The Boeing Company

Kenneth D Bouvier

John M Boggio

Stephen P Jewett

Peter James Lake

Matthew Kyle Fay

Benjamin J Brelje

Diana L Herman

Techniques are described for analyzing text. Embodiments tokenize a plurality of documents into a plurality of sets of terms. An average top dimension weight corresponding to the plurality of documents is calculated based on performing singular value decomposition (SVD) factorization for a plurality of dimension counts. An average inverse top dimension top term ranking for the plurality of documents is further calculated based on the SVD factorization for the plurality of dimension counts. A number of dimensions is determined based on the average top dimension weight and the average inverse top dimension top term ranking. A topic model is built for the plurality of documents based on the number of dimensions. The topic model is adapted to identify patterns of terms that correspond to semantic topics in at least the plurality of documents.

Dimension optimization in singular value decomposition-based topic models

Techniques are described for optimizing a number of dimensions for performing a singular value decomposition (SVD) factorization. Embodiments tokenize each of a plurality of documents into a respective set of terms. For each of a plurality of dimension counts, embodiments perform the SVD factorization to determine a respective plurality of dimensions, the respective plurality of dimensions corresponding to the dimension count, determine, for each of the plurality of documents, a respective set of dimension weights for each of the plurality of dimensions, calculate an average top dimension weight across the sets of dimension weights for the plurality of documents and calculate an average inverse top dimension top term ranking across the sets of dimension weights for the plurality of documents. An optimal number of dimensions is calculated, based on the average top dimension weight and the average inverse top dimension top term ranking.

Method and apparatus for unsupervised aircraft design. A plurality of design problem data and service event data for an aircraft is received from an electronic data repository. Embodiments communicate with sensors on the aircraft during flight operations and capturing service data and sensor data. A high order vector is generated for each received problem report and service event data and each high order vector is concatenated into a high order vector matrix. Embodiments generate a reduced order symptom-normalized matrix by factorization of the concatenated high order vector matrix and generate a similarity matrix from the symptom-normalized matrix. An impact score is computed for each in-service event data as a function of similar problem reports using the similarity matrix. Embodiments generate a priority matrix configured to identify service event data having high impact scores and communicate a real-time alert of the high impact scored service event.

Providing early warning and assessment of vehicle design problems with potential operational impact

In accordance with one or more embodiments, a system for facilitating transfer of data and information over a network includes a database component for storing data and information related to a machine and at least one part thereof, a communication component adapted to communicate with a user via a user device over the network, and a processing component adapted to receive a request for data and information from the user over the network via the user device and process the request by retrieving data and information from the database component related to the machine or the at least one part thereof specified by the user passed with the request. The communication component is adapted to transfer the data and information related to the machine or the at least one part thereof from the database component to the user device for viewing by the user on the user device.

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Renton, WA, United States of America

Kenneth D Bouvier