-10-21

åç¢ç§æè¡ä»½æéå¬å¸

Resource management system and resource management method

US12253860B2

( en )

2020-11-12

2025-03-18

Accenture Global Solutions Limited

Fault tolerant systems for distributed supervision of robots

KR102503794B1

( en )

2020-11-17

2023-02-23

ì¡°ì ëíêµì°ííë ¥ë¨

Transmission power allocation method based on user clustering and reinforcement learning

US12111632B1

( en )

2020-12-09

2024-10-08

Amazon Technologies, Inc.

Edge device architecture within logistics center for increasing throughput

US12393915B2

( en )

2020-12-18

2025-08-19

Strong Force Vcn Portfolio 2019, Llc

Variable-focus dynamic vision for robotic system

EP4264518A4

( en )

2020-12-18

2024-12-11

Strong Force VCN Portfolio 2019, LLC

ROBOT FLEET MANAGEMENT AND ADDITIVE MANUFACTURING FOR VALUE CHAIN NETWORKS

US20230109096A1

( en )

2020-12-18

2023-04-06

Strong Force Vcn Portfolio 2019, Llc

Maintenance Prediction and Health Monitoring for Robotic Fleet Management

US20220215332A1

( en )

2021-01-04

2022-07-07

Capital One Services, Llc

Using machine learning to predict impacts to a supply chain by analyzing current events

US12257949B2

( en )

2021-01-25

2025-03-25

Autobrains Technologies Ltd

Alerting on driving affecting signal

CN114819242B

( en )

2021-01-28

2025-05-09

èåçµåè¡ä»½æéå¬å¸

Semiconductor process prediction method and device considering overall characteristics and local characteristics

US20220253780A1

( en )

2021-02-11

2022-08-11

Solutioneering, LLC

Analytical tool for collaborative competitive pursuit analysis and creation of enterprise value

US11145208B1

( en )

2021-03-15

2021-10-12

Samsara Networks Inc.

Customized route tracking

DE102021202682A1

( en )

2021-03-19

2022-09-22

Trumpf Werkzeugmaschinen Gmbh + Co. Kg

Docking station for workpiece carriers in a driverless transport system

CN115119172B

( en )

2021-03-22

2025-01-07

æ¬ç°æç å·¥ä¸æ ªå¼ä¼ç¤¾

Wireless communication system

US11829450B2

( en )

2021-03-26

2023-11-28

Siemens Healthineers International Ag

Dual domain tracking of target structures

US20220343227A1

( en )

2021-04-14

2022-10-27

Locomation, Inc.

Freight optimization

US11657112B2

( en )

2021-04-21

2023-05-23

Dell Products L.P.

Artificial intelligence-based cache distribution

EP4337467A4

( en )

2021-05-11

2025-05-07

Strong Force VCN Portfolio 2019, LLC

Systems, methods, kits, and apparatuses for edge-distributed storage and querying in value chain networks

EP4092587A1

( en )

2021-05-21

2022-11-23

Robert Bosch GmbH

Scheduling jobs of a manufacturing or logistics process

US12511873B2

( en )

2021-06-07

2025-12-30

Cortica, Ltd.

Isolating unique and representative patterns of a concept structure

Value chain network planning using machine learning and digital twin simulation — Strong Force Vcn Portfolio 2019, Llc (US12314060B2)

Related documents