International Business Machines Corporation

Joel C Dubbels

Richard Joseph Stevens

Michael W Schroeder

John Edward Petri

Mark Gregory Megerian

Andrew Ronald Freed

William Craig Rapp

Janice R Glowacki

Adam Clark

Jeffrey K Huebert

Thomas John Eggebraaten

David Alan Wall

Elizabeth T Dettman

Travis Michael Drucker

Eric W Will

Michael Terry Payne

Patrick Michael Wildt

Frances E Stewart

Richard D Dettinger

Fang Wang

Vanessa V Michelini

Kirk Alan Beaty

Judy I Djugash

Kevin Patrick Gibson

Cheryl L Eifert

Claudia S Huettner

Jia Xu

Marta Sanchez-Martin

Jeffrey Bernard Nowicki

Computer based methods, systems, and computer readable media for classifying documents within a content repository or documents within the document subsets are provided. Documents may be pre-processed to render document sections visible to machine readers. Document subsets may be generated based on user-defined terms. The machine readable documents may be classified within the content repository into one of a group of categories, based-upon the number of times classification terms appear in a specific document section of the document. Documents may be ranked based upon the frequency of classification terms in the specific section. Documents may be associated with specific diseases such as cancer, genes, gene variants, and drugs or synonyms thereof by comparing relevant search terms to specific sections of the documents.

Automated document filtration and prioritization for document searching and access

Auditing database access in a distributed medical computing environment may include receiving from a user a query of one or more databases within the distributed medical administration computing environment; determining by an audit manager in dependence upon audit policies for the medical computing environment whether any of the results of the query require auditing access; and if any of the results of the query require auditing access, creating an audit record and storing the audit record in an audit database.

Auditing database access in a distributed medical computing environment

In some NLP systems, queries are compared to different data sources stored in a corpus to provide an answer to the query. However, the best data sources for answering the query may not currently be contained within the corpus or the data sources in the corpus may contain stale data that provides an inaccurate answer. When receiving a query, the NLP system may evaluate the query to identify a data source that is likely to contain an answer to the query. If the data source is not currently contained within the corpus, the NLP system may ingest the data source. If the data source is already within the corpus, however, the NLP may determine a time-sensitivity value associated with at least some portion of the query. This value may then be used to determine whether the data source should be re-ingested&#x2014;e.g., the information contained in the corpus is stale.

Identifying a stale data source to improve NLP accuracy

Corpus-scoped annotation and analysis. Enrichment analysis data is generated including annotations and metadata for a plurality of documents that are part of a corpus. Whether to generate a second set of annotations is determined, based on a correlation of the annotations and metadata. A relational database is populated with the enrichment analysis data. A corpus-scoped query is resolved, initiated by an application, using the enrichment analysis data and content of the corpus.

Corpus-scoped annotation and analysis

Embodiments presented herein disclose techniques for transforming input documents having disparate formats into a normalized format (e.g., Atom, RSS, HTML, customized XML, etc.). According to one embodiment, a plurality of fields is identified in an input document that has a given format. Each field includes a descriptor and text content associated with the descriptor. For each field, semantic properties are evaluated for the descriptor and text content against a plurality of mapping rules to determine whether the field is consistent with one of a plurality of fields of a target format. Each mapping rule specifies characteristics associated with one of the fields in the target format. Once so determined, a mapping from the first field to the second field is defined.

Natural language processing&#x2014;assisted extract, transform, and load techniques

Determining the likelihood that an input descriptor and associated text content match a target field using natural language processing techniques in preparation for an extract, transform and load process

Auditing database access in a distributed medical computing environment includes receiving from a user a query of one or more databases within the distributed medical administration computing environment; determining by an audit manager in dependence upon audit policies for the medical computing environment whether any of the results of the query require auditing access; and if any of the results of the query require auditing access, creating an audit record and storing the audit record in an audit database.

Dynamically allocating business workflows, each workflow comprising a reusable component of a business transaction, including: receiving, by a workflow orchestrator, a request for a business transaction; determining, by the workflow orchestrator, a desired result for the business transaction in dependence upon the request; selecting, by the workflow orchestrator, one or more workflows from a set of available workflows in dependence upon the request and the desired result; determining an execution order for the one or more workflows; and executing, by the workflow orchestrator, the one or more selected workflows in the execution order.

Dynamically allocating business workflows

Method, system, and computer program product to analyze a plurality of candidate answers identified as responsive to a question presented to a deep question answering system, by computing a first feature score for a first feature of an item of evidence, of a plurality of items of evidence, the first feature score being based on at least one attribute of the first feature, the item of evidence relating to a first candidate answer, of the plurality of candidate answers, and computing a merged feature score for the first candidate answer by applying the first feature score to a second feature score for a second feature of the item of evidence.

Multi-dimensional feature merging for supporting evidence in a question and answering system

Dynamically allocating data processing components

Mechanisms are provided, in a Question and Answer (QA) system comprising a processor and a memory, for debugging code. An input question identifying an error during execution of code is processed by the QA system using a corpus corresponding to a software product associated with the code, thereby generating a first candidate answer set. The QA system processes the input question using a code change record repository identifying changes to the code performed over time to generate a second candidate answer set. The QA system generates a final answer to the input question based on the first and second candidate answer sets and outputs the final answer to the input question. The final answer to the input question identifies at least one of a source, in the code, of the error or a solution to resolving the error.

Debugging code using a question and answer system based on documentation and code change records

Growing community of inventors

Eyota, MN, United States of America

Joel C Dubbels