International Business Machines Corporation

Charles Evan Beller

Edward Graham Katz

Kristen Maria Summers

Christopher F Ackermann

Michael Drzewucki

William G Dubyak

Palani Sakthi

Christopher Phipps

Stephen Arthur Boxwell

Paul J Chase, Jr

Sean Thomas Thatcher

Richard L Darden

Alexander Charles Tonetti

Gregory J Boss

Stanley John Vernier

Elinna Shek

Chengmin Ding

John A Riendeau

Benjamin L Johnson

Julie T Yu

Jeremy R Fox

Zachary A Silverstein

Donna Karen Byron

Clement Decrop

Allen Ginsberg

Andrew Doyle

Craig M Trim

Edgar Adolfo Zamora Duran

Mary Swift

Shaila Pervin

Abhishek Malvankar

Carlos A Fonseca

Rob High

Adam D Dumey

John Michael Ganci, Jr

Aaron K Baughman

Christopher L Molloy

Bernadette Ann Pierson

Sean M Fuoco

Sean L Bethard

Brian L Keith

Joshua G Hong

Michael S Purdy

Chennakesavalu Govindasamy

Chris F Ackermann

David McQuenney

James E Ramirez

Richard Behrens, Jr

Robert A Sheets

Rick A Hamilton, Ii

Shikhar Kwatra

Cesar Augusto Rodriguez Bravo

Florian Pinel

Hemant Kumar Sivaswamy

Mohamed Nooman Ahmed

Renee F Decker

Carmine Dimascio

Andeep S Toor

Jayashree Vaidyanathan

Richard Obinna Osuala

Akanksha Joshi

David McQueeney

Sunil Nambiar

Suzanne Miller

Marcell Streile

Ben J Schaper

Christopher M Lohse

A method, computer system, and a computer program product for translating a classifier construction into a graphical representation is provided. The present invention may include observing a classifier handshape by an augmented reality device. The present invention may include analyzing the observed classifier handshape according to an object recognition algorithm to determine a contextual meaning of the classifier handshape. The present invention may include converting the contextual meaning of the observed classifier handshape into a graphical representation. The present invention may include displaying the graphical representation alongside the observed classifier handshape on the augmented reality device.

Augmented reality translation of sign language classifier constructions

An artificial intelligence (AI) platform to support selective replacement of one or more image layers of a container image build. A metadata file is subject to natural language processing and one or more corresponding vector representations are created and subject to evaluation by a set of artificial neural networks (ANNs). A first ANN assesses each vector representation with respect to compliance and operability, and the second ANN selectively assesses the vector representation(s) with respect to similarity with one or more compliant vector representations. In response to the assignment of the second score, at least one vector representation of the received metadata file is selectively replaced with at least one compliant vector representation. The metadata file is selectively provisioned with the selectively replaced vector representation(s).

Risk assessment of a container build

The method provides for receiving a plurality of documents including mentions of a target entity from a search query about the entity. The mentions of the target entity are identified in respective documents of the plurality of documents. Content surrounding the one or more mentions of the target entity are extracted with the mentions within the respective documents and form section. A respective document includes a plurality of sections. Metrics of relevance and irrelevance to the target entity are determined within the plurality of sections of the respective documents. A density score is generated for the plurality of sections of the respective documents. A relevancy score is assigned to respective documents of the plurality of documents, based on the density scores of the sections of the respective documents. The documents are ranked based on the relevancy score and presented in an order based on the ranking.

Ranking entity search results based on information density

A system for receiving a corpus of sign language data in which a plurality of known signs each correspond to known meanings, generate a model for identifying new sign language signs using the corpus, and identifying, using the model, a new sign language sign that does not match any of the plurality of known signs.

Aggregating and identifying new sign language signs

An artificial intelligence (AI) platform to support optimization of container builds and virtual machine mounts in a distributed computing environment. A provisioning file is subject to natural language processing (NLP) and a corresponding vector representation of the file is created and subject to evaluation by a set of artificial neural networks (ANN). A first ANN assesses the representation of the file with respect to compliance and operability, and the second ANN selectively assesses the representation of the file with respect to provisioning efficiency. The provisioning file is selectively process based on the provisioning efficiency, with the processing directed at provisioning a container build or mounting a VM.

A method for adjusting device system settings in response to analysis of displayed content. In some embodiments, a processor may extract content from contents displayed on a screen of a device. The processor may analyze features of the content. The processor may classify the content as belonging to a content class, based on analysis of the content features, utilizing a machine learning model. The processor may update the device system settings based on the content class.

Adjusting system settings based on displayed content

Provided are a method, system, and computer program product in which operations are performed to receive a question that includes a descriptor and an indication that indicates that a unique answer to the question is expected. A determination is made of instances of matching descriptors and descriptor targets from a set of documents. The determined descriptor targets are compared for consistency. In response to determining that the determined descriptor targets are inconsistent, more restrictive descriptors are iteratively generated via a selection model based on metadata associated with the question until the descriptor targets are consistent. An answer to the question is returned from consistent descriptor targets.

Identification of restrictors to form unique descriptions for generation of answers to questions

A computer device receives a request to search a corpus of documents for an entity, wherein the request includes a non-name identifier of the entity. The computing device identifies entries of text within the corpus of documents that reference the non-name identifier. The computing device applies natural language processing (NLP) to content associated with the identified entries within the corpus of documents, wherein the NLP identifies candidate entities associated with the non-name identifier. The computing device selects an entity from the candidate entities based, at least in part, on distances between the candidate entities and references to the non-name identifier in the identified entries. The computing device returns the selected entity to a submitter of the request.

Entity search system

An embodiment for expanding a search query is provided. The embodiment may include receiving a stopping criterion for stopping a search. The embodiment may also include receiving an initial search query. The embodiment may further include submitting the initial search query to an information retrieval system. The embodiment may also include identifying enrichment terms from the retrieved initial set of documents. The embodiment may further include generating a subsequent search query that includes one or more enrichment terms from the retrieved initial set of documents. The embodiment may also include submitting the subsequent search query to the information retrieval system. The embodiment may further include determining whether the stopping criterion is met, and in response to determining the stopping criterion is not met, iterating identifying, generating, submitting steps until the stopping criterion is met.

Iterative query expansion for document discovery

A controller generating a knowledge graph of entries, each entry comprising a separate entity identifier and a separate entity mention identifier within a separate document of a corpus of documents with a located relationship and one or more computed prefix-based geotemporal values determined from geotemporal information associated with the separate entity mention identifier within the separate document. The controller, in response to receiving an input comprising a particular entity and a threshold value, mapping the threshold value to a geospatial hash prefix type and a temporal hash prefix type. The controller applying geospatial hash prefix type and the temporal hash prefix type to the entries in the knowledge graph to determine a response to the input indicating one or more geotemporal proximate entities identified within a degree of geotemporal proximity to the particular entity set by the threshold value.

Analyzing geotemporal proximity of entities through a knowledge graph

A computer assigns a similarity value to a comparison document. The computer receives, reference document contextual word embeddings in first set of topic clusters, each with a representative embedding. The computer receives comparison document contextual word embeddings. The computer determines, using a trained neural network model classifier, for each comparison document contextual word embedding, topic correspondence values relative to the representative embeddings of said first set of clusters. The computer generates a second set of clusters by assigning comparison document embeddings to best matching one of the first clusters, according to the topic correspondence values. The computer determines a second set of representative embeddings and uses a comparison method, to determine a cluster similarity value for second set clusters compared to first set representative embeddings. The computer determines document similarity values based, at least in part, on at least one of cluster similarity values.

Semantic text comparison using artificial intelligence identified source document topics

Provided is a system, method, and computer program product for generating a three-dimensional (3D) printable file of a complete object by re-assembling pieces of a broken object using generative adversarial network techniques. A processor may generate a 3D scan of each piece of a plurality of pieces of a broken object. The processor may assemble the 3D scan of each piece of the plurality of pieces to generate a re-assembled object, where the re-assembled object includes one or more gaps. The processor may fill the one or more gaps in the re-assembled object to create a complete object. The processor may generate a 3D printable file of the complete object.

Three-dimensional printing using generative adversarial network techniques

A method learns approximate translations of unfamiliar measurement units during deep question answering (DeepQA) system training and usage. The DeepQA system receives a training set containing Question-Answer (QA) pairs having known unit-of-measurement terms, where each QA pair contains an answer having a known numeric value for a corresponding question from the QA pair. The DeepQA system receives a question from each QA pair from the training set to the DeepQA system in order to find answers and passage phrases to the question from each QA pair, and then identifies all found answers and passage phrases having values that are within a predetermined range of answer values of the training set, where one or more of the identified all found answers and passage phrases contain unfamiliar unit-of-measurement terms, in order to learn approximate translations of the unfamiliar unit-of-measurement terms.

Learning approximate translations of unfamiliar measurement units during deep question answering system training and usage

A candidate document is received, for example, by a document filter. A determination is made based on the content of the candidate document, whether the candidate document is relevant to a document corpus. A determination is made based on the content of the candidate document, whether the candidate document is novel with respect to the document corpus. In response to determining that the candidate document is relevant to the document corpus and novel with respect to the document corpus, the candidate document is added to the document corpus to make at least a portion of the content of the candidate document available for a response to a search query.

Automated determination of document utility for a document corpus

Embodiments relate to an intelligent computer platform to selectively amend one or more document elements. A first document is subjected to natural language processing (NLP) and two or more document characteristics are subjected to an assessment to produce a characteristic value. The document characteristics and corresponding characteristic values are analyzed to produce a characteristic profile for each identified document characteristic. Upon receipt of a new document, document characteristic data and corresponding characteristic value(s) are identified. The corresponding characteristic value(s) of the new document is applied against the produced characteristic profile. New document characteristic data is selectively amended responsive to the comparison, and a new document version is created from the selective amendment.

Removal of personality signatures

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Baltimore, MD, United States of America

Charles Evan Beller