Patents by Inventor Sumant Sudhir Kawale

Sumant Sudhir Kawale 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).

  • PREDICTIVE, MACHINE-LEARNING, LOCALE-AWARE COMPUTER MODELS SUITABLE FOR LOCATION- AND TRAJECTORY-AWARE TRAINING SETS
    Publication number: 20230135619
    Abstract: Provided is a process including: obtaining, for a plurality of entities, entity logs, wherein: the entity logs comprise events involving the entities, a first subset of the events are actions by the entities, at least some of the actions by the entities are targeted actions, and the events are labeled according to an ontology of events having a plurality of event types; training, with one or more processors, based on the entity logs, a predictive machine learning model to predict whether an entity characterized by a set of inputs to the model will engage in a targeted action in a given geographic locale in the future; and storing the training the trained predictive machine learning model in memory.
    Type: Application
    Filed: October 7, 2022
    Publication date: May 4, 2023
    Inventors: Alain Charles Briancon, Eyal Ben Zion, Sumant Sudhir Kawale, Sara Amini
  • Predictive, machine-learning, locale-aware computer models suitable for location- and trajectory-aware training sets
    Patent number: 11501213
    Abstract: Provided is a process including: obtaining, for a plurality of entities, entity logs, wherein: the entity logs comprise events involving the entities, a first subset of the events are actions by the entities, at least some of the actions by the entities are targeted actions, and the events are labeled according to an ontology of events having a plurality of event types; training, with one or more processors, based on the entity logs, a predictive machine learning model to predict whether an entity characterized by a set of inputs to the model will engage in a targeted action in a given geographic locale in the future; and storing the training the trained predictive machine learning model in memory.
    Type: Grant
    Filed: May 6, 2020
    Date of Patent: November 15, 2022
    Assignee: Cerebri AI Inc.
    Inventors: Alain Charles Briancon, Eyal Ben Zion, Sumant Sudhir Kawale, Sara Amini
  • PREDICTIVE, MACHINE-LEARNING, LOCALE-AWARE COMPUTER MODELS SUITABLE FOR LOCATION- AND TRAJECTORY-AWARE TRAINING SETS
    Publication number: 20200356900
    Abstract: Provided is a process including: obtaining, for a plurality of entities, entity logs, wherein: the entity logs comprise events involving the entities, a first subset of the events are actions by the entities, at least some of the actions by the entities are targeted actions, and the events are labeled according to an ontology of events having a plurality of event types; training, with one or more processors, based on the entity logs, a predictive machine learning model to predict whether an entity characterized by a set of inputs to the model will engage in a targeted action in a given geographic locale in the future; and storing the training the trained predictive machine learning model in memory.
    Type: Application
    Filed: May 6, 2020
    Publication date: November 12, 2020
    Inventors: Alain Charles Briancon, Eyal Ben Zion, Sumant Sudhir Kawale, Sara Amini