Patents by Inventor Gaurav Singh Thakur

Gaurav Singh Thakur 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).

  • Detecting a page for a real-world entity, an imposter of a real-world entity, or a non-real-world entity that complies with or violates a policy of an online system
    Patent number: 10893070
    Abstract: An online system maintains pages and accesses a graph of nodes representing the pages. Each node is labeled to indicate that a corresponding page is for a real-world entity, an imposter of the real-world entity, or a derived entity complying with or violating a policy. The online system retrieves machine-learning models, each of which is trained based on labels for a set of the nodes and features of corresponding pages. A first model predicts whether a page is for a derived entity based on features of the page. Responsive to predicting the page is not for a derived entity, a second model predicts whether the page is for a real-world entity or an imposter based on features of the page. Responsive to predicting the page is for a derived entity, a third model predicts whether the derived entity complies with or violates the policy based on features of the page.
    Type: Grant
    Filed: April 18, 2019
    Date of Patent: January 12, 2021
    Assignee: Facebook, Inc.
    Inventors: Haotian Wang, Komal Kapoor, Gaurav Singh Thakur
  • DETECTING A PAGE FOR A REAL-WORLD ENTITY, AN IMPOSTER OF A REAL-WORLD ENTITY, OR A NON-REAL-WORLD ENTITY THAT COMPLIES WITH OR VIOLATES A POLICY OF AN ONLINE SYSTEM
    Publication number: 20200336509
    Abstract: An online system maintains pages and accesses a graph of nodes representing the pages. Each node is labeled to indicate that a corresponding page is for a real-world entity, an imposter of the real-world entity, or a derived entity complying with or violating a policy. The online system retrieves machine-learning models, each of which is trained based on labels for a set of the nodes and features of corresponding pages. A first model predicts whether a page is for a derived entity based on features of the page. Responsive to predicting the page is not for a derived entity, a second model predicts whether the page is for a real-world entity or an imposter based on features of the page. Responsive to predicting the page is for a derived entity, a third model predicts whether the derived entity complies with or violates the policy based on features of the page.
    Type: Application
    Filed: April 18, 2019
    Publication date: October 22, 2020
    Inventors: Haotian Wang, Komal Kapoor, Gaurav Singh Thakur