International Business Machines Corporation

Lenovo (singapore) Pte. Ltd.

Globalfoundries Inc.

Eric Woods

Corville O Allen

Adrian Xavier Rodriguez

Scott Robert Carrier

Albert D Bennah

Michael M Skeen

Eileen Jiwon Min

Scott N Gerard

Sterling Richardson Smith

David B Styles

Kelly L Cook

Jeffrey Douglas Haggar

Richard A Salmon

Jared M D Smythe

Lydia Mai Do

Nicholas V Bruno

Menlo Wuu

Liam S Harpur

Robert E Loredo

Clark A Dudek

Kobina Kweenu Inkumsah

Saeid Sakhitab

Nathan A Bost

Paul P Mariduena

Donna Karen Byron

William Gabriel Pagan

Alexander Pikovsky

Joshua S Allen

Paula Besterman

Phillip David Jones

Liviu Rodean

Balasubramanian Sivasubramanian

Philip Mullins

Rupal S Soneji

Pamela S Ross

Edwin W Peters

Sherry L Platek

Nitty T Pulikan

Balaji Ranganathan

Ovies C Brabson

Andrew Ronald Freed

Todd Eric Kaplinger

Pamela Ann Nesbitt

Albert Alexander Chung

Andrew Bryan Smith

John P Bufe, Iii

Scott A Will

David G Robinson

Robert L Nielsen

Spenser Edward Shumaker

Erik Bradley Craig

Daniel Brian Varga

Carlos Santana

Harold B Moss, Iii

Jared Thomas Siirila

Kathryn Lemanski Mercer

Charles A Cole

Ping Zhou

Zachary W Abrams

Adrian X Rodriquez

Kathryn M Whaley

Kathryn M Lamont

Utilizing social information for recommending an application includes providing an application recommendation system based on social characterizations, and responsive to a user searching for an application meeting a criteria, utilizing the application recommendation system by searching for applications meeting the criteria, characterizing the applications according to a social proximity factor to the user, and presenting the applications ordered by the social proximity factor.

Utilizing social information for recommending an application

A recommendation engine for works of literature uses patterns of flow and element similarities for scoring a first user-rated work of literature against one or more recommendation candidate works of literature. Cluster models are created using meta-data modeling the works of literature, the meta-data having literary element categories and instances within each category. Each instance is described by an index value (position in the literature) and significance value (e.g. weight or significance). Cluster finding process(es) invoked for each instance in each category find Similarity Concept clusters and Consistency Trend clusters, which are recorded into the cluster models representing each work of literature. The cluster model can be printed or displayed so that a user can visually understand the ebb and flow of each literary element in the literature, and may be digitally compared to other cluster models of other works of literature for potential recommendation to a user.

Recommendation engine using inferred deep similarities for works of literature

Deep semantic analysis is performed on an electronic literary work in order to detect plot elements and optional other storyline elements such as characters within the work. Multiple levels of abstract are generated into a model representing the literary work, wherein each element in each abstraction level may be independently rated for preference by a user. Through comparison of multiple abstraction models and one or more user rating preferences, one or more alternative literary works may be automatically recommended to the user.

Automatic semantic rating and abstraction of literature

A visualization of literary elements of a work of literature, such as a novel or short story, is generated from meta-data records representing a digital work of literature including literary elements (humor, drama, adventure, etc.), characters, and plot devices related to a position within the work of literature where each appears. A significance level is determined for each of the elements, characters and plot devices at each position within the work of literature, and these are plotted into a sequential graph having position (e.g. timeline) axis and a significance level axis. The sequential graph is then output for printing or display. Human-generated and machine-generated meta-data may be ingested equally well by the method. Colors, line thickness, and a broken line patterns may be employed for greater visual meaning. And, the sequential graph may be annotated according to segments (e.g. chapters, sections) and dominant genre within each segment.

Producing visualizations of elements in works of literature

Automatic semantic analysis for characterizing and correlating literary elements within a digital work of literature is accomplished by employing natural language processing and deep semantic analysis of text to create annotations for the literary elements found in a segment or in the entirety of the literature, a weight to each literary element and its associated annotations, wherein the weight indicates an importance or relevance of a literary element to at least the segment of the work of literature; correlating and matching the literary elements to each other to establish one or more interrelationships; and producing an overall weight for the correlated matches.

Detecting literary elements in literature and their importance through semantic analysis and literary correlation

Methods and apparatus, including computer program products and a computer system, implement and use techniques for preserving referential integrity. A first input receiving component receives a first input selecting a target file within a first directory. A second input receiving component receives a second input selecting all files referenced by the target file. A transfer component automatically performs a moving or copying operation of all referenced files from a first directory to a second directory when a moving or copying operation of the target file is performed.

Preservation of referential integrity

Software that uses machine-logic based algorithms to assess the value of idea descriptions by performing the following operations: (i) receiving an idea description pertaining to an idea; (ii) generating a density score for the idea, wherein the density score is based, at least in part, on one or more publication date densities of a set of works related to the idea description; (iii) generating a novelty score for the idea, wherein the novelty score is based, at least in part, on a comparison of the idea description to the set of works related to the idea description using natural language processing; and (iv) assessing the value of the idea based, at least in part, on the generated density score and the generated novelty score.

Value scorer in an automated disclosure assessment system

Reducing a negative social interaction includes receiving a response to a post from a user, the response includes content to be posted on an activity stream of a social network, analyzing the content of the response to determine a negative response risk to the post, analyzing a profile of the user to determine a tendency of the user to respond negatively in responses, and executing, based on the negative response risk and the tendency, an action for the response to reduce negative responses directed towards the post.

Analysis of social interaction sentiment

Arrangements relate to automatically taking notes in a virtual meeting. The virtual meeting has meeting content that includes a plurality of meeting content streams. One or more of the meeting content streams is in a non-text format. The one or more meeting content streams in a non-text format can be converted into text. As a result, the plurality of meeting content streams is in text format. The text of the plurality of meeting content streams can be analyzed to identify a key element within the text. Consolidated system notes that include the key element can be generated.

Automatic note taking within a virtual meeting

Mechanisms for clarifying an input question are provided. A question is received for generation of an answer. A set of candidate answers is generated based on an analysis of a corpus of information. Each candidate answer has an evidence passage supporting the candidate answer. Based on the set of candidate answers, a determination is made as to whether clarification of the question is required. In response to a determination that clarification of the question is required, a request is sent for user input to clarify the question. User input is received from the computing device in response to the request and at least one candidate answer in the set of candidate answers is selected as an answer for the question based on the user input.

Clarification of submitted questions in a question and answer system

A search engine is enhanced to employ user preferences to customize answer output by, receiving a first plurality of candidate answers and weights to a first search query for a first user from a first stage of a non-semantic search engine routing and processing path; augmenting the weights for the plurality of candidate answers according to a set of preference coefficients according to a first set of preferences for the first user; selecting one or more of the first plurality of candidate answers according to the augmented weights; and producing an output including the selected one or more candidate answers.

Enhanced answer in DeepQA system according to user preferences

A method, system and computer-usable medium are disclosed for analyzing and deducing criteria-related content for evaluation in a system capable of answering questions. A criteria text is processed to identify criteria. The criteria text is then analyzed to identify a set of criteria-related content associated with the criteria, which in turn is processed to determine its relationship to the criteria. Once the relationship has been determined, the set of criteria-related content is annotated accordingly. The set of criteria-related content is then used in accordance with its annotation when processing the criteria for evaluation.

System and method for analyzing and deducing criteria-related content for evaluation

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Durham, NC, United States of America

Eric Woods