Patents by Inventor Jason Tyler Smith

Jason Tyler Smith 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).

  • Fluorescence lifetime imaging using deep learning
    Patent number: 11887298
    Abstract: One embodiment provides an apparatus for fluorescence lifetime imaging (FLI). The apparatus includes a deep neural network (DNN). The DNN includes a first convolutional layer, a plurality of intermediate layers and an output layer. The first convolutional layer is configured to receive FLI input data. Each intermediate layer is configured to receive a respective intermediate input corresponding to an output of a respective prior layer. Each intermediate layer is further configured to provide a respective intermediate output related to the received respective intermediate input. The output layer is configured to provide estimated FLI output data corresponding to the received FLI input data. The DNN is trained using synthetic data.
    Type: Grant
    Filed: January 7, 2021
    Date of Patent: January 30, 2024
    Assignee: Rensselaer Polytechnic Institute
    Inventors: Jason Tyler Smith, Ruoyang Yao, Xavier Intes, Pingkun Yan, Marien Ochoa-Mendoza
  • FLUORESCENCE LIFETIME IMAGING USING DEEP LEARNING
    Publication number: 20210209759
    Abstract: One embodiment provides an apparatus for fluorescence lifetime imaging (FLI). The apparatus includes a deep neural network (DNN). The DNN includes a first convolutional layer, a plurality of intermediate layers and an output layer. The first convolutional layer is configured to receive FLI input data. Each intermediate layer is configured to receive a respective intermediate input corresponding to an output of a respective prior layer. Each intermediate layer is further configured to provide a respective intermediate output related to the received respective intermediate input. The output layer is configured to provide estimated FLI output data corresponding to the received FLI input data. The DNN is trained using synthetic data.
    Type: Application
    Filed: January 7, 2021
    Publication date: July 8, 2021
    Applicant: RENSSELAER POLYTECHNIC INSTITUTE
    Inventors: Jason Tyler Smith, Ruoyang Yao, Xavier Intes, Pingkun Yan, Marien Ochoa-Mendoza