Patents by Inventor Shankar Bhamidi

Shankar Bhamidi 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).

  • Systems and methods for modeling neural architecture
    Patent number: 11200672
    Abstract: Systems and methods are described herein for modeling neural architecture. Regions of interest of a brain of a subject can be identified based on image data characterizing the brain of the subject. the identified regions of interest can be mapped to a connectivity matrix. The connectivity matrix can be a weighted and undirected network. A multivariate transformation can be applied to the connectivity matrix to transform the connectivity matrix into a partial correlation matrix. The multivariate transformation can maintain a positive definite constraint for the connectivity matrix. The partial correlation matrix can be transformed into a neural model indicative of the connectivity matrix.
    Type: Grant
    Filed: March 5, 2021
    Date of Patent: December 14, 2021
    Assignees: Ohio State Innovation Foundation, The Penn State Research Foundation, University of North Carolina, University of San Francisco
    Inventors: Skyler Cranmer, Bruce Desmarais, Shankar Bhamidi, James Wilson, Matthew Denny, Zhong-Lin Lu, Paul Stillman
  • Systems and methods for modeling neural architecture
    Patent number: 11062450
    Abstract: Systems and methods are described herein for modeling neural architecture. Regions of interest of a brain of a subject can be identified based on image data characterizing the brain of the subject. the identified regions of interest can be mapped to a connectivity matrix. The connectivity matrix can be a weighted and undirected network. A multivariate transformation can be applied to the connectivity matrix to transform the connectivity matrix into a partial correlation matrix. The multivariate transformation can maintain a positive definite constraint for the connectivity matrix. The partial correlation matrix can be transformed into a neural model indicative of the connectivity matrix.
    Type: Grant
    Filed: September 13, 2017
    Date of Patent: July 13, 2021
    Assignees: Ohio State Innovation Foundation, The Penn State Research Foundation, University of North Carolina, University of San Francisco
    Inventors: Skyler Cranmer, Bruce Desmarais, Shankar Bhamidi, James Wilson, Matthew Denny, Zhong-Lin Lu, Paul Stillman
  • SYSTEMS AND METHODS FOR MODELING NEURAL ARCHITECTURE
    Publication number: 20210209761
    Abstract: Systems and methods are described herein for modeling neural architecture. Regions of interest of a brain of a subject can be identified based on image data characterizing the brain of the subject. the identified regions of interest can be mapped to a connectivity matrix. The connectivity matrix can be a weighted and undirected network. A multivariate transformation can be applied to the connectivity matrix to transform the connectivity matrix into a partial correlation matrix. The multivariate transformation can maintain a positive definite constraint for the connectivity matrix. The partial correlation matrix can be transformed into a neural model indicative of the connectivity matrix.
    Type: Application
    Filed: March 5, 2021
    Publication date: July 8, 2021
    Applicants: Ohio State Innovation Foundation, The Penn State Research Foundation, University of North Carolina, University of San Francisco
    Inventors: Skyler Cranmer, Bruce Desmarais, Shankar Bhamidi, James Wilson, Matthew Denny, Zhong-Lin Lu, Paul Stillman
  • SYSTEMS AND METHODS FOR MODELING NEURAL ARCHITECTURE
    Publication number: 20190206057
    Abstract: Systems and methods are described herein for modeling neural architecture. Regions of interest of a brain of a subject can be identified based on image data characterizing the brain of the subject. the identified regions of interest can be mapped to a connectivity matrix. The connectivity matrix can be a weighted and undirected network. A multivariate transformation can be applied to the connectivity matrix to transform the connectivity matrix into a partial correlation matrix. The multivariate transformation can maintain a positive definite constraint for the connectivity matrix. The partial correlation matrix can be transformed into a neural model indicative of the connectivity matrix.
    Type: Application
    Filed: September 13, 2017
    Publication date: July 4, 2019
    Applicants: Ohio State Innovation Foundation, The Penn State Research Foundation, University of North Carolina, University of San Francisco
    Inventors: Skyler Cranmer, Bruce Desmarais, Shankar Bhamidi, James Wilson, Matthew Denny, Zhong-Lin Lu, Paul Stillman