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: Systems and methods for transaction platforms include various systems interacting with each other and transacting in various ways. A method for configuring and launching a marketplace includes: identifying, by a processing system having one or more processors, an opportunity to facilitate configuration of a new marketplace; receiving marketplace opportunity data, wherein the marketplace opportunity data includes information related to a set of assets of one or more types; determining configuration parameters to be implemented in the new marketplace; determining the feasibility of implementing the configuration parameters in the new marketplace; determining data resources to support the new marketplace; determining an architecture of the new marketplace; determining the configuration of the data resources in a data model for the marketplace; configuring a marketplace object; connecting selected data resources to populate the marketplace object; and launching the new marketplace.

Abstract: Systems and methods for transaction platforms include various systems interacting with each other and transacting in various ways. A method for configuring and launching a marketplace includes: identifying, by a processing system having one or more processors, an opportunity to facilitate configuration of a new marketplace; receiving marketplace opportunity data, wherein the marketplace opportunity data includes information related to a set of assets of one or more types; determining configuration parameters to be implemented in the new marketplace; determining the feasibility of implementing the configuration parameters in the new marketplace; determining data resources to support the new marketplace; determining an architecture of the new marketplace; determining the configuration of the data resources in a data model for the marketplace; configuring a marketplace object; connecting selected data resources to populate the marketplace object; and launching the new marketplace.

Abstract: Systems and methods for transaction platforms include various systems interacting with each other and transacting in various ways. A method for configuring and launching a marketplace includes: identifying, by a processing system having one or more processors, an opportunity to facilitate configuration of a new marketplace; receiving marketplace opportunity data, wherein the marketplace opportunity data includes information related to a set of assets of one or more types; determining configuration parameters to be implemented in the new marketplace; determining the feasibility of implementing the configuration parameters in the new marketplace; determining data resources to support the new marketplace; determining an architecture of the new marketplace; determining the configuration of the data resources in a data model for the marketplace; configuring a marketplace object; connecting selected data resources to populate the marketplace object; and launching the new marketplace.

Abstract: Systems and methods for transaction platforms include various systems interacting with each other and transacting in various ways. A method for configuring and launching a marketplace includes: identifying, by a processing system having one or more processors, an opportunity to facilitate configuration of a new marketplace; receiving marketplace opportunity data, wherein the marketplace opportunity data includes information related to a set of assets of one or more types; determining configuration parameters to be implemented in the new marketplace; determining the feasibility of implementing the configuration parameters in the new marketplace; determining data resources to support the new marketplace; determining an architecture of the new marketplace; determining the configuration of the data resources in a data model for the marketplace; configuring a marketplace object; connecting selected data resources to populate the marketplace object; and launching the new marketplace.

Abstract: 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.

Abstract: Systems and methods for transaction platforms include various systems interacting with each other and transacting in various ways. A method for configuring and launching a marketplace includes: identifying, by a processing system having one or more processors, an opportunity to facilitate configuration of a new marketplace; receiving marketplace opportunity data, wherein the marketplace opportunity data includes information related to a set of assets of one or more types; determining configuration parameters to be implemented in the new marketplace; determining the feasibility of implementing the configuration parameters in the new marketplace; determining data resources to support the new marketplace; determining an architecture of the new marketplace; determining the configuration of the data resources in a data model for the marketplace; configuring a marketplace object; connecting selected data resources to populate the marketplace object; and launching the new marketplace.

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: 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 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.

Abstract: 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.

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

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

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

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

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

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

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

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