Patents by Inventor Ayush Choure

Ayush Choure 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).

  • Patent number: 11886971
    Abstract: Systems and methods for entity recommendation can make use of rich data by allowing the items to be recommended and the recipients of the recommendation (e.g., users) to be modeled as “complex entities” composed of one or more static sub-entities and/or a dynamic component, and by utilizing information about multiple relationships between the sub-entities as reflected in bipartite graphs. Generating recommendations from such information may involve creating vector representations of the sub-entities based on the bipartite graphs (e.g., using graph-based convolutional networks), and combining these vector representations into representations of the items and users (or other recipients) to be fed into a classifier model.
    Type: Grant
    Filed: August 15, 2019
    Date of Patent: January 30, 2024
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Lekshmi Menon, Amar Budhiraja, Gaurush Hiranandani, Prateek Jain, Darshatkumar Anandji Shah, Ayush Choure, Navya Yarrabelly, Anurag Mishra, Mohammad Luqman, Shivangi Dhakad, Juhi Dua
  • Patent number: 11184301
    Abstract: Systems and methods for entity recommendation can make use of rich data by allowing the items to be recommended and the recipients of the recommendation (e.g., users) to be modeled as “complex entities” composed of one or more static sub-entities and/or a dynamic component, and by utilizing information about multiple relationships between the sub-entities as reflected in bipartite graphs. Generating recommendations from such information may involve creating vector representations of the sub-entities based on the bipartite graphs (e.g., using graph-based convolutional networks), and combining these vector representations into representations of the items and users (or other recipients) to be fed into a classifier model.
    Type: Grant
    Filed: August 15, 2019
    Date of Patent: November 23, 2021
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Lekshmi Menon, Amar Budhiraja, Gaurush Hiranandani, Prateek Jain, Darshatkumar Anandji Shah, Ayush Choure, Navya Yarrabelly, Anurag Mishra, Mohammad Luqman, Shivangi Dhakad, Juhi Dua
  • Publication number: 20210051121
    Abstract: Systems and methods for entity recommendation can make use of rich data by allowing the items to be recommended and the recipients of the recommendation (e.g., users) to be modeled as “complex entities” composed of one or more static sub-entities and/or a dynamic component, and by utilizing information about multiple relationships between the sub-entities as reflected in bipartite graphs. Generating recommendations from such information may involve creating vector representations of the sub-entities based on the bipartite graphs (e.g., using graph-based convolutional networks), and combining these vector representations into representations of the items and users (or other recipients) to be fed into a classifier model.
    Type: Application
    Filed: August 15, 2019
    Publication date: February 18, 2021
    Inventors: Lekshmi Menon, Amar Budhiraja, Gaurush Hiranandani, Prateek Jain, Darshatkumar Anandji Shah, Ayush Choure, Navya Yarrabelly, Anurag Mishra, Mohammad Luqman, Shivangi Dhakad, Juhi Dua
  • Publication number: 20210049442
    Abstract: Systems and methods for entity recommendation can make use of rich data by allowing the items to be recommended and the recipients of the recommendation (e.g., users) to be modeled as “complex entities” composed of one or more static sub-entities and/or a dynamic component, and by utilizing information about multiple relationships between the sub-entities as reflected in bipartite graphs. Generating recommendations from such information may involve creating vector representations of the sub-entities based on the bipartite graphs (e.g., using graph-based convolutional networks), and combining these vector representations into representations of the items and users (or other recipients) to be fed into a classifier model.
    Type: Application
    Filed: August 15, 2019
    Publication date: February 18, 2021
    Inventors: Lekshmi Menon, Amar Budhiraja, Gaurush Hiranandani, Prateek Jain, Darshatkumar Anandji Shah, Ayush Choure, Navya Yarrabelly, Anurag Mishra, Mohammad Luqman, Shivangi Dhakad, Juhi Dua