Patents by Inventor Suresh LADAPURAM SOUNDARAJAN

Suresh LADAPURAM SOUNDARAJAN 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).

  • Updating data models to manage data drift and outliers
    Patent number: 11954129
    Abstract: The present invention relates to a system and a method for updating data models. Input data received from a data source and/or prediction data obtained from a data model is reduced based on baseline reference data to obtain a plurality of representative points. The plurality of representative points are clustered to generate a plurality of clusters. An outlier cluster is detected from the plurality of clusters based on a maximum distance of the plurality of clusters from a highest density cluster and/or comparison of quantity and values of the plurality of representative points with predefined rules. Data drift is identified based on changes in densities of the plurality of clusters. The data model is updated using information corresponding to the outlier cluster and the data drift.
    Type: Grant
    Filed: April 8, 2021
    Date of Patent: April 9, 2024
    Assignee: Hewlett Packard Enterprise Development LP
    Inventors: Satish Kumar Mopur, Sridhar Balachandriah, Gunalan Perumal Vijayan, Suresh Ladapuram Soundarajan, Krishna Prasad Lingadahalli Shastry
  • UPDATING DATA MODELS TO MANAGE DATA DRIFTAND OUTLIERS
    Publication number: 20210365478
    Abstract: The present invention relates to a system and a method for updating data models. Input data received from a data source and/or prediction data obtained from a data model is reduced based on baseline reference data to obtain a plurality of representative points. The plurality of representative points are clustered to generate a plurality of clusters. An outlier cluster is detected from the plurality of clusters based on a maximum distance of the plurality of clusters from a highest density cluster and/or comparison of quantity and values of the plurality of representative points with predefined rules. Data drift is identified based on changes in densities of the plurality of clusters. The data model is updated using information corresponding to the outlier cluster and the data drift.
    Type: Application
    Filed: April 8, 2021
    Publication date: November 25, 2021
    Inventors: Satish Kumar MOPUR, Sridhar BALACHANDRIAH, Gunalan PERUMAL VIJAYAN, Suresh LADAPURAM SOUNDARAJAN, Krishna Prasad Lingadahalli SHASTRY