Patents by Inventor Sofiane Mahiou

Sofiane Mahiou 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).

  • Synthetic generation of data with many to many relationships
    Patent number: 12373469
    Abstract: Embodiments described herein relate to the efficient generation of synthetic datasets that represent many-to-many relationships. In particular, certain embodiments implement a particular factorization for many-to-many generative models, which leads to a scalable generation framework by combining random graph theory and representation learning. Further embodiments we extend the framework to establish the notion of differential privacy within the synthetically generated data. The embodiments described herein are therefore able to generate synthetic datasets efficiently while preserving information within and across many-to-many datasets with improved accuracy.
    Type: Grant
    Filed: November 8, 2024
    Date of Patent: July 29, 2025
    Assignee: SAS Institute Inc.
    Inventors: Kai Xu, Georgi Valentinov Ganev, Emile Isak Joubert, Rees Stephen Davison, Olivier Rene Maurice Van Acker, Luke Anthony William Robinson, Sofiane Mahiou
  • SYNTHETIC GENERATION OF DATA WITH MANY TO MANY RELATIONSHIPS
    Publication number: 20250068658
    Abstract: Embodiments described herein relate to the efficient generation of synthetic datasets that represent many-to-many relationships. In particular, certain embodiments implement a particular factorization for many-to-many generative models, which leads to a scalable generation framework by combining random graph theory and representation learning. Further embodiments we extend the framework to establish the notion of differential privacy within the synthetically generated data. The embodiments described herein are therefore able to generate synthetic datasets efficiently while preserving information within and across many-to-many datasets with improved accuracy.
    Type: Application
    Filed: November 8, 2024
    Publication date: February 27, 2025
    Applicant: SAS Institute Inc.
    Inventors: Kai Xu, Georgi Valentinov Ganev, Emile Isak Joubert, Rees Stephen Davison, Olivier Rene Maurice Van Acker, Luke Anthony William Robinson, Sofiane Mahiou