Patents by Inventor Liat ELBOIM

Liat ELBOIM 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).

  • Artificial intelligence system for anomaly detection in transaction data sets
    Patent number: 12124933
    Abstract: An artificial intelligence system configured to detect anomalies in transaction data sets. The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform modeling operations which include receiving a first data set for training a first machine learning model to detect anomalies in the transaction data sets using a machine learning technique, accessing at least one micro-model trained using at least one second data set separate from the first data set, determining risk scores from the first data set using the at least one micro-model, enriching the first data set with the risk scores, and determining the first machine learning model for the enriched first data set using the machine learning technique.
    Type: Grant
    Filed: May 4, 2020
    Date of Patent: October 22, 2024
    Assignee: ACTIMIZE LTD.
    Inventors: Amir Shachar, Einat Neumann Ben Ari, Danny Butvinik, Yoav Avneon, Gabrielle Zaghdoun Azoulay, Liat Elboim
  • ARTIFICIAL INTELLIGENCE SYSTEM FOR ANOMALY DETECTION IN TRANSACTION DATA SETS
    Publication number: 20210342847
    Abstract: An artificial intelligence system configured to detect anomalies in transaction data sets. The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform modeling operations which include receiving a first data set for training a first machine learning model to detect anomalies in the transaction data sets using a machine learning technique, accessing at least one micro-model trained using at least one second data set separate from the first data set, determining risk scores from the first data set using the at least one micro-model, enriching the first data set with the risk scores, and determining the first machine learning model for the enriched first data set using the machine learning technique.
    Type: Application
    Filed: May 4, 2020
    Publication date: November 4, 2021
    Inventors: Amir SHACHAR, Einat Neumann BEN ARI, Danny BUTVINIK, Yoav AVNEON, Gabrielle Zaghdoun AZOULAY, Liat ELBOIM