Patents by Inventor Prathosh A.P

Prathosh A.P 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).

  • System and method for cross domain generalization for industrial artificial intelligence applications class
    Patent number: 12032929
    Abstract: A cross domain generalization system for industrial artificial intelligence (AI) applications is disclosed. A target encoder subsystem obtains target data from a target machine product and generates lower dimensional data for obtained target data using a target artificial intelligence (AI) model. The generated lower dimensional data are corresponding to a plurality of target embeddings data. The target encoder subsystem further applies the plurality of target embeddings data into a source classifier AI model. A source classifier subsystem predicts a quality of the target machine product by generating class labels for each of the plurality of target embeddings data based on a result of the classifier AI model. The goal of the present invention is to learn features or representations such that the correlation with a label space is similar both in source and target domains while being invariant of data distributions.
    Type: Grant
    Filed: December 8, 2022
    Date of Patent: July 9, 2024
    Assignee: TVARIT GMBH
    Inventors: Aditya Srivastava, Sanjay Shekhawat, Rushil Gupta, Sachin Kumar, Kamal Galrani, Rahul Prajapat, Naga Sai Pranay Modukuru, Rishabh Agrahari, Nihal Rajan Barde, Arnab Kumar Mondal, Prathosh A.P
  • SYSTEM AND METHOD FOR CROSS DOMAIN GENERALIZATION FOR INDUSTRIAL ARTIFICIAL INTELLIGENCE APPLICATIONS
    Publication number: 20230185540
    Abstract: A cross domain generalization system for industrial artificial intelligence (AI) applications is disclosed. A target encoder subsystem obtains target data from a target machine product and generates lower dimensional data for obtained target data using a target artificial intelligence (AI) model. The generated lower dimensional data are corresponding to a plurality of target embeddings data. The target encoder subsystem further applies the plurality of target embeddings data into a source classifier AI model. A source classifier subsystem predicts a quality of the target machine product by generating class labels for each of the plurality of target embeddings data based on a result of the classifier AI model. The goal of the present invention is to learn features or representations such that the correlation with a label space is similar both in source and target domains while being invariant of data distributions.
    Type: Application
    Filed: December 8, 2022
    Publication date: June 15, 2023
    Inventors: Aditya Srivastava, Sanjay Shekhawat, Rushil Gupta, Sachin Kumar, Kamal Galrani, Rahul Prajapat, Naga Sai Pranay Modukuru, Rishabh Agrahari, Nihal Rajan Barde, Arnab Kumar Mondal, Prathosh A.P