Strong Force Vcn Portfolio 2019, LLC

Verona Holdings Sezc

Brent Bliven

Charles Howard Cella

Kunal Sharma

Teymour S El-Tahry

Andrew Cardno

Joshua Dobrowitsky

Jonathan Yantis

William Edward Quigley

Sava Marinkovich

Brad Kell

Teymour El-tahry

Jason Seldon

Charles Cella

Jenna Parenti

Systems and methods for tokenizing real-world items for use in digital commerce are disclosed. In some embodiments, a digital token transaction system configures a set of attributes for a set of digital tokens that are cryptographically linked to a set of virtual representations of a set of items. The set of attributes includes at least one temporal attribute that defines a time after which redemption rights of the set of tokens for the items expire.

Configuring a set of digital tokens with a temporal attribute that determines a timing of redemption of the set of digital tokens for a corresponding set of items

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

Systems and methods for facilitating electronic transactions for real world items linked to digital tokens. The systems and methods leverage an item management system configured to provide an interface, generate virtual representations of real-world items, generate digital tokens, and generate links between the tokens and the virtual representations. The systems and methods further leverage an integration system configured to handle a request from a streaming platform, server an electronic advertisement corresponding to a real-world item, receive a transaction notification, and initiate an assignment of a digital token corresponding to the real-world item to an account.

Integrating cryptographic tokens representing real world items into media streams

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 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.

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Austin, TX, United States of America

Brent Bliven