Patents by Inventor Chandra Khatri

Chandra Khatri 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: 12282743
    Abstract: Described herein is an Autonomous Conversational AI system, which does not require any human configuration or annotation, and is used to have multi-turn dialogs with a user. A typical Conversational AI system consists of three main models: Natural Language Understanding (NLU), Dialog Manager (DM) and Natural Language Generation (NLG), which requires human provided data and configuration. The system proposed herein leverages novel Conversational AI methods which automatically generates conversational AI configuration from any historical conversation logs. The automatically generated configuration contains Auto-Topics, Auto-Subtopics, Auto-Intents, Auto-Responses and Auto-Flows which are used to automatically train NLU, DM and NLG models. Once these models are trained for given conversation logs, the system can be used to have dialog with any user.
    Type: Grant
    Filed: January 6, 2022
    Date of Patent: April 22, 2025
    Assignee: GICRM AI LLC
    Inventors: Amol Kelkar, Nikhil Varghese, Chandra Khatri, Utkarsh Mittal, Nachiketa Rajpurohit, Peter Relan, Hung Tran
  • Publication number: 20240028658
    Abstract: One or more of the systems, apparatuses, or methods discussed herein can include a quality score for a plurality of item listings or collections of item listings. Data sparseness can be avoided, as the quality score is based on inherent properties of the listing. An item listing can be recommended to a user based on the quality score. In one or more embodiments, a method can include determining a plurality of quality scores including a quality score for each of a plurality of item listings or a plurality of collections of item listings, the quality scores determined independent of a user's attributes and independent of the user's contextual information, the contextual information corresponding to details of the user's access to a website, and recommending an item listing or collection of item listings to a user based on the quality scores and the contextual information.
    Type: Application
    Filed: October 2, 2023
    Publication date: January 25, 2024
    Applicant: eBay Inc.
    Inventors: Chandra Khatri, Steven Hui Luan, Michael Tanaka, Praveen K. Boinapalli
  • Patent number: 11809501
    Abstract: One or more of the systems, apparatuses, or methods discussed herein can include a quality score for a plurality of item listings or collections of item listings. Data sparseness can be avoided, as the quality score is based on inherent properties of the listing. An item listing can be recommended to a user based on the quality score. In one or more embodiments, a method can include determining a plurality of quality scores including a quality score for each of a plurality of item listings or a plurality of collections of item listings, the quality scores determined independent of a user's attributes and independent of the user's contextual information, the contextual information corresponding to details of the user's access to a website, and recommending an item listing or collection of item listings to a user based on the quality scores and the contextual information.
    Type: Grant
    Filed: December 30, 2014
    Date of Patent: November 7, 2023
    Assignee: eBay Inc.
    Inventors: Chandra Khatri, Steven Hui Luan, Michael Tanaka, Praveen K. Boinapalli
  • Publication number: 20230274095
    Abstract: Described herein is an Autonomous Conversational AI system, which does not require any human configuration or annotation, and is used to have multi-tum dialogs with a user. A typical Conversational AI system consists of three main models: Natural Language Understanding (NLU), Dialog Manager (DM) and Natural Language Generation (NLG), which requires human provided data and configuration. The system proposed herein leverages novel Conversational AI methods which automatically generates conversational AI configuration from any historical conversation logs. The automatically generated configuration contains Auto-Topics, Auto-Subtopics, Auto-Intents, Auto-Responses and Auto-Flows which are used to automatically train NLU, DM and NLG models. Once these models are trained for given conversation logs, the system can be used to have dialog with any user.
    Type: Application
    Filed: January 6, 2022
    Publication date: August 31, 2023
    Inventors: Amol Kelkar, Nikhil Varghese, Chandra Khatri, Utkarsh Mittal, Nachiketa Rajpurohit, Peter Relan, Hung Tran
  • Publication number: 20160063065
    Abstract: One or more of the systems, apparatuses, or methods discussed herein can include a quality score for a plurality of item listings or collections of item listings. Data sparseness can be avoided, as the quality score is based on inherent properties of the listing. An item listing can be recommended to a user based on the quality score. In one or more embodiments, a method can include determining a plurality of quality scores including a quality score for each of a plurality of item listings or a plurality of collections of item listings, the quality scores determined independent of a user's attributes and independent of the user's contextual information, the contextual information corresponding to details of the user's access to a website, and recommending an item listing or collection of item listings to a user based on the quality scores and the contextual information.
    Type: Application
    Filed: December 30, 2014
    Publication date: March 3, 2016
    Inventors: Chandra Khatri, Steven Hui Luan, Michael Tanaka, Praveen K. Boinapalli