Patents by Inventor ABHIRUT GUPTA

ABHIRUT GUPTA 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: 20240211688
    Abstract: Systems and methods for generating phonetic spelling variations of a given word based on locale-specific pronunciations. A phoneme-letter density model may be configured to identify a phoneme sequence corresponding to an input word, and to identify all character sequences that may correspond to an input phoneme sequence and their respective probabilities. The phoneme-phoneme error model may be configured to identify locale-specific alternative phoneme sequences that may correspond to a given phoneme sequence, and their respective probabilities. Using these two models, a processing system may be configured to generate, for a given input word, a list of alternative character sequences that may correspond to the input word based on locale-specific pronunciations, and/or a probability distribution representing how likely each alternative character sequence is to correspond to the input word.
    Type: Application
    Filed: December 19, 2023
    Publication date: June 27, 2024
    Inventors: Abhirut Gupta, Aravindan Raghuveer, Abhay Sharma, Nitin Raut, Manish Kumar
  • Patent number: 11893349
    Abstract: Systems and methods for generating phonetic spelling variations of a given word based on locale-specific pronunciations. A phoneme-letter density model may be configured to identify a phoneme sequence corresponding to an input word, and to identify all character sequences that may correspond to an input phoneme sequence and their respective probabilities. The phoneme-phoneme error model may be configured to identify locale-specific alternative phoneme sequences that may correspond to a given phoneme sequence, and their respective probabilities. Using these two models, a processing system may be configured to generate, for a given input word, a list of alternative character sequences that may correspond to the input word based on locale-specific pronunciations, and/or a probability distribution representing how likely each alternative character sequence is to correspond to the input word.
    Type: Grant
    Filed: April 8, 2022
    Date of Patent: February 6, 2024
    Assignee: Google LLC
    Inventors: Abhirut Gupta, Aravindan Raghuveer, Abhay Sharma, Nitin Raut, Manish Kumar
  • Publication number: 20220391588
    Abstract: Systems and methods for generating phonetic spelling variations of a given word based on locale-specific pronunciations. A phoneme-letter density model may be configured to identify a phoneme sequence corresponding to an input word, and to identify all character sequences that may correspond to an input phoneme sequence and their respective probabilities. The phoneme-phoneme error model may be configured to identify locale-specific alternative phoneme sequences that may correspond to a given phoneme sequence, and their respective probabilities. Using these two models, a processing system may be configured to generate, for a given input word, a list of alternative character sequences that may correspond to the input word based on locale-specific pronunciations, and/or a probability distribution representing how likely each alternative character sequence is to correspond to the input word.
    Type: Application
    Filed: April 8, 2022
    Publication date: December 8, 2022
    Inventors: Abhirut Gupta, Aravindan Raghuveer, Abhay Sharma, Nitin Raut, Manish Kumar
  • Patent number: 11157920
    Abstract: A technique for instance-specific feature-based cross-document sentiment aggregation includes analyzing input data to determine an entity referenced in the input data. One or more salient features of the entity are identified. Multiple documents that reference the entity are located. The salient features in each of the multiple documents are identified. Respective sentiment values are assigned to each of the salient features identified in the multiple documents. Respective sub-graphs are created for each of the multiple documents. The sub-graphs specify the sentiment values assigned to each of the salient features identified in an associated one of the multiple documents. The assigned sentiment values for each of the salient features are aggregated based on the sub-graphs. Finally, output data that is associated with the input data is generated. The output data provides an indication of the aggregated sentiment value for each of the salient features.
    Type: Grant
    Filed: November 10, 2015
    Date of Patent: October 26, 2021
    Assignee: International Business Machines Corporation
    Inventors: John P. Bufe, III, Abhirut Gupta, William G. O'Keeffe, Srikanth G. Tamilselvam
  • Publication number: 20180068330
    Abstract: Methods, systems, and computer program products for deep learning based unsupervised event learning for economic indicator predictions are provided herein. A computer-implemented method includes extracting multiple events from a collection of documents; determining characteristics of the extracted events, wherein the characteristics comprise (i) one or more actions occurring within each event, (ii) one or more actors participating in each event, and (iii) one or more objects affected by each event; deriving structured data, related to an economic indicator value to be predicted, from multiple data sources; combining items of the derived structured data into one or more groups based on (i) semantic similarity of the items and (ii) a temporal aspect attributed to each of the items; and generating a prediction for the economic indicator value based on a comparison of the one or more groups to the characteristics of each of the extracted events.
    Type: Application
    Filed: September 7, 2016
    Publication date: March 8, 2018
    Inventors: Abhirut Gupta, Rahul D. Sharnagat, Srikanth G. Tamilselvam
  • Publication number: 20170132309
    Abstract: A technique for instance-specific feature-based cross-document sentiment aggregation includes analyzing input data to determine an entity referenced in the input data. One or more salient features of the entity are identified. Multiple documents that reference the entity are located. The salient features in each of the multiple documents are identified. Respective sentiment values are assigned to each of the salient features identified in the multiple documents. Respective sub-graphs are created for each of the multiple documents. The sub-graphs specify the sentiment values assigned to each of the salient features identified in an associated one of the multiple documents. The assigned sentiment values for each of the salient features are aggregated based on the sub-graphs. Finally, output data that is associated with the input data is generated. The output data provides an indication of the aggregated sentiment value for each of the salient features.
    Type: Application
    Filed: November 10, 2015
    Publication date: May 11, 2017
    Inventors: JOHN P. BUFE, III, ABHIRUT GUPTA, WILLIAM G. O'KEEFFE, SRIKANTH G. TAMILSELVAM