Patents by Inventor Garima Gupta

Garima 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: 20220093249
    Abstract: In presence of high-cardinality treatment variables, number of counterfactual outcomes to be estimated is much larger than number of factual observations, rendering the problem to be ill-posed. Furthermore, lack of information regarding the confounders among large number of covariates pose challenges in handling confounding bias. Essential is to find lower-dimensional manifold where an equivalent problem of causal inference can be posed, and counterfactual outcomes can be computed.
    Type: Application
    Filed: July 13, 2021
    Publication date: March 24, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: ANKIT SHARMA, GARIMA GUPTA, RANJITHA PRASAD, ARNAB CHATTERJEE, LOVEKESH VIG, GAUTAM SHROFF
  • Publication number: 20210326727
    Abstract: Causality is a crucial paradigm in several domains where observational data is available. Primary goal of Causal Inference (CI) is to uncover cause-effect relationship between entities. Conventional methods face challenges in providing an accurate CI framework due to cofounding and selection bias in multiple treatment scenario. The present disclosure computes a Propensity Score (PS) from a received CI data for the plurality of subjects under test for a treatment. A Generalized Propensity Score (GPS) is computed for a plurality of treatments corresponding to the plurality of subjects by using the PS. Further, a plurality of task batches are created using the GPS and given as input to the DNN for training. Errors in factual data and in balancing representation of the DNN are rectified using a novel loss function. The trained DNN is further used for predicting the counter factual treatment response corresponding to the factual treatment data.
    Type: Application
    Filed: March 2, 2021
    Publication date: October 21, 2021
    Applicant: Tata Consultancy Services Limited
    Inventors: Garima GUPTA, Ankit SHARMA, Ranjitha PRASAD, Arnab CHATTERJEE, Lovekesh VIG, Gautam SHROFF
  • Patent number: 10769157
    Abstract: This disclosure relates generally to data processing, and more particularly to a system and a method for mapping heterogeneous data sources. For a product being sold globally, there might be one global database listing characteristics of the product, and from various System and method for mapping attributes of entities are disclosed. In an embodiment, the system uses a combination of Supervised Bayesian Model (SBM) and an Unsupervised Textual Similarity (UTS) model for data analysis. A weighted ensemble of the SBM and the UTS is used, wherein the ensemble is weighted based on a confidence measure. The system, by performing data processing, identifies data match between different data sources (a local databases and a corresponding global database) being compared, and based on matching data found, performs mapping between the local databases and the global database.
    Type: Grant
    Filed: March 13, 2018
    Date of Patent: September 8, 2020
    Assignee: Tata Consultancy Services Limited
    Inventors: Karamjit Singh, Garima Gupta, Gautam Shroff, Puneet Agarwal
  • Patent number: 10515002
    Abstract: A device receives application information associated with a cloud application provided in a cloud computing environment, and utilizes a first AI model to generate test cases and test data based on the application information. The device utilizes a second AI model to generate optimized test cases and optimized test data based on the test cases and the test data, and utilizes a third AI model to generate test classes based on the optimized test cases and the optimized test data. The device executes the test classes to generate results, and utilizes a fourth AI model to generate an analysis of the results, recommendations for the cloud application based on the analysis of the results, or a code coverage report associated with the cloud application. The device automatically causes an action to be performed based on the analysis of the results, the recommendations, or the code coverage report.
    Type: Grant
    Filed: January 8, 2018
    Date of Patent: December 24, 2019
    Assignee: Accenture Global Solutions Limited
    Inventors: Paresh Takawale, Dnyaneshwar Gangaram Dhumal, Garima Gupta, Mukul Dilip Patidar
  • Patent number: 10430417
    Abstract: System and method for visual Bayesian data fusion are disclosed. In an example, a plurality of datasets associated with a topic are obtained from a data lake. Each of the plurality of datasets include information corresponding to various attributes of the topic. Further, the plurality of datasets are joined to obtain a joined dataset. Furthermore, distribution associated with a target attribute is predicted using Bayesian modeling by selecting a plurality of attributes (k) based on mutual information with the target attribute in the joined dataset, learning a minimum spanning tree based Bayesian structure using the selected attributes and the target attribute, learning conditional probabilistic tables at each node of the minimum spanning tree based Bayesian structure; and predicting the distribution associated with the target attribute by querying the conditional probabilistic tables, thereby facilitating visual Bayesian data fusion.
    Type: Grant
    Filed: March 9, 2017
    Date of Patent: October 1, 2019
    Assignee: Tata Consultancy Services Limited
    Inventors: Geetika Sharma, Karamjit Singh, Garima Gupta, Gautam Shroff, Puneet Agarwal, Aditeya Pandey, Kaushal Ashokbhai Paneri, Gunjan Sehgal
  • Publication number: 20190213115
    Abstract: A device receives application information associated with a cloud application provided in a cloud computing environment, and utilizes a first AI model to generate test cases and test data based on the application information. The device utilizes a second AI model to generate optimized test cases and optimized test data based on the test cases and the test data, and utilizes a third AI model to generate test classes based on the optimized test cases and the optimized test data. The device executes the test classes to generate results, and utilizes a fourth AI model to generate an analysis of the results, recommendations for the cloud application based on the analysis of the results, or a code coverage report associated with the cloud application. The device automatically causes an action to be performed based on the analysis of the results, the recommendations, or the code coverage report.
    Type: Application
    Filed: January 8, 2018
    Publication date: July 11, 2019
    Inventors: Paresh Takawale, Dnyaneshwar Gangaram Dhumal, Garima Gupta, Mukul Dilip Patidar
  • Publication number: 20180260396
    Abstract: This disclosure relates generally to data processing, and more particularly to a system and a method for mapping heterogeneous data sources. For a product being sold globally, there might be one global database listing characteristics of the product, and from various System and method for mapping attributes of entities are disclosed. In an embodiment, the system uses a combination of Supervised Bayesian Model (SBM) and an Unsupervised Textual Similarity (UTS) model for data analysis. A weighted ensemble of the SBM and the UTS is used, wherein the ensemble is weighted based on a confidence measure. The system, by performing data processing, identifies data match between different data sources (a local databases and a corresponding global database) being compared, and based on matching data found, performs mapping between the local databases and the global database.
    Type: Application
    Filed: March 13, 2018
    Publication date: September 13, 2018
    Applicant: Tata Consultancy Services Limited
    Inventors: Karamjit SINGH, Garima GUPTA, Gautam SHROFF, Puneet AGARWAL
  • Publication number: 20170262506
    Abstract: System and method for visual Bayesian data fusion are disclosed. In an example, a plurality of datasets associated with a topic are obtained from a data lake. Each of the plurality of datasets include information corresponding to various attributes of the topic. Further, the plurality of datasets are joined to obtain a joined dataset. Furthermore, distribution associated with a target attribute is predicted using Bayesian modeling by selecting a plurality of attributes (k) based on mutual information with the target attribute in the joined dataset, learning a minimum spanning tree based Bayesian structure using the selected attributes and the target attribute, learning conditional probabilistic tables at each node of the minimum spanning tree based Bayesian structure; and predicting the distribution associated with the target attribute by querying the conditional probabilistic tables, thereby facilitating visual Bayesian data fusion.
    Type: Application
    Filed: March 9, 2017
    Publication date: September 14, 2017
    Applicant: Tata Consultancy Services Limited
    Inventors: Geetika Sharma, Karamjit Singh, Garima Gupta, Gautam Shroff, Puneet Agarwal, Aditeya Pandey, Kaushal Ashokbhai Paneri, Gunjan Sehgal