Patents by Inventor Atharv Tyagi

Atharv Tyagi 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).

  • Patent number: 12182829
    Abstract: A system includes a representation generator subsystem configured to execute a user representation model and a task prediction model to generate a user representation for a user. The user representation model receives user event sequence data comprises a sequence of user interactions with the system. The task prediction model is configured to train the user representation model. The user representation includes a vector of a predetermined size that represents the user event sequence data and is generated by applying the trained user representation model to the user event sequence data. A storage requirement of the user representation is less than a storage space requirement of the user event sequence data. The system includes a data store configured for storing the user representation in a user profile associated with the user.
    Type: Grant
    Filed: June 24, 2022
    Date of Patent: December 31, 2024
    Assignee: Adobe Inc.
    Inventors: Sarthak Chakraborty, Sunav Choudhary, Atanu R. Sinha, Sapthotharan Krishnan Nair, Manoj Ghuhan Arivazhagan, Yuvraj, Atharva Anand Joshi, Atharv Tyagi, Shivi Gupta
  • Publication number: 20240394407
    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that implements a secure distributed data collaboration architecture for generating synthetic datasets. For example, the disclosed system sends a request to perform a data collaboration with a first dataset of a first local node and a second dataset of a second local node. The disclosed system receives intermediate feature maps from the local nodes that correspond with the datasets and generates a combined feature map. Further, the disclosed system generates a synthetic dataset from the combined feature map by utilizing a central generative model. Moreover, the synthetic dataset generated by the disclosed system is statistically representative of the first dataset and the second dataset.
    Type: Application
    Filed: May 26, 2023
    Publication date: November 28, 2024
    Inventors: Sunav Choudhary, Subrata Mitra, Sanjay Sukumaran, Priyanshu Yadav, Munish Gupta, Jashn Arora, Iftikhar Ahamath Burhanuddin, Gautam Choudhary, Atharv Tyagi
  • Publication number: 20240320538
    Abstract: Systems and methods identify anomalous data in tabular data. A set of tabular data records is received. Each tabular data record includes data elements for a numbers of attributes, with each data element providing a value for a corresponding attribute. An anomaly score is generated for each data element of each tabular data record. Additionally, an evidence set is defined for each attribute and each tabular data record based on the anomaly scores for the data elements. An anomaly score is generated for each attribute and each tabular data record using the evidence sets. An output is provided that identifies one or more anomalous data subsets determined based on the anomaly scores for the attributes and tabular data records. Each anomalous data subset identifies a subset of attributes and a subset of tabular data records.
    Type: Application
    Filed: March 20, 2023
    Publication date: September 26, 2024
    Inventors: Ramasuri NARAYANAM, Shiv Kumar SAINI, Koyel MUKHERJEE, Manisha PADALA, Keshav VADREVU, Gautam CHOUDHARY, Atharv TYAGI
  • Patent number: 12045272
    Abstract: A text summarization system auto-generates text summarization models using a combination of neural architecture search and knowledge distillation. Given an input dataset for generating/training a text summarization model, neural architecture search is used to sample a search space to select a network architecture for the text summarization model. Knowledge distillation includes fine-tuning a language model for a given text summarization task using the input dataset, and using the fine-tuned language model as a teacher model to inform the selection of the network architecture and the training of the text summarization model. Once a text summarization model has been generated, the text summarization model can be used to generate summaries for given text.
    Type: Grant
    Filed: July 8, 2021
    Date of Patent: July 23, 2024
    Assignee: ADOBE INC.
    Inventors: Saurabh Mahapatra, Niyati Chhaya, Snehal Raj, Sharmila Reddy Nangi, Sapthotharan Nair, Sagnik Mukherjee, Jay Mundra, Fan Du, Atharv Tyagi, Aparna Garimella
  • Publication number: 20230419339
    Abstract: A system includes a representation generator subsystem configured to execute a user representation model and a task prediction model to generate a user representation for a user. The user representation model receives user event sequence data comprises a sequence of user interactions with the system. The task prediction model is configured to train the user representation model. The user representation includes a vector of a predetermined size that represents the user event sequence data and is generated by applying the trained user representation model to the user event sequence data. A storage requirement of the user representation is less than a storage space requirement of the user event sequence data. The system includes a data store configured for storing the user representation in a user profile associated with the user.
    Type: Application
    Filed: June 24, 2022
    Publication date: December 28, 2023
    Inventors: Sarthak Chakraborty, Sunav Choudhary, Atanu R. Sinha, Sapthotharan Krishnan Nair, Manoj Ghuhan Arivazhagan, Yuvraj, Atharva Anand Joshi, Atharv Tyagi, Shivi Gupta
  • Publication number: 20230020886
    Abstract: A text summarization system auto-generates text summarization models using a combination of neural architecture search and knowledge distillation. Given an input dataset for generating/training a text summarization model, neural architecture search is used to sample a search space to select a network architecture for the text summarization model. Knowledge distillation includes fine-tuning a language model for a given text summarization task using the input dataset, and using the fine-tuned language model as a teacher model to inform the selection of the network architecture and the training of the text summarization model. Once a text summarization model has been generated, the text summarization model can be used to generate summaries for given text.
    Type: Application
    Filed: July 8, 2021
    Publication date: January 19, 2023
    Inventors: Saurabh Mahapatra, Niyati Chhaya, Snehal Raj, Sharmila Reddy Nangi, Sapthotharan Nair, Sagnik Mukherjee, Jay Mundra, Fan Du, Atharv Tyagi, Aparna Garimella