Patents by Inventor Sergey Yurgenson

Sergey Yurgenson 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).

  • Patent number: 11922329
    Abstract: A predictive modeling method may include obtaining a fitted, first-order predictive model configured to predict values of output variables based on values of first input variables; and performing a second-order modeling procedure on the fitted, first-order model, which may include: generating input data including observations including observed values of second input variables and predicted values of the output variables; generating training data and testing data from the input data; generating a fitted second-order model of the fitted first-order model by fitting a second-order model to the training data; and testing the fitted, second-order model of the first-order model on the testing data. Each observation of the input data may be generated by (1) obtaining observed values of the second input variables, and (2) applying the first-order predictive model to corresponding observed values of the first input variables to generate the predicted values of the output variables.
    Type: Grant
    Filed: December 20, 2019
    Date of Patent: March 5, 2024
    Assignee: DataRobot, Inc.
    Inventors: Jeremy Achin, Thomas DeGodoy, Timothy Owen, Xavier Conort, Sergey Yurgenson, Mark L. Steadman, Glen Koundry, Hon Nian Chua
  • Publication number: 20230051833
    Abstract: Systems and methods of epidemiological modeling using machine learning are provided, and can include receiving values for an occurrence of the infectious disease during a first time period, generating, from a model trained by a machine learning system, predictions for the occurrence of the infectious disease over a second time period, performing, by a simulator using the predictions, one or more simulations of the occurrence of the infectious disease in one or more geographic regions during one or more time periods subsequent to the second time period, and providing, to a user interface, a first simulation of the one or more simulations performed by the simulator for a first geographic region of the one or more geographic regions during a time period of the one or more time periods.
    Type: Application
    Filed: July 28, 2022
    Publication date: February 16, 2023
    Applicant: DataRobot, Inc.
    Inventors: Jeremy Achin, Michael Schmidt, Mackenzie Heiser, Jona Sassenhagen, Oleg Baranovskiy, Jared Shamwell, Hon Nian Chua, Joao Paulo Gomes, Maxence Jeunesse, Yung Siang Liau, Julian Wergieluk, Jay Cameron Schuren, Mark Steadman, Mohak Saxena, Samuel Clark, Noa Flaherty, Jarred Bultema, Nathan Robert Cameron, Amanda Schierz, Vinay Venkata Wunnava, Xavier Conort, Gregory Michaelson, Anton Suslov, Madeleine Mott, Sergey Yurgenson, Christopher James Monsour, Matthew Joseph Nitzken, Patrick Allen Farrell, Jared Bowns, Dustin Burke, Ievgenii Baliuk, Rishabh Raman
  • Publication number: 20200134489
    Abstract: A predictive modeling method may include obtaining a fitted, first-order predictive model configured to predict values of output variables based on values of first input variables; and performing a second-order modeling procedure on the fitted, first-order model, which may include: generating input data including observations including observed values of second input variables and predicted values of the output variables; generating training data and testing data from the input data; generating a fitted second-order model of the fitted first-order model by fitting a second-order model to the training data; and testing the fitted, second-order model of the first-order model on the testing data. Each observation of the input data may be generated by (1) obtaining observed values of the second input variables, and (2) applying the first-order predictive model to corresponding observed values of the first input variables to generate the predicted values of the output variables.
    Type: Application
    Filed: December 20, 2019
    Publication date: April 30, 2020
    Inventors: Jeremy Achin, Thomas DeGodoy, Timothy Owen, Xavier Conort, Sergey Yurgenson, Mark L. Steadman, Glen Koundry, Hon Nian Chua
  • Publication number: 20200090075
    Abstract: A method for determining the predictive value of a feature may include: (a) performing predictive modeling procedures associated with respective predictive models, wherein performing each modeling procedure includes fitting the associated model to an initial dataset representing an initial prediction problem; (b) determining a first accuracy score of each of the fitted models, representing an accuracy with which the fitted model predicts an outcome of the initial prediction problem; (c) shuffling values of a feature across observations included in the initial dataset, thereby generating a modified dataset representing a modified prediction problem; (d) determining a second accuracy score of each of the fitted models, representing an accuracy with which the fitted model predicts an outcome of the modified prediction problem; and (e) determining a model-specific predictive value of the feature for each of the fitted models based on the first and second accuracy scores of the fitted model.
    Type: Application
    Filed: June 20, 2019
    Publication date: March 19, 2020
    Applicant: DataRobot, Inc.
    Inventors: Jeremy Achin, Thomas DeGodoy, Xavier Conort, Sergey Yurgenson, Mark L. Steadman, Glen Koundry
  • Patent number: 10558924
    Abstract: A predictive modeling method may include obtaining a fitted, first-order predictive model configured to predict values of output variables based on values of first input variables; and performing a second-order modeling procedure on the fitted, first-order model, which may include: generating input data including observations including observed values of second input variables and predicted values of the output variables; generating training data and testing data from the input data; generating a fitted second-order model of the fitted first-order model by fitting a second-order model to the training data; and testing the fitted, second-order model of the first-order model on the testing data. Each observation of the input data may be generated by (1) obtaining observed values of the second input variables, and (2) applying the first-order predictive model to corresponding observed values of the first input variables to generate the predicted values of the output variables.
    Type: Grant
    Filed: October 23, 2017
    Date of Patent: February 11, 2020
    Assignee: DataRobot, Inc.
    Inventors: Jeremy Achin, Thomas DeGodoy, Timothy Owen, Xavier Conort, Sergey Yurgenson, Mark L. Steadman, Glen Koundry, Hon Nian Chua
  • Patent number: 10496927
    Abstract: A predictive modeling method may include determining a time interval of time-series data; identifying one or more variables of the data as targets; determining a forecast range and a skip range associated with a prediction problem represented by the data; generating training data and testing data from the time-series data; fitting a predictive model to the training data; and testing the fitted model on the testing data. The forecast range may indicate a duration of a period for which values of the targets are to be predicted. The skip range may indicate a temporal lag between the time period corresponding to the data used to make predictions and the time period corresponding to the predictions. The skip range may separate input data subsets representing model inputs from subsets representing model outputs, and separate test data subsets representing model inputs from subsets representing validation data.
    Type: Grant
    Filed: October 23, 2017
    Date of Patent: December 3, 2019
    Assignee: DataRobot, Inc.
    Inventors: Jeremy Achin, Thomas DeGodoy, Timothy Owen, Xavier Conort, Sergey Yurgenson, Mark L. Steadman, Glen Koundry, Peter Prettenhofer
  • Patent number: 10366346
    Abstract: A method for determining the predictive value of a feature may include: (a) performing predictive modeling procedures associated with respective predictive models, wherein performing each modeling procedure includes fitting the associated model to an initial dataset representing an initial prediction problem; (b) determining a first accuracy score of each of the fitted models, representing an accuracy with which the fitted model predicts an outcome of the initial prediction problem; (c) shuffling values of a feature across observations included in the initial dataset, thereby generating a modified dataset representing a modified prediction problem; (d) determining a second accuracy score of each of the fitted models, representing an accuracy with which the fitted model predicts an outcome of the modified prediction problem; and (e) determining a model-specific predictive value of the feature for each of the fitted models based on the first and second accuracy scores of the fitted model.
    Type: Grant
    Filed: October 21, 2016
    Date of Patent: July 30, 2019
    Assignee: DataRobot, Inc.
    Inventors: Jeremy Achin, Thomas DeGodoy, Xavier Conort, Sergey Yurgenson, Mark L. Steadman, Glen Koundry