Patents by Inventor Neha Bindumadhav Kulkarni

Neha Bindumadhav Kulkarni 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).

  • Computer implemented systems for automatic hierarchy for large scale time series data sets
    Patent number: 10540377
    Abstract: A hierarchical structure (e.g., a hierarchy) for use in hierarchical analysis (e.g., hierarchical forecasting) of timestamped data can be automatically generated. This automated approach to determining a hierarchical structure involves identifying attributes of the timestamped data, clustering the timestamped data to select attributes for the hierarchy, ordering the attributes to achieve a recommended hierarchical order, and optionally modifying the hierarchical order based on user input. Through the approach disclosed herein, a hierarchy can be generated that is designed to perform well under hierarchical models. This recommended hierarchy for use in hierarchical analysis may be agnostic to any planned hierarchy provided by or used by a user to otherwise interpret the timestamped data.
    Type: Grant
    Filed: April 9, 2019
    Date of Patent: January 21, 2020
    Assignee: SAS INSTITUTE INC.
    Inventors: Yue Li, Neha Bindumadhav Kulkarni, Yung-Hsin Chien, Sagar Arun Mainkar, Bhupendra Suresh Bendale
  • COMPUTER IMPLEMENTED SYSTEMS FOR AUTOMATIC HIERARCHY FOR LARGE SCALE TIME SERIES DATA SETS
    Publication number: 20190317952
    Abstract: A hierarchical structure (e.g., a hierarchy) for use in hierarchical analysis (e.g., hierarchical forecasting) of timestamped data can be automatically generated. This automated approach to determining a hierarchical structure involves identifying attributes of the timestamped data, clustering the timestamped data to select attributes for the hierarchy, ordering the attributes to achieve a recommended hierarchical order, and optionally modifying the hierarchical order based on user input. Through the approach disclosed herein, a hierarchy can be generated that is designed to perform well under hierarchical models. This recommended hierarchy for use in hierarchical analysis may be agnostic to any planned hierarchy provided by or used by a user to otherwise interpret the timestamped data.
    Type: Application
    Filed: April 9, 2019
    Publication date: October 17, 2019
    Applicant: SAS Institute Inc.
    Inventors: Yue Li, Neha Bindumadhav Kulkarni, Yung-Hsin Chien, Sagar Arun Mainkar, Bhupendra Suresh Bendale