Patents by Inventor Tomas Cacicedo

Tomas Cacicedo 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).

  • Publication number: 20240232655
    Abstract: Systems and methods for classifying gaps in network activity as normal or anomalous are disclosed. A computer system can identify time gaps between successive network events, which can comprise communications or interactions between entities or devices on a network. The computer system can identify network event data records corresponding to network events that occurred both before and after the identified time gaps. The computer system can use data contained in network event data records corresponding to these network events to derive data features that can be used to train a machine learning to classify time gaps based on those features. After training the machine learning model, the computer system can then extract data features corresponding to unlabeled time gaps, and input those data features into the trained machine learning model in order to classify those time gaps as normal or anomalous.
    Type: Application
    Filed: March 31, 2023
    Publication date: July 11, 2024
    Applicant: Visa International Service Association
    Inventors: Tomas Cacicedo, Arya Eskamani, Debesh Kumar
  • Publication number: 20240135203
    Abstract: Systems and methods for classifying gaps in network activity as normal or anomalous are disclosed. A computer system can identify time gaps between successive network events, which can comprise communications or interactions between entities or devices on a network. The computer system can identify network event data records corresponding to network events that occurred both before and after the identified time gaps. The computer system can use data contained in network event data records corresponding to these network events to derive data features that can be used to train a machine learning to classify time gaps based on those features. After training the machine learning model, the computer system can then extract data features corresponding to unlabeled time gaps, and input those data features into the trained machine learning model in order to classify those time gaps as normal or anomalous.
    Type: Application
    Filed: March 30, 2023
    Publication date: April 25, 2024
    Applicant: Visa International Service Association
    Inventors: Tomas Cacicedo, Arya Eskamani, Debesh Kumar