Autonomous Contingency-Responsive Smart Contract Configuration System

    公开(公告)号:US20230222531A1

    公开(公告)日:2023-07-13

    申请号:US18179960

    申请日:2023-03-07

    CPC classification number: G06Q30/0206

    Abstract: A system for managing future costs associated with a product includes a future requirement system programmed to estimate an amount of resources required for manufacturing, distributing, and selling the product at a future point in time. The system includes an adverse contingency system configured to identify adverse contingencies and calculate changes in costs associated with obtaining the amount of resources at the future point in time. The system includes a smart contract system programmed to autonomously configure and execute a smart futures contract based on the amount of resources required and on the changes in costs to manage the future costs associated with the product

    Artificial-Intelligence-Based Preventative Maintenance for Robotic Fleet

    公开(公告)号:US20230222454A1

    公开(公告)日:2023-07-13

    申请号:US18180033

    申请日:2023-03-07

    Abstract: A robotic fleet management platform includes a resources data store maintaining a fleet resource inventory indicating fleet resources that can be assigned to a robotic fleet. For each respective fleet resource, the fleet resource inventory indicates maintenance status data, a predicted maintenance need, and/or a preventive maintenance schedule. A maintenance management library of fleet resource maintenance requirements facilitates determining maintenance workflows, service actions, and/or service parts for fleet resources. The platform calculates the predicted maintenance need of a fleet resource based anticipated component wear. The anticipated wear/failure is derived from machine learning-based analysis of the maintenance status data. The platform monitors a health state of the fleet resource from sensor data. The platform adapts the preventive maintenance schedule. The platform initiates a service action of the at least one item of maintenance for the fleet resource based on the fleet resource maintenance requirements and/or the new preventive maintenance schedule.

    Dynamic-Ledger-Enabled Edge-Device Query Processing

    公开(公告)号:US20230222413A1

    公开(公告)日:2023-07-13

    申请号:US18180023

    申请日:2023-03-07

    CPC classification number: G06Q10/06315 G06Q10/0833 G06Q10/087 G06Q2220/00

    Abstract: A method for processing a query for data stored in a distributed database includes receiving, at an edge device, the query for data stored in the distributed database from a query device. The method includes causing, by the edge device, the query to be stored on a dynamic ledger maintained by the distributed database. The method includes detecting, by the edge device, that summary data has been stored on the dynamic ledger. The method includes generating, by the edge device, an approximate response to the query based on the summary data stored on the dynamic ledger. The method includes transmitting, to the query device, the approximate response.

    Robot Fleet Management with Workflow Simulation for Value Chain Networks

    公开(公告)号:US20230097438A1

    公开(公告)日:2023-03-30

    申请号:US17942048

    申请日:2022-09-09

    Abstract: A robot fleet management platform includes one or more processors configured to execute instructions. The instructions include receiving a job request comprising information descriptive of job deliverable and request-specific constraints for delivering the job deliverable. The instructions include applying content and structural filters to content received in association with a job request to identify portions thereof suitable for robot automation. The instructions include establishing a set of robot tasks, each defining at least a type of robot and a task objective, based on the portions of the job request that are suitable for robot automation and meet a first fleet objective. The instructions include applying fleet configuration services to the job content and the set of robot tasks to produce a fleet resource configuration data structure for the job request that associates at least one robot operating unit with each task in the set of tasks and robot adaptation instructions.

    Distributed Additive Manufacturing Platform for Value Chain Networks

    公开(公告)号:US20230080545A1

    公开(公告)日:2023-03-16

    申请号:US17942078

    申请日:2022-09-09

    Abstract: An information technology system for a distributed manufacturing network includes an additive manufacturing management platform configured to manage process workflows for a set of distributed manufacturing network entities associated with the distributed manufacturing network. A modeling stage of a process workflow includes a digital twin modeling system defined by a product instruction or a control tower instruction to encode a set of digital twins representing a product for use by the additive manufacturing management platform. The information technology system includes an artificial intelligence system executable by a data processing system. The artificial intelligence system is trained to generate process parameters for the process workflows managed by the additive manufacturing management platform using data collected from the distributed manufacturing network entities. The information technology system includes a control system configured to adjust the process parameters during an additive manufacturing process performed by at least one of the distributed manufacturing network entities.

    Robotic Fleet Provisioning for Value Chain Networks

    公开(公告)号:US20230078448A1

    公开(公告)日:2023-03-16

    申请号:US17942081

    申请日:2022-09-09

    Abstract: A robotic fleet resource provisioning system includes a storage system storing a fleet resources data store and resource provisioning rules that are accessible to an intelligence layer to ensure that provisioned resources comply with the provisioning rules. The fleet resources data store maintains a fleet resource inventory indicating fleet resources that can be provisioned and, for each respective fleet resource, features, configuration requirements, and a respective status. The robotic fleet resource provisioning system receives a request for a job, determines a job definition data structure, defining a set of tasks, based on the request, determines a robotic fleet configuration data structure corresponding to the job based on the set of tasks and the fleet resource inventory, determines a respective provisioning configuration for each respective fleet resource, provisions the respective fleet resource based on the respective provisioning configuration and the provisioning rules, and deploys the robotic fleet to perform the job.

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