Patents by Inventor Udit Saini

Udit Saini 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: 20250013621
    Abstract: An example system for parsing and transforming input data that includes processing circuitry and memory, the memory configured to store the input data. The processing circuitry is configured to determine a first delimiter in the input data. The processing circuitry is configured to determine a plurality of second delimiter hypotheses and parse the input data according to the first delimiter and the plurality of second delimiter hypotheses to generate a plurality of tables that are each associated with a respective one of the plurality of second delimiter hypotheses. The processing circuitry is configured to determine a respective consistency score for each of the plurality of tables and select a table from among the plurality of tables based on the respective consistency score associated with the table. The processing circuitry is configured to format the input data based on the selected table to generate formatted data and output the formatted data.
    Type: Application
    Filed: July 6, 2023
    Publication date: January 9, 2025
    Inventors: Sanjay Kumar Singh, Subhasis Jethy, Udit Saini, Ranju Das, Vasant Manohar, Rahul Bhotika, Carlos Morato
  • Publication number: 20240411732
    Abstract: Various embodiments of the present disclosure provide machine learning techniques for transforming disparate, third-party datasets to canonical representations. The techniques include generating, using a machine learning prediction model, a canonical representation for an input dataset. The machine learning prediction model is previously trained using permutative input embeddings for a training dataset based on canonical data entity features, such that each permutative input embedding corresponds to a different sequence of the canonical data entity features. The permutative input embeddings are leveraged to generate a latent representation for the training dataset. The latent representation is combined with a canonical data map to generate an alignment vector, which is refined to generate an output vector for the input dataset. The machine learning prediction model is trained using a model loss generated based on a comparison of the output vector with a corresponding labeled vector.
    Type: Application
    Filed: June 8, 2023
    Publication date: December 12, 2024
    Inventors: Sanjay Kumar SINGH, Subhasis JETHY, Udit SAINI, Carlos W. MORATO, Rahul BHOTIKA, Ranju DAS, Vasant MANOHAR
  • Publication number: 20240394526
    Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for disambiguating data fields mapped to a plurality of data tables according to a common data model by generating disambiguation embeddings based on a matrix representation of the common data model and one or more logical data type weights, generating a plurality of input embedding vectors for one or more prediction inputs based on the disambiguation embeddings, generating a plurality of prediction vectors based on the plurality of input embedding vectors, and assigning one or more select data fields to respective one or more candidate data tables based on the plurality of prediction vectors.
    Type: Application
    Filed: May 23, 2023
    Publication date: November 28, 2024
    Inventors: Sanjay Kumar Singh, Subhasis Jethy, Udit Saini, Ranju Das, Vasant Manohar, Rahul Bhotika, Carlos Morato