Patents by Inventor Dimitrios BRISIMITZIS

Dimitrios BRISIMITZIS 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).

  • Time-factored performance prediction
    Patent number: 11829855
    Abstract: Training query intents are allocated for multiple training entities into training time intervals in a time series based on a corresponding query intent time for each training query intent. Training performance results for the multiple training entities are allocated into the training time intervals in the time series based on a corresponding performance time of each training performance result. A machine learning model for a training milestone of the time series is trained based on the training query intents allocated to a training time interval prior to the training milestone and the training performance results allocated to a training time interval after the training milestone. Target performance for the target entity for an interval after a target milestone in the time series is predicted by inputting to the trained machine learning model target query intents allocated to the target entity in a target time interval before the target milestone.
    Type: Grant
    Filed: May 25, 2022
    Date of Patent: November 28, 2023
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Mayank Shrivastava, Hui Zhou, Pushpraj Shukla, Emre Hamit Kok, Sonal Prakash Mane, Dimitrios Brisimitzis
  • TIME-FACTORED PERFORMANCE PREDICTION
    Publication number: 20220284350
    Abstract: Training query intents are allocated for multiple training entities into training time intervals in a time series based on a corresponding query intent time for each training query intent. Training performance results for the multiple training entities are allocated into the training time intervals in the time series based on a corresponding performance time of each training performance result. A machine learning model for a training milestone of the time series is trained based on the training query intents allocated to a training time interval prior to the training milestone and the training performance results allocated to a training time interval after the training milestone. Target performance for the target entity for an interval after a target milestone in the time series is predicted by inputting to the trained machine learning model target query intents allocated to the target entity in a target time interval before the target milestone.
    Type: Application
    Filed: May 25, 2022
    Publication date: September 8, 2022
    Inventors: Mayank SHRIVASTAVA, Hui ZHOU, Pushpraj SHUKLA, Emre Hamit KOK, Sonal Prakash MANE, Dimitrios BRISIMITZIS
  • Time-factored performance prediction
    Patent number: 11361244
    Abstract: Training query intents are allocated for multiple training entities into training time intervals in a time series based on a corresponding query intent time for each training query intent. Training performance results for the multiple training entities are allocated into the training time intervals in the time series based on a corresponding performance time of each training performance result. A machine learning model for a training milestone of the time series is trained based on the training query intents allocated to a training time interval prior to the training milestone and the training performance results allocated to a training time interval after the training milestone. Target performance for the target entity for an interval after a target milestone in the time series is predicted by inputting to the trained machine learning model target query intents allocated to the target entity in a target time interval before the target milestone.
    Type: Grant
    Filed: June 8, 2018
    Date of Patent: June 14, 2022
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Mayank Shrivastava, Hui Zhou, Pushpraj Shukla, Emre Hamit Kok, Sonal Prakash Mane, Dimitrios Brisimitzis
  • TIME-FACTORED PERFORMANCE PREDICTION
    Publication number: 20190378048
    Abstract: Training query intents are allocated for multiple training entities into training time intervals in a time series based on a corresponding query intent time for each training query intent. Training performance results for the multiple training entities are allocated into the training time intervals in the time series based on a corresponding performance time of each training performance result. A machine learning model for a training milestone of the time series is trained based on the training query intents allocated to a training time interval prior to the training milestone and the training performance results allocated to a training time interval after the training milestone. Target performance for the target entity for an interval after a target milestone in the time series is predicted by inputting to the trained machine learning model target query intents allocated to the target entity in a target time interval before the target milestone.
    Type: Application
    Filed: June 8, 2018
    Publication date: December 12, 2019
    Inventors: Mayank SHRIVASTAVA, Hui ZHOU, Pushpraj SHUKLA, Emre Hamit KOK, Sonal Prakash MANE, Dimitrios BRISIMITZIS