Patents by Inventor Tsahi FLYSHER

Tsahi FLYSHER 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).

  • Event-based machine learning for a time-series metric
    Patent number: 12101343
    Abstract: A method for generating samples for an anomaly detection system includes receiving events that occurred during a time series of a target metric. Each respective event includes an event attribute characterizing the respective event. The method includes generating a set of event groups for the events. Each event shares a respective attribute with one or more other events of the respective event group. For each respective event group of the set of event groups, the method includes determining an influence pattern that identifies an influence of the respective event group on the target metric. The method includes clustering the set of event groups into event clusters based on a respective influence pattern of each respective event group. Each event cluster includes one or more event groups that share a similar influence pattern. The method includes generating training samples for an anomaly detection system based on a respective event cluster.
    Type: Grant
    Filed: March 25, 2022
    Date of Patent: September 24, 2024
    Assignee: Anodot Ltd.
    Inventors: Tsahi Flysher, Ira Cohen, Inbal Tadeski, Meir Toledano
  • Event-Based Machine Learning for a Time-Series Metric
    Publication number: 20230308461
    Abstract: A method for generating samples for an anomaly detection system includes receiving events that occurred during a time series of a target metric. Each respective event includes an event attribute characterizing the respective event. The method includes generating a set of event groups for the events. Each event shares a respective attribute with one or more other events of the respective event group. For each respective event group of the set of event groups, the method includes determining an influence pattern that identifies an influence of the respective event group on the target metric. The method includes clustering the set of event groups into event clusters based on a respective influence pattern of each respective event group. Each event cluster includes one or more event groups that share a similar influence pattern. The method includes generating training samples for an anomaly detection system based on a respective event cluster.
    Type: Application
    Filed: March 25, 2022
    Publication date: September 28, 2023
    Inventors: Tsahi FLYSHER, Ira COHEN, Inbal TADESKI, Meir TOLEDANO