Patents by Inventor Scott Christopher Locklin

Scott Christopher Locklin 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).

  • LOAD FORECASTING FROM INDIVIDUAL CUSTOMER TO SYSTEM LEVEL
    Publication number: 20220172233
    Abstract: The present invention relates to system and method for providing near real-time DR events and price signals to the customer end-points to optimally manage the available DR resources. The system utilizes bottom up load forecasting for accurate individualized forecasts for customer loads in the presence of dynamic pricing signals. For better efficiency and reliability of grid operation the system utilizes advanced machine learning and robust optimization techniques for real-time and “personalized” DR-offer dispatch.
    Type: Application
    Filed: July 6, 2021
    Publication date: June 2, 2022
    Applicant: Autogrid Systems, Inc.
    Inventors: Amit NARAYAN, Scott Christopher LOCKLIN, Vijay Srikrishna BHAT, Henry SCHWARZ
  • System for Asymmetric Just-in-time Human Intervention in Automated Vehicle Fleets
    Publication number: 20180039265
    Abstract: The claimed subject matter provides a system and method that anticipates a particular coming failure in the automated vehicle industry, which is that it will not actually be fully automated, and introduces a novel computer and processor-based mechanism for economically working around this deficiency, which is to provide on-demand access to oversubscribed banks of remote human operators, the technical details of which follow.
    Type: Application
    Filed: August 4, 2016
    Publication date: February 8, 2018
    Inventors: Kevin Christopher Lawler, Scott Christopher Locklin
  • LOAD FORECASTING FROM INDIVIDUAL CUSTOMER TO SYSTEM LEVEL BASED ON PRICE
    Publication number: 20150046221
    Abstract: The present invention relates to system and method for providing near real-time DR events and price signals to the customer end-points to optimally manage the available DR resources. The system utilizes bottom up load forecasting for accurate individualized forecasts for customer loads in the presence of dynamic pricing signals. For better efficiency and reliability of grid operation the system utilizes advanced machine learning and robust optimization techniques for real-time and “personalized” DR-offer dispatch.
    Type: Application
    Filed: September 14, 2012
    Publication date: February 12, 2015
    Applicant: AUTOGRID INC.
    Inventors: Amit Narayan, Scott Christopher Locklin, Vijay Srikrishna Bhat, Henry Schwarz