Patents by Inventor Brian F. White

Brian F. White has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 9652719
    Abstract: A system and computer program product that facilitates authoring of a Bayesian Belief Networks by: accessing text content stored in a content storage device; identifying statements within said accessed text content indicating a dependence relation; extracting said statements indicating said dependence relation from said text content; and aggregating said extracted statements into a form suitable for representation as a BBN network structure. To identify statements indicating a dependence relation, the system identifies one or more lexical and semantic attributes of variables within a text unit indicating a conditional dependence relation between two or more variables. The system further processes the text content to extract probabilistic information and probability statements and aggregate the probability statements into a quantitative layer of the BBN structure.
    Type: Grant
    Filed: September 17, 2013
    Date of Patent: May 16, 2017
    Assignee: SINOEAST CONCEPT LIMITED
    Inventors: Lamia T. Bounouane, Lea Deleris, Bogdan E. Sacaleanu, Brian F. White
  • Patent number: 9361587
    Abstract: A system and method that facilitates authoring of a Bayesian Belief Networks by: accessing text content stored in a content storage device; identifying statements within said accessed text content indicating a dependence relation; extracting said statements indicating said dependence relation from said text content; and aggregating said extracted statements into a form suitable for representation as a BBN network structure. To identify statements indicating a dependence relation, the method includes identifying one or more lexical and semantic attributes of variables within a text unit indicating a conditional dependence relation between two or more variables. The method further processes the text content to extract probabilistic information and probability statements and aggregate the probability statements into a quantitative layer of the BBN structure.
    Type: Grant
    Filed: March 1, 2013
    Date of Patent: June 7, 2016
    Assignee: International Business Machines Corporation
    Inventors: Lamia T. Bounouane, Lea Deleris, Bogdan E. Sacaleanu, Brian F. White
  • Publication number: 20140250047
    Abstract: A system and computer program product that facilitates authoring of a Bayesian Belief Networks by: accessing text content stored in a content storage device; identifying statements within said accessed text content indicating a dependence relation; extracting said statements indicating said dependence relation from said text content; and aggregating said extracted statements into a form suitable for representation as a BBN network structure. To identify statements indicating a dependence relation, the system identifies one or more lexical and semantic attributes of variables within a text unit indicating a conditional dependence relation between two or more variables. The system further processes the text content to extract probabilistic information and probability statements and aggregate the probability statements into a quantitative layer of the BBN structure.
    Type: Application
    Filed: September 17, 2013
    Publication date: September 4, 2014
    Applicant: International Business Machines Corporation
    Inventors: Lamia T. Bounouane, Lea Deleris, Bogdan E. Sacaleanu, Brian F. White
  • Publication number: 20140250045
    Abstract: A system and method that facilitates authoring of a Bayesian Belief Networks by: accessing text content stored in a content storage device; identifying statements within said accessed text content indicating a dependence relation; extracting said statements indicating said dependence relation from said text content; and aggregating said extracted statements into a form suitable for representation as a BBN network structure. To identify statements indicating a dependence relation, the method includes identifying one or more lexical and semantic attributes of variables within a text unit indicating a conditional dependence relation between two or more variables. The method further processes the text content to extract probabilistic information and probability statements and aggregate the probability statements into a quantitative layer of the BBN structure.
    Type: Application
    Filed: March 1, 2013
    Publication date: September 4, 2014
    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Lamia T. Bounouane, Lea Deleris, Bogdan E. Sacaleanu, Brian F. White
  • Patent number: 8793106
    Abstract: A system, method and computer program product for predicting at least one feature of at least one product being manufactured. The system receives, from at least one sensor installed in equipment performing one or more manufacturing process steps, at least one measurement of the feature of the product being manufactured. The system selects one or more of the received measurement of the feature of the product. The system estimates additional measurements of the feature of the product at a current manufacturing process step. The system creates a computational model for predicting future measurements of the feature of the product, based on the selected measurement and the estimated additional measurements. The system predicts the future measurements of the feature of the product based on the created computational model. The system outputs the predicted future measurements of the feature of the product.
    Type: Grant
    Filed: September 23, 2011
    Date of Patent: July 29, 2014
    Assignee: International Business Machines Corporation
    Inventors: Robert J. Baseman, Amit Dhurandhar, Sholom M. Weiss, Brian F. White
  • Publication number: 20130080125
    Abstract: A system, method and computer program product for predicting at least one feature of at least one product being manufactured. The system receives, from at least one sensor installed in equipment performing one or more manufacturing process steps, at least one measurement of the feature of the product being manufactured. The system selects one or more of the received measurement of the feature of the product. The system estimates additional measurements of the feature of the product at a current manufacturing process step. The system creates a computational model for predicting future measurements of the feature of the product, based on the selected measurement and the estimated additional measurements. The system predicts the future measurements of the feature of the product based on the created computational model. The system outputs the predicted future measurements of the feature of the product.
    Type: Application
    Filed: September 23, 2011
    Publication date: March 28, 2013
    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Robert J. Baseman, Amit Dhurandhar, Sholom M. Weiss, Brian F. White
  • Patent number: 6654739
    Abstract: A procedure for clustering documents that operates in high dimensions, processes tens of thousands of documents and groups them into several thousand clusters or, by varying a single parameter, into a few dozen clusters. The procedure is specified in two parts: computing a similarity score representing the k most similar documents (typically the top ten) for each document in the collection, and grouping the documents into clusters using the similarly scores.
    Type: Grant
    Filed: January 31, 2000
    Date of Patent: November 25, 2003
    Assignee: International Business Machines Corporation
    Inventors: Chidanand Apte, Sholom M. Weiss, Brian F. White
  • Patent number: 6286000
    Abstract: A lightweight document matcher employs minimal processing and storage. The lightweight document matcher matches new documents to those stored in a database. The matcher lists, in order, those stored documents that are most similar to the new document. The new documents are typically problem statements or queries, and the stored documents are potential solutions such as FAQs (Frequently Asked Questions). Given a set of documents, titles, and possibly keywords, an automatic back-end process constructs a global dictionary of unique keywords and local dictionaries of relevant words for each document. The application front-end uses this information to score the relevance of stored documents to new documents. The scoring algorithm uses the count of matched words as a base score, and then assigns bonuses to words that have high predictive value. It optionally assigns an extra bonus for a match of words in special sections, e.g., titles.
    Type: Grant
    Filed: December 1, 1998
    Date of Patent: September 4, 2001
    Assignee: International Business Machines Corporation
    Inventors: Chidanand Apte, Frederick J. Damerau, Sholom M. Weiss, Brian F. White
  • Patent number: 5237502
    Abstract: A computer implemented system creates natural language paraphrases of information contained in a logical form, where the logical form may be a representation of a natural language expression. (Logical forms are widely used by database query systems and machine translation systems and are typically forms of first-order logic, with the possible addition of higher-order operators.) The paraphraser is implicitly defined via the BNF description of CLF (Baclis-Naur Forms) of Initial Trees and of the paraphrase rules. The paraphraser uses a technique for mapping logical forms to natural language. The natural language paraphrases which are created could be used either as input to a query system, as part of a machine translation system, or to generate natural language corresponding to an interpretation created by a natural language database query system of a user's query for the purpose of verification of the paraphrase by the user.
    Type: Grant
    Filed: August 26, 1991
    Date of Patent: August 17, 1993
    Assignee: International Business Machines Corporation
    Inventors: Brian F. White, Ivan P. Bretan, Mohammad A. Sanamrad