The Boeing Company

Robert E Cranfill

William R Ferng

Yuan-Jye Wu

Stephen Robert Poteet

Anne S-w Kao

Changzhou Wang

Jai Joon Choi

Dragos Dorin Margineantu

Haiqin Wang

Sidney Ly

Tomas Singliar

William R Murray

Jan S Koontz

Methods and systems are provided for processing natural language for machine learning analytical systems. The method includes receiving, at a processor, an input including text representing one or more observed parameters of an environment. The inputted text is in a natural language format. The processor parses the input and extracts the one or more parameters. A function is defined representing a domain of the one or more observed parameters based upon the one or more extracted parameters.

Methods and systems for processing natural language for machine learning

The embodiments described herein describe a computerized system and method for retrieving and processing data to provide dependency and root cause information for a process. The computerized system and method include receiving historic data of the process, detecting temporal dependency or precedence tasks in the process from the historic data, generating a historic dependency graph, aggregating the historic dependency graph into a probabilistic precedence network (PPN), pruning the PPN, and presenting results to a user.

System and method for dependency and root cause discovery

A streaming text data comparator performs real-time text data mining on streaming text data. The comparator receives a streaming text data document and generates a vector representation of the term frequencies relating to an existing document collection. The comparator then transforms the term frequency vector into a projection in a precomputed multidimensional subspace that represents the original document collection. The comparator further calculates a relationship value representing the similarities or differences between the vector representation and the subspace, and compares the relationship value to a predetermined threshold to determine whether the streaming text data document is related to the original document collection. If the streaming text data document is related, the streaming text data comparator intercalates the new document into the document collection. If the new document is not related, the comparator may store or delete the unrelated document.

Streaming text data mining method and apparatus using multidimensional subspaces

A text summarizer identifies relevant terms in a document, weights the terms and extracts one or more segments to produce a summary or abstract. The various terms in a particular are weighted in relation to an existing document collection. A term weight computer computes term weights for terms in the document, and a threshold comparator compares the term weights to determine if the corresponding terms are relevant to the document collection. Next, a term weight summer adds the term weights for each occurrence of each relevant term in the various segments of the document, and a summation comparator compares the summations to identify a text summarization segment representative of the document. Optionally, relevant terms can be highlighted in the term summarization segment.

Text summarization method and apparatus using a multidimensional subspace

Query-based text summarization

Provided are improved methods, apparatus, and computer program products for text differentiation which involves identifying differences between documents with similar content, not merely similar terms, and generating results. Text differentiation provides the ability to find non-similar, or different, content hidden within documents with similar overall content, but not exactly the same content. Text differentiation may be used to quickly identify key differences between similar documents.

Text differentiation methods, systems, and computer program products for content analysis

An electronic scanner having a light source and a light sensitive head is connected via a cable to a computer. The scanner head detects reflected light from the surface of the composite material and generates an electronic representation of the surface. A conventional software driver interprets the scanner output to produce an electronic bit-mapped image. The electronic image is then displayed so that gaps are readily visible. The electronic representation is also analyzed to determine the presence of laps and gaps, to measure gap widths, to measure the distance between points on the display, and to determine the percentage of the surface covered by fiber material. The electronic representation may also be stored for later analysis.

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Seattle, WA, United States of America

Robert E Cranfill