International Business Machines Corporation

Mary Swift

Donna Karen Byron

Ashok Kumar

James W Murdock, Iv

Gregory J Boss

Edgar Adolfo Zamora Duran

Jonathan D Dunne

Charles Evan Beller

Timothy P Winkler

John P Bufe, Iii

Stephan Joseph Roorda

Kelley Anders

Scott Harlan Isensee

Alexander Pikovsky

Radha Mohan De

Michael D Whitley

Cale R Vardy

Irene Lizeth Manotas Gutiérrez

Mark Marrara

Nisarga Markandaiah

Eun Ha

Rick A Hamilton, Ii

Yuk Lung Chan

Qi Cheng Li

Florian Pinel

Anne Elizabeth Gattiker

David Deidou Taieb

Benjamin L Johnson

Ladislav Kunc

Edward Eugene Seabolt

James William Murdock, Iv

Christopher Munjal Nolan

Suman Patra

Sujoy Roy

Dimple Bhatia

Armageddon Rhabdizo Brown

Sai Prathyusha Peddi

Lin Pan

Mayoore Selvarasa Jaiswal

Ambal Balakrishnan

Brian Hull

Charles Kichler

Vishesh Raimugia

Subbarao Seethamsetty

Matthew Comer

Chung-Wei Hang

Jaymin Desai

Caesar Chatterjee

Kazi Hasan

Sneha Srinivasan

Naga Katreddi

Irene Lizeth Manotas

Various systems and methods are presented regarding automatically generating a document for a particular topic, and further utilizing an algorithm to automatically identify information in a resource that pertains to the topic. The document can be a purpose-specific document, created by a template configured to combine data fields to build the document. Each data field can be subject-matter specific with an associated algorithm. The algorithm can be trained to recognize and extract content that potentially meets the subject matter of the data field. The data field can be populated with the content. Subsequent editing of the content populating the data field can be monitored to determine an effectiveness of the algorithm to identify data of interest in the data repository. In the event of low success, the algorithm can be retrained. Further, the document can be regenerated if an algorithm is retrained, new information added to the repository, etc.

Automated document harvesting and regenerating by crowdsourcing in enterprise social networks

A method, computer program product, and a system where a processor(s) obtains data related to medical treatment and a current physical state of a given patient. The processor(s) cognitively analyzes the data to identify potential patient behavioral patterns of the given patient. The processor(s) obtains general patient data and correlates the general patient data with a portion of the data to identify elements in the general patient data with a potential impact on the identified potential patient behavioral patterns. The processor(s) determines impacts of the elements on the identified potential behavioral patterns and applies the impacts to generate a data structure comprising baseline behavioral patterns, i.e., a predictive model to utilize in determining probabilities that the given patient will exhibit behaviors comprising the baseline behavioral patterns during a defined future time interval.

Hybrid predictive model for alertness monitoring

Techniques are described with respect to a system, method, and computer product for generating relevance alerts. An associated method includes analyzing a multi-party discussion based on a generated profile associated with a user and assigning at least one relevance value associated with the user to the multi-party discussion based on the analysis and an amount of multi-party discussion participation associated with the user. The method further includes generating an alert for the user to participate in the multi-party discussion in response to determining the relevance value exceeding a relevance threshold associated with the multi-party discussion.

Generating alerts on demand for participants in multiparty discussion channels

Provided is a method, computer program product, and system for predicting image sharing decisions using machine learning. A processor may receive a set of annotated images and an associated text input from each user of a plurality of users. The processor may train, using the set of annotated images and the associated text input from each user, a neural network model to output an image sharing decision that is specific to a user.

Image dispositioning using machine learning

Computer-implemented methods, computer program products, and computer systems for mitigating frequency loss may include one or more processors configured for receiving first audio data corresponding to unobstructed user utterances, receiving second audio data corresponding to first obstructed user utterances, generating a frequency loss (FL) model representing frequency loss between the first audio data and the second audio data, receiving third audio data corresponding to one or more second obstructed user utterances, processing the third audio data using the FL model to generate fourth audio data corresponding to a frequency loss mitigated version of the second obstructed user utterances, and transmitting the fourth audio data to a recipient computing device. The first obstructed user utterances are obstructed by a facemask and the one or more second obstructed user utterances is obstructed by the facemask. The FL model may be executed as an audio plugin in a web conferencing program.

Mitigating voice frequency loss

A communication session between two or more computing devices is monitored. One or more share actions, that include one or more media documents, are identified in the communication session responsive to the media documents and based on the monitoring the communication session. Reaction data, that is related to the share actions, in the communication session is analyzed in response the share actions and based on the media documents. One or more relevant media documents are determined based on the analyzing the reaction data. The relevant media documents are analyzed based on the media documents. A first depiction rendered by a first of the devices of the communication session is modified based on the relevant media documents.

Multimedia content differentiation

A method of configuring a widget defining a user interface includes parsing content from a corpus comprising documents describing a plurality of products, selecting a plurality of pillars from the content, wherein the pillars are descriptors describing a product space of the products, determining an affinity of each of the products to each of the pillars, receiving, via the widget, a selection of a first pillar, and displaying a given product in the widget, the given product selected from the products given the selection of the first pillar.

Automated curation of digital information and user interface for same

In response to determining that a native language of a first user is different from a target language of a message to be transmitted by the first user to a second user, a translation model based on a plurality of language efficacies of the first user is created. An optimal action associated with a translation of the message from the native language to the target language is determined based on the created model and a language efficacy of the first user in the native language. The determined optimal action is performed. The message translation comprising the performed optimal action is transmitted to the second user.

Real-time optimized translation

Embodiments for responding to computing system incidents are provided. Information associated with a computing system is analyzed to generate a base system model of the computing system. The information includes at least one of system logs and metrics data of the computing system. An indication of an incident associated with the computing system is received. The base system model is utilized to analyze the incident. The analysis of the incident includes characterizing the incident as a previously detected incident or an unknown incident. A strategy for responding to the incident is identified based on the analysis of the incident.

Cognitive methods and systems for responding to computing system incidents

A computer-implemented method for building a semantic analysis model. In one embodiment, the computer-implemented method includes creating proxy tags comprising a set of surface form variants. The computer-implemented method creates training examples comprising a combination of terminal tokens and at least one of the proxy tags. The computer-implemented method builds the semantic analysis model using the training examples.

Abstraction and portability to intent recognition

A method, computer program product, and computer system, for receiving a first set of ground truth instances from a first source. A second set of ground truth instances may be received from a second source. The first and second sets of ground truth instances may be weighed differently based on a level of trust associated with each of the first and second sources. The weighted first and second sets of ground truth instances may be applied in a machine learning task executed by a computer.

System and method for defining and using different levels of ground truth

A method, computer program product, and computer system for receiving, by a computing device, a question from a user. A first answer provided by a first subject matter expert is identified. A second answer provided by a second subject matter expert is identified. It is determined that a profile of the user matches a first profile of the first subject matter expert more than a second profile of the second subject matter expert. The first answer provided by the first subject matter expert is sent to the user with a preference over the second answer provided by the second subject matter expert based upon, at least in part, determining that the profile of the user matches the first profile of the first subject matter expert more than the second profile of the second subject matter expert.

System and method for providing more appropriate question/answer responses based upon profiles

Travel itineraries are automatically prepared based upon user interests and sentiments inferred by deep semantic analysis of user-commented and user-preferred digital works of literature by receiving interests and associated sentiment levels for at least one user according to a deep semantic analysis of a plurality of works of literature, wherein the works of literature have been rated, commented, or both rated and commented by the user; searching repositories of travel items to find one or more matching travel items to the received more interests and associated sentiment levels; and preparing at least one travel itinerary including at least one found matching travel item. Travel itineraries may be prepared responsive to a user-initiated trip planning session, responsive to notification of a new travel item's availability, periodically, or a combination thereof.

Travel itinerary recommendation engine using inferred interests and sentiments

Mechanisms for training a Question and Answer (QA) system are provided. The QA system receives a training question for processing by the QA system and processes the training question to generate an answer to the training question, from a portion of content. The QA system identifies a repeatable pattern of content present in the portion of content in association with the answer to the training question. The QA system applies the repeatable pattern of content to other portions of content to generate at least one additional training question and at least one additional entry in a ground truth data structure to thereby expand a set of training questions and expand the ground truth data structure. The QA system is then trained using the expanded set of training questions and expanded ground truth data structure.

Expanding training questions through contextualizing feature search

A method, system or computer usable program product for utilizing a dialectical model for providing an answer to a user query including applying NLP to the query to generate a first set of NLP structures; generating a contrary set of NLP structures contrary to the user query; evaluating the first set of NLP structures to identify a first set of evidence; evaluating the contrary set of NLP structures to identify a second set of evidence; evaluating a first set of hypotheses from the first set of NLP structures and a contrary set of hypotheses from the contrary set of NLP structures to determine a set of answers to the user query; summarizing the set of answers including indicating derivation thereof; and providing the summarized set of answers to the user.

User interface for a query answering system

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Rochester, NY, United States of America

Mary Swift