Patents by Inventor Patrick Ryan DRISCOLL

Patrick Ryan DRISCOLL 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).

  • Reversal-point-based detection and ranking
    Patent number: 12112263
    Abstract: In an example embodiment, a model is trained to specifically identify reversal points in data and then to rank these reversal points in order of importance. A reversal point shall be defined as a point in which a particular metric, specifically a first order derivative, crosses over from positive to negative or vice-versa. Users are more likely to be interested in abnormal and significant changes in data, and thus the machine-learned model is trained to evaluate a reversal point based on two dimensions: abnormality and significance.
    Type: Grant
    Filed: December 9, 2020
    Date of Patent: October 8, 2024
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Bo Yang, Chaofan Huang, Songtao Guo, Robert Perrin Reeves, Wan Qi Gao, Patrick Ryan Driscoll, Kristina Caroline Ryan, Michael Mario Jennings, Jeremy Lwanga, Manzarul Azad Kazi
  • TIME SERIES ANOMALY RANKING
    Publication number: 20220198264
    Abstract: In an example embodiment, a machine-learned model is trained to rank anomaly points in time series data. The model is capable of being applied in parallel to many different time series simultaneously, allowing for a scalable solution for large scale online networks. The model outputs a ranking score for an input anomaly and allows for ranking of anomalies not just in the same time series but anomalies across multiple time series as well. This ranking can then be used to determine how best to present the ranked anomalies to users in a graphical user interface.
    Type: Application
    Filed: December 23, 2020
    Publication date: June 23, 2022
    Inventors: Songtao Guo, Robert Perrin REEVES, Bo YANG, Wan Qi GAO, William TANG, Patrick Ryan DRISCOLL, Shan ZHOU, Taylor Shelby BURFIELD, Adriana Dominique MEZA
  • TIME SERIES ANOMALY DETECTION
    Publication number: 20220198263
    Abstract: In an example embodiment, a machine-learned model is trained to specifically identify anomaly points in time series data. The model is capable of being applied in parallel to many different time series simultaneously, allowing for a scalable solution for large scale online networks. The model classifies each data point in a specified time window and outputs rich contextual information for downstream applications, such as ranking and display of the anomalous data points.
    Type: Application
    Filed: December 23, 2020
    Publication date: June 23, 2022
    Inventors: Songtao Guo, Patrick Ryan Driscoll, Michael Mario Jennings, Robert Perrin Reeves, Bo Yang
  • REVERSAL-POINT-BASED DETECTION AND RANKING
    Publication number: 20220180181
    Abstract: In an example embodiment, a model is trained to specifically identify reversal points in data and then to rank these reversal points in order of importance. A reversal point shall be defined as a point in which a particular metric, specifically a first order derivative, crosses over from positive to negative or vice-versa. Users are more likely to be interested in abnormal and significant changes in data, and thus the machine-learned model is trained to evaluate a reversal point based on two dimensions: abnormality and significance.
    Type: Application
    Filed: December 9, 2020
    Publication date: June 9, 2022
    Inventors: Bo YANG, Chaofan HUANG, Songtao GUO, Robert Perrin REEVES, Wan Qi GAO, Patrick Ryan DRISCOLL, Kristina Caroline RYAN, Michael Mario JENNINGS, Jeremy LWANGA, Manzarul Azad KAZI