Patents by Inventor YIJIN WEI

YIJIN WEI 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).

  • Detection of runtime errors using machine learning
    Patent number: 11599447
    Abstract: Runtime errors in a source code program are detected in advance of execution by machine learning models. Features representing a context of a runtime error are extracted from source code programs to train a machine learning model, such as a random forest classifier, to predict the likelihood that a code snippet has a particular type of runtime error. The features are extracted from a syntax-type tree representation of each method in a program. A model is generated for distinct runtime errors, such as arithmetic overflow, and conditionally uninitialized variables.
    Type: Grant
    Filed: July 4, 2022
    Date of Patent: March 7, 2023
    Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC.
    Inventors: Shaun Miller, Kalpathy Sitaraman Sivaraman, Neelakantan Sundaresan, Yijin Wei, Roshanak Zilouchian Moghaddam
  • Cloud resource management using machine learning
    Patent number: 11526370
    Abstract: A cloud resource management system trains, through ensemble learning, multiple time series forecasting models to forecast a future idle time of a virtual machine operating on a cloud computing service. The models are trained on historical usage and metric data of the virtual machine. The metric data includes CPU usage, disk usage and network usage. A select one of the models having the best accuracy for a target virtual machine is used in a production run to predict when the virtual machine will be idle. At this time, the virtual machine may be automatically shutdown in order to reduce the expense associated with the continued operation of the virtual machine.
    Type: Grant
    Filed: March 10, 2019
    Date of Patent: December 13, 2022
    Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC.
    Inventors: Yiping Dou, Tanmayee Prakash Kamath, Arun Ramanathan Chandrasekhar, Claude Remillard, Mark Steven Schnitzer, Balan Subramanian, Neelakantan Sundaresan, Yijin Wei
  • DETECTION OF RUNTIME ERRORS USING MACHINE LEARNING
    Publication number: 20220342800
    Abstract: Runtime errors in a source code program are detected in advance of execution by machine learning models. Features representing a context of a runtime error are extracted from source code programs to train a machine learning model, such as a random forest classifier, to predict the likelihood that a code snippet has a particular type of runtime error. The features are extracted from a syntax-type tree representation of each method in a program. A model is generated for distinct runtime errors, such as arithmetic overflow, and conditionally uninitialized variables.
    Type: Application
    Filed: July 4, 2022
    Publication date: October 27, 2022
    Inventors: SHAUN MILLER, KALPATHY SITARAMAN SIVARAMAN, NEELAKANTAN SUNDARESAN, YIJIN WEI, ROSHANAK ZILOUCHIAN MOGHADDAM
  • Detection of runtime errors using machine learning
    Patent number: 11403207
    Abstract: Runtime errors in a source code program are detected in advance of execution by machine learning models. Features representing a context of a runtime error are extracted from source code programs to train a machine learning model, such as a random forest classifier, to predict the likelihood that a code snippet has a particular type of runtime error. The features are extracted from a syntax-type tree representation of each method in a program. A model is generated for distinct runtime errors, such as arithmetic overflow, and conditionally uninitialized variables.
    Type: Grant
    Filed: February 28, 2020
    Date of Patent: August 2, 2022
    Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC.
    Inventors: Shaun Miller, Kalpathy Sitaraman Sivaraman, Neelakantan Sundaresan, Yijin Wei, Roshanak Zilouchian Moghaddam
  • DETECTION OF RUNTIME ERRORS USING MACHINE LEARNING
    Publication number: 20210271587
    Abstract: Runtime errors in a source code program are detected in advance of execution by machine learning models. Features representing a context of a runtime error are extracted from source code programs to train a machine learning model, such as a random forest classifier, to predict the likelihood that a code snippet has a particular type of runtime error. The features are extracted from a syntax-type tree representation of each method in a program. A model is generated for distinct runtime errors, such as arithmetic overflow, and conditionally uninitialized variables.
    Type: Application
    Filed: February 28, 2020
    Publication date: September 2, 2021
    Inventors: SHAUN MILLER, KALPATHY SITARAMAN SIVARAMAN, NEELAKANTAN SUNDARESAN, YIJIN WEI, ROSHANAK ZILOUCHIAN MOGHADDAM
  • CLOUD RESOURCE MANAGEMENT USING MACHINE LEARNING
    Publication number: 20200285503
    Abstract: A cloud resource management system trains, through ensemble learning, multiple time series forecasting models to forecast a future idle time of a virtual machine operating on a cloud computing service. The models are trained on historical usage and metric data of the virtual machine. The metric data includes CPU usage, disk usage and network usage. A select one of the models having the best accuracy for a target virtual machine is used in a production run to predict when the virtual machine will be idle. At this time, the virtual machine may be automatically shutdown in order to reduce the expense associated with the continued operation of the virtual machine.
    Type: Application
    Filed: March 10, 2019
    Publication date: September 10, 2020
    Inventors: YIPING DOU, TANMAYEE PRAKASH KAMATH, ARUN RAMANATHAN CHANDRASEKHAR, CLAUDE REMILLARD, MARK STEVEN SCHNITZER, BALAN SUBRAMANIAN, NEELAKANTAN SUNDARESAN, YIJIN WEI