Patents by Inventor Paul Baseotto

Paul Baseotto 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).

  • Enhanced machine learning refinement and alert generation system
    Patent number: 12261872
    Abstract: Systems and methods are provided for enhanced machine learning refinement and alert generation. An example method includes accessing datasets storing customer information reflecting transactions of customers. Individual risk scores are generated for the customers based on the customer information. Generating the risk score includes providing identified occurrences of scenario definitions and customer information as input to one or more machine learning models, the scenario definitions identifying occurrences of specific information reflected in the datasets, with the machine learning models assign respective risk scores to the customers. An interactive user interface is presented.
    Type: Grant
    Filed: August 16, 2021
    Date of Patent: March 25, 2025
    Assignee: Palantir Technologies Inc.
    Inventors: Corentin Petit, Jacob Albertson, Marissa Kimball, Paul Baseotto, Pierre Cholet, Timur Iskhakov, Victoria Galano
  • ENTITY MATCHING WITH MACHINE LEARNING FUZZY LOGIC
    Publication number: 20240427783
    Abstract: A fuzzy matching system matching data records in one or more data sets based on user-customized selection of multiple fuzzy matching algorithms. Possible matches may be displayed to a user, who provides feedback on the accuracy of the matches, which may then be used by a machine learning algorithm to update weightings and parameters of the multiple fuzzy matching algorithms, such as based on machine learning analysis of the matching results and the user feedback.
    Type: Application
    Filed: September 9, 2024
    Publication date: December 26, 2024
    Inventors: Elliot Hirsch, Johannes Beil, Lauren Brown, Nicolas Prettejohn, Paul Baseotto
  • Entity matching with machine learning fuzzy logic
    Patent number: 12111839
    Abstract: A fuzzy matching system matching data records in one or more data sets based on user-customized selection of multiple fuzzy matching algorithms. Possible matches may be displayed to a user, who provides feedback on the accuracy of the matches, which may then be used by a machine learning algorithm to update weightings and parameters of the multiple fuzzy matching algorithms, such as based on machine learning analysis of the matching results and the user feedback.
    Type: Grant
    Filed: June 13, 2023
    Date of Patent: October 8, 2024
    Assignee: Palantir Technologies Inc.
    Inventors: Elliot Hirsch, Johannes Beil, Lauren Brown, Nicolas Prettejohn, Paul Baseotto
  • Entity matching with machine learning fuzzy logic
    Patent number: 11720580
    Abstract: A fuzzy matching system matching data records in one or more data sets based on user-customized selection of multiple fuzzy matching algorithms. Possible matches may be displayed to a user, who provides feedback on the accuracy of the matches, which may then be used by a machine learning algorithm to update weightings and parameters of the multiple fuzzy matching algorithms, such as based on machine learning analysis of the matching results and the user feedback.
    Type: Grant
    Filed: March 1, 2022
    Date of Patent: August 8, 2023
    Assignee: Palantir Technologies Inc.
    Inventors: Elliot Hirsch, Johannes Beil, Lauren Brown, Nicolas Prettejohn, Paul Baseotto
  • ENHANCED MACHINE LEARNING REFINEMENT AND ALERT GENERATION SYSTEM
    Publication number: 20220201030
    Abstract: Systems and methods are provided for enhanced machine learning refinement and alert generation. An example method includes accessing datasets storing customer information reflecting transactions of customers. Individual risk scores are generated for the customers based on the customer information. Generating the risk score includes providing identified occurrences of scenario definitions and customer information as input to one or more machine learning models, the scenario definitions identifying occurrences of specific information reflected in the datasets, with the machine learning models assign respective risk scores to the customers. An interactive user interface is presented.
    Type: Application
    Filed: August 16, 2021
    Publication date: June 23, 2022
    Inventors: Corentin Petit, Jacob Albertson, Marissa Kimball, Paul Baseotto, Pierre Cholet, Timur Iskhakov, Victoria Galano