New Bis Safe Luxco S.&#xe0; R.l

Strong Force Vcn Portfolio 2019, LLC

Strong Force Ee Portfolio 2022, LLC

New Bis Safe Luxco S.a R.l

Quick Custom Intelligence, LLC

Strong Force Tx Portfolio 2018, LLC

Strong Force Tp Portfolio 2022, LLC

New Bis Luxco S.&#xe0; R.l

New Bis Safe Luxco S.&#xe1;.r.l.

Andrew Cardno

Charles Howard Cella

Ashok Kumar Singh

Peter Stewart Ingham

Bart Andrew Lewin

Teymour S El-Tahry

Daniel Cardno

Brad Kell

Jenna Parenti

Ralph J Thomas

Ralph W Thomas

Kunal Sharma

Andrew S Locke

Brent Bliven

Leon Fortin, Jr

Paul Allan Cardno

Joshua Dobrowitsky

Taylor D Charon

Sava Marinkovich

Andrew Bunin

Eric P Vetter

Taylor Charon

David Stein

Hristo Malchev

Benjamin D Goodman

Mukesh Gordhan

Guillermo Quiros Manser

Jenna Lynn Parenti

Paul Valer'evich Sheliastovich

Matthew John Paul Simmonds

Babu Arunachalam

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 a machine learning/artificial intelligence managing sensor and the camera feeds into digital twin

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.

Variable focus liquid lens optical assembly for value chain networks

Systems and methods for controlling rights related to digital knowledge are disclosed. A sample system may include an input system to receive digital knowledge from a user, a tokenization system to tokenize the digital knowledge and a ledger management system to create, manage, and store things on a distributed ledger and provide provable access to the digital knowledge. A smart contract system may create a smart contract including triggering action is and respond with a defined smart contract action on an occurrence of the triggering event. The smart contract system may also process commitments to the smart contract.

Systems and methods for controlling rights related to digital knowledge

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.

Variable-focus dynamic vision for robotic system

A method for updating one or more properties of one or more transportation system digital twins includes receiving a request to update the one or more transportation system digital twins; retrieving the one or more transportation system digital twins to fulfill the request from a digital twin datastore; and retrieving one or more dynamic models to fulfill the request from a dynamic model datastore. The method further includes selecting data sources from a set of available data sources for one or more inputs for the one or more dynamic models; retrieving data from the selected data sources; running the one or more dynamic models using the retrieved data as input data to determine one or more output values; and updating the one or more properties of the one or more transportation system digital twins based on the one or more output values of the one or more dynamic models.

Digital twin systems and methods for transportation systems

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.

Machine-learning-driven supply chain out-of-stock inventory resolution and contract negotiation

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. The central server communicates with the real word non-random number outcome generator and entertainment gaming machine wherein the central server further transmit the processed outcome to the entertainment gaming machine to generate the game result requested by the triggering event on the entertainment gaming machine.

Entertainment gaming machine based on non-random outcomes

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.

Edge device query processing of distributed database

Disclosed herein are AI-based platforms for enabling intelligent orchestration and management of power and energy. In various embodiments, a machine learning system is trained on a set of energy intelligence data and deployed on an edge device, wherein the machine learning system is configured to receive additional training by the edge device to improve energy management. In some embodiments, the energy management includes management of generation of energy by a set of distributed energy generation resources, management of storage of energy by a set of distributed energy storage resources management of delivery of energy by a set of distributed energy delivery resources, management of delivery of energy by a set of distributed energy delivery resources, and/or management of consumption of energy by a set of distributed energy consumption resources.

Intelligent orchestration systems for energy and power management of edge devices

An AI-based platform for enabling intelligent orchestration and management of power and energy is provided herein. The AI-based platform includes a set of adaptive, autonomous data handling systems, wherein each of the adaptive, autonomous data handling systems is configured to collect data relating to energy generation, energy storage, energy delivery, and/or energy consumption, wherein the data is collected from a set of edge devices that are in operational control of a set of distributed energy resources; and a set of intelligent agents configured to, by robotic process automation, autonomously adjust, based on the collected data, a set of operational parameters for such operational control.

AI-based energy edge platform, systems, and methods having a robotic process automation system

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.

Robot fleet resource configuration in value chain networks

An AI-based platform for automated labor law compliance associated with mining operations is disclosed. The AI-based platform includes a governance system for a mining operation and a reporting system for conveying at least one parameter that is sensed by a sensor of a mine of the mining operation, wherein the at least one parameter is associated with a compliance of the mining operation with a set of labor standards. In some embodiments, the set of labor standards is associated with at least one activity performed by a laborer of the mine, and the AI-based platform conveys an indication of a performance of the activity by the laborer. In some embodiments, the set of labor standards is associated with at least one object associated with a laborer of the mine, and the AI-based platform conveys an indication of a detection of the at least one object.

Ai-based platform for automated labor law compliance associated with mining operations

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.

Value chain network planning using machine learning and digital twin simulation

An AI-based platform for enabling intelligent orchestration and management of power and energy is provided herein. The AI-based platform includes a digital twin system including a plurality of digital twins of energy operating assets, the plurality of digital twins of energy operating assets including at least one energy generation digital twin, energy storage digital twin, energy delivery digital twin, and/or energy consumption digital twin, and a set of energy simulation systems configured to generate a simulation of energy-related behavior of at least one of the plurality of digital twins of energy operating assets, and a machine-learning system configured to generate a predicted state of at least one of the energy operating assets. The simulation of energy-related behavior is based on historical patterns, current states, and the predicted state of at least one of the energy operating assets.

AI-based energy edge platform, systems, and methods having a digital twin of decentralized infrastructure

An AI-based platform for enabling intelligent orchestration and management of power and energy is disclosed. The platform includes a system configured to perform automated and coordinated governance of a set of energy entities that are operationally coupled within an energy grid and a set of distributed edge energy resources. At least one of the distributed edge energy resources is operationally independent of the energy grid.

Growing community of inventors

San Diego, CA, United States of America

Andrew Cardno