Strong Force Iot Portfolio 2016, LLC

Strong Force Tx Portfolio 2018, LLC

Strong Force Tp Portfolio 2022, LLC

Strong Force Vcn Portfolio 2019, LLC

Strong Force Ee Portfolio 2022, LLC

Olive Seed Industries, LLC

The Ticket Reserve, Inc.

Microsoft Technology Licensing, LLC

Philips Solid-state Lighting Solutions, Inc.

Strong Force Intellectual Capital, LLC

Color Kinetics, Inc.

Energywell Technology Licensing, LLC

Merck Patent Gmbh

Staton Techiya, LLC

Habi, Inc.

Charles Howard Cella

Gerald William Duffy, Jr

Jeffrey P McGuckin

Mehul Desai

Andrew Cardno

Teymour S El-Tahry

Andrew S Locke

Kunal Sharma

Jenna Lynn Parenti

Taylor D Charon

Richard Spitz

Christine Soule

Kevin Dowling

Brent Bliven

Ihor A Lys

George G Mueller

Edward J Kelly

Matthew P Vincent

Brad Kell

Jenna Parenti

Ralph F Osterhout

John D Haddick

Frederick Marshall Morgan

Robert Michael Lohse

Edward H Nortrup

Leon Fortin, Jr

Robert J Nortrup

Tracey Ho

John Segui

Alfred D Ducharme

Steven Jay Blumenthal

Chun Meng

Sava Marinkovich

John Patrick Keady

Russell A Gaudiana

Colin N Piepgras

Michael Blackwell

Brian Roberge

Derek Logan

Kevin J Dowling

Joshua Dobrowitsky

Andrew Bunin

Anwar Rohan

Eric P Vetter

Taylor Charon

Bardia Keyvani

David Stein

Kabir Shukla

Nicholas Jordan Bain

Gregory Craig

David Robert Cass

Luke Dallafior

Jason Rudder

Benjamin D Goodman

Hristo Malchev

Jeremy Zarowitz

Emily Cipes

Teymour El-tahry

Josh Strauss

Daniel Patrick McGahn

Hern Kim

Carol E Bramson

Marney Prince

Elizabeth Ann Boh

Olivier Jean Claude Fischer

Stephen Paul Elias

Eric Roger Giler

Katherine Lavin Hall

Vijeesh Thattante Valappil

Vimal M Gopal

Thaher Majeed

Nithin Mohan

Riswin Mangarathayyil Habeebulla

Praveen Shridhar

Nabeel Km

Sanoj Sen Tp

Jr Gerald William Duffy

George G Müller

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 value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.

Control tower and enterprise management platform with robotic process automation systems managing product outcomes and activities

A method for providing personalized non-profit venue visit recommendations to a visitor at a non-profit venue, comprising: providing a management interface for management of a set of multimedia assets; receiving a mapping of multimedia assets to a display in at least one site plan; receiving metadata for the set of multimedia assets; providing an interface for receiving data indicating personal interests of a visitor, including visitor-provided preference data and passively-collected visitor interaction data; receiving personal interest data for a plurality of non-profit venue visitors; receiving preference data relating to a prospective visitor; applying a machine learning system to analyze the metadata, the data indicating personal interests of the visitor, the personal interest data for the plurality of non-profit venue visitors, and the preference data relating to the prospective visitor; and generating a selection or sequence of non-profit venue location recommendations for the prospective visitor.

Methods and systems for personalizing a prospective visitor experience at a non-profit venue

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

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

Transaction-enabled methods for providing provable access to a distributed ledger with a tokenized instruction set for polymer production processes are described. A method may include accessing a distributed ledger comprising an instruction set for a polymer production process and tokenizing the instruction set. The method may further include interpreting an instruction set access request and providing provable access to the instruction set. The method may further include providing commands to a production tool of the polymer production process and recording the transaction on the distributed ledger.

Smart contract management of licensing and apportionment using a distributed ledger

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

Systems and methods for forward market purchase of machine resources using artificial intelligence are disclosed. An example transaction-enabling system may include a fleet of machines, each one of the fleet of machines having a resource requirement comprising at least one of a plurality of machine-related resources. The system may further include a controller including an artificial intelligence (AI) circuit to aggregate data for the plurality of machine-related resources from at least one data source comprising an external data source or an internal data source; an expert system circuit to configure a purchase of at least one of the plurality of machine-related resources; and a machine resource acquisition circuit to automatically solicit the configured purchase of the at least one of the plurality of machine-related resources in a forward market for at least one resource of the plurality of machine-related resources.

Systems and methods for forward market purchase of machine resources using artificial intelligence

A method of configuring a robot of a fleet of robots for use of an AI chipset includes receiving a request for a robotic fleet to perform a job. The method includes defining a set of tasks that are to be performed by the robotic fleet in performance of the job. The method includes assigning at least one task of the set of tasks to a robot. The method includes determining a configuration for the robot based on the assigned task and a components inventory that indicates different components that can be provisioned to the robot including at least one AI chipset, and for each component, a set of extended capabilities and a status of the component. The method includes configuring the robot based on the determined configuration to use the at least one AI chipset. The method includes deploying the robotic fleet to perform the job.

Robot fleet management configured for use of an artificial intelligence chipset

Systems and methods for forward market purchase of attention resources are disclosed. An example transaction-enabling system may include a fleet of machines and a controller. The controller may include an artificial intelligence (AI) circuit to aggregate data for a plurality of attention-related resources, wherein each one of the plurality of attention-related resources are related to a task of the fleet of machines, an expert system circuit to configure a purchase of at least one of the plurality of attention-related resources on an attention market, and an attention acquisition circuit to automatically solicit the configured purchase of the at least one of the plurality of attention-related resources in a forward market for the at least one of the plurality of attention-related resources.

Systems and methods for forward market purchase of attention resources

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 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

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Pembroke, MA, United States of America

Charles Howard Cella