Patents by Inventor Mrinal RAWAT

Mrinal RAWAT 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).

  • Publication number: 20220342919
    Abstract: For various applications (for example, a Virtual Assistant), mechanisms that are capable of collecting user queries and generating responses are being used. While such systems handle structured queries well, they struggle to or fail to interpret an unstructured Natural Language (NL) query. The disclosure herein generally relates to data processing, and, more particularly, to a method and a system for generating responses to unstructured Natural Language (NL) queries. The system collects at least one NL query as input at a time, and generates a sketch, where the sketch is a structured representation of the unstructured NL query. Further by processing the sketch, the system generates one or more database queries. The one or more database queries are then used to search in one or more associated databases and to retrieve matching results, which are then used to generate response to the at least one NL query.
    Type: Application
    Filed: March 4, 2020
    Publication date: October 27, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: AMIT SANGROYA, GAUTAM SHROFF, CHANDRASEKHAR ANANTARAM, MRINAL RAWAT, PRATIK SAINI
  • Publication number: 20220284215
    Abstract: This disclosure relates to a method and system for extracting information from images of one or more templatized documents. A knowledge graph with a fixed schema based on background knowledge is used to capture spatial and semantic relationships of entities present in scanned document. An adaptive lattice-based approach based on formal concepts analysis (FCA) is used to determine a similarity metric that utilizes both spatial and semantic information to determine if the structure of the scanned document image adheres to any of the known document templates, If known document template whose structure is closely matching the structure of the scanned document is detected, then an inductive rule learning based approach is used to learn symbolic rules to extract information present in scanned document image. If a new document template is detected, then any future scanned document images belonging to new document template are automatically processed using the learnt rules.
    Type: Application
    Filed: May 27, 2021
    Publication date: September 8, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: Mouli RASTOGI, Syed Afshan ALI, Mrinal RAWAT, Lovekesh VIG, Puneet AGARWAL, Gautam SHROFF, Ashwin SRINIVASAN
  • Patent number: 11294946
    Abstract: This disclosure relates generally to methods and systems for generating a textual summary from a tabular data. During the textual summary generation using conventional end-to-end neural network-based techniques, a numeric data present in the tables is encoded via textual embeddings. However, the textual embeddings cannot reliably encode information about numeric concepts and relationships. The methods and systems generate the textual summary from the tabular data, by incorporating rank information for different records present in the tabular data. Then, a two-stage encoder-decoder network is used to learn correlations between the rank information and the probability of including the records based on the rank information, to obtain the textual summary generation model. The textual summary generation model identifies the content selection having the records present in the tables to be included in the textual summary and generates the textual summary from the identified content selection.
    Type: Grant
    Filed: May 13, 2021
    Date of Patent: April 5, 2022
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Mrinal Rawat, Lovekesh Vig, Amit Sangroya, Gautam Shroff
  • Publication number: 20210357443
    Abstract: This disclosure relates generally to methods and systems for generating a textual summary from a tabular data. During the textual summary generation using conventional end-to-end neural network-based techniques, a numeric data present in the tables is encoded via textual embeddings. However, the textual embeddings cannot reliably encode information about numeric concepts and relationships. The methods and systems generate the textual summary from the tabular data, by incorporating rank information for different records present in the tabular data. Then, a two-stage encoder-decoder network is used to learn correlations between the rank information and the probability of including the records based on the rank information, to obtain the textual summary generation model. The textual summary generation model identifies the content selection having the records present in the tables to be included in the textual summary and generates the textual summary from the identified content selection.
    Type: Application
    Filed: May 13, 2021
    Publication date: November 18, 2021
    Applicant: Tata Consultancy Services Limited
    Inventors: Mrinal RAWAT, Lovekesh VIG, Amit SANGROYA, Gautam SHROFF