Strong Force Vcn Portfolio 2019, LLC

Jpmorgan Chase Bank, N.a.

Kunal Sharma

Charles Howard Cella

Teymour S El-Tahry

Brent Bliven

Andrew Cardno

Brad Kell

Andrew S Locke

Sava Marinkovich

Jenna Parenti

Leon Fortin, Jr

Joshua Dobrowitsky

Benjamin D Goodman

Hristo Malchev

Andrew Bunin

Eric P Vetter

Taylor Charon

David Stein

Ravi Pasupuleti

Shariq Javed

Pushkar Deshpande

Harsha Ravella

Nikhil Patkar

Sriram Balasubramaniam

Ashok Jha

Vini Pandya

Charles Hannum, Iv

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

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

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.

Adaptive additive manufacturing for value chain networks

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

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.

Distributed-ledger-based manufacturing for value chain networks

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.

AI-managed additive manufacturing for value chain networks

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

Various methods, apparatuses/systems, and media for data migration readiness of a target data source are disclosed. A processor invokes, in response to triggering a process, an application programming interface (API) to call corresponding source application or microservice hosted on a source database; updates, by the source application or microservice, the source database to reflect the changes made to one or more fields data of user profile; generates a mapping identifier (ID) in response to updating the source database; stores the mapping ID onto a mapping database in a predefined format; invokes another API call to obtain source table field details of the updated source database and the mapping ID that maps source column of the source table to a corresponding target column of a target table of a target database; and automatically updates the target database to match the updated data of the source database based on the mapping ID.

System and method for microservices to monolith—data syncback solution

Growing community of inventors

Mumbai, India

Kunal Sharma