International Business Machines Corporation

Sinoeast Concept Limited

Brian F White

Sholom M Weiss

Chidanand V Apte

Lea A Deleris

Lamia T Bounouane

Bogdan E Sacaleanu

Robert J Baseman

Amit Dhurandhar

Frederick J Damerau

Mohammad Ali Sanamrad

Barry Kumin Rosen

Fateh Ali Tipu

Edna Grossman

Edwin Peter Pednault

Hsueh-ju Wang

Ivan P Bretan

Chidanand Vinayak Apte

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.

Authoring system for bayesian networks automatically extracted from text

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.

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.

Continuous prediction of expected chip performance throughout the production lifecycle

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.

Lightweight document clustering

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. The method uses minimal data structures and lightweight scoring algorithms to compute efficiently even in restricted environments, such as mobile or small desktop computers.

Light weight document matcher

A computer implemented method of underwriting profitability analysis delivers the analytic process to a wide cross section of insurance decision makers. The underwriting profitability analysis system leverages an existing investment in databases and improves underwriting business processes. Data mining techniques are applied to historical policy and claims to extract rules that describe policy holders with homogeneous claim frequency and severity characteristics. These rule sets are used to classify policy holders into distinct risk groups, each with its own set of characteristics, including pure premium. Breaking up a book of business into segments allows identification of sub-populations of policy holders that distinctly deviate from the expected normal pure premium. This identification allow the insurance business analysts to interactively adjust eligibility criteria and examine altered characteristics of the covered segments until satisfactory. The system is implemented on a client server using network centric language technology.

Data mining based underwriting profitability analysis

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.

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Yorktown Heights, NY, United States of America

Brian F White