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

Verona Holdings Sezc

Charles Howard Cella

Gerald William Duffy, Jr

Jeffrey P McGuckin

Mehul Desai

Andrew Cardno

Teymour S El-Tahry

Kunal Sharma

Brent Bliven

Andrew S Locke

Richard Spitz

Christine Soule

Jenna Lynn Parenti

Taylor D Charon

Brad Kell

Kevin Dowling

Ihor A Lys

George G Mueller

Edward J Kelly

Matthew P Vincent

Leon Fortin, Jr

Ralph F Osterhout

John D Haddick

Frederick Marshall Morgan

Robert Michael Lohse

Edward H Nortrup

Jenna Parenti

Robert J Nortrup

Sava Marinkovich

Joshua Dobrowitsky

Tracey Ho

John Segui

Alfred D Ducharme

Steven Jay Blumenthal

Chun Meng

Teymour El-tahry

Anwar Rohan

Andrew Bunin

David Stein

Nicholas Jordan Bain

Gregory Craig

David Robert Cass

Jason Rudder

Benjamin D Goodman

Hristo Malchev

John Patrick Keady

Russell A Gaudiana

Colin N Piepgras

William Edward Quigley

Jonathan Yantis

Lukasz Jakub Sliwka

Michael Blackwell

Brian Roberge

Jason Seldon

Kevin J Dowling

Bardia Keyvani

Eric P Vetter

Kabir Shukla

Luke Dallafior

Jeremy Zarowitz

Taylor Charon

Emily Cipes

Derek Logan

Josh Strauss

Daniel Patrick McGahn

Eric Roger Giler

Katherine Lavin Hall

Carol E Bramson

Hern Kim

Marney Prince

Elizabeth Ann Boh

Olivier Jean Claude Fischer

Stephen Paul Elias

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 system may include a non-transitory computer-readable storage medium storing instructions for execution and one or more processors that execute the instructions. The instructions may cause the one or more processors to configure at least one parameter of a crowdsourcing request related to obtaining information relating to a collateral for a loan, publish the crowdsourcing request related to obtaining the information relating to the collateral for the loan to a group of information suppliers, collect and process a response from an information supplier of the group of information suppliers, where the response includes information on a condition of the collateral for the loan, process the response provided by the information supplier to determine whether an information supply event relating to the response is successful, and respond to a determination of a successful information supply event.

Adaptive intelligence and shared infrastructure lending transaction enablement platform responsive to crowd sourced information

A dynamic vision system includes a variable focus liquid lens optical assembly. The dynamic vision system includes a variable lighting 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 control system configured to adjust the variable lighting assembly. 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 a set of machine learning models to control the variable focus liquid lens optical assembly to optimize collection of data for processing by the set of machine learning models.

Robotic vision system with variable lens for value chain networks

The present disclosure relates to systems and methods that leverage smart contracts and distributed ledgers, such as blockchains, to provide a trustless or substantially trustless ecosystem that includes distributed ledger crawler functions, advertising functions, risk mitigation for secondary sales of distributed ledger tokens, and various methods of acquiring and using distributed ledger tokens, among other uses.

Systems and methods for crawling and analyzing distributed ledger data

A method of configuring robot fleets with additive manufacturing capabilities includes receiving a request for a robotic fleet to perform a job and determining a job definition data structure based on the request. The job definition data structure defines a set of tasks to be performed in furtherance of the job. The method includes determining a provisioning configuration for each additive manufacturing system based on the task to which the additive manufacturing system is assigned, the set of 3D printing requirements, the printing instructions, and the status of the additive manufacturing system. The method includes provisioning the additive manufacturing system based on the provisioning configuration and a set of additive manufacturing system provisioning rules that are accessible to an intelligence layer to ensure that provisioned systems comply with the provisioning rules. The method includes deploying the robotic fleet based on the robotic fleet configuration data structure to perform the job.

Robotic fleet configuration method for additive manufacturing systems

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

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

Growing community of inventors

Pembroke, MA, United States of America

Charles Howard Cella