IP Library › Granted Patent US 12,488,307
Granted Patent B2
US 12,488,307 · App. 17/461,173 · Granted Dec 2, 2025

Control tower and enterprise management platform with robotic process automation systems managing product outcomes and activities

Inventors: Charles Howard Cella (Pembroke, MA); Richard Spitz (Fort Lauderdale, FL); Teymour S. El-Tahry (Birmingham, MI)
Assignee: STRONG FORCE VCN PORTFOLIO 2019, LLC
G06Q10/0833B25J9/163G05B13/0265G05B19/4155G06F16/2315G06F16/27G06F16/284G06F18/214G06F18/2178G06F30/27G06N3/04G06N3/08G06N20/00G06Q10/06313G06Q10/06315G06Q10/0633G06Q10/0635G06Q10/06375G06Q10/067G06Q10/08G06Q10/083G06Q10/0835G06Q10/0838G06Q10/087G06Q10/103G06Q30/018G06Q30/0201G06Q30/0202G06Q30/0206G06Q30/0631G06Q50/26H04L67/1097H04L67/12B64U2101/64G05B2219/40408G06Q2220/00G06Q2220/10G16Y10/40G16Y10/45G16Y10/75G16Y40/10H04W84/042
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Quick Facts
Patent No.
US 12,488,307
App. No.
17/461,173
Filed
Aug 30, 2021
Granted
Dec 2, 2025
Kind
B2
Art Unit
3625
USPC
705/7.28
Abstract

A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.

Claims (43)

1 . An information technology system, comprising:

a cloud-based management platform with a micro-services architecture, the platform having a set of interfaces that are configured to access and configure features of the platform, a set of network connectivity facilities that are configured to direct a set of value chain network entities to connect to the features of the platform, a set of adaptive intelligence facilities that are configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform, or a set of monitoring facilities that are configured to monitor the value chain network entities, wherein the interfaces, the set of network connectivity facilities, the set of adaptive intelligence facilities, and the set of monitoring facilities are coordinated for monitoring and management of the value chain network entities;

a set of applications that are configured to direct an enterprise to manage the value chain network entities of the platform from a point of origin to a point of customer use; and

a set of microservices layers including an application layer supporting at least one supply chain application and at least one demand management application, wherein the set of microservices layers include a robotic process automation layer executing on at least one processor of the platform and that uses a first set of information collected by a data collection layer and a second set of information of outcomes and activities involving the applications of the application layer to automate a set of actions for at least a subset of the applications with respect to the value chain network entities of the platform, wherein one of the actions automated by the robotic process automation layer involves at least one of classification of a product defect in an image or inspection of a product in an image, the robotic process automation layer trained with the first set of information by the at least one processor to use the information collected by the data collection layer for the classification of the product defect and for the inspection of the product, the robotic process automation layer retrained with a portion of the second set of information for the classification of the product defect and for the inspection of the product, the portion of the second set of information for retraining the robotic process automation layer including a portion of the set of outcomes and activities involving the subset of applications for which the robotic process automation layer automates the set of actions.

2 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves selection of a quantity of product for an order.

3 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves selection of a carrier for a shipment.

4 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves at least one of a selection of a vendor for a component or a vendor for a finished goods order.

5 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves selection of a variation of a product for marketing.

6 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves selection of an assortment of goods for a shelf.

7 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves determination of a price for a finished good.

8 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves configuration of at least one of a service offer related to a product or configuration of product bundle.

9 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves at least one of configuration of a product kit, a product package, a product display, a product image, or a product description.

10 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves configuration of a website navigation path related to a product.

11 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves determination of an inventory level for a product.

12 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves selection at least one of a logistics type or a schedule for product delivery.

13 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves configuration of a logistics schedule.

14 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves configuration of a set of inputs for machine learning.

15 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves at least one of preparation of product documentation or preparation of disclosures about a product.

16 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves ordering of equipment for a at least one of a warehouse or a fulfillment center.

17 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves inspection of data from a set of onboard diagnostics on a product.

18 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves review of sensor data from environmental sensors in a set of supply chain environments.

19 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves selection of inputs for a digital twin.

20 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves selection of outputs from a digital twin.

21 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves selection of visual elements for presentation in a digital twin.

22 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves diagnosis of sources of at least one of delay, congestion, or scarcity in a supply chain.

23 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves diagnosis of sources of cost overruns in a supply chain.

24 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves diagnosis of sources of product defects in a supply chain.

25 . The system of claim 1 , wherein one of the actions automated by the robotic process automation layer involves prediction of maintenance requirements in supply chain infrastructure.

26 . An information technology system, comprising:

a cloud-based management platform with a micro-services architecture, the platform having a set of interfaces that are configured to access and configure features of the platform, a set of network connectivity facilities that are configured to direct a set of value chain network entities to connect to the features of the platform, a set of adaptive intelligence facilities that are configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform, or a set of monitoring facilities that are configured to monitor the value chain network entities, wherein the interfaces, the network connectivity facilities, the adaptive intelligence facilities, and the monitoring facilities are coordinated for monitoring and management of the value chain network entities;

a set of applications that are configured to direct an enterprise to manage the value chain network entities of the platform from a point of origin to a point of customer use; and

a set of microservices layers including an application layer supporting at least one supply chain application and at least one demand management application, wherein the microservices layers include a robotic process automation layer executing on at least one processor of the platform and that uses a first set of information collected by a data collection layer and a second set of information of outcomes and activities involving the applications of the application layer to automate a set of actions for at least a subset of the applications with respect to the value chain network entities of the platform, wherein one of the actions automated by the robotic process automation layer involves inspection of product quality data from a set of sensors, the robotic process automation layer trained with the first set of information by the at least one processor to use the information collected by the data collection layer for the inspection of product quality data, the robotic process automation layer retrained with a portion of the second set of information for the inspection of product quality data, the portion of the second set of information for retraining the robotic process automation layer including a portion of the set of outcomes and activities involving the subset of applications for which the robotic process automation layer automates the set of actions.

27 . A method of automating value chain network entity actions, the method comprising:

directing, with a set of network connectivity facilities, a set of value chain network entities to connect to features of a value chain network;

configuring a cloud-based platform with a micro-services architecture to manage or monitor the set of value chain network entities from a point of origin to a point of customer use;

automating, with a robotic process automation layer of a set of adaptive intelligence facilities executing on at least one processor, a set of actions for at least a subset of applications with respect to the set of value chain network entities, the set of actions including at least one of classification of a product defect in an image, inspection of a product in an image, or inspection of product quality data from a set of sensors;

preparing a first set of training data based on information associated with the subset of applications collected by a data collection layer;

training, with the at least one processor, the robotic process automation layer based on the first set of training data to use the information collected by the data collection layer for automating at least one of the set of actions; and

retraining the robotic process automation layer for automating the at least one of the set of actions using a second set of information including a portion of a set of outcomes and activities involving the subset of applications for which the robotic process automation layer automates the set of action.

28 . The method of claim 27 , wherein inspection of product quality data involves inspection of data from a set of onboard diagnostics on a product.

29 . The method of claim 27 , wherein one of the actions automated by the robotic process automation layer involves configuration of a product for a set of local requirements.

30 . The method of claim 27 , wherein one of the actions automated by the robotic process automation layer involves configuration of a set of products for compatibility.

31 . The method of claim 27 , wherein one of the actions automated by the robotic process automation layer involves configuration of a request for proposals.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 12, 2023
From: CELLA, CHARLES HOWARD; SPITZ, RICHARD; EL-TAHRY, TEYMOUR S.
To: STRONG FORCE VCN PORTFOLIO 2019, LLC
Reel/Frame 065201/0457 →
Continuity (8)
Continuation PCTUS2020059227 · Nov 5, 2020
Provisional Application 63087292 · Oct 4, 2020
Provisional Application 63069533 · Aug 24, 2020
Provisional Application 63054606 · Jul 21, 2020
Provisional Application 63016976 · Apr 28, 2020
Provisional Application 62969153 · Feb 3, 2020
Provisional Application 62931193 · Nov 5, 2019
Related Publication 20220035342A1 · Feb 3, 2022
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