Patents by Inventor Babou Srinivasan

Babou Srinivasan 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).

  • Machine-learning processing at native-location storage system to generate collections action plan
    Patent number: 10904298
    Abstract: Techniques are disclosed for using machine-learning processing for generating resource-allocation specifications. A first data set may be received from a first data source. The first data set can include a first resource request and a first timestamp associated with entities. A second data set can be received from a second data source that includes communication data and allocation data associated with the entities. Target characteristics may be defined for training instances. The training instances can be used to train a machine-learning model using the first data set and the second data set. A third data set may be accessed and used to generate a user session within which, the trained machine-learning model may execute to generate a resource-allocation specification. The resource-allocation specification including a communication schedule. One or more communications compliant with the communication schedule may be output to an entity.
    Type: Grant
    Filed: September 27, 2019
    Date of Patent: January 26, 2021
    Assignee: Oracle International Corporation
    Inventors: Bhupinder Sondhi, Keshava Mangipudi, Babou Srinivasan, Madhavi Makkapati, Gerhard Schmidt, Venugopal Bheemreddy, Balaji Dasarath, Christopher Bharath, Tripti Nair
  • MACHINE-LEARNING PROCESSING AT NATIVE-LOCATION STORAGE SYSTEM TO GENERATE COLLECTIONS ACTION PLAN
    Publication number: 20200128051
    Abstract: Techniques are disclosed for using machine-learning processing for generating resource-allocation specifications. A first data set may be received from a first data source. The first data set can include a first resource request and a first timestamp associated with entities. A second data set can be received from a second data source that includes communication data and allocation data associated with the entities. Target characteristics may be defined for training instances. The training instances can be used to train a machine-learning model using the first data set and the second data set. A third data set may be accessed and used to generate a user session within which, the trained machine-learning model may execute to generate a resource-allocation specification. The resource-allocation specification including a communication schedule. One or more communications compliant with the communication schedule may be output to an entity.
    Type: Application
    Filed: September 27, 2019
    Publication date: April 23, 2020
    Applicant: Oracle International Corporation
    Inventors: Bhupinder Sondhi, Keshava Mangipudi, Babou Srinivasan, Madhavi Makkapati, Gerhard Schmidt, Venugopal Bheemreddy, Balaji Dasarath, Christopher Bharath, Tripti Nair