Patents by Inventor Craig Vermeer

Craig Vermeer 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).

  • METHODS AND APPARATUS FOR SELF-ADAPTIVE TIME SERIES FORECASTING ENGINE
    Publication number: 20220358528
    Abstract: An apparatus has a memory with processor-executable instructions and a processor operatively coupled to the memory. The apparatus receives datasets including time series data points that are descriptive of a feature of a given entity. The processor determines a time series characteristic based on the data content, and selects, based on the determined characteristic, a set of entrant forecasting models from a pool of forecasting models stored in the memory. Next, the processor trains each entrant forecasting model with the time series data points to produce a set of trained entrant forecasting models. The processor executes each trained entrant forecasting model to generate a set of forecasted values indicating estimations of the feature of the given entity. Thereafter the processor selects at least one forecasting model from the set of trained entrant forecasting models based on computed accuracy evaluations performed over the set of forecasted values.
    Type: Application
    Filed: February 14, 2022
    Publication date: November 10, 2022
    Applicant: DataRobot, Inc.
    Inventors: John Bledsoe, Jeff Gabriel, Jason Montgomery, Ryan Sevey, Matt Steinpreis, Craig Vermeer, Ryan West
  • Methods for self-adaptive time series forecasting, and related systems and apparatus
    Patent number: 11250449
    Abstract: An apparatus has a memory with processor-executable instructions and a processor operatively coupled to the memory. The apparatus receives datasets including time series data points that are descriptive of a feature of a given entity. The processor determines a time series characteristic based on the data content, and selects, based on the determined characteristic, a set of entrant forecasting models from a pool of forecasting models stored in the memory. Next, the processor trains each entrant forecasting model with the time series data points to produce a set of trained entrant forecasting models. The processor executes each trained entrant forecasting model to generate a set of forecasted values indicating estimations of the feature of the given entity. Thereafter the processor selects at least one forecasting model from the set of trained entrant forecasting models based on computed accuracy evaluations performed over the set of forecasted values.
    Type: Grant
    Filed: July 9, 2019
    Date of Patent: February 15, 2022
    Assignee: DataRobot, Inc.
    Inventors: John Bledsoe, Jeff Gabriel, Jason Montgomery, Ryan Sevey, Matt Steinpreis, Craig Vermeer, Ryan West
  • Methods and apparatus for self-adaptive time series forecasting engine
    Patent number: 10387900
    Abstract: An apparatus has a memory with processor-executable instructions and a processor operatively coupled to the memory. The apparatus receives datasets including time series data points that are descriptive of a feature of a given entity. The processor determines a time series characteristic based on the data content, and selects, based on the determined characteristic, a set of entrant forecasting models from a pool of forecasting models stored in the memory. Next, the processor trains each entrant forecasting model with the time series data points to produce a set of trained entrant forecasting models. The processor executes each trained entrant forecasting model to generate a set of forecasted values indicating estimations of the feature of the given entity. Thereafter the processor selects at least one forecasting model from the set of trained entrant forecasting models based on computed accuracy evaluations performed over the set of forecasted values.
    Type: Grant
    Filed: April 17, 2017
    Date of Patent: August 20, 2019
    Assignee: DataRobot, Inc.
    Inventors: John Bledsoe, Jeff Gabriel, Jason Montgomery, Ryan Sevey, Matt Steinpreis, Craig Vermeer, Ryan West
  • METHODS AND APPARATUS FOR SELF-ADAPTIVE TIME SERIES FORECASTING ENGINE
    Publication number: 20180300737
    Abstract: An apparatus has a memory with processor-executable instructions and a processor operatively coupled to the memory. The apparatus receives datasets including time series data points that are descriptive of a feature of a given entity. The processor determines a time series characteristic based on the data content, and selects, based on the determined characteristic, a set of entrant forecasting models from a pool of forecasting models stored in the memory. Next, the processor trains each entrant forecasting model with the time series data points to produce a set of trained entrant forecasting models. The processor executes each trained entrant forecasting model to generate a set of forecasted values indicating estimations of the feature of the given entity. Thereafter the processor selects at least one forecasting model from the set of trained entrant forecasting models based on computed accuracy evaluations performed over the set of forecasted values.
    Type: Application
    Filed: April 17, 2017
    Publication date: October 18, 2018
    Inventors: John Bledsoe, Jeff Gabriel, Jason Montgomery, Ryan Sevey, Matt Steinpreis, Craig Vermeer, Ryan West