Patents by Inventor Ramtin Mehdizadeh SERAJ

Ramtin Mehdizadeh SERAJ 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).

  • Distributed machine learning via secure multi-party computation and ensemble learning
    Patent number: 11811934
    Abstract: Systems and methods for combining input data and machine learning models that remain secret to each entity are described. This disclosure can allow groups of entities to compute predictions based on datasets that are larger and more detailed collectively than individually, without revealing their data to other parties. This is of particular use in artificial intelligence (AI) tasks in domains which deal with sensitive data, such as medical, financial, or cybersecurity.
    Type: Grant
    Filed: February 10, 2022
    Date of Patent: November 7, 2023
    Assignee: Dapper Labs, Inc.
    Inventors: Ramtin Mehdizadeh Seraj, Nicholas Chow
  • Distributed machine learning via secure multi-party computation and ensemble learning
    Patent number: 11792010
    Abstract: Systems and methods for combining input data and machine learning models that remain secret to each entity are described. This disclosure can allow groups of entities to compute predictions based on datasets that are larger and more detailed collectively than individually, without revealing their data to other parties. This is of particular use in artificial intelligence (AI) tasks in domains which deal with sensitive data, such as medical, financial, or cybersecurity.
    Type: Grant
    Filed: February 8, 2022
    Date of Patent: October 17, 2023
    Assignee: Dapper Labs, Inc.
    Inventors: Ramtin Mehdizadeh Seraj, Nicholas Chow
  • DISTRIBUTED MACHINE LEARNING VIA SECURE MULTI-PARTY COMPUTATION AND ENSEMBLE LEARNING
    Publication number: 20220239487
    Abstract: Systems and methods for combining input data and machine learning models that remain secret to each entity are described. This disclosure can allow groups of entities to compute predictions based on datasets that are larger and more detailed collectively than individually, without revealing their data to other parties. This is of particular use in artificial intelligence (AI) tasks in domains which deal with sensitive data, such as medical, financial, or cybersecurity.
    Type: Application
    Filed: February 10, 2022
    Publication date: July 28, 2022
    Inventors: Ramtin Mehdizadeh Seraj, Nicholas Chow
  • DISTRIBUTED MACHINE LEARNING VIA SECURE MULTI-PARTY COMPUTATION AND ENSEMBLE LEARNING
    Publication number: 20220166624
    Abstract: Systems and methods for combining input data and machine learning models that remain secret to each entity are described. This disclosure can allow groups of entities to compute predictions based on datasets that are larger and more detailed collectively than individually, without revealing their data to other parties. This is of particular use in artificial intelligence (AI) tasks in domains which deal with sensitive data, such as medical, financial, or cybersecurity.
    Type: Application
    Filed: February 8, 2022
    Publication date: May 26, 2022
    Applicant: Dapper Labs Inc.
    Inventors: Ramtin Mehdizadeh SERAJ, Nicholas CHOW
  • Distributed machine learning via secure multi-party computation and ensemble learning
    Patent number: 11265166
    Abstract: Systems and methods for combining input data and machine learning models that remain secret to each entity are described. This disclosure can allow groups of entities to compute predictions based on datasets that are larger and more detailed collectively than individually, without revealing their data to other parties. This is of particular use in artificial intelligence (AI) tasks in domains which deal with sensitive data, such as medical, financial, or cybersecurity.
    Type: Grant
    Filed: June 30, 2021
    Date of Patent: March 1, 2022
    Assignee: Dapper Labs Inc.
    Inventors: Ramtin Mehdizadeh Seraj, Nicholas Chow
  • DISTRIBUTED MACHINE LEARNING VIA SECURE MULTI-PARTY COMPUTATION AND ENSEMBLE LEARNING
    Publication number: 20210409220
    Abstract: Systems and methods for combining input data and machine learning models that remain secret to each entity are described. This disclosure can allow groups of entities to compute predictions based on datasets that are larger and more detailed collectively than individually, without revealing their data to other parties. This is of particular use in artificial intelligence (AI) tasks in domains which deal with sensitive data, such as medical, financial, or cybersecurity.
    Type: Application
    Filed: June 30, 2021
    Publication date: December 30, 2021
    Applicant: Dapper Labs Inc.
    Inventors: Ramtin Mehdizadeh SERAJ, Nicholas CHOW