California Institute of Technology

Chevron U.s.a. Inc.

Wayne Moses Burke

Shuxing Cheng

Grant Lee

Hamsa Shwetha Venkataram

Chris Mattmann

Annie Didier

Vishal Lall

Asitang Mishra

A computer-implemented, machine learning-based method of converting an unstructured technical report into a structured technical report includes obtaining an unstructured technical report, tokenizing the unstructured technical report into an n-gram array, identifying and filtering non-interesting n-grams from the first n-gram array based on common language usage of the non-interesting n-grams and a determination that the non-interesting n-grams do not appear on a confirmed technical entity database, generating and displaying a technical entity candidate list from the filtered n-gram array, displaying, obtaining, from a pattern matching model and/or a graphical user interface, an indication that a technical entity candidate is a technical entity of interest, appending the technical entity of interest to the confirmed technical entity database, generating and displaying a structured technical report with the confirmed technical entities and corresponding technical entity value parameters, and iterating the process to refine the pattern matching model.

Converting unstructured technical reports to structured technical reports using machine learning

Growing community of inventors

Ann Arbor, MI, United States of America

Wayne Moses Burke