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 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: 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 platform for facilitating development of intelligence in an Industrial Internet of Things (IIoT) system generally includes a plurality of distinct data-handling layers comprising an industrial monitoring systems layer that collects data from or about a plurality of industrial entities in the IIoT system; an industrial entity-oriented data storage systems layer that stores the data collected by the industrial monitoring systems layer; an adaptive intelligent systems layer that provisions available computing resources within the platform; and an industrial management application platform layer that manages the platform in a common application environment.

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: 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 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.

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 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 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: An information technology platform for personalizing an experience of a visitor at a non-profit venue comprising: a network; a server in communication with the network; a mobile computing device to be carried by a visitor to a non-profit venue, wherein the platform determines personalized information about the visitor and a recommendation engine outputs a suggestion for a personalized experience to the visitor to encourage philanthropic activity related to an ongoing philanthropic campaign; and a machine learning system for learning on a training set of photographic data or video data, the learning to train the machine learning system to predict a sentiment towards the ongoing philanthropic campaign of the visitor based, at least in part, on photographic images or video images that are taken by the visitor, that are shared to the visitor, that are shared by the visitor or that include the visitor as a subject.

Abstract: An information technology platform for personalizing an experience of a visitor at a non-profit venue comprising: a network; a server in communication with the network; a mobile computing device to be carried by the visitor, the mobile computing device in communication with the server via the network, wherein the information technology platform determines personalized information about the visitor; a recommendation engine associated with the server, wherein the recommendation engine is to receive the personalized information about the visitor and to output a suggestion for a personalized experience for the visitor, wherein the information technology platform conveys the suggestion for the personalized experience to the visitor, wherein the personalized experience is to encourage philanthropic activity; and a social networking system for digital communication between the visitor and other visitors, and for sharing information or media related to the non-profit venue or to a philanthropic campaign associated wi

Abstract: According to some embodiments of the present disclosure, a method for recommending pet food for a pet is disclosed. The method includes receiving pet information corresponding to the pet from a client user device of a user associated with the pet and generating a set of attributes relating to the pet based on the pet information. The method further includes determining a temperature classification corresponding to the pet based on the set of attributes and determining a recipe score corresponding to the pet based upon the temperature classification and the set of attributes. The method further includes determining a pet food recommendation from a pet product database based on the temperature classification, and providing a diet recommendation indicating the pet food recommendation the user via a communication network.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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.