Strong Force Vcn Portfolio 2019, LLC

Other

Andrew S Locke

Charles Howard Cella

Andrew Cardno

Teymour S El-Tahry

Jenna Parenti

Kunal Sharma

Brad Kell

Leon Fortin, Jr

Andrew Bunin

Eric P Vetter

Taylor Charon

Benjamin D Goodman

David Stein

Hristo Malchev

A raw material system includes a product manufacturing demand estimation system programmed to calculate an expected demand for a product at a future point in time. An environment detection system identifies at least one of an environmental condition or an environmental event. A raw material production system estimates a raw material availability at the future point in time based on the expected demand and the environmental condition/event. A raw material requirement system calculates a required raw material amount to manufacture the product at the future point in time based on the expected demand and the environmental condition/event. A raw material procurement system autonomously configures 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.

Demand-responsive raw material management system

A system for managing future costs associated with a product includes a future requirement system programmed to estimate an amount of resources required for manufacturing, distributing, and selling the product at a future point in time. The system includes an adverse contingency system configured to identify adverse contingencies and calculate changes in costs associated with obtaining the amount of resources at the future point in time. The system includes a smart contract system programmed to autonomously configure and execute a smart futures contract based on the amount of resources required and on the changes in costs to manage the future costs associated with the product.

Autonomous contingency-responsive smart contract configuration system

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 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

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 method includes causing, by the edge device, the query to be stored on a dynamic ledger maintained by the distributed database. The method includes detecting, by the edge device, that summary data has been stored on the dynamic ledger. The method includes generating, by the edge device, an approximate response to the query based on the summary data stored on the dynamic ledger. The method includes transmitting, to the query device, the approximate response.

Dynamic edge-distributed storage in value chain network

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 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-distributed query processing in value chain networks

A digital product network system includes a set of digital products each having a product processor, a product memory, and a product network interface. The digital product network system includes a product network control tower having a control tower processor, a control tower memory, and a control tower network interface. The product processor and the control tower processor collectively include non-transitory instructions that program the digital product network system to generate product level data at the product processor, transmit the product level data from the product network interface, receive the product level data at the control tower network interface, encode the product level data as a product level data structure configured to convey parameters indicated by the product level data across the set of digital products, and write the product level data structure to at least one of the product memory and the control tower memory.

Control tower encoding of cross-product data structure

An illumination device having a plurality of light emitting diodes is provided. The light emitting diode may include a plurality of semiconductor layers at least one of which has a light emitting surface which may include a rough surface pattern having a pre-determined pattern. The pre-determined pattern may include one or more impurity regions with each region having a recess for guiding current across the light emitting surface and maximizing the emission of light (i.e. light intensity) of the illumination device. Each recess may include a lower internal portion having a bottom contact point located on a bottom surface and an upper internal portion integrally connected to the lower internal portion by a plurality of center contact points. The gaps created between the center and bottom contact points in adjacent recesses may act as spark gaps allowing for the current to flow through the entire light emitting surface.

Growing community of inventors

Farmington, MI, United States of America

Andrew S Locke