International Business Machines Corporation

Kyndryl, Inc.

Optum, Inc.

Unitedhealth Group Incorporated

Optum Technology, Inc.

Globalfoundries Inc.

Servicenow, Inc.

Optum Services (ireland) Limited

Other

Hulu, LLC

Internationla Business Machines Corporation

Gregory J Boss

Rick A Hamilton, Ii

John Elbert Moore, Jr

Kevin Charles Mcconnell

Andrew R Jones

Kulvir Singh Bhogal

Anne R Sand

Brian M O'Connell

Timothy Moffett Waters

Christopher J Dawson

Jeremy R Fox

Charles Steven Lingafelt

Tamer E Abuelsaad

Jason L Anderson

Michael Bender

James W Seaman

Randy Allan Rendahl

Clifford A Pickover

Alexandre Polozoff

Peter George Finn

James R Kozloski

Julianne Frances Haugh

Keith R Walker

Sarbajit Kumar Rakshit

Edgar Adolfo Zamora Duran

Ira L Allen

Jeffrey Robert Hoy

Kelly Abuelsaad

Minh Q Pham

Yen-Fu Chen

Cesar Augusto Rodriguez Bravo

Katalin Klara Bartfai-Walcott

Luis Carlos Cruz Huertas

Craig M Trim

Jeffrey David Amsterdam

John Michael Ganci, Jr

James R Doran

Rhonda L Childress

Adekunle Bello

Ninad D Sathaye

Charles Evan Beller

Romelia H Flores

Brian Daniel Goodman

Christian Compton

P Daniel Kangas

Kelley Anders

Swaminathan Balasubramanian

Soobaek Jang

Shubhadip Ray

John Steven Langford

Lisa Seacat DeLuca

Lawrence Alfred Clevenger

Liam S Harpur

Guy M Cohen

Maharaj Mukherjee

Zachary A Silverstein

Nitin Gaur

David Jaramillo

Dhandapani Shanmugam

Paul N Krystek

Barry Michael Graham

Mary Swift

Siddique M Adoni

Norbert Herman

Martin G Keen

Robert Beverley Basham

Carl Phillip Gusler

Robert Ross Peterson

Jacquelyn Annette Martino

Vishal Chittranjan Aslot

Daniel Everett Jemiolo

Hari Shankar

James Edward Bostick

Aaron K Baughman

Shikhar Kwatra

Ruthie D Lyle

William Kress Bodin

Itzhack Goldberg

Trudy L Hewitt

Pamela Ann Nesbitt

David Bruce Lection

Neil Sondhi

Mark Gregory Megerian

Michael D Kendzierski

Kerrie Lamont Holley

Agueda Martinez Hernandez Magro

Dana Lea Price

Lydia Mai Do

Jenny S Li

John David Wilson

Chung-Sheng Li

Neil A Katz

Yin Xia

Jianying Hu

Vincenzo Valentino DiLuoffo

Youssef Drissi

Natalie Brooks Powell

Robert R Wentworth

Ori Pomerantz

Barton Wayne Emanuel

A controller for an artificial heart enables activity-specific adjustments to the operation of an artificial heart by obtaining sensor data from a plurality of sensors monitoring characteristics of a patient's body, and using the sensor data as input to one or more control parameter models for identifying control parameters to be provided to the artificial heart to adjust the operational parameters of the artificial heart. The controller is in wireless communication with the artificial heart via an application program interface (API)-based communication channel that facilitates communication between the controller and the artificial heart. Moreover, a cloud-based management computing entity may be utilized to train and/or execute one or more models to enable real-time updates to the operational characteristics of the artificial heart to enable the artificial heart to appropriately accommodate activities of the patient.

Artificial heart control systems and methods

Various embodiments of the present disclosure provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for detecting and preventing seizure events in real-time. For example, various embodiments provide techniques for detecting and preventing seizure events in real-time that use seizure prediction machine learning models.

Seizure prediction machine learning models

To schedule a future interaction between a caller and a call-center, a management computing system associated with the call center is configured to determine an estimated call time for addressing the caller's issues, based at least in part on a predicted call intent as well as feature enrichment data received from one or more memory storage areas. Upon determining a predicted call duration for the call, the management computing entity accesses the caller's calendar, such as through an internet-based communication between the caller's user computing entity and the management computing entity, or via third-party access permissions provided by the caller. The management computing entity identifies one or more candidate timeslots based at least in part on the predicted call duration, and receives user input selecting a candidate timeslot for scheduling the callback.

Computer systems and computer-based methods for automated callback scheduling utilizing call duration prediction

An automated system and corresponding method is configured to predict a call duration of a customer service interaction between a caller and a customer-service agent of a call center, based at least in part on information provided orally by the caller to the automated system. The automated system transcribes the orally provided information, preprocesses the transcribed data, adds feature enrichment data to supplement the transcribed data, and executes a machine-learning model to predict the caller's intent. If the predicted caller's intent does not have an adequate confidence score associated therewith, the system requests additional data from the caller, and supplements the original data with newly provided data, and again determines a predicted call intent. This process may iterate until the confidence score satisfies applicable confidence criteria prior to utilizing two additional machine-learning models to predict a call duration of the interaction between the caller and a customer-service agent.

Computer systems and computer-based methods for automated caller intent prediction

Various embodiments of the present disclosure provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for monitoring a user's movement in real-time and providing or augmenting stimulation. For example, various embodiments provide techniques generating movement prediction profiles using movement prediction machine learning models and for use in conjunction with wearable devices.

Movement prediction machine learning models

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing optimized equipment allocation. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform optimized equipment allocation using at least one of equipment evaluation scoring machine learning models, equipment optimized allocation machine learning models, and equipment history events.

Machine learning techniques for optimized equipment allocation

Aspects identify users as trusted assistant candidates in response to associations to purchases of vehicle repair items. Embodiments further select a one of the trusted assistant candidates that is linked to a vehicle operator as a social network contact and that is located closest to a vehicle location relative to remaining others of the trusted assistant candidates, in response to determining that the vehicle operator needs remote location assistance at the vehicle location; and dispatch the selected one of the trusted assistant candidates to the vehicle location to provide remote location assistance to the vehicle operator.

Dynamic trusted assistance network

A computing device may receive, from a remote computing device, a request to establish a communication session with the remote computing device and contextual information indicative of an urgency level of the communication session. The computing device may output, at one or more output devices, the contextual information indicative of the urgency level of the communication session and at least one of: haptic output or an audible alert that indicates the request to establish the communication session.

Providing health urgency context for incoming calls

A method comprising: monitoring, by a computing system, a callee device for missed phone calls from a caller; determining, by the computing system, based on a behavioral history of the callee, callee health parameters of the callee, and a number of the missed phone calls from the caller, whether an unresponsiveness condition is satisfied; and based on a determination that the unresponsiveness condition is satisfied and in response to a missed phone call from the caller, causing a caller device to present callee health data to the caller.

Vulnerable callee monitoring system

Systems and methods are configured to perform machine-learning-based predictive behavioral response. In various embodiments, one or more behavioral monitoring data objects are identified and processed using a behavioral pattern prediction machine learning model to generate a behavioral pattern prediction model. The behavioral pattern prediction model is processed using a risk generation machine learning model to generate a risk model, wherein: (i) the risk generation machine learning model is generated based at least in part by one or more risk factors, and (ii) the risk model comprises a per-risk factor score for each risk factor of the one or more risk factors. The risk model is processed using an adjustment generation machine learning model to generate an adjustment model and one or more prediction-based actions are performed based on the adjustment model.

Machine-learning-based predictive behavioral monitoring

An example method includes obtaining first audio data representing one or more initial utterances during an interactive voice session with an interactive voice system (IVS); generating, based on the first audio data, a prediction regarding whether a subsequent utterance of the user in the interactive voice session will contain sensitive information; obtaining second audio data representing the subsequent utterance; determining, based on the prediction, whether to transmit the second audio data to the IVS via a first communication channel; based on a determination not to transmit the second audio data to the IVS via the first communication channel: transmitting third audio data to the IVS via the first communication channel in place of the second audio data; and transmitting the second audio data to a server via a second communication channel that bypasses the IVS.

Routing of sensitive-information utterances through secure channels in interactive voice sessions

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing (NLP) operations on multi-segment documents. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform NLP operations on multi-segment documents by generating document segmentation machine learning models, using document segmentation machine learning models to determine document segments of input multi-segment documents, enabling adaptive multi-segment summarization of multi-segment documents, and enabling guided interaction with multi-segment documents.

Natural language processing operations performed on multi-segment documents

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive prioritization. Certain embodiments utilize systems, methods, and computer program products that perform predictive prioritization using a combination of supervised machine learning models and unsupervised machine learning models that are in turn used to generate target features for a resultant prioritization machine learning model.

Machine learning techniques for predictive prioritization

An example method includes obtaining, by processing circuitry, an image depicting a face of a person wearing eyewear and a reflection pattern generated by projecting light onto a lens of the eyewear and determining, by the processing circuitry, an optical parameter of the lens based on the image. The method further includes receiving, by the processing circuitry and from an electronic database, a personal characteristic data relating to a previously identified person and comparing, by the processing circuitry, the optical parameter of the lens to the personal characteristic data. The method further includes determining, by the processing circuitry and based on the comparison, a score indicative of a match between the person wearing eyewear and the previously identified person.

Authentication and facial recognition through analysis of optometric prescription data

An example method includes obtaining, by a computing system, first audio data representing one or more initial utterances during an interactive voice session with an interactive voice system; generating, by the computing system, based on the first audio data, a prediction regarding whether a subsequent utterance of a user in the interactive voice session will contain sensitive information, wherein the subsequent utterance follows the one or more initial utterances in time; obtaining, by the computing system, second audio data representing the subsequent utterance; determining, by the computing system, based on the prediction and based on a risk profile of the interactive voice system, whether to transmit the second audio data to the interactive voice system; and based on the determination to transmit the second audio data to the interactive voice system, transmitting the second audio data to the interactive voice system.

Cybersecurity for sensitive-information utterances in interactive voice sessions using risk profiles

Growing community of inventors

Saginaw, MI, United States of America

Gregory J Boss