Patents by Inventor Manisha MUKHERJEE

Manisha MUKHERJEE 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).

  • Source code retrieval
    Patent number: 11651014
    Abstract: A method may include obtaining training code and extracting features from the training code. The extracted features of the training code may be mapped to natural language code vectors by a deep neural network. A natural language search query requesting source-code suggestions may be received, and the natural language search query may be mapped to a natural language search vector by the deep neural network. The method may include mapping the natural language search query to the natural language search vector in the same or a similar method as mapping the extracted features of the training code to natural language code vectors, and the natural language search vector may be compared to the natural language code vectors. Source code responsive to the natural language search query may be suggested based on the comparison between the natural language search vector and the natural language code vectors.
    Type: Grant
    Filed: October 30, 2020
    Date of Patent: May 16, 2023
    Assignee: FUJITSU LIMITED
    Inventors: Mehdi Bahrami, Manisha Mukherjee, Wei-Peng Chen
  • SOURCE CODE RETRIEVAL
    Publication number: 20220138240
    Abstract: A method may include obtaining training code and extracting features from the training code. The extracted features of the training code may be mapped to natural language code vectors by a deep neural network. A natural language search query requesting source-code suggestions may be received, and the natural language search query may be mapped to a natural language search vector by the deep neural network. The method may include mapping the natural language search query to the natural language search vector in the same or a similar method as mapping the extracted features of the training code to natural language code vectors, and the natural language search vector may be compared to the natural language code vectors. Source code responsive to the natural language search query may be suggested based on the comparison between the natural language search vector and the natural language code vectors.
    Type: Application
    Filed: October 30, 2020
    Publication date: May 5, 2022
    Applicant: FUJITSU LIMITED
    Inventors: Mehdi BAHRAMI, Manisha MUKHERJEE, Wei-Peng CHEN