Patents by Inventor Avid Boustani

Avid Boustani 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).

  • SYSTEMS AND METHODS FOR UTILIZING MACHINE LEARNING TO IDENTIFY NON-TECHNICAL LOSS
    Publication number: 20240045659
    Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating to a plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.
    Type: Application
    Filed: October 23, 2023
    Publication date: February 8, 2024
    Inventors: Thomas M. Siebel, Edward Y. Abbo, Houman Behzadi, Avid Boustani, Nikhil Krishnan, Kuenley Chiu, Henrik Ohlsson, Louis Poirier, Jeremy Kolter
  • Systems and methods for utilizing machine learning to identify non-technical loss
    Patent number: 11886843
    Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating toa plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.
    Type: Grant
    Filed: August 1, 2022
    Date of Patent: January 30, 2024
    Assignee: C3.ai, Inc.
    Inventors: Thomas M. Siebel, Edward Y. Abbo, Houman Behzadi, Avid Boustani, Nikhil Krishnan, Kuenley Chiu, Henrik Ohlsson, Louis Poirier, Jeremy Kolter
  • SYSTEMS AND METHODS FOR UTILIZING MACHINE LEARNING TO IDENTIFY NON-TECHNICAL LOSS
    Publication number: 20230027296
    Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating toa plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.
    Type: Application
    Filed: August 1, 2022
    Publication date: January 26, 2023
    Inventors: Thomas M. Siebel, Edward Y. Abbo, Houman Behzadi, Avid Boustani, Nikhil Krishnan, Kuenley Chiu, Henrik Ohlsson, Louis Poirier, Jeremy Kolter
  • Systems and methods for utilizing machine learning to identify non-technical loss
    Patent number: 11449315
    Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating to a plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.
    Type: Grant
    Filed: April 5, 2019
    Date of Patent: September 20, 2022
    Assignee: C3.AI, INC.
    Inventors: Thomas M. Siebel, Edward Y. Abbo, Houman Behzadi, Avid Boustani, Nikhil Krishnan, Kuenley Chiu, Henrik Ohlsson, Louis Poirier, Zico Kolter
  • SYSTEMS AND METHODS FOR UTILIZING MACHINE LEARNING TO IDENTIFY NON-TECHNICAL LOSS
    Publication number: 20190340535
    Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating to a plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.
    Type: Application
    Filed: April 5, 2019
    Publication date: November 7, 2019
    Inventors: Thomas M. Siebel, Edward Y. Abbo, Houman Behzadi, Avid Boustani, Nikhil Krishnan, Kuenley Chiu, Henrik Ohlsson, Louis Poirier, Zico Kolter
  • Systems and methods for utilizing machine learning to identify non-technical loss
    Patent number: 10296843
    Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating to a plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.
    Type: Grant
    Filed: September 24, 2014
    Date of Patent: May 21, 2019
    Assignee: C3 IoT, Inc.
    Inventors: Thomas M. Siebel, Edward Y. Abbo, Houman Behzadi, Avid Boustani, Nikhil Krishnan, Kuenley Chiu, Henrik Ohlsson, Louis Poirier, Zico Kolter
  • SYSTEMS AND METHODS FOR UTILIZING MACHINE LEARNING TO IDENTIFY NON-TECHNICAL LOSS
    Publication number: 20180107941
    Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating to a plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.
    Type: Application
    Filed: September 24, 2014
    Publication date: April 19, 2018
    Inventors: Thomas M. Siebel, Edward Y. Abbo, Houman Behzadi, Avid Boustani, Nikhil Krishnan, Kuenley Chiu, Henrik Ohlsson, Louis Poirier, Zico Kolter
  • SYSTEMS AND METHODS FOR UTILIZING MACHINE LEARNING TO IDENTIFY NONTECHNICAL LOSS
    Publication number: 20150046378
    Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating to a plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.
    Type: Application
    Filed: September 24, 2014
    Publication date: February 12, 2015
    Inventors: Thomas M. Siebel, Edward Y. Abbo, Houman Behzadi, Avid Boustani, Nikhil Krishnan, Kuenley Chiu, Henrik Ohlsson, Louis Poirier, Zico Kolter