Abstract: Methods generally including determining a structure in the data; by the controller, determining a relevance of the determined structure in the data to at least one role type stored within a role taxonomy; by the controller, determining a reduced dimensionality view of the data in response to the determined structure in the data. The reduced dimensionality view comprises fewer dimensions than the data from the plurality of input sensors. The reduced dimensionality view further comprises a graphical element representing at least one of: mechanical portions of a machine of the industrial environment, or a sensor from the plurality of input sensors that provided data; and providing the reduced dimensionality view to a user interface that is associated with at least one entity associated with the at least one role type stored within the role taxonomy.

Abstract: Data storage structured to store a plurality of detection values relating to aspects of an industrial production process and data relating to at least one role type stored within a role taxonomy; a data analysis circuit structured to interpret at least a subset of the plurality of detection values to determine a state value comprising at least one of a process state or a component state; an optimization circuit structured to analyze a subset of the plurality of detection values and the state value using at least one of a neural net or an expert system to determine a signal effectiveness of at least one of the plurality of input channels relative to the state value, and to provide an adjustment recommendation based, at least in part, on the signal effectiveness; and an analysis response circuit structured to adjust the industrial production process in response to the adjustment recommendation.

Abstract: Method for data collection in an industrial environment generally including receiving, at a switch, data from one or more variable groups of sensor inputs; monitoring the data from the one or more variable groups of sensor inputs; adaptively scheduling data collection at the switch; determining one or more noise values including one of an ambient noise, a local noise, or a vibration noise; using machine learning to forecast a future state of the industrial environment based at least in part on the determined one or more noise values; and reporting the forecasted future state of the industrial environment to an entity associated with a role type stored within a role taxonomy.

Abstract: Systems and methods for integrating a gaming engine and a smart contract system in a platform are provided. The gaming engine is programmed with a software development environment and an architecture that provides a set of gaming engine services with predefined tools for digital content developers to create a set of game engine generated environments. The smart contract system programmed with smart contract services associated with transactions that are based on electronically verifiable conditions. The integration platform is programmed with an execution framework that is common to the gaming engine and to the smart contract system to integrate the smart contract services with at least one of the gaming engine and the set of game engine generated environments.

Abstract: Computer-implemented methods for fault diagnosis in an industrial environment generally includes processing the plurality of sensor data values to determine a recognized pattern therefrom; retrieving at least one industrial-environment digital twin corresponding to the industrial environment, the at least one industrial-environment digital twin comprising a plurality of component digital twins, with each of the plurality of component digital twins corresponding to one of the plurality of components in the industrial environment, and wherein the at least one industrial-environment digital twin and the plurality of component digital twins are visual digital twins that are configured to be rendered in a visual manner; and rendering the at least one industrial-environment digital twin and the at least one respective component digital twin corresponding to the particular component in the client application in response to the received request and based on the operational condition of the particular component.

Abstract: Computer-implemented methods for fault diagnosis in an industrial environment generally includes processing the plurality of sensor data values to determine a recognized pattern therefrom; retrieving at least one industrial-environment digital twin corresponding to the industrial environment, the at least one industrial-environment digital twin comprising a plurality of component digital twins, with each of the plurality of component digital twins corresponding to one of the plurality of components in the industrial environment, and wherein the at least one industrial-environment digital twin and the plurality of component digital twins are visual digital twins that are configured to be rendered in a visual manner; and rendering the at least one industrial-environment digital twin and the at least one respective component digital twin corresponding to the particular component in the client application in response to the received request and based on the operational condition of the particular component.

Abstract: Embodiments of the present invention provide entertainment gaming systems and methods comprising, among other things, a real world non-random number outcome generator machine and an entertainment gaming machine wherein the non-random outcome from the real world non-random number generator machine is processed into an entertainment gaming machine outcome. The entertainment gaming system and method of embodiments of the invention may be configured to collect non-random number based outcome from a real-world event and transmitting it to the entertainment gaming machine. The non-random number outcome from the real-world is processed into an entertainment gaming machine outcome event by the entertaining game machine processor or by the central server.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A platform for updating one or more properties of one or more digital twins including receiving a request for one or more digital twins; retrieving the one or more digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models corresponding to one or more properties that are depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating the one or more properties of the one or more digital twins based on the one or more outputs of the one or more dynamic models.

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

Abstract: 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.