Patents by Inventor Samuel Kit Bunn Lai

Samuel Kit Bunn Lai 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).

  • Methods, systems, and computer readable media for using synthetically trained deep neural networks for automated tracking of particles in diverse video microscopy data sets
    Patent number: 10664978
    Abstract: A method for using a synthetically trained neural network for tracking particle movement in video microscopy data includes receiving, as input, video microscopy data representing images of particles that move between video frames. The method includes using a synthetically trained neural network to track movement of the particles between the video frames, wherein the synthetically trained neural network comprises a neural network that is trained on a plurality of different simulated video microscopy data sets. The method further includes outputting, by the synthetically trained neural network, an indication of movement of the particles between the video frames.
    Type: Grant
    Filed: April 9, 2019
    Date of Patent: May 26, 2020
    Assignee: The University of North Carolina at Chapel Hill
    Inventors: Jay Mack Newby, M. Gregory Forest, Samuel Kit Bunn Lai
  • METHODS, SYSTEMS, AND COMPUTER READABLE MEDIA FOR USING SYNTHETICALLY TRAINED DEEP NEURAL NETWORKS FOR AUTOMATED TRACKING OF PARTICLES IN DIVERSE VIDEO MICROSCOPY DATA SETS
    Publication number: 20190311482
    Abstract: A method for using a synthetically trained neural network for tracking particle movement in video microscopy data includes receiving, as input, video microscopy data representing images of particles that move between video frames. The method includes using a synthetically trained neural network to track movement of the particles between the video frames, wherein the synthetically trained neural network comprises a neural network that is trained on a plurality of different simulated video microscopy data sets. The method further includes outputting, by the synthetically trained neural network, an indication of movement of the particles between the video frames.
    Type: Application
    Filed: April 9, 2019
    Publication date: October 10, 2019
    Inventors: Jay Mack Newby, M. Gregory Forest, Samuel Kit Bunn Lai