Patents by Inventor Benjamin J. LOFTON

Benjamin J. LOFTON 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).

  • DATA ANOMALY DETECTION
    Publication number: 20240160554
    Abstract: Systems and methods for data anomaly detection include recommending one or more algorithms from a set of algorithms to process received time series data, wherein the one or more algorithms are recommended based at least in part on a type of workload for processing the received time series data. Assisted parameter tuning is provided for a detected anomaly alert and calibration, and the received time series data is processed based on a user selected algorithm that is parameter tuned, thereby resulting in more efficient and reliable anomaly detection.
    Type: Application
    Filed: January 18, 2024
    Publication date: May 16, 2024
    Inventors: Roman BATOUKOV, Richard WYDROWSKI, Sai Sankalp ARRABOLU, Zeqiang WANG, Lech GUDALEWICZ, Keiji KANAZAWA, Benjamin J. LOFTON, Thomas W. POTTHAST, Suren AGHAJANYAN, Khoa TRAN, Jian ZHANG
  • Data anomaly detection
    Patent number: 11921609
    Abstract: Systems and methods for data anomaly detection include recommending one or more algorithms from a set of algorithms to process received time series data, wherein the one or more algorithms are recommended based at least in part on a type of workload for processing the received time series data. Assisted parameter tuning is provided for a detected anomaly alert and calibration, and the received time series data is processed based on a user selected algorithm that is parameter tuned.
    Type: Grant
    Filed: May 13, 2022
    Date of Patent: March 5, 2024
    Assignee: Microsoft Technology Licensing, LLC.
    Inventors: Roman Batoukov, Richard Wydrowski, Sai Sankalp Arrabolu, Zeqiang Wang, Lech Gudalewicz, Keiji Kanazawa, Benjamin J. Lofton, Thomas W. Potthast, Suren Aghajanyan, Khoa Tran, Jian Zhang
  • DATA ANOMALY DETECTION
    Publication number: 20220269908
    Abstract: Systems and methods for data anomaly detection include recommending one or more algorithms from a set of algorithms to process received time series data, wherein the one or more algorithms are recommended based at least in part on a type of workload for processing the received time series data. Assisted parameter tuning is provided for a detected anomaly alert and calibration, and the received time series data is processed based on a user selected algorithm that is parameter tuned, thereby resulting in more efficient and reliable anomaly detection.
    Type: Application
    Filed: May 13, 2022
    Publication date: August 25, 2022
    Inventors: Roman BATOUKOV, Richard WYDROWSKI, Sai Sankalp ARRABOLU, Zeqiang WANG, Lech GUDALEWICZ, Keiji KANAZAWA, Benjamin J. LOFTON, Thomas W. POTTHAST, Suren AGHAJANYAN, Khoa TRAN, Jian ZHANG
  • Data anomaly detection
    Patent number: 11341374
    Abstract: Systems and methods for data anomaly detection include recommending one or more algorithms from a set of algorithms to process received time series data, wherein the one or more algorithms are recommended based at least in part on a type of workload for processing the received time series data. Assisted parameter tuning is provided for a detected anomaly alert and calibration, and the received time series data is processed based on a user selected algorithm that is parameter tuned.
    Type: Grant
    Filed: January 14, 2019
    Date of Patent: May 24, 2022
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Roman Batoukov, Richard Wydrowski, Sai Sankalp Arrabolu, Zeqiang Wang, Lech Gudalewicz, Keiji Kanazawa, Benjamin J. Lofton, Thomas W. Potthast, Suren Aghajanyan, Khoa Tran, Jian Zhang
  • DATA ANOMALY DETECTION
    Publication number: 20190370610
    Abstract: Systems and methods for data anomaly detection include recommending one or more algorithms from a set of algorithms to process received time series data, wherein the one or more algorithms are recommended based at least in part on a type of workload for processing the received time series data. Assisted parameter tuning is provided for a detected anomaly alert and calibration, and the received time series data is processed based on a user selected algorithm that is parameter tuned, thereby resulting in more efficient and reliable anomaly detection.
    Type: Application
    Filed: January 14, 2019
    Publication date: December 5, 2019
    Inventors: Roman BATOUKOV, Richard WYDROWSKI, Sai Sankalp ARRABOLU, Zeqiang WANG, Lech GUDALEWICZ, Keiji KANAZAWA, Benjamin J. LOFTON, Thomas W. POTTHAST, Suren AGHAJANYAN, Khoa TRAN, Jian ZHANG