# US 2022/0035342 A1 · Control tower and enterprise management platform with robotic process automation systems managing product outcomes and activities

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- Number: US 2022/0035342 A1
- Document ID: US20220035342A1
- Status: Published application
- Kind: A1
- Application number: 17/461,173
- Filed: 2021-08-30
- Published: 2022-02-03
- Earliest claimed: 2019-11-05
- Assignees: Strong Force Vcn Portfolio 2019, LLC
- Applicants: Strong Force Vcn Portfolio 2019, LLC
- Inventors: Charles Howard CELLA; Richard SPITZ; Teymour S. EL-TAHRY
- Web page: https://patentgrep.com/patent/US20220035342A1

## CPC

- G06Q 10/10: Office automation; Time management
- B25J 9/163: {learning, adaptive, model based, rule based expert control}
- G05B 13/0265: {the criterion being a learning criterion}
- G05B 19/042: using digital processors (G05B19/05 takes precedence)
- G05B 19/4155: characterised by program execution, i.e. part program or machine function execution, e.g. selection of a program
- G06F 16/2308: {Concurrency control (transaction processing G06F9/466)}
- G06F 16/2315: {Optimistic concurrency control}
- G06F 16/254: {Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses}
- G06F 16/27: Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
- G06F 16/283: {Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP}
- G06F 16/284: {Relational databases}
- G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06F 18/2178: {based on feedback of a supervisor}
- G06F 30/27: using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
- G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
- G06N 3/04: Architecture, e.g. interconnection topology
- G06N 3/042: Knowledge-based neural networks; Logical representations of neural networks
- G06N 3/044: Recurrent networks, e.g. Hopfield networks
- G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
- G06N 3/045: Combinations of networks
- G06N 3/0455: Auto-encoder networks; Encoder-decoder networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/047: Probabilistic or stochastic networks
- G06N 3/049: Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
- G06N 3/082: modifying the architecture, e.g. adding, deleting or silencing nodes or connections
- G06N 3/084: Backpropagation, e.g. using gradient descent
- G06N 3/088: Non-supervised learning, e.g. competitive learning
- G06N 3/0895: Weakly supervised learning, e.g. semi-supervised or self-supervised learning
- G06N 3/09: Supervised learning
- G06N 3/091: Active learning
- G06N 3/092: Reinforcement learning
- G06N 3/096: Transfer learning
- G06N 3/098: Distributed learning, e.g. federated learning
- G06N 3/0985: Hyperparameter optimisation; Meta-learning; Learning-to-learn
- G06N 3/126: Evolutionary algorithms, e.g. genetic algorithms or genetic programming
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 5/022: Knowledge engineering; Knowledge acquisition
- G06N 5/025: Extracting rules from data
- G06N 5/04: Inference or reasoning models
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 20/00: Machine learning
- G06N 20/10: using kernel methods, e.g. support vector machines [SVM]
- G06N 20/20: Ensemble learning
- G06Q 10/04: Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202)
- G06Q 10/063: Operations research, analysis or management
- G06Q 10/0631: Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q 10/063118: {Staff planning in a project environment}
- G06Q 10/06313: {Resource planning in a project environment}
- G06Q 10/06315: {Needs-based resource requirements planning or analysis}
- G06Q 10/06316: {Sequencing of tasks or work}
- G06Q 10/0633: Workflow analysis
- G06Q 10/0635: Risk analysis of enterprise or organisation activities
- G06Q 10/0637: Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
- G06Q 10/06375: {Prediction of business process outcome or impact based on a proposed change}
- G06Q 10/0639: Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q 10/067: Enterprise or organisation modelling
- G06Q 10/0674: {using process simulation}
- G06Q 10/08: Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q 10/083: Shipping
- G06Q 10/0833: Tracking
- G06Q 10/0834: Choice of carriers
- G06Q 10/0835: Relationships between shipper or supplier and carriers
- G06Q 10/0838: {Historical data}
- G06Q 10/0843: {using forecasting or optimisation}
- G06Q 10/087: Inventory or stock management, e.g. order filling, procurement or balancing against orders
- G06Q 10/103: {Workflow collaboration or project management}
- G06Q 30/018: Certifying business or products
- G06Q 30/02: Marketing; Price estimation or determination; Fundraising
- G06Q 30/0201: Market modelling; Market analysis; Collecting market data
- G06Q 30/0202: Market predictions or forecasting for commercial activities
- G06Q 30/0206: {Price or cost determination based on market factors}
- G06Q 30/06: Buying, selling or leasing transactions
- G06Q 30/0631: {Recommending goods or services}
- G06Q 30/0635: {replenishment orders; recurring orders}
- G06Q 50/26: Government or public services (business processes related to the transportation industry G06Q50/40)
- H04L 9/3239: {involving non-keyed hash functions, e.g. modification detection codes [MDCs], MD5, SHA or RIPEMD}
- H04L 41/0631: using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
- H04L 67/1097: for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
- H04L 67/12: specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
- B25J 9/1656: {characterised by programming, planning systems for manipulators}
- B64U 2101/60: for transporting passengers; for transporting goods other than weapons
- B64U 2101/64: for parcel delivery or retrieval
- G05B 13/04: involving the use of models or simulators
- G05B 2219/31229: Supervisor, leader, workstation controller, automation, machine control
- G05B 2219/40408: Intention learning
- G05B 2219/50391: Robot
- G06N 3/08: Learning methods
- G06Q 10/0872: {using inventory planning}
- G06Q 10/08744: {by distributed inventory management}
- G06Q 10/0877: {by inventory control or reporting using inventory tracking or counting}
- G06Q 2220/00: Business processing using cryptography
- G06Q 2220/10: Usage protection of distributed data files
- G16Y 10/40: Transportation
- G16Y 10/45: Commerce
- G16Y 10/75: Information technology; Communication
- G16Y 40/10: Detection; Monitoring
- H04L 9/3236: {using cryptographic hash functions}
- H04L 9/50: {using hash chains, e.g. blockchains or hash trees}
- H04L 2209/56: Financial cryptography, e.g. electronic payment or e-cash
- H04W 84/042: {Public Land Mobile systems, e.g. cellular systems}
- Y02P 90/90: Financial instruments for climate change mitigation, e.g. environmental taxes, subsidies or financing
- Y04S 40/18: Network protocols supporting networked applications, e.g. including control of end-device applications over a network
- Y04S 40/20: Information technology specific aspects, e.g. CAD, simulation, modelling, system security
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- B25J: MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES ({manipulators specially adapted for use in surgery A61B34/70; manipulators used in cleaning hollow articles B08B9/04}; manipulators associated with rolling mills B21B39/20; manipulators associated with forging machines B21J13/10; {manipulators associated with picking-up and placing mechanisms B23P19/007}; means for holding wheels or parts thereof B60B30/00; {vehicles with ground-engaging propulsion means, e.g. walking members B62D57/02, B62D57/032; devices for picking-up and depositing articles or materials between conveyors B65G47/90, B65G47/91; manipulators with gripping or holding means for transferring packages B65H67/065}; cranes B66C; {manipulators used in the protection or supervision of pipe-line installations F17D5/00; walking equipment adapted for nuclear steam-generators F22B37/006}; manipulators specially adapted for, or associated with, nuclear reactors G21C; {apparatus used for handling wafers during manufacture or treatment of semiconductor H10P72/50})
- G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)
- B64U: UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
- G16Y: INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)
- Y02P: CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y04S: SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS

## 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

### Claim 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 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 that uses information collected by a data collection layer and a set 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.

### Claim 2 (depends on 1)

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.

### Claim 3 (depends on 1)

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.

### Claim 4 (depends on 1)

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.

### Claim 5 (depends on 1)

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.

### Claim 6 (depends on 1)

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.

### Claim 7 (depends on 1)

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.

### Claim 8 (depends on 1)

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.

### Claim 9 (depends on 1)

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.

### Claim 10 (depends on 1)

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.

### Claim 11 (depends on 1)

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.

### Claim 12 (depends on 1)

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.

### Claim 13 (depends on 1)

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

### Claim 14 (depends on 1)

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.

### Claim 15 (depends on 1)

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.

### Claim 16 (depends on 1)

The system of claim 1, wherein one of the actions automated by the robotic process automation layer involves configuration of a product for a set of local requirements.

### Claim 17 (depends on 1)

The system of claim 1, wherein one of the actions automated by the robotic process automation layer involves configuration of a set of products for compatibility.

### Claim 18 (depends on 1)

The system of claim 1, wherein one of the actions automated by the robotic process automation layer involves configuration of a request for proposals.

### Claim 19 (depends on 1)

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.

### Claim 20 (depends on 1)

The system of claim 1, 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.

### Claim 21 (depends on 1)

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

### Claim 22 (depends on 1)

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.

### Claim 23 (depends on 1)

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.

### Claim 24 (depends on 1)

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.

### Claim 25 (depends on 1)

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.

### Claim 26 (depends on 1)

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.

### Claim 27 (depends on 1)

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.

### Claim 28 (depends on 1)

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.

### Claim 29 (depends on 1)

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.

### Claim 30 (depends on 1)

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.

### Claim 31 (depends on 1)

The system of claim 1, wherein the robotic process automation layer automates a process including at least one of selection of a quantity of product for an order, selection of a carrier for a shipment, selection of a vendor for a component, selection of a vendor for a finished goods order, selection of a variation of a product for marketing, selection of an assortment of goods for a shelf, determination of a price for a finished good, configuration of a service offer related to a product, configuration of product bundle, configuration of a product kit, configuration of a product package, configuration of a product display, configuration of a product image, configuration of a product description, configuration of a website navigation path related to a product, determination of an inventory level for a product, selection of a logistics type, configuration of a schedule for product delivery, configuration of a logistics schedule, configuration of a set of inputs for machine learning, preparation of product documentation, preparation of disclosures about a product, configuration of a product for a set of local requirements, configuration of a set of products for compatibility, configuration of a request for proposals, ordering of equipment for a warehouse, ordering of equipment for a fulfillment center, classification of a product defect in an image, inspection of a product in an image, inspection of product quality data from a set of sensors, inspection of data from a set of onboard diagnostics on a. product, inspection of diagnostic data from an Internet of Things system, review of sensor data from environmental sensors in a set of supply chain environments, selection of inputs for a digital twin, selection of outputs from a digital twin, selection of visual elements for presentation in a digital twin, diagnosis of sources of delay in a supply chain, diagnosis of sources of scarcity in a supply chain, diagnosis of sources of congestion in a supply chain, diagnosis of sources of cost overruns in a supply chain, diagnosis of sources of product defects in a supply chain, or prediction of maintenance requirements in supply chain infrastructure.