Patents by Inventor Janis Timosenko

Janis Timosenko 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).

  • System and method for structural characterization of materials by supervised machine learning-based analysis of their spectra
    Patent number: 11193884
    Abstract: A method of supervised machine learning-based spectrum analysis information, using a neural network trained with spectrum information, to identify a specified feature of a given material, a system for supervised machine learning-based spectrum analysis, and a method of training a neural network to analyze spectrum data. The method of supervised machine learning-base spectrum analysis comprises inputting into the neural network spectrum data obtained from a sample of the given material; and the neural network processing the spectrum data, in accordance with the training of the neural network, and outputting one or more values for the specified feature of the sample of the material. In an embodiment, the training set of data includes x-ray absorption spectroscopy data for the given material. In an embodiment, the training set of data includes electron energy loss spectra (EELS) data.
    Type: Grant
    Filed: July 2, 2019
    Date of Patent: December 7, 2021
    Assignee: The Research Foundation for The State University of New York
    Inventors: Anatoly Frenkel, Janis Timosenko
  • SYSTEM AND METHOD FOR STRUCTURAL CHARACTERIZATION OF MATERIALS BY SUPERVISED MACHINE LEARNING-BASED ANALYSIS OF THEIR SPECTRA
    Publication number: 20200003682
    Abstract: A method of supervised machine learning-based spectrum analysis information, using a neural network trained with spectrum information, to identify a specified feature of a given material, a system for supervised machine learning-based spectrum analysis, and a method of training a neural network to analyze spectrum data. The method of supervised machine learning-base spectrum analysis comprises inputting into the neural network spectrum data obtained from a sample of the given material; and the neural network processing the spectrum data, in accordance with the training of the neural network, and outputting one or more values for the specified feature of the sample of the material. In an embodiment, the training set of data includes x-ray absorption spectroscopy data for the given material. In an embodiment, the training set of data includes electron energy loss spectra (EELS) data.
    Type: Application
    Filed: July 2, 2019
    Publication date: January 2, 2020
    Applicant: The Research Foundation for The State University of New York
    Inventors: Anatoly Frenkel, Janis Timosenko