Patents by Inventor David Cavazos Woo

David Cavazos Woo 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).

  • Duplicate bug report detection using machine learning algorithms and automated feedback incorporation
    Patent number: 10789149
    Abstract: Duplicate bug report detection using machine learning algorithms and automated feedback incorporation is disclosed. For each set of bug reports, a user-classification of the set of bug reports as including duplicate bug reports or non-duplicate bug reports is identified. Also for each set of bug reports, correlation values corresponding to a respective feature, of a plurality of features, between bug reports in the set of bug reports is identified. Based on the user-classifications and the correlation values, a model is generated to identify any set of bug reports as including duplicate bug reports or non-duplicate bug reports. The model is applied to classify a particular bug report and a candidate bug report as duplicate bug reports or non-duplicate bug reports.
    Type: Grant
    Filed: April 12, 2019
    Date of Patent: September 29, 2020
    Assignee: Oracle International Corporation
    Inventors: Prasad V. Bagal, Sameer Arun Joshi, Hanlin Daniel Chien, Ricardo Rey Diez, David Cavazos Woo, Emily Ronshien Su, Sha Chang
  • Duplicate bug report detection using machine learning algorithms and automated feedback incorporation
    Patent number: 10379999
    Abstract: Duplicate bug report detection using machine learning algorithms and automated feedback incorporation is disclosed. For each set of bug reports, a user-classification of the set of bug reports as including duplicate bug reports or non-duplicate bug reports is identified. Also for each set of bug reports, correlation values corresponding to a respective feature, of a plurality of features, between bug reports in the set of bug reports is identified. Based on the user-classifications and the correlation values, a model is generated to identify any set of bug reports as including duplicate bug reports or non-duplicate bug reports. The model is applied to classify a particular bug report and a candidate bug report as duplicate bug reports or non-duplicate bug reports.
    Type: Grant
    Filed: January 11, 2016
    Date of Patent: August 13, 2019
    Assignee: Oracle International Corporation
    Inventors: Prasad V. Bagal, Sameer Arun Joshi, Hanlin Daniel Chien, Ricardo Rey Diez, David Cavazos Woo, Emily Ronshien Su, Sha Chang
  • DUPLICATE BUG REPORT DETECTION USING MACHINE LEARNING ALGORITHMS AND AUTOMATED FEEDBACK INCORPORATION
    Publication number: 20190235987
    Abstract: Duplicate bug report detection using machine learning algorithms and automated feedback incorporation is disclosed. For each set of bug reports, a user-classification of the set of bug reports as including duplicate bug reports or non-duplicate bug reports is identified. Also for each set of bug reports, correlation values corresponding to a respective feature, of a plurality of features, between bug reports in the set of bug reports is identified. Based on the user-classifications and the correlation values, a model is generated to identify any set of bug reports as including duplicate bug reports or non-duplicate bug reports. The model is applied to classify a particular bug report and a candidate bug report as duplicate bug reports or non-duplicate bug reports.
    Type: Application
    Filed: April 12, 2019
    Publication date: August 1, 2019
    Applicant: Oracle International Corporation
    Inventors: Prasad V. Bagal, Sameer Arun Joshi, Hanlin Daniel Chien, Ricardo Rey Diez, David Cavazos Woo, Emily Ronshien Su, Sha Chang
  • Duplicate bug report detection using machine learning algorithms and automated feedback incorporation
    Patent number: 10339030
    Abstract: Duplicate bug report detection using machine learning algorithms and automated feedback incorporation is disclosed. For each set of bug reports, a user-classification of the set of bug reports as including duplicate bug reports or non-duplicate bug reports is identified. Also for each set of bug reports, correlation values corresponding to a respective feature, of a plurality of features, between bug reports in the set of bug reports is identified. Based on the user-classifications and the correlation values, a model is generated to identify any set of bug reports as including duplicate bug reports or non-duplicate bug reports. The model is applied to classify a particular bug report and a candidate bug report as duplicate bug reports or non-duplicate bug reports.
    Type: Grant
    Filed: January 11, 2016
    Date of Patent: July 2, 2019
    Assignee: Oracle International Corporation
    Inventors: Prasad V. Bagal, Sameer Arun Joshi, Hanlin Daniel Chien, Ricardo Rey Diez, David Cavazos Woo, Emily Ronshien Su, Sha Chang
  • DUPLICATE BUG REPORT DETECTION USING MACHINE LEARNING ALGORITHMS AND AUTOMATED FEEDBACK INCORPORATION
    Publication number: 20170199803
    Abstract: Duplicate bug report detection using machine learning algorithms and automated feedback incorporation is disclosed. For each set of bug reports, a user-classification of the set of bug reports as including duplicate bug reports or non-duplicate bug reports is identified. Also for each set of bug reports, correlation values corresponding to a respective feature, of a plurality of features, between bug reports in the set of bug reports is identified. Based on the user-classifications and the correlation values, a model is generated to identify any set of bug reports as including duplicate bug reports or non-duplicate bug reports. The model is applied to classify a particular bug report and a candidate bug report as duplicate bug reports or non-duplicate bug reports.
    Type: Application
    Filed: January 11, 2016
    Publication date: July 13, 2017
    Inventors: Prasad V. Bagal, Sameer Arun Joshi, Hanlin Daniel Chien, Ricardo Rey Diez, David Cavazos Woo, Emily Ronshien Su, Sha Chang