Patents by Inventor Joshua Bates Stevens

Joshua Bates Stevens 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).

  • Code development using continued machine learnings
    Patent number: 10481879
    Abstract: Improving how a codebase, which may include source code, related databases, test files, code history, and/or changes, is drafted, edited, debugged, or otherwise developed. Machine learning is performed on a model codebase to establish a machine learning model. When a change to a codebase occurs, the machine learning model may be applied to evaluate that change. A change context providing context for this change is accessed. An analyzer then analyzes the change using the machine learning model and at least a part of the change context to generate an analysis result. Some information about the result is rendered. After rendering that information, a determination regarding how a user responded to the information is performed, and a subsequent analysis is then modified based on the user's response.
    Type: Grant
    Filed: March 30, 2018
    Date of Patent: November 19, 2019
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Joshua Bates Stevens, John S. Tilford, Guillermo Serrato Castilla, Srivatsn Narayanan, Simon Calvert, Mark Alistair Wilson-Thomas, Deborah Chen, Miltiadis Allamanis, Marc Manuel Johannes Brockschmidt, Kesavan Shanmugam
  • CODE DEVELOPMENT USING CONTINUED MACHINE LEARNINGS
    Publication number: 20190243617
    Abstract: Improving how a codebase, which may include source code, related databases, test files, code history, and/or changes, is drafted, edited, debugged, or otherwise developed. Machine learning is performed on a model codebase to establish a machine learning model. When a change to a codebase occurs, the machine learning model may be applied to evaluate that change. A change context providing context for this change is accessed. An analyzer then analyzes the change using the machine learning model and at least a part of the change context to generate an analysis result. Some information about the result is rendered. After rendering that information, a determination regarding how a user responded to the information is performed, and a subsequent analysis is then modified based on the user's response.
    Type: Application
    Filed: March 30, 2018
    Publication date: August 8, 2019
    Inventors: Joshua Bates STEVENS, John S. TILFORD, Guillermo Serrato CASTILLA, Srivatsn NARAYANAN, Simon CALVERT, Mark Alistair WILSON-THOMAS, Deborah CHEN, Miltiadis ALLAMANIS, Marc Manuel Johannes BROCKSCHMIDT, Kesavan SHANMUGAM