International Business Machines Corporation

Kyndryl, Inc.

Wayfair LLC

Nuance Communications, Inc.

Airbnb, Inc.

Daedalus Blue LLC

Aaron K Baughman

Mauro Marzorati

Brian M O'Connell

Shikhar Kwatra

Craig M Trim

Stephen Carl Hammer

Todd Russell Whitman

Diwesh Pandey

Gray Franklin Cannon

Nicholas Albert McCrory

Gary Francis Diamanti

Peter Kenneth Malkin

Barry Michael Graham

Christian Eggenberger

John P Perrino

Micah Forster

Sarbajit Kumar Rakshit

David C Bastian

James R Kozloski

Thomas Benjamin Harrison

Mohamed Nooman Ahmed

Christopher J Dawson

Martin G Keen

Garfield Winston Vaughn

Palani Sakthi

Rick A Hamilton, Ii

John F Behnken

Herbert Daniel Pearthree

Brian James Snitzer

Christopher Eythan Holladay

Ahmed M Nassar

Moncef Benboubakeur

John Michael Ganci, Jr

Michael Bender

Jeremy R Fox

Hernan A Cunico

Timothy Michael Lynar

John Michael Wagner

Mary E Rudden

Clifford A Pickover

Cesar Augusto Rodriguez Bravo

Raghuveer Prasad Nagar

Sathya Santhar

Blaine H Dolph

Malolan Chetlur

Aditya Anand Kalyanpur

Jeffrey David Amsterdam

Linda M Boyer

Marc Yvon

Gregory J Boss

Valentina Salapura

Kulvir Singh Bhogal

Vijay Ekambaram

Gandhi Sivakumar

Dong Chen

Charles Evan Beller

Marco Aurelio Stelmar Netto

Qiqing Christine Ouyang

Robert G Farrell

Aaron Roger Cox

Kimberly Greene Starks

Louis Bennie Capps, Jr

Tiberiu (Jim) Suto

Boaz Mizrachi

Arun Joseph

Michael Man Behrendt

Brenda Lynn Dietrich

David W Buchanan

Oded Dubovsky

James Edward Bostick

Fang Lu

Zachary A Silverstein

John David Wilson

Joseph N Kozhaya

Vinod A Valecha

Sreekanth Ramakrishna Iyer

Brian Paul Gaucher

Hemant Kumar Sivaswamy

Nader M Nassar

Rudolf Maarten Bolle

Guillermo A Cecchi

Uri Kartoun

James J Fan

Vagner Figueredo De Santana

Prasenjit Dey

Brian Frederick Ober

Gopal Sarma Pingali

Victor Povar

Nancy Anne Greco

Ann Corrao

Natalie Brooks Powell

Phani Kumar V U Ayyagari

Indervir Singh Banipal

William G Dubyak

Sherif Ahmed Goma

Madan K Chukka

Yaser K Doleh

Guy Feigenblat

Tamer M Nassar

Iwao Hatanaka

Using a classical data model executing on a classical processor, a set of classical features is scored. A classical feature comprises a first attribute of a resource, and a score of the classical feature comprises an evaluation of a utility of the classical feature in predicting a result involving the resource. Using a quantum data model executing on a quantum processor and the scored set of classical features, a set of quantum features is scored. The scored set of classical features and the scored set of quantum features are correlated, forming a combined set of scored features. Using the combined set of scored features and a first set of input data of a resource, a valuation of the resource is calculated.

Combined classical/quantum predictor evaluation

Using a model executing on a classical processor, a set of classical features is scored. The scored set of classical features is divided into a set of feature groups, a number of classical features in a group determined according to a qubit capability of a quantum processor. Using a model executing on the quantum processor and a group of the scored set of classical features, a set of quantum features is scored. The score of a quantum feature is adjusted according to an accuracy of the quantum data model. The scored set of classical features and the scored set of quantum features are combined according to a measure of differences between the scored set of classical features and the scored set of quantum features. Using the combined set of scored features and a first set of input data of a resource, a valuation of a resource is calculated.

Combined classical/quantum predictor evaluation with model accuracy adjustment

An embodiment includes identifying, from among the plurality of digital content datasets, a set of candidate textual items based on relevance to a specified subtopic using one or more natural language processing techniques. The embodiment groups candidate textual items into a predetermined number of groups using relevance scores and feature vectors. The embodiment trains a pre-trained encoder-decoder model using a designated group of selected textual items, where the pre-trained encoder-decoder model is pretrained to generate textual content according to a particular style of writing. The embodiment generates, using the pre-trained encoder-decoder model, machine-authored textual content in the particular style of writing resulting in an article about the specified subtopic based on the designated group of selected textual items. The embodiment also includes transmitting the article to a remote web server as an update for a website.

Elucidated natural language artifact recombination with contextual awareness

Using a classical data model executing on a classical processor, a set of classical features is scored. A score of a classical feature comprises an evaluation of a utility of the classical feature in predicting a result involving a resource. Using a quantum data model executing on a quantum processor and the scored set of classical features, a set of quantum features is scored. The quantum data model is executed a number of times previously determined using a set of results of executing the quantum data model on a set of annotated training data. The scored set of classical features and the scored set of quantum features are correlated, forming a combined set of scored features. Using the combined set of scored features and a first set of input data of a resource, a valuation of the resource is calculated.

Identifying related messages in a natural language interaction in multiple iterations

An embodiment of the invention may include a method, computer program product, and computer system for managing a machine learning algorithm. The embodiment may include a computing device that distributes a first algorithm to a plurality of computing devices. The embodiment may include updates to the first algorithm, to create a second algorithm, by a first device of the plurality of computing devices. The first device is grouped with other devices in a first cluster of devices of the plurality of computing devices. The first cluster of computing devices comprises more than one computing device. Updating the first algorithm is performed based on information shared amongst the first cluster of devices.

Sharing and executing custom machine learning algorithms

A computer-implemented method includes: presenting, by a computing device, a virtual event user interface to an attendee of a virtual event; obtaining, by the computing device, data about actions performed by the attendee within the virtual event; creating, by the computing device, a tensile sphere kernel that is specific to the attendee using the obtained data; creating, by the computing device, a mixed tensile sphere kernel using the tensile sphere kernel that is specific to the attendee and another tensile sphere kernel of another attendee; determining, by the computing device, an event recommendation of the virtual event using the mixed tensile sphere kernel; and presenting, by the computing device, the event recommendation to the attendee by changing the virtual event user interface to show the event recommendation.

Event experience representation using tensile sphere mixing and merging

A computer captures a voice of a user. The computer determines a frequency spectrum and a voice pattern of the voice. The computer identifies one or more topics of the voice by transcribing the voice by a natural language processing. The computer identifies the user based on matching the frequency spectrum of the voice to the frequency spectrum of the conversation and the pattern of the voice to the pattern of the conversation when a conversation is intercepted and determines a trust score based on comparing the one or more topics to the one or more topics extracted from the conversation.

AI based system and method for corners of trust for a caller

A computer-implemented method includes monitoring, by a computing device, sensor data during gameplay of a sporting event; determining, by the computing device, predictive factors associated with a target based on the monitoring the sensor data; determining, by the computing device, a real-time region of effectiveness for the target based on the predictive factors and training data identifying historical effectiveness of the target; and outputting, by the computing device, the real-time region of effectiveness for displaying the real-time region of effectiveness around the target.

Dynamically determining a region of effectiveness of athletes during game play based on predictive factors

A route from a first physical location to a second physical location is divided into a set of segments using a route optimization engine. Using a user response analysis model, a response to a physical environment associated with a segment in the set of segments is scored, the scoring resulting in a score. Using a content generation model and the score, the physical environment is augmented, the augmenting combining the physical environment and a generated environment.

Vehicle based external environment augmentation for operator alertness

Using a digital twin model of a user, a digital twin model of a geographical location, a digital twin model of an event venue located at the geographical location, and a plurality of digital twin models of clothing items, a product recommendation customized to the user and a planned event is generated, the planned event planned to occur at the event venue. A product recommendation depiction is generated, the product recommendation depiction comprising a depiction of the product recommendation being worn by the user at the planned event. An answer to a natural language query regarding the product recommendation depiction is generated.

Digital and physical experience correlation for product recommendation

Using a first trained residual neural network, an event analysis is generated from a set of event description encodings, an event description encoding in the set of event description encodings encoding a natural language description of an event, the event analysis comprising a first set of coordinates in an event space. Using a second trained residual neural network, the event analysis and a user description encoding, a personalization analysis is generated, the user description encoding a user interaction with the event, the personalization analysis comprising a component of a second set of coordinates in the event space. Using a combination of the event analysis and the personalization analysis, a first event component is activated, the first event component comprising a component of the event.

Geometric shape based event component personalization

Methods, computer program products, and systems are presented. The methods can include, for instance: generating a plurality of deep transfer learning networks. Further, the methods can include, for instance: encoding one or more transfer layers.

Transfer learning based on cross-domain homophily influences

A method, system, and computer program product for implementing machine learning natural language digital package generation and selection is provided. The method includes receiving from hardware and software sources, factual data associated with an event. In response, natural language digital templates comprising natural language phrase variants for each portion of the factual data is generated. Factual data phrases are generated and packaged into digital packages including at least one natural language phrase variant with respect to each portion of factual data. An initial package is selected by minimizing a number of repetitions of the factual data phrases across the digital packages and digital summaries are extracted. Alignment attributes associated with the digital summaries are determined with respect to the initial package and a final package is selected. A hardware device is enabled for presenting a video stream including the final package with respect to the event.

Automated natural language generation and selection

A computer-implemented method for detecting a change in a meta-environment is disclosed. The method comprises collecting historic parameter values from at least one actor being active in the meta-environment, wherein each of the at least one actors has a set of abilities represented by parameters relating to the collected parameter values, collecting current parameter values from the at least one actor being active in the meta-environment, determining weight values of the collected historic parameters and weight values of the collected current parameters, and upon a value of a function of the determined weight values of the historic parameters and the weight values of the collected current parameters being greater than a predefined threshold value, determining a detection of the change in the meta-environment.

Detecting meta-environment changes

Using a logistic regression classification model executing on a processor, a topic is classified into an interaction type in a set of predefined interaction types. A set of documents corresponding to the topic is extracted from a document repository. Using a generative adversarial model executing on a processor, a sentiment corresponding to a reaction to a previous presentation is scored, the scoring resulting in a scored sentiment. Using a trained attention layer model, the interaction type, the set of documents, and the scored sentiment are weighted, the weighting generating a weighted interaction type, a weighted set of documents, and a weighted scored sentiment. Using a natural language generation transformer model executing on the processor, a document in the weighted set of documents is weighted according to the weighted interaction type and the weighted scored sentiment.

Automated document adaptation based on topic classification

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Cary, NC, United States of America

Aaron K Baughman