Patents by Inventor Aravindakshan B.

Aravindakshan B. 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: 20220147711
    Abstract: Example techniques for processing service notes are described. In an example, labeled service notes, associated with fuser units of a plurality of image rendering devices, are processed to generate a vector corresponding to each of the labeled service notes, a labeled service note comprising natural language text describing an error event and a corresponding service activity associated with a fuser unit, wherein the labeled service note is assigned a label based on a category of failure of the fuser unit. Based on the processing, a relationship between vectors and labels corresponding to the labeled service notes is generated.
    Type: Application
    Filed: November 25, 2019
    Publication date: May 12, 2022
    Applicant: Hewlett-Packard Development Company, L.P.
    Inventors: Anton Wiranata, Niranjan Damera Ventkata, Prasad Hegde, Aravindakshan B.
  • Patent number: 10747785
    Abstract: An optimized and efficient method of identifying one or more points within a dataset that are close to the centers of clumps similar records in a large, multi-element dataset uses Monte Carlo techniques to compute approximate clustering costs at significantly reduced computational expense. The inaccuracy caused by the approximate methods is also estimated, and if it is too high, the method may be repeated with a larger Monte Carlo sample size to improve accuracy.
    Type: Grant
    Filed: November 1, 2017
    Date of Patent: August 18, 2020
    Assignee: Mad Street Den, Inc.
    Inventors: Aravindakshan B, Sudarshan B, Hariharan Chandrasekaran
  • Publication number: 20190130017
    Abstract: An optimized and efficient method of identifying one or more points within a dataset that are close to the centers of clumps similar records in a large, multi-element dataset uses Monte Carlo techniques to compute approximate clustering costs at significantly reduced computational expense. The inaccuracy caused by the approximate methods is also estimated, and if it is too high, the method may be repeated with a larger Monte Carlo sample size to improve accuracy.
    Type: Application
    Filed: November 1, 2017
    Publication date: May 2, 2019
    Applicant: Mad Street Den, Inc.
    Inventors: Aravindakshan B., Sudarshan B., Hariharan CHANDRASEKARAN