Patents by Inventor Lukas Dylan OSBORNE

Lukas Dylan OSBORNE 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).

  • Self learning machine learning transaction scores adjustment via normalization thereof accounting for underlying transaction score bases relating to an occurrence of fraud in a transaction
    Patent number: 11694208
    Abstract: Provided are a system and methodology for iteratively measuring data, as between multiple sets thereof, that accounts for underlying data generation sources and bases. Doing so, via normalization of the data, enables uniformity of interpretation and presentation of the data no matter the machine learning model that produced the data.
    Type: Grant
    Filed: December 5, 2022
    Date of Patent: July 4, 2023
    Assignee: Socure, Inc.
    Inventors: Pablo Ysrrael Abreu, Omar Gutierrez, Ali Haddad, Stanislav Palatnik, Lukas Dylan Osborne
  • Self Learning Machine Learning Transaction Scores Adjustment via Normalization Thereof Accounting for Underlying Transaction Score Bases
    Publication number: 20230169511
    Abstract: Provided are a system and methodology for iteratively measuring data, as between multiple sets thereof, that accounts for underlying data generation sources and bases. Doing so, via normalization of the data, enables uniformity of interpretation and presentation of the data no matter the machine learning model that produced the data.
    Type: Application
    Filed: December 5, 2022
    Publication date: June 1, 2023
    Applicant: Socure, Inc.
    Inventors: Pablo Ysrrael ABREU, Omar GUTIERREZ, Ali HADDAD, Stanislav PALATNIK, Lukas Dylan OSBORNE
  • Self learning machine learning transaction scores adjustment via normalization thereof accounting for underlying transaction score bases
    Patent number: 11544715
    Abstract: Provided are a system and methodology for iteratively transforming data, as between multiple sets thereof, that account for underlying data generation sources and bases. Doing so, via normalization of the data, enables uniformity of interpretation and presentation of the data no matter the machine learning model that produced the data.
    Type: Grant
    Filed: March 7, 2022
    Date of Patent: January 3, 2023
    Assignee: Socure, Inc.
    Inventors: Pablo Ysrrael Abreu, Omar Gutierrez, Ali Haddad, Stansilav Palatnik, Lukas Dylan Osborne
  • Self Learning Machine Learning Transaction Scores Adjustment via Normalization Thereof Accounting for Underlying Transaction Score Bases
    Publication number: 20220327542
    Abstract: Provided are a system and methodology for iteratively transforming data, as between multiple sets thereof, that account for underlying data generation sources and bases. Doing so, via normalization of the data, enables uniformity of interpretation and presentation of the data no matter the machine learning model that produced the data.
    Type: Application
    Filed: March 7, 2022
    Publication date: October 13, 2022
    Applicant: Socure, Inc.
    Inventors: Pablo Ysrrael ABREU, Omar GUTIERREZ, Ali HADDAD, Stanislav PALATNIK, Lukas Dylan OSBORNE
  • Self Learning Machine Learning Pipeline for Enabling Binary Decision Making
    Publication number: 20220027916
    Abstract: The system and methodology of the present invention uses feedback data received as a result of previous binary decisions made and uses an applicable score as well as other data, in some cases, to update and optimize a rules base such that scoring is incrementally improved over time as more and more feedback data is provided to the system.
    Type: Application
    Filed: July 23, 2020
    Publication date: January 27, 2022
    Applicant: Socure, Inc.
    Inventors: Pablo Ysrrael ABREU, Omar GUTIERREZ, Lukas Dylan OSBORNE