Patents by Inventor Syuan-Ming Guo

Syuan-Ming Guo 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).

  • Bayesian inference of particle motion and dynamics from single particle tracking and fluorescence correlation spectroscopy
    Patent number: 8542898
    Abstract: Techniques for inferring particle dynamics from certain data include determining multiple models for motion of particles in a biological sample. Each model includes a corresponding set of one or more parameters. Measured data is obtained based on measurements at one or more voxels of an imaging system sensitive to motion of particles in the biological sample; and, determining noise correlation of the measured data. Based at least in part on the noise correlation, a marginal likelihood is determined of the measured data given each model of the multiple models. A relative probability for each model is determined based on the marginal likelihood. Based at least in part on the relative probability for each model, a value is determined for at least one parameter of the set of one or more parameters corresponding to a selected model of the multiple models.
    Type: Grant
    Filed: December 16, 2011
    Date of Patent: September 24, 2013
    Assignee: Massachusetts Institute of Technology
    Inventors: Mark Bathe, Jun He, Syuan-Ming Guo, Nilah Monnier
  • Bayesian Inference of Particle Motion and Dynamics from Single Particle Tracking and Fluorescence Correlation Spectroscopy
    Publication number: 20120155725
    Abstract: Techniques for inferring particle dynamics from certain data include determining multiple models for motion of particles in a biological sample. Each model includes a corresponding set of one or more parameters. Measured data is obtained based on measurements at one or more voxels of an imaging system sensitive to motion of particles in the biological sample; and, determining noise correlation of the measured data. Based at least in part on the noise correlation, a marginal likelihood is determined of the measured data given each model of the multiple models. A relative probability for each model is determined based on the marginal likelihood. Based at least in part on the relative probability for each model, a value is determined for at least one parameter of the set of one or more parameters corresponding to a selected model of the multiple models.
    Type: Application
    Filed: December 16, 2011
    Publication date: June 21, 2012
    Applicant: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
    Inventors: Mark Bathe, Jun He, Syuan-Ming Guo, Nilah Monnier