Patents by Inventor Amita Surendra Gajewar

Amita Surendra Gajewar 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).

  • Predictive modeling across multiple horizons combining time series and external data
    Patent number: 10671931
    Abstract: A multi-horizon predictor system that predicts a future parameter value for multiple horizons based on time-series data of the parameter, external data, and machine-learning. For a given time horizon, a time series data splitter splits the time into training data corresponding to a training time period, and a validation time period corresponding to a validation time period between the training time period and the given horizon. A model tuner tunes the prediction model of the given horizon fitting an initial prediction model to the parameter using the training data thereby using machine learning. The model tuner also tunes the initial prediction model by adjusting an effect of the external data on the prediction to generate a final prediction model for the given horizon using the validation data. A multi-horizon predictor causes the time series data splitter and the model tuner to operate for each of multiple horizons.
    Type: Grant
    Filed: June 9, 2016
    Date of Patent: June 2, 2020
    Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC
    Inventors: Gagan Bansal, Amita Surendra Gajewar, Debraj GuhaThakurta, Konstantin Golyaev, Mayank Shrivastava, Vijay Krishna Narayanan, Walter Sun
  • PREDICTIVE MODELING ACROSS MULTIPLE HORIZONS COMBINING TIME SERIES & EXTERNAL DATA
    Publication number: 20170220939
    Abstract: A multi-horizon predictor system that predicts a future parameter value for multiple horizons based on time-series data of the parameter, external data, and machine-learning. For a given time horizon, a time series data splitter splits the time into training data corresponding to a training time period, and a validation time period corresponding to a validation time period between the training time period and the given horizon. A model tuner tunes the prediction model of the given horizon fitting an initial prediction model to the parameter using the training data thereby using machine learning. The model tuner also tunes the initial prediction model by adjusting an effect of the external data on the prediction to generate a final prediction model for the given horizon using the validation data. A multi-horizon predictor causes the time series data splitter and the model tuner to operate for each of multiple horizons.
    Type: Application
    Filed: June 9, 2016
    Publication date: August 3, 2017
    Inventors: Gagan Bansal, Amita Surendra Gajewar, Debraj GuhaThakurta, Konstantin Golyaev, Mayank Shrivastava, Vijay Krishna Narayanan, Walter Sun