Patents by Inventor Xuye Liu

Xuye Liu 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).

  • Learning-based automated machine learning code annotation with graph neural network
    Patent number: 11928156
    Abstract: Obtain, at a computing device, a segment of computer code. With a classification module of a machine learning system executing on the computing device, determine a required annotation category for the segment of computer code. With an annotation generation module of the machine learning system executing on the computing device, generate a natural language annotation of the segment of computer code based on the segment of computer code and the required annotation category. Provide the natural language annotation to a user interface for display adjacent the segment of computer code.
    Type: Grant
    Filed: November 3, 2020
    Date of Patent: March 12, 2024
    Assignee: International Business Machines Corporation
    Inventors: Dakuo Wang, Lingfei Wu, Xuye Liu, Yi Wang, Chuang Gan, Jing Xu, Xue Ying Zhang, Jun Wang, Jing James Xu
  • Learning-based automation machine learning code annotation in computational notebooks
    Patent number: 11360763
    Abstract: One embodiment of the invention provides a method for automated code annotation in machine learning (ML) and data science. The method comprises receiving, as input, a section of executable code. The method further comprises classifying, via a ML model, the section of executable code with a stage classification label indicative of a stage within a workflow for automated ML that the executable code applies to. The method further comprises categorizing, based on the stage classification label, the section of executable code with a category of annotation that is most appropriate for the section of executable code. The method further comprises generating a suggested annotation for the section of executable code based on the category of annotation. The method further comprises providing, as output, the suggested annotation to a display of an electronic device for user review. The suggested annotation is user interactable via the electronic device.
    Type: Grant
    Filed: October 13, 2020
    Date of Patent: June 14, 2022
    Assignee: International Business Machines Corporation
    Inventors: Dakuo Wang, Lingfei Wu, Yi Wang, Xuye Liu, Chuang Gan, Si Er Han, Bei Chen, Ji Hui Yang
  • LEARNING-BASED AUTOMATED MACHINE LEARNING CODE ANNOTATION WITH GRAPH NEURAL NETWORK
    Publication number: 20220138266
    Abstract: Obtain, at a computing device, a segment of computer code. With a classification module of a machine learning system executing on the computing device, determine a required annotation category for the segment of computer code. With an annotation generation module of the machine learning system executing on the computing device, generate a natural language annotation of the segment of computer code based on the segment of computer code and the required annotation category. Provide the natural language annotation to a user interface for display adjacent the segment of computer code.
    Type: Application
    Filed: November 3, 2020
    Publication date: May 5, 2022
    Inventors: Dakuo Wang, Lingfei Wu, Xuye Liu, Yi Wang, Chuang Gan, Jing Xu, Xue Ying Zhang, Jun Wang
  • LEARNING-BASED AUTOMATION MACHINE LEARNING CODE ANNOTATION IN COMPUTATIONAL NOTEBOOKS
    Publication number: 20220113964
    Abstract: One embodiment of the invention provides a method for automated code annotation in machine learning (ML) and data science. The method comprises receiving, as input, a section of executable code. The method further comprises classifying, via a ML model, the section of executable code with a stage classification label indicative of a stage within a workflow for automated ML that the executable code applies to. The method further comprises categorizing, based on the stage classification label, the section of executable code with a category of annotation that is most appropriate for the section of executable code. The method further comprises generating a suggested annotation for the section of executable code based on the category of annotation. The method further comprises providing, as output, the suggested annotation to a display of an electronic device for user review. The suggested annotation is user interactable via the electronic device.
    Type: Application
    Filed: October 13, 2020
    Publication date: April 14, 2022
    Inventors: Dakuo Wang, Lingfei Wu, Yi Wang, Xuye Liu, Chuang Gan, Si Er Han, Bei Chen, Ji Hui Yang