Patents by Inventor Liang KUO-CHING

Liang KUO-CHING 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).

  • Patent number: 12080427
    Abstract: A method for predicting a depressive state using a wearable device. Each data type of biological data for several days from plurality of subjects is converted into data in predetermined time unit (S201). Next, quantiles of the distribution of obtained sample data are determined for each subject and each data type (S202). The standard deviation of distribution of the obtained sample data is calculated for each subject and each data type (S203). The Pearson correlation coefficient is calculated for each combination of data types for each subject (S204). Next, a prediction model for the classification problem of whether subject is in a depressive state is trained by machine leaning (S206), wherein quantiles, standard deviations, and Pearson correlation coefficients extracted from the biological data are features used for an input vector, and an evaluation of existence or non-existence of a depressive state by an expert is a label used as teacher data.
    Type: Grant
    Filed: December 13, 2019
    Date of Patent: September 3, 2024
    Assignee: KEIO UNIVERSITY
    Inventors: Taishiro Kishimoto, Yuki Tazawa, Liang Kuo-Ching, Takanori Fujita, Michitaka Yoshimura, Momoko Kitazawa, Masaru Mimura
  • Publication number: 20220059226
    Abstract: A method for predicting a depressive state using a wearable device. Each data type of biological data for several days from plurality of subjects is converted into data in predetermined time unit (S201). Next, quantiles of the distribution of obtained sample data are determined for each subject and each data type (S202). The standard deviation of distribution of the obtained sample data is calculated for each subject and each data type (S203). The Pearson correlation coefficient is calculated for each combination of data types for each subject (S204). Next, a prediction model for the classification problem of whether subject is in a depressive state is trained by machine leaning (S206), wherein quantiles, standard deviations, and Pearson correlation coefficients extracted from the biological data are features used for an input vector, and an evaluation of existence or non-existence of a depressive state by an expert is a label used as teacher data.
    Type: Application
    Filed: December 13, 2019
    Publication date: February 24, 2022
    Inventors: Taishiro KISHIMOTO, Yuki TAZAWA, Liang KUO-CHING, Takanori FUJITA, Michitaka YOSHIMURA, Momoko KITAZAWA, Masaru MIMURA