International Business Machines Corporation

Boston Scientific Neuromodulation Corporation

Slingshot Iot LLC

Maplebear Inc.

University of Illinois

Activision Publishing, Inc.

Doordash, Inc.

Granite Vehicle Ventures LLC

James R Kozloski

Clifford A Pickover

Michael S Gordon

Ashish Kundu

Peter Kenneth Malkin

Rick A Hamilton, Ii

Brian M O'Connell

Maja Vukovic

Irina Rish

Neal Martin Keller

Lorraine M Herger

Guillermo A Cecchi

Matthew A McCarthy

Timothy Michael Lynar

Gregory J Boss

Keith R Walker

John Michael Wagner

Komminist S Weldemariam

Andrew Paul Wyskida

John Jeremy Rice

Aaron K Baughman

Anne R Sand

Jeremy A Greenberger

Kevin W Brew

Anup Kalia

Mark C H Lamorey

Valentina Salapura

Jeffrey L Rogers

Sara E Berger

Lisa Seacat DeLuca

Guy M Cohen

Justin David Weisz

Itzhack Goldberg

Dimitri Kanevsky

Jonathan Lenchner

Tara N Sainath

Shikhar Kwatra

James W Seaman

Neil Sondhi

Robert William Wisniewski

Kulvir Singh Bhogal

Rhonda L Childress

Robert G Farrell

Justin Gregory Manweiler

Umut Topkara

Suraj Pandey

Boaz Mizrachi

Ajay K Royyuru

Maryam Ashoori

Sarbajit Kumar Rakshit

Christopher J Dawson

Rogerio Schmidt Feris

George Frederick Walker

Thomas E Chefalas

James W Murdock, Iv

Sasha P Caskey

Jason L Crawford

Rosanna Mannan

Mauro Marzorati

John Maxwell Cohn

Rahil Garnavi

Jinjun Xiong

Julianne Frances Haugh

Minkyong Kim

Barry Michael Graham

Jose Roberto Mosqueda Mejia

Michael Peter Perrone

Ravishankar Rao

Elham Khabiri

Agueda Martinez Hernandez Magro

James R Doran

Jill S Dhillon

Oded Dubovsky

Devendra K Sadana

Stephen W Bedell

Fang Lu

Kevin Charles Mcconnell

Alejandro Gabriel Schrott

Douglas Morgan Freimuth

Levente Klein

John Alan Bivens

Sara H Basson

Edgar Adolfo Zamora Duran

Jeffrey Owen Kephart

Brian Paul Gaucher

Anuradha Bhamidipaty

Daby Mousse Sow

Kenney Ng

John F Behnken

Ninad D Sathaye

Ruhong Zhou

Dario Gil

Stephen John Heisig

Ravi Tejwani

David O S Melville

Stacy Fay Hobson

Marco Cavallo

Pablo Meyer Rojas

Wendy Anne Kellogg

Alan Michael Webb

Techniques regarding inferring parameters of one or more mechanistic models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a machine learning component that can identify a causal relationship in a mechanistic model via a machine learning architecture that employs a parameter space of the mechanistic model as a learned distribution sampled within a generative adversarial network.

Mechanistic model parameter inference through artificial intelligence

Systems and techniques are disclosed to establish programming of an implantable electrical neurostimulation device for treating pain of a human subject, through the use and adjustment of analgesic stimulation parameters based on trust dynamics and trust measurements. In an example, the system to establish programming of the neurostimulation device performs operations that: determine a trust measurement value that is derived from results of at least one commitment made with a human subject, via observable interactions; determine a modification of at least one neurostimulation programming parameter, based on the trust measurement value; and to cause the implantable neurostimulation device to implement the modification of the at least one neurostimulation programming parameter. Further examples are provided to produce and track the trust measurement value, as well as identify a pain susceptibility value and determine a receptiveness to analgesic effects based on these and other trust dynamics.

Adjustment of analgesic stimulation parameters based on trust dynamic measurements

A method, a structure, and a computer system for digital twin management. The exemplary embodiments may include gathering data corresponding to one or more bodily tissues and generating one or more digital twin versions of the one or more bodily tissues. In addition, the exemplary embodiments may further include identifying a most appropriate digital twin version of the one or more digital twin versions based on a health and a compatibility of the one or more digital twin versions, and distributing the most appropriate digital twin version.

Digital twin manager

A system and method that allows use of a product to review its users and generates a report summarizing how closely the product has been used according to its intended use. The product includes on-board and associated I-o-T sensors to differentiate between different users of the same product and provide data correlating usage how the product is used by each user. The system and method provide recommendations regarding how a typical behavior of each user can change during its use to optimize a product's its function, lifespan, efficiency. The report additionally serves to evaluate how effective each user is at rating the product by creating a feedback evaluation report and reliability score for each user, giving weight to basically all product reviews that the user may have submitted on other products. All customer reviews submitted online by a given user can be linked, for example by username, IP address, etc.

User feedback system on product usage

Techniques that facilitate altering a targeted brain therapeutic are provided. In one example, a system determines parameter data associated with a circuit model of a biological brain. The system also simulates the circuit model based on the parameter data to generate treatment data associated with the biological brain.

Altering a targeted brain therapeutic based on a brain circuit model

Techniques for facilitating the autonomous presentation of a self-driving vehicle are provided. In one example, a method can include a system operatively coupled to a processor, where the system: determines a feature of a self-driving vehicle based on information regarding an entity in a pending transaction; determines a task to be performed by the self-driving vehicle based on the feature; and generates an instruction for the self-driving vehicle to perform the task.

Autonomous presentation of a self-driving vehicle

A computer-implemented method, system, and/or computer program product controls a driving mode of a self-driving vehicle (SDV). One or more processors compare a control processor competence level of an on-board SDV control processor in controlling the SDV to a human driver competence level of a human driver in controlling the SDV while the SDV encounters a current roadway condition which is a result of current weather conditions of the roadway on which the SDV is currently traveling. One or more processors then selectively assign control of the SDV to the SDV control processor or to the human driver while the SDV encounters the current roadway condition based on which of the control processor competence level and the human driver competence level is relatively higher to one another.

Controlling driving modes of self-driving vehicles

An example operation may include one or more of receiving sensor data from one or more sensors associated with a building, storing a block including the sensor data to a shared ledger of a blockchain network, the one or more sensors associated with one or more buildings, requesting a risk assessment for the sensor data, by a blockchain node, calculating the risk assessment with one or more machine learning algorithms based on the sensor data, historical sensor blockchain data, importance of the one or more sensors, and a degree of concern related to the sensor data, providing the risk assessment to the blockchain node, and generating an alert in response to the risk assessment above a threshold.

Sensor risk assessment database

A moving target service receives path of locations of a moving target monitored by a navigation device. The moving target service calculates at least one optimized course for at least one user to follow to intersect with the moving target at one or more future locations predicted based on the path of the locations and based on at least one context record. The moving target service outputs the at least one optimized course to a display interface accessible to the at least one user for directing the user to navigate from a starting point to at least one of the one or more future locations.

Navigating to a moving target

A queue management system including a user data retrieving circuit configured to retrieve a user property of the user and a queue managing circuit configured to model the plurality of users as a non-Newtonian fluid, to analyze the user properties to assign each user an anisometric polarity parameter, and to solve an objective function for a queue property to cause the plurality of users to collectively act as the non-Newtonian fluid in the queue.

Cognitive and contextual queue management

A risk management method, system, and non-transitory computer readable medium, include a data analyzing circuit configured to analyze user data, site data, and equipment data to map prior behavior types to an event on a site, a relationship determining circuit configured to determine a relationship between the mapped data and the event based on behaviors exhibited by the user and an impact on a performance factor and a risk factor, and a reinforcement learning circuit configured to use reinforcement learning to learn the performance factor to the risk factor ratio to optimize an overall site productivity.

Risk optimization through reinforcement learning

A computer-implemented method for capturing a user constructed map of bodily region of interest for remote telemedicine navigation includes receiving, by a user-device, a request for capturing data for constructing a model of the bodily region using a designated capture-device. The method further includes generating, by the user-device, a visual feedback for capturing the data. Generating the visual feedback includes displaying a representation of the bodily region, and modulating, in response to the capture-device scanning the bodily region, the representation with a first indicator that indicates a duration to maintain a position of the capture-device. Generating the visual feedback includes further modulating, in response to completion of scanning the bodily region, the representation with a second indicator that indicates completion of the scan. The method further includes constructing, by the user-device, the model of the bodily region using data that is acquired by the capture-device by scanning the bodily region.

Capturing user constructed map of bodily region of interest for remote telemedicine navigation

Growing community of inventors

New Fairfield, CT, United States of America

James R Kozloski