Patents by Inventor Ashwani Kumar Luhaniwal

Ashwani Kumar Luhaniwal 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).

  • Significant correlation framework for command translation
    Patent number: 11281862
    Abstract: A significant correlation framework is provided herein for translating input commands to intents. The input commands may be natural language commands, received from a variety of input channels, which may be translated to intents or other runtime-bindable execution objects. The significant correlation framework may use interpreter nodes for translating the input commands by calculating the strength of correlation between an input command and an intent. The significant correlation framework may analyze the sequence of intents or the timing of translated intents to enhance the accuracy of the translation. The significant correlation framework may maintain a history of command translations, and may compare current translations against the history to improve accuracy of the translations. The significant correlation framework may switch between a depth-first mapping method and a breadth-first mapping method. Depth-first mapping may translate commands through a single interpreter node.
    Type: Grant
    Filed: June 28, 2019
    Date of Patent: March 22, 2022
    Assignee: SAP SE
    Inventors: Aavishkar Bharara, Anbarasu Ayyasami, Anil Rao Arun, Ramya K S, Deepanshi Katoch, Shrijan Shrivastav, Ankita Prabhu, Ashwani Kumar Luhaniwal
  • SIGNIFICANT CORRELATION FRAMEWORK FOR COMMAND TRANSLATION
    Publication number: 20200349228
    Abstract: A significant correlation framework is provided herein for translating input commands to intents. The input commands may be natural language commands, received from a variety of input channels, which may be translated to intents or other runtime-bindable execution objects. The significant correlation framework may use interpreter nodes for translating the input commands by calculating the strength of correlation between an input command and an intent. The significant correlation framework may analyze the sequence of intents or the timing of translated intents to enhance the accuracy of the translation. The significant correlation framework may maintain a history of command translations, and may compare current translations against the history to improve accuracy of the translations. The significant correlation framework may switch between a depth-first mapping method and a breadth-first mapping method. Depth-first mapping may translate commands through a single interpreter node.
    Type: Application
    Filed: June 28, 2019
    Publication date: November 5, 2020
    Applicant: SAP SE
    Inventors: Aavishkar Bharara, Anbarasu Ayyasami, Anil Rao Arun, Ramya KS, Deepanshi Katoch, Shrijan Shrivastav, Ankita Prabhu, Ashwani Kumar Luhaniwal