A system may include a product-to-product communication module configured to exchange inter-product communications for a plurality of digitally connected products. A system may include a product-to-user communication module configured to exchange product-to-user communications between the plurality of digitally connected products and their respective users. A system may include a product-to-business communication module configured to exchange product-to-user communications between the plurality of digitally connected products and their associated enterprises. A system may include a data processing module configured to process the inter-product communications, product-to-user communications, and the product-to-business communications to determine time-sensitive alerts related to corresponding one of the plurality of digitally connected products. A system may include a graphical user interface (GUI) module configured to generate one or more user interfaces for displaying a time-sensitive alerts.
SYSTEMS, METHODS, KITS, AND APPARATUSES FOR GENERATIVE ARTIFICIAL INTELLIGENCE, GRAPHICAL NEURAL NETWORKS, TRANSFORMER MODELS, AND CONVERGING TECHNOLOGY STACKS IN VALUE CHAIN NETWORKS
A system may execute, by a generative artificial intelligence system, generative artificial intelligence algorithms trained on value chain network data. A system may receive input data including at least one of images, video, audio, text, programmatic code, and data, process the input data using the generative artificial intelligence algorithms to generate output content, wherein the output content includes at least one of structured prose, images, video, audio content, software source code, formatted data, algorithms, definitions, and context-specific structures, and generate an internal state of the generative artificial intelligence system, including a set of weights and/or biases as a result of prior processing. A system may provide the generated output content to a user interface for presentation to a user.
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
A VCN process may receive, 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. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models. A VCN process may determine a procurement action to be taken in the value chain network based upon, at least in part, an output of the set of AI-based learning models. A VCN process may execute the procurement action.
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
4.
SYSTEMS, METHODS, KITS, AND APPARATUSES FOR PREDICTIVE SOURCING IN VALUE CHAIN NETWORKS
A VCN process may receive information associated with a set of value chain network entities. A VCN process may provide the information 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 is trained to generate a prediction of future demand for an item. A VCN process may provide the information to a second set of AI-based learning models, wherein at least one member of the second 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 at least one value chain entity. A VCN process may determine a potential risk in the value chain network associated with the at least one value chain network entity based upon, at least in part, an output of the AI-based learning models.
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
G06Q 10/0635 - Risk analysis of enterprise or organisation activities
5.
SYSTEMS, METHODS, KITS, AND APPARATUSES FOR USING ARTIFICIAL INTELLIGENCE FOR AUTOMATION IN VALUE CHAIN NETWORKS
A VCN process may receive 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 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 VCN process may determine a task to be completed for the value chain network based upon, at least in part, on an output of the set of AI-based learning models. A VCN process may execute the task to facilitate an improvement in the value chain network.
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
6.
SYSTEMS, METHODS, KITS, AND APPARATUSES FOR AI- DRIVEN DIGITAL TWINS FOR VALUE CHAIN NETWORK CONTROL TOWERS
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.
Methods and systems described herein may provide control towers and/or mini control towers and/or digital twins to manage a value chain network (VCN). Information associated with a set of value chain network entities of a VCN may be received from a variety of sources. That information may be provided to a set of Artificial Intelligence (Al)-based learning models. The Al-based learning models may use that information along with the control towers and/or mini control towers and/or digital twins to determine a potential risk in the VCN, to determine a potential action to take in the VCN, to configure intelligent procurement in the VCN, to configure predictive sourcing in the VCN, to automate VCN entities, to instruct smart machines in the VCN, and to configure robotic process automation in the VCN.
A VCN process may configure a set of sub-level computing devices for communication with a primary computing device, wherein the primary computing device manages the set of sub-level computing devices to orchestrate performance of a set of value chain network entities. A VCN process may receive, by the set of sub-level computing devices, a primary command from the primary computing device, wherein the primary command is one of a task or a request associated with the value chain network. A VCN process may assign at least a portion of one or more operating devices capable of fulfilling the primary command as a set of one or more computing devices to be managed by the set of sub-level computing devices, wherein the sub-level computing device is a computing device that manages or executes performance of a particular entity or relationship of the value chain network.
A VCN process may receive, 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. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models. A VCN process may determine a potential risk in the value chain based upon, at least in part, an output of the AI-based learning classification. A VCN process may execute an action to mitigate the potential risk in the value chain network.
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
G06Q 10/0635 - Risk analysis of enterprise or organisation activities
10.
SYSTEMS, METHODS, KITS, AND APPARATUSES FOR USING ARTIFICIAL INTELLIGENCE FOR INSTRUCTING SMART MACHINES IN VALUE CHAIN NETWORKS
A VCN process may receive 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 on the training data set to determine, upon receiving the classification of the at least one of: the operating state, the fault condition, the operating flow, or the behavior, a task to be completed for the value chain network. A VCN process may provide a computer code instruction set to a machine to execute the task to facilitate an improvement in the operation of the value chain network.
A VCN process may configure 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 manages the set of secondary computing devices. A VCN process may receive, by at least one member of the set of secondary computing devices, a set of primary commands from the primary computing device, wherein each of the set of primary commands is at least one of a task or a request. A VCN process may assign at least a portion of one or more computing devices capable of fulfilling the set of primary commands as a set of one or more computing devices to be managed by the set of secondary computing devices.
G05D 1/223 - Command input arrangements on the remote controller, e.g. joysticks or touch screens
G05B 19/4155 - Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by programme execution, i.e. part programme or machine function execution, e.g. selection of a programme
A method of configuring a robot of a fleet of robots for use of an AI chipset includes receiving a request for a robotic fleet to perform a job. The method includes defining a set of tasks that are to be performed by the robotic fleet in performance of the job. The method includes assigning at least one task of the set of tasks to a robot. The method includes determining a configuration for the robot based on the assigned task and a components inventory that indicates different components that can be provisioned to the robot including at least one AI chipset, and for each component, a set of extended capabilities and a status of the component. The method includes configuring the robot based on the determined configuration to use the at least one AI chipset. The method includes deploying the robotic fleet to perform the job.
A system includes a fleet resources data store that maintains a fleet resource inventory indicating fleet resources that can be assigned to perform tasks. For each fleet resource, the inventory indicates features of each fleet resource and a respective status. A set of task definitions is accessible to an intelligence layer to facilitate improving task definition based on feedback from task-specific outcomes. The system receives a job request for a robotic fleet to perform a job and determines a job definition data structure indicating a set of tasks to be performed for the job. The system applies an outcome of performing a task by a resource assigned to perform the task to a machine learning system of the intelligence layer that facilitates improving, based on the outcome, the set of task definitions. The system updates the set of task definitions based on a result of applying the machine learning system.
A system includes a job definition data structure, based on a request for a robotic fleet to perform a job, that defines tasks for the job. A robotic fleet configuration data structure corresponding to the job is based on the tasks and a fleet resource inventory. The robotic fleet configuration data structure assigns fleet resources from the fleet resource inventory to the tasks defined in the job definition data structure. The system provisions the respective fleet resource based on the configuration data structure, deploys the robotic fleet, monitors task completion status, and configures a distributed ledger for tracking task or job completion. The system, in response to completion of a task, updates a set of job completion data in the distributed ledger that reflects at least one of robotic task completion data, allocation of robotic resources to parties associated with the job, and actions triggered in response to the completion.
A digital twin system includes a library of different types of robot operating unit digital twins stored in a storage system. The digital twin system includes one or more interfaces through which information associated with a physical robot operating unit corresponding to an instance of the robot operating unit digital twins is communicated. The digital twin system includes a set of processors that execute a set of computer-readable instructions to collectively operate one or more execution environments for executing instances of a portion of the different types of robot operating unit digital twins. The digital twin system also generates digital twin instances for individual robot operating units, a team of robot operating units, or a fleet of robot operating units. The digital twin system simulates operation of a physical robot by executing an instance of a digital twin generated for the physical robot based on information communicated through the interfaces.
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 fleet resource, the fleet resource inventory indicates maintenance status data, a predicted maintenance need, and/or a preventive maintenance schedule. A library of fleet resource maintenance requirements facilitates determining maintenance workflows, service actions, and/or service parts. The platform calculates predicted maintenance need of a fleet resource based on anticipated component wear or failure. The anticipated component wear/failure is derived from machine learning-based analysis of the maintenance status data in the fleet resource inventory. The platform monitors a health state of the fleet resource based on sensor data. The platform predicts the anticipated component wear/failure based on a clustering algorithm to identify at least one failure pattern in a set of failure data. The prediction is executed by a predictive maintenance intelligence service layer.
A computer-implemented method for optimizing a distributed database includes receiving, at an aggregator, one or more query logs comprising past queries received by the distributed database. The computer-implemented method includes determining, by the aggregator, common queries received by one or more edge devices. The computer-implemented method includes determining, by the aggregator, that at least one edge device was not able to respond to a common query received by the at least one edge device. The computer-implemented method includes causing, by the aggregator, data for responding to the common query to be transmitted to the at least one edge device.
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
A raw material system includes a product manufacturing demand estimation system programmed to calculate an expected demand for a product at a future point in time. An environment detection system identifies at least one of an environmental condition or an environmental event. A raw material production system estimates a raw material availability at the future point in time based on the expected demand and the environmental condition/event. A raw material requirement system calculates a required raw material amount to manufacture the product at the future point in time based on the expected demand and the environmental condition/event. A raw material procurement system autonomously configures a futures contract for procurement of at least a portion of the required raw material amount in response to the required raw material amount calculation exceeding the raw material availability estimation.
A method for prioritizing predictive model data streams includes receiving, by a device, a plurality of predictive model data streams. Each predictive model data stream includes a set of model parameters for a corresponding predictive model. Each predictive model is trained to predict future data values of a data source. The method includes prioritizing, by the device, each of the plurality of predictive model data streams. The method includes selecting at least one of the predictive model data streams based on a corresponding priority. The method includes parameterizing, by the device, a predictive model using the set of model parameters included in the selected at least one predictive model data stream. The method includes predicting, by the device, the future data values of the data source using the parameterized predictive model.
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
G06Q 10/087 - Inventory or stock management, e.g. order filling, procurement or balancing against orders
20.
Control tower encoding of cross-product data structure
A digital product network system includes a set of digital products each having a product processor, a product memory, and a product network interface. The digital product network system includes a product network control tower having a control tower processor, a control tower memory, and a control tower network interface. The product processor and the control tower processor collectively include non-transitory instructions that program the digital product network system to generate product level data at the product processor, transmit the product level data from the product network interface, receive the product level data at the control tower network interface, encode the product level data as a product level data structure configured to convey parameters indicated by the product level data across the set of digital products, and write the product level data structure to at least one of the product memory and the control tower memory.
G06Q 30/0201 - Market modellingMarket analysisCollecting market data
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
G06F 9/48 - Program initiatingProgram switching, e.g. by interrupt
G06N 10/60 - Quantum algorithms, e.g. based on quantum optimisation, or quantum Fourier or Hadamard transforms
G06N 10/80 - Quantum programming, e.g. interfaces, languages or software-development kits for creating or handling programs capable of running on quantum computersPlatforms for simulating or accessing quantum computers, e.g. cloud-based quantum computing
A robotic fleet platform includes a fleet resources data store with a fleet resource inventory indicating additive manufacturing systems that can be provisioned with a set of fleet resources. The fleet resource inventory indicates 3D printing requirements, printing instructions, and a status of each additive manufacturing system. Provisioning rules are accessible to an intelligence layer to ensure compliance. The platform receives a request for a robotic fleet to perform a job and determines a job definition data structure defining tasks. The platform determines a robotic fleet configuration data structure that assigns additive manufacturing systems to one or more of the tasks. The platform determines a respective provisioning configuration for each of the additive manufacturing systems. The platform provisions each additive manufacturing system based on the respective provisioning configuration and the provisioning rules. The platform deploys the robotic fleet based on the robotic fleet configuration data structure to perform the job.
A computer-implemented method for optimizing a distributed database includes receiving, at an aggregator, one or more query logs comprising past queries received by the distributed database. The method includes generating, by the aggregator, a query prediction model based on the one or more query logs. The method includes predicting, by the aggregator, a future query using the query prediction model. The predicted future query is predicted to be received by an edge device. The method includes causing, by the aggregator, data for responding to the predicted future query to be transmitted to the edge device.
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
G06F 16/2458 - Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
A robotic fleet resource provisioning system includes a computer-readable storage system storing a fleet resources data store and resource provisioning rules. The fleet resources data store maintains a fleet resource inventory indicating fleet resources, each with features, configuration requirements, and a status. The resource provisioning rules are accessible to an intelligence layer to ensure that provisioned resources comply with the resource provisioning rules. The system receives a request for a robotic fleet to perform a job and determine a job definition data structure. The definition data structure defines a set of tasks that are to be performed in performance of the job. The system determines a robotic fleet configuration data structure corresponding to the job based on the set of tasks and the fleet resource inventory. The system determines a respective provisioning configuration for each respective fleet resource. The system deploys the robotic fleet to perform the job.
A dynamic vision system for a robotic system includes an optical assembly including a lens containing a liquid. The lens is deformable to generate variable focus for the lens. The optical assembly is configured to capture optical data. A robotic system is configured to simulate human or animal species capabilities having a control system configured to adjust one or more optical parameters. The one or more optical parameters modify the variable focus of the lens while the optical assembly captures current optical data relating to the robotic system. A processing system is configured to train a machine learning model to recognize an object relating to the robotic system from training data generated from the optical data captured by the optical assembly. The optical data includes the current optical data relating to the robotic system.
An autonomous futures contract orchestration platform includes a set of processors programmed with a set of non-transitory computer-readable instructions. The instructions include receiving, from a data source, an indication associated with a product that relates to an entity that purchases or sells the product. The instructions include predicting a baseline cost of purchasing or selling the product at a future point in time based on the indication. The instructions include retrieving a futures cost, at a current point in time, of a futures contract for an obligation to the purchasing or selling the product for delivery or performance of the product at the future point in time. The instructions include executing a smart contract for the futures contract based on the baseline cost and the futures cost. The instructions include orchestrating the delivery or performance of the product at the future point in time.
G06Q 30/0201 - Market modellingMarket analysisCollecting market data
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
G06F 9/48 - Program initiatingProgram switching, e.g. by interrupt
G06N 10/60 - Quantum algorithms, e.g. based on quantum optimisation, or quantum Fourier or Hadamard transforms
G06N 10/80 - Quantum programming, e.g. interfaces, languages or software-development kits for creating or handling programs capable of running on quantum computersPlatforms for simulating or accessing quantum computers, e.g. cloud-based quantum computing
A computer-implemented method for executing a quantum computing task includes providing a quantum computing system. The computer-implemented method includes receiving a request, from a quantum computing client, to execute a quantum computing task via the quantum computing system. The computer-implemented method includes executing the requested quantum computing task via the quantum computing system. The executing the requested quantum computing task includes trapping a set of ions. The computer-implemented method includes returning a response related to the executed quantum computing task to the quantum computing client.
G06N 10/80 - Quantum programming, e.g. interfaces, languages or software-development kits for creating or handling programs capable of running on quantum computersPlatforms for simulating or accessing quantum computers, e.g. cloud-based quantum computing
G06F 9/48 - Program initiatingProgram switching, e.g. by interrupt
27.
Digital-Twin-Enabled Artificial Intelligence System for Distributed Additive Manufacturing
An information technology system for a distributed manufacturing network includes an additive manufacturing platform configured to manage workflows for a set of distributed manufacturing network entities associated with the distributed manufacturing network. The information technology system includes a set of digital twins generated by the additive manufacturing platform. The information technology system includes an artificial intelligence system configured to be executed by a data processing system in communication with the additive manufacturing platform. The artificial intelligence system is trained to generate process parameters for the workflows managed by the additive manufacturing platform using data collected from the set of 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 set of distributed manufacturing network entities.
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G06Q 10/087 - Inventory or stock management, e.g. order filling, procurement or balancing against orders
B33Y 50/02 - Data acquisition or data processing for additive manufacturing for controlling or regulating additive manufacturing processes
28.
Robotic Fleet Configuration Method for Additive Manufacturing Systems
A method of configuring robot fleets with additive manufacturing capabilities includes receiving a request for a robotic fleet to perform a job and determining a job definition data structure based on the request. The job definition data structure defines a set of tasks to be performed in furtherance of the job. The method includes determining a provisioning configuration for each additive manufacturing system based on the task to which the additive manufacturing system is assigned, the set of 3D printing requirements, the printing instructions, and the status of the additive manufacturing system. The method includes provisioning the additive manufacturing system based on the provisioning configuration and a set of additive manufacturing system provisioning rules that are accessible to an intelligence layer to ensure that provisioned systems comply with the provisioning rules. The method includes deploying the robotic fleet based on the robotic fleet configuration data structure to perform the job.
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.
G06F 16/2458 - Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
A robot fleet management platform includes a robot inventory that indicates robots that can be assigned to a robot fleet and, for each robot, a set of baseline features and a status. A components inventory indicates different components that can be provisioned to one or more multi-purpose robots and, for each component, a set of extended capabilities and a status. The platform receives a request for a robotic fleet to perform a job and determines a job definition data structure defining a set of tasks. The platform determines a respective configuration for each assigned multi-purpose robots based on the respective set of tasks that is assigned to the one or more assigned multi-purpose robots and the components inventory. The platform configures the more assigned multi-purpose robots based on the respective configuration. The platform deploys the robotic fleet including the one or more assigned multi-purpose robots to perform the job.
A method includes receiving a request for a robotic fleet to perform a job and defining a set of tasks that are to be performed in performance of the job. The method includes assigning robots selected from a robot inventory to the set of tasks based on a robot inventory data structure that indicates, for each robot, a status and set of baseline features. The robots include one or more assigned multi-purpose robots that can be configured for different tasks and different environments. The method includes determining a configuration for each assigned robot based on the respective task that is assigned and a components inventory. The components inventory indicates multiple components and, for each component, a status and a set of extended capabilities. The method includes configuring the one or more assigned multi-purpose robots based on the respective configurations. The method includes deploying the robotic fleet to perform the job.
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
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 query is a request for data stored at the edge device and for data stored at other edge devices. The method includes executing, by the edge device, the query to find partial query results comprising the data stored at the edge device. The method includes generating, by the edge device, statistical information based on the partial query results. The method includes determining, by the edge device, a statistical confidence associated with the partial query results based on the statistical information. The method includes generating, by the edge device, an approximate response to the query based on the statistical information. The method includes transmitting the approximate response to the query device.
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.
A method includes receiving a request for a robotic fleet to perform a job and defining a set of tasks that are to be performed in performance of the job. The method includes assigning robots selected from a robot inventory to the set of tasks based on a robot inventory data structure that indicates, for each robot, a status and set of baseline features. The robots include one or more assigned multi-purpose robots that can be configured for different tasks and different environments. The method includes determining a configuration for each assigned robot based on the respective task that is assigned and a components inventory. The components inventory indicates multiple components and, for each component, a status and a set of extended capabilities. The method includes configuring the one or more assigned multi-purpose robots based on the respective configurations. The method includes deploying the robotic fleet to perform the job.
A system for product replacement includes a product logistics system for a product in a product condition. The system includes an exposure data collection system configured to collect exposure data indicating at least one of an event or an environmental condition that may impact the product condition of the product. The system includes a replacement determination system programmed to calculate a probability for the need to replace the product based on the at least one of the event or the environmental condition. The system includes a replacement procurement system programmed to autonomously configure an option-type futures contract for replacement of the product based on the probability for the need to replace the product.
A distributed manufacturing network information technology system includes a cloud-based additive manufacturing management platform with a user interface, connectivity facilities, data storage facilities, and monitoring facilities. The distributed manufacturing network information technology system includes a set of applications for enabling the additive manufacturing management platform to manage a set of distributed manufacturing network entities. The distributed manufacturing network information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the distributed manufacturing network entities to optimize manufacturing and value chain workflows.
G05B 19/4099 - Surface or curve machining, making 3D objects, e.g. desktop manufacturing
B29C 64/393 - Data acquisition or data processing for additive manufacturing for controlling or regulating additive manufacturing processes
G05B 19/402 - Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by control arrangements for positioning, e.g. centring a tool relative to a hole in the workpiece, additional detection means to correct position
38.
Digital-Twin-Enabled Digital Product Network System
A digital product network system includes a set of digital products each having a product memory, a product network interface, and a product processor programmed with product instructions. The digital product network system includes a product network control tower having a control tower memory, a control tower network interface, and a control tower processor programmed with control tower instructions. The digital product network system includes a digital twin system defined at least in part by at least one of the product instructions or the control tower instructions to encode a set of digital twins representing the set of digital products
A method for prioritizing predictive model data streams includes receiving, by a first device, a plurality of predictive model data streams. Each predictive model data stream includes a set of model parameters for a corresponding predictive model. Each predictive model is trained to predict future data values of a data source. The method includes prioritizing, by the first device, priorities to each of the plurality of predictive model data streams. The method includes selecting at least one of the predictive model data streams based on a corresponding priority. The method includes parameterizing, by the first device, a predictive model using the set of model parameters included in the selected predictive model data stream. The method includes predicting, by the first device, future data values of the data source using the parameterized predictive model.
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
G06Q 10/087 - Inventory or stock management, e.g. order filling, procurement or balancing against orders
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
A dynamic vision system for robot fleet management includes an optical assembly including a lens containing a liquid. The lens is deformable to generate variable focus for the lens. The optical assembly is configured to capture optical data. The dynamic vision system includes a robot fleet management platform having a control system configured to adjust one or more optical parameters. The one or more optical parameters modify the variable focus of the lens while the optical assembly captures current optical data relating to a robotic fleet. The dynamic vision system includes a processing system configured to train a machine learning model to recognize an object relating to the robotic fleet using training data generated from the optical data captured by the optical assembly. The optical data includes the current optical data relating to the robotic fleet.
A method of provisioning robotic fleet resources includes receiving a request for a robotic fleet to perform a job and determining a job definition data structure based on the request. The job definition data structure defines a set of tasks that are to be performed in performance of the job. The method includes determining a robotic fleet configuration data structure corresponding to the job based on the set of tasks and a fleet resource inventory that indicates fleet resources. The method includes determining a respective provisioning configuration for each respective fleet resource. The method includes provisioning the respective fleet resource based on the respective provisioning configuration and a set of resource provisioning rules that are accessible to an intelligence layer to ensure that provisioned resources comply with the provisioning rules. The method includes deploying the robotic fleet to perform the job.
A robotic fleet platform for configuring robot fleets with additive manufacturing capabilities includes a fleet resources data store that maintains a fleet resource inventory indicating additive manufacturing systems that can be provisioned with fleet resources and, for each additive manufacturing system, a set of 3D printing requirements, printing instructions that define configuring an on-demand production system for 3D printing, and a status of the additive manufacturing system. The platform includes additive manufacturing system provisioning rules that are accessible to an intelligence layer to ensure that provisioned additive manufacturing systems comply with the provisioning rules. The platform receives a request for a robotic fleet to perform a job and determine a job definition data structure based on the request. The job definition data structure defines a set of tasks for the job. The platform deploys the robotic fleet based on the robotic fleet configuration data structure to perform the job.
B29C 64/379 - Handling of additively manufactured objects, e.g. using robots
B29C 64/386 - Data acquisition or data processing for additive manufacturing
B29C 64/393 - Data acquisition or data processing for additive manufacturing for controlling or regulating additive manufacturing processes
B33Y 50/02 - Data acquisition or data processing for additive manufacturing for controlling or regulating additive manufacturing processes
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
A raw material system includes a product manufacturing demand estimation system programmed to calculate an expected demand for a product. The system includes an environment detection system configured to identify an environmental condition or event. The system includes a raw material production system programmed to estimate a raw material availability at the future point in time based on the expected demand and the environmental condition/event. The system includes a raw material requirement system programmed to calculate a required raw material amount to manufacture the product based on the expected demand and the environmental condition/event. The system includes a raw material procurement system programmed to autonomously configure a futures contract for procurement of at least a portion of the required raw material amount in response to the required raw material amount calculation exceeding the raw material availability estimation.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
An autonomous futures contract orchestration platform includes processors programmed with non-transitory computer-readable instructions to collectively execute receiving, from a data source, an indication associated with a product that relates to an entity that at least one of purchases or sells the product. The instructions include predicting a baseline cost of at least one of purchasing or selling the product at a future point in time based on the indication. The instructions include retrieving a futures cost, at a current point in time, of a futures contract for the product. The instructions include generating a risk threshold based on a specified risk tolerance of the entity indicating a difference between the baseline cost and the futures cost. The instructions include executing a smart contract for the futures contract based on the baseline cost, the futures cost, and the risk threshold.
A robotic fleet management platform includes a resources data store that maintains a fleet resource inventory indicating fleet resources that can be assigned to a robotic fleet and, for each fleet resource, maintenance history, predicted maintenance need, and a preventive maintenance schedule. The platform includes a maintenance management library of fleet resource maintenance requirements for determining maintenance workflows, service actions, and service parts for at least one fleet resource in the fleet resource inventory. The platform calculates predicted maintenance need of a fleet resource based on anticipated component wear and anticipated component failure of the at least one fleet resource according to machine learning-based analysis of the maintenance status data. The platform monitors a health state of the fleet resource based on sensor data. 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.
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.
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 query is a request for data stored at the edge device and for data stored at other edge devices. The method includes executing, by the edge device, the query to find partial query results comprising the data stored at the edge device. The method includes generating, by the edge device, statistical information based on the partial query results. The method includes determining, by the edge device, a statistical confidence associated with the partial results based on the statistical information. The method includes generating, by the edge device, an approximate response to the query based on the statistical information. The method includes transmitting the approximate response to the query device.
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
A method of configuring robot fleets with additive manufacturing capabilities includes receiving a request for a robotic fleet to perform a job and determining a job definition data structure based on the request. The job definition data structure defines a set of tasks to be performed in furtherance of the job. The method includes determining a provisioning configuration for each additive manufacturing system based on the task to which the additive manufacturing system is assigned, the set of 3D printing requirements, the printing instructions, and the status of the additive manufacturing system. The method includes provisioning the additive manufacturing system based on the provisioning configuration and a set of additive manufacturing system provisioning rules that are accessible to an intelligence layer to ensure that provisioned systems comply with the provisioning rules. The method includes deploying the robotic fleet based on the robotic fleet configuration data structure to perform the job.
A distributed manufacturing network includes a distributed ledger system and an artificial intelligence system. The distributed ledger system is integrated with digital threads of a set of distributed manufacturing network entities for storing information on event, activities and transactions related to the distributed manufacturing network entities. The artificial intelligence system is configured to learn on a training set of outcomes, parameters, and data collected from the distributed manufacturing network entities to optimize manufacturing and value chain workflows.
A robot fleet management platform includes a resources data store that maintains a robot inventory indicating robots that can be assigned to a robot fleet and, for each respective robot, a set of baseline features of the robot and a respective status. The resources data store maintains a components inventory indicating different components that can be provisioned to one or more multi-purpose robots and, for each component, a respective set of extended capabilities corresponding to the component and a respective status. The robot fleet management platform receives a request for a robotic fleet to perform a job, determines a job definition data structure based on the request, determines a robot fleet configuration data structure corresponding to the job based on the set of tasks and the robot inventory, determines a respective configuration for each assigned robot based on the components inventory, configures the assigned robots, and deploys the robotic fleet.
B25J 19/00 - Accessories fitted to manipulators, e.g. for monitoring, for viewingSafety devices combined with or specially adapted for use in connection with manipulators
51.
Demand-Responsive Robot Fleet Management for Value Chain Networks
A robot fleet platform for preparing a job request includes one or more processors configured to execute instructions. The instructions include a job request ingestion system configured to receive job content relating to at least one of picking, packing, moving, storing, warehousing, transporting or delivering of items in a supply chain. The job content includes an electronic job request and related data. The instructions include a job content parsing system configured to apply filters to the received job content to identify candidate portions thereof for robot automation. The instructions include a fleet intelligence layer that activates a set of intelligence services to process terms in the candidate portions of the job content and receive therefrom at least one recommended robot task and associated contextual information. The instructions include a demand intelligence layer that provides real time information relating to a parameter of demand for the items in the supply chain.
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.
G06F 16/2458 - Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
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.
A digital product network system includes a set of digital products each having a product processor, a product memory, and a product network interface. The digital product network system includes a product network control tower having a control tower processor, a control tower memory, and a control tower network. The product processor and the control tower processor collectively include non-transitory instructions that program the digital product network system to generate product level data at the product processor, transmit the product level data from the product network interface, receive the product level data at the control tower network interface, encode the product level data as a product level data structure configured to convey parameters indicated by the product level data across the set of digital products, and write the product level data structure to at least one of the product memory and the control memory.
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.
Methods and systems described herein provide a distributed database configured to leverage the storage and processing capabilities of edge devices and a query language for efficiently querying the distributed database system. A database layer of an application stack can be distributed across the nodes of a network including the edge nodes such that vast amounts of data may be stored locally at these nodes to provide access to the data in response to a query. In such a distributed database environment, queries may be received and/or executed by edge distributed node points. According to techniques described herein, the entire network environment may appear as a seamless database, and an Edge Query Language may provide for resolution of the query.
Methods and systems described herein provide a distributed database configured to leverage the storage and processing capabilities of edge devices and a query language for efficiently querying the distributed database system. A database layer of an application stack can be distributed across the nodes of a network including the edge nodes such that vast amounts of data may be stored locally at these nodes to provide access to the data in response to a query. In such a distributed database environment, queries may be received and/or executed by edge distributed node points. According to techniques described herein, the entire network environment may appear as a seamless database, and an Edge Query Language may provide for resolution of the query.
B33Y 30/00 - Apparatus for additive manufacturingDetails thereof or accessories therefor
B33Y 50/02 - Data acquisition or data processing for additive manufacturing for controlling or regulating additive manufacturing processes
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
G06N 10/60 - Quantum algorithms, e.g. based on quantum optimisation, or quantum Fourier or Hadamard transforms
H04L 67/51 - Discovery or management thereof, e.g. service location protocol [SLP] or web services
G08B 13/196 - Actuation by interference with heat, light, or radiation of shorter wavelengthActuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
H04L 67/568 - Storing data temporarily at an intermediate stage, e.g. caching
58.
SYSTEMS, METHODS, KITS, AND APPARATUSES FOR DIGITAL PRODUCT NETWORK SYSTEMS AND BIOLOGY-BASED VALUE CHAIN NETWORKS
A digital product network system generally includes a set of digital products each having a product memory, a product network interface, and a product processor programmed with product instructions; a product network control tower having a control tower memory, a control tower network interface, and a control tower processor programmed with control tower instructions; and a digital twin system defined at least in part by at least one of the product instructions or the control tower instructions to encode a set of digital twins representing the set of digital products.
A digital product network system generally includes a set of digital products each having a product memory, a product network interface, and a product processor programmed with product instructions; a product network control tower having a control tower memory, a control tower network interface, and a control tower processor programmed with control tower instructions; and a digital twin system defined at least in part by at least one of the product instructions or the control tower instructions to encode a set of digital twins representing the set of digital products.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/08 - Logistics, e.g. warehousing, loading or distributionInventory or stock management
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
60.
Robot Fleet Resource Configuration in Value Chain Networks
A robot fleet management platform includes a job configuration system that determines tasks to be performed by robots of a robot fleet based on a job request and a first fleet objective. A proxy service applies fleet configuration services to the tasks to produce a data structure. An intelligence layer activates intelligence services to produce a robot task and associated contextual information that facilitates robot selection and task ordering. A job workflow system generates a workflow defining a performance order of the tasks. A workflow simulation system simulates performance of the job request based on the workflow to recursively redefine the tasks, the data structure, or the workflow until the simulation result satisfies a second fleet objective. In response to the simulation result satisfying the set of fleet objectives, a plan generator generates a job execution plan based on the set of robot tasks, the data structure, and the workflow.
An information technology system for a distributed manufacturing network includes an additive manufacturing management platform configured to manage process and production workflows for a set of distributed manufacturing network entities through design, modeling, printing, and supply chain stages. The information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities of the distributed manufacturing network to optimize digital production processes and workflows. The information technology system includes a distributed ledger system integrated with a digital thread configured to provide unified views of workflow and transaction information to entities in the distributed manufacturing network.
B33Y 50/02 - Data acquisition or data processing for additive manufacturing for controlling or regulating additive manufacturing processes
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
A value chain network automation system includes a supply chain robotic fleet data set including attributes of a set of states and capabilities of a set of robotic systems in a supply chain for a set of goods. The system includes a demand intelligence robotic process automation data set including attributes of a set of states of a set of robotic process automation systems that undertake automation of a set of demand forecasting tasks for the set of goods. The system includes a coordination system that provides a set of robotic task instructions for the supply chain robotic fleet based on processing the supply chain robotic fleet data set and the demand intelligence robotic process automation data set to coordinate supply and demand for the set of goods.
B33Y 30/00 - Apparatus for additive manufacturingDetails thereof or accessories therefor
B33Y 50/02 - Data acquisition or data processing for additive manufacturing for controlling or regulating additive manufacturing processes
B29C 64/386 - Data acquisition or data processing for additive manufacturing
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
G02B 3/14 - Fluid-filled or evacuated lenses of variable focal length
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
G05B 17/02 - Systems involving the use of models or simulators of said systems electric
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
63.
Digital-Twin-Assisted Additive Manufacturing for Value Chain Networks
An autonomous additive manufacturing platform includes sensors positioned in, on, and/or near a part and configured to collect sensor data related to the part. An adaptive intelligence system is configured to receive the sensor data from the sensors. The adaptive intelligence system includes a machine learning system configured to input the sensor data as training data into one or more machine learning models. The machine learning models are configured to transform the sensor data into simulation data. A digital twin system is configured to create a part twin based on the simulation data. The part twin provides for representation of the part and simulation of a possible future state of the part via the simulation data. An artificial intelligence system is configured to execute simulations on the digital twin system. The machine learning models are utilized to make classifications, predictions, and other decisions relating to the part.
An information technology system for a distributed manufacturing network includes an additive manufacturing management platform with an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from a set of distributed manufacturing network entities and execute simulations on digital twins of the set of distributed manufacturing network entities to make classifications, predictions, and optimization-related decisions for the set of distributed manufacturing network entities. The information technology system includes a distributed ledger system integrated with a digital thread and configured to provide unified views of workflow and transaction information to the set of distributed manufacturing network entities.
A dynamic vision system includes a variable focus liquid lens optical assembly. The dynamic vision system includes a variable lighting assembly. The dynamic vision system includes a control system configured to adjust one or more optical parameters and data collected from the variable focus liquid lens optical assembly in real time. The dynamic vision system includes a control system configured to adjust the variable lighting assembly. The dynamic vision system includes a processing system that dynamically learns on a training set of outcomes, parameters, and data collected from the variable focus liquid lens optical assembly to train a set of machine learning models to control the variable focus liquid lens optical assembly to optimize collection of data for processing by the set of machine learning models.
A robot fleet management platform includes a job parsing system that applies filters to identify portions of a job request suitable for robot automation. Based on the identified portions and a first fleet objective of the job request, a task system establishes tasks that define a robot type and task objective. A proxy service associates a robot of a robot fleet to each task and adaptation instructions to define how to adapt the robot fleet to perform the tasks. A workflow system generates a workflow defining a performance order of the tasks. A simulation system applies the workflow in an environment that includes digital models of the robot fleet and the tasks. The simulation is used to iteratively redefine the tasks and workflow until a second fleet objective is satisfied. A generation system generates a job execution plan in response to the simulation satisfying the first and second fleet objectives.
A dynamic vision system includes a variable focus liquid lens optical assembly. The dynamic vision system includes a control system configured to adjust one or more optical parameters and data collected from the variable focus liquid lens optical assembly in real time. The dynamic vision system includes a processing system that dynamically learns on a training set of outcomes, parameters, and data collected from the variable focus liquid lens optical assembly to train one or more machine learning models to recognize an object.
A value chain network automation system includes a supply chain robotic fleet data set including attributes of a set of states and capabilities of a set of robotic systems in a supply chain for a set of goods. The system includes a demand intelligence robotic process automation data set including attributes of a set of states of a set of robotic process automation systems that undertake automation of a set of demand forecasting tasks for the set of goods. The system includes a coordination system that provides a set of robotic task instructions for the supply chain robotic fleet based on processing the supply chain robotic fleet data set and the demand intelligence robotic process automation data set to coordinate supply and demand for the set of goods.
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
G05B 19/4097 - Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by using design data to control NC machines, e.g. CAD/CAM
G05B 19/4099 - Surface or curve machining, making 3D objects, e.g. desktop manufacturing
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
A robot fleet management platform includes datastores configured to store a governance library defining governance standards. Processors execute computer-readable instructions to implement a governance-enabling intelligence layer that receives and responds to intelligence requests received from intelligence service clients. The intelligence layer includes artificial intelligence services including machine learning, rules-based intelligence, digital twin, robot process automation, and machine vision. The set of governance standards is applied to decisions made by one or more of the set of artificial intelligence services. An intelligence layer controller coordinates performance of the artificial intelligence services on behalf of the intelligence service clients and performance of analyses corresponding to the artificial intelligence services based on the set of governance standards. The intelligence layer returns decisions determined by the artificial intelligence services in response to the intelligence requests. The decisions are determined based on intelligence service data sources and the set of analyses.
G05B 19/4155 - Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by programme execution, i.e. part programme or machine function execution, e.g. selection of a programme
70.
ARTIFICIAL INTELLIGENCE SYSTEM FOR CONTROL TOWER AND ENTERPRISE MANAGEMENT PLATFORM MANAGING LOGISTICS SYSTEM
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.
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.
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.
G06Q 10/08 - Logistics, e.g. warehousing, loading or distributionInventory or stock management
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
73.
DIGITAL TWIN FOR CONTROL TOWER AND ENTERPRISE MANAGEMENT PLATFORM MANAGING ENTITY REPLICAS AND E-COMMERCE SYSTEMS
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.
G06Q 10/08 - Logistics, e.g. warehousing, loading or distributionInventory or stock management
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
G06N 3/04 - Architecture, e.g. interconnection topology
74.
ARTIFICIAL INTELLIGENCE SYSTEM FOR CONTROL TOWER AND ENTERPRISE MANAGEMENT PLATFORM MANAGING SENSORS AND CAMERAS
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.
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
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.
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.
G05B 19/4155 - Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by programme execution, i.e. part programme or machine function execution, e.g. selection of a programme
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06Q 10/08 - Logistics, e.g. warehousing, loading or distributionInventory or stock management
77.
CONTROL TOWER AND ENTERPRISE MANAGEMENT PLATFORM WITH ROBOTIC PROCESS AUTOMATION SYSTEMS MANAGING SYSTEM INTERFACES WITH ADAPTIVE INTELLIGENCE
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.
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.
G05B 19/4155 - Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by programme execution, i.e. part programme or machine function execution, e.g. selection of a programme
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
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.
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.
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.
A sensor system for determining occupancy in a space generally includes a transmitter radio device that transmits radio signals over a channel in the space; a receiver radio device that receives the transmitted radio signals that have traveled through the space; and at least one processor implementing an occupancy-centric algorithm that determines occupancy in the space based on the radio signals. The at least one processor determines channel state information based on the radio signals transmitted over the channel, determines occupancy in the space based on the channel state information, and outputs an occupancy signal based on the determined occupancy.
A sensor system for determining occupancy in a space generally includes a transmitter radio device that transmits radio signals over a channel in the space; a receiver radio device that receives the transmitted radio signals that have traveled through the space; and at least one processor implementing an occupancy-centric algorithm that determines occupancy in the space based on the radio signals. The at least one processor determines channel state information based on the radio signals transmitted over the channel, determines occupancy in the space based on the channel state information, and outputs an occupancy signal based on the determined occupancy.
G01S 13/00 - Systems using the reflection or reradiation of radio waves, e.g. radar systemsAnalogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
G01S 7/00 - Details of systems according to groups , ,
G01S 13/56 - Discriminating between fixed and moving objects or between objects moving at different speeds for presence detection
84.
CONTROL TOWER AND ENTERPRISE MANAGEMENT PLATFORM WITH WORKFORCE DIGITAL TWINS FOR VALUE CHAIN NETWORKS
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.
A sensor system for determining occupancy in a space generally includes a transmitter radio device that transmits radio signals over a channel in the space; a receiver radio device that receives the transmitted radio signals that have traveled through the space; and at least one processor implementing an occupancy-centric algorithm that determines occupancy in the space based on the radio signals. The at least one processor determines channel state information based on the radio signals transmitted over the channel, determines occupancy in the space based on the channel state information, and outputs an occupancy signal based on the determined occupancy.
G01S 13/00 - Systems using the reflection or reradiation of radio waves, e.g. radar systemsAnalogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
G01S 7/00 - Details of systems according to groups , ,
G01S 13/56 - Discriminating between fixed and moving objects or between objects moving at different speeds for presence detection
86.
SYSTEMS FOR DETECTING OCCUPANCY AND DETERMINING VALUE CHAIN RECOMMENDATIONS USING RADIO SIGNALS
A sensor system for determining occupancy in a space generally includes a transmitter radio device that transmits radio signals over a channel in the space; a receiver radio device that receives the transmitted radio signals that have traveled through the space; and at least one processor implementing an occupancy-centric algorithm that determines occupancy in the space based on the radio signals. The at least one processor determines channel state information based on the radio signals transmitted over the channel, determines occupancy in the space based on the channel state information, and outputs an occupancy signal based on the determined occupancy.
F24F 11/49 - Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring ensuring correct operation, e.g. by trial operation or configuration checks
G01S 13/00 - Systems using the reflection or reradiation of radio waves, e.g. radar systemsAnalogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
G06K 9/62 - Methods or arrangements for recognition using electronic means
A sensor system for determining occupancy in a space generally includes a transmitter radio device that transmits radio signals over a channel in the space; a receiver radio device that receives the transmitted radio signals that have traveled through the space; and at least one processor implementing an occupancy-centric algorithm that determines occupancy in the space based on the radio signals. The at least one processor determines channel state information based on the radio signals transmitted over the channel, determines occupancy in the space based on the channel state information, and outputs an occupancy signal based on the determined occupancy.
G01S 13/00 - Systems using the reflection or reradiation of radio waves, e.g. radar systemsAnalogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
G01S 7/00 - Details of systems according to groups , ,
G01S 13/56 - Discriminating between fixed and moving objects or between objects moving at different speeds for presence detection
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 50/28 - Logistics, e.g. warehousing, loading, distribution or shipping
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
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.
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.
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.
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.
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.
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
H04L 12/24 - Arrangements for maintenance or administration
93.
CONTROL TOWER AND ENTERPRISE MANAGEMENT PLATFORM FOR MANAGING VALUE CHAIN NETWORK ENTITIES FROM POINT OF ORIGIN OF ONE OR MORE PRODUCTS OF THE ENTERPRISE TO POINT OF CUSTOMER USE
An information technology system generally includes a cloud-based management platform with a micro-services architecture deploying a set of adaptive intelligence facilities that can be 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 and a set of data storage facilities that can be configured to store data collected and handled by the platform. The data can be related to at least one of the value chain network entities and the features of the platform. A set of monitoring facilities can be configured to monitor the value chain network entities. The platform can be configured to host a set of applications for directing an enterprise to manage the value chain network entities from a point of origin of a product of the enterprise to a point of customer use.
An information technology system generally includes a cloud-based management platform with a micro-services architecture having a set of microservices layers including an application layer supporting at least one supply chain application and at least one demand management application. The microservices layers can include a data collection layer that can collect information from a set of Internet of Things resources that collect information with respect to supply chain entities and demand management entities related to the value chain network entities of the platform.
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.
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.
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.
CONTROL TOWER AND ENTERPRISE MANAGEMENT PLATFORM WITH ROBOTIC PROCESS AUTOMATION LAYER TO AUTOMATE ACTIONS FOR SUBSET OF APPLICATIONS BENEFITTING VALUE CHAIN NETWORK ENTITIES
An information technology system generally includes a set of microservices layers including an application layer supporting at least one supply chain application and at least one demand management application. The microservices layers can 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.
CONTROL TOWER AND ENTERPRISE MANAGEMENT PLATFORM WITH A MACHINE LEARNING/ARTIFICIAL INTELLIGENCE MANAGING SENSOR AND THE CAMERA FEEDS INTO DIGITAL TWIN
An information technology generally including a set of monitoring facilities that are configured to monitor 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 machine learning/artificial intelligence system configured to generate recommendations for placing at least one of an additional sensor and a camera on and/or in proximity to a value chain network entity of the value chain network entities, and wherein data from the at least one of the additional sensor and the camera feeds into a digital twin that represents the value chain network entities.
CONTROL TOWER AND ENTERPRISE MANAGEMENT PLATFORM WITH UNIFIED SET OF ROBOTIC PROCESS AUTOMATION SYSTEMS FOR COORDINATED AUTOMATION AMONG VALUE CHAIN APPLICATIONS
An information technology system generally including a cloud-based management platform with a micro-services architecture having a unified set of robotic process automation systems that provide coordinated automation among at least two types of applications from among a set of demand management applications, a set of supply chain applications, a set of intelligent product applications, and a set of enterprise resource management applications for a category of goods with respect to the value chain network entities of the platform.
patents
