Patents by Inventor Sandeep Repaka

Sandeep Repaka 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).

  • Publication number: 20230229941
    Abstract: Rule induction is used to produce human readable descriptions of patterns within a dataset. A rule induction algorithm or classifier is a type supervised machine learning classification algorithm. A rule induction classifier is trained, which involves using labelled examples in the dataset to produce a set of rules. Rather than using the rules/classifier to make predictions on new unlabeled samples, the training of the rule induction model outputs human-readable descriptions of patterns (rules) within the dataset that gave rise to the rules (rather than using the rules to predict new unlabeled samples). Parameters of the rule induction algorithm are tuned to favor simple and understandable rules, instead of only tuning for predictive accuracy. The learned set of rules are outputted during the training process in a human-friendly format.
    Type: Application
    Filed: March 24, 2023
    Publication date: July 20, 2023
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Edmund Chi Man Tse, Brett Owens Simons, Sandeep Repaka, Yatpang Cheung
  • Patent number: 11640543
    Abstract: Rule induction is used to produce human readable descriptions of patterns within a dataset. A rule induction algorithm or classifier is a type supervised machine learning classification algorithm. A rule induction classifier is trained, which involves using labelled examples in the dataset to produce a set of rules. Rather than using the rules/classifier to make predictions on new unlabeled samples, the training of the rule induction model outputs human-readable descriptions of patterns (rules) within the dataset that gave rise to the rules (rather than using the rules to predict new unlabeled samples). Parameters of the rule induction algorithm are tuned to favor simple and understandable rules, instead of only tuning for predictive accuracy. The learned set of rules are outputted during the training process in a human-friendly format.
    Type: Grant
    Filed: June 17, 2019
    Date of Patent: May 2, 2023
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Edmund Chi Man Tse, Brett Owens Simons, Sandeep Repaka, Yatpang Cheung
  • Publication number: 20220283864
    Abstract: Described are examples for tracking memory usage of a driver. A memory allocation request related to the driver to allocate a portion of memory for the driver can be traced in a kernel mode of an operating system. One or more associated allocation parameters can be recorded, and an allocation history of the driver over a period of time can be reported during execution of the driver and based on the one or more allocation parameters indicated by the memory allocation request.
    Type: Application
    Filed: March 4, 2021
    Publication date: September 8, 2022
    Inventors: Hyuk Joon KWON, Sandeep REPAKA, Andrew M. KLUEMKE, Jakob F. LICHTENBERG, Sebastian LERNER, Matthew John WOOLMAN, Swati KANCHAN
  • Publication number: 20200394530
    Abstract: Rule induction is used to produce human readable descriptions of patterns within a dataset. A rule induction algorithm or classifier is a type supervised machine learning classification algorithm. A rule induction classifier is trained, which involves using labelled examples in the dataset to produce a set of rules. Rather than using the rules/classifier to make predictions on new unlabeled samples, the training of the rule induction model outputs human-readable descriptions of patterns (rules) within the dataset that gave rise to the rules (rather than using the rules to predict new unlabeled samples). Parameters of the rule induction algorithm are tuned to favor simple and understandable rules, instead of only tuning for predictive accuracy. The learned set of rules are outputted during the training process in a human-friendly format.
    Type: Application
    Filed: June 17, 2019
    Publication date: December 17, 2020
    Inventors: Edmund Chi Man Tse, Brett Owens Simons, Sandeep Repaka, Yatpang Cheung