Patents by Inventor Harsh Bhattachar

Harsh Bhattachar 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).

  • Predicting network anomaly events
    Patent number: 12113686
    Abstract: In one example, a method for prediction future network anomaly events is disclosed. The method includes generating a machine learning model for a computer communication network. Generation of the machine learning model can be by applying a historical set of time series data metrics of the computer communication network to train the machine learning model. The method may determine a data metric threshold that indicates a limit for future data metrics associated with the computer communication network. The method may analyze current data metrics associated with the computer communication network, and predict a future time when the data metrics associated with the computer communication network will meet or exceed the data metric threshold value. The method may flag the prediction of the future time to avoid a network anomaly.
    Type: Grant
    Filed: December 6, 2022
    Date of Patent: October 8, 2024
    Assignee: JPMORGAN CHASE BANK, N.A.
    Inventors: Garima Juneja, Wanpeng Liu, David D. Lin, Harsh Bhattachar, Bing Zhang
  • Determining false positive and active event data
    Patent number: 12061516
    Abstract: In one example of the present disclosure, a method for determining false positive and active indications is disclosed. The method may apply anomaly network event data to a machine learning model. The machine learning model is trained with historic and correlated anomaly network event data. The method then determines which one of the anomaly network event data is a false positive indication and which one is an active indication.
    Type: Grant
    Filed: October 25, 2022
    Date of Patent: August 13, 2024
    Assignee: JPMORGAN CHASE BANK, N.A.
    Inventors: Garima Juneja, Wanpeng Liu, David D. Lin, Harsh Bhattachar, Bing Zhang
  • DETERMINING FALSE POSITIVE AND ACTIVE EVENT DATA
    Publication number: 20240231990
    Abstract: In one example of the present disclosure, a method for determining false positive and active indications is disclosed. The method may apply anomaly network event data to a machine learning model. The machine learning model is trained with historic and correlated anomaly network event data. The method then determines which one of the anomaly network event data is a false positive indication and which one is an active indication.
    Type: Application
    Filed: October 25, 2022
    Publication date: July 11, 2024
    Applicant: JPMorgan Chase Bank, N.A.
    Inventors: Garima Juneja, Wanpeng Liu, David D. Lin, Harsh Bhattachar, Bing Zhang
  • PREDICTING NETWORK ANOMALY EVENTS
    Publication number: 20240187321
    Abstract: In one example, a method for prediction future network anomaly events is disclosed. The method includes generating a machine learning model for a computer communication network. Generation of the machine learning model can be by applying a historical set of time series data metrics of the computer communication network to train the machine learning model. The method may determine a data metric threshold that indicates a limit for future data metrics associated with the computer communication network. The method may analyze current data metrics associated with the computer communication network, and predict a future time when the data metrics associated with the computer communication network will meet or exceed the data metric threshold value. The method may flag the prediction of the future time to avoid a network anomaly.
    Type: Application
    Filed: December 6, 2022
    Publication date: June 6, 2024
    Inventors: Garima Juneja, Wanpeng Liu, David D. Lin, Harsh Bhattachar, Bing Zhang
  • DETERMINING FALSE POSITIVE AND ACTIVE EVENT DATA
    Publication number: 20240134734
    Abstract: In one example of the present disclosure, a method for determining false positive and active indications is disclosed. The method may apply anomaly network event data to a machine learning model. The machine learning model is trained with historic and correlated anomaly network event data. The method then determines which one of the anomaly network event data is a false positive indication and which one is an active indication.
    Type: Application
    Filed: October 24, 2022
    Publication date: April 25, 2024
    Applicant: JPMorgan Chase Bank, N.A.
    Inventors: Garima Juneja, Wanpeng Liu, David D. Lin, Harsh Bhattachar, Bing Zhang