Patents by Inventor William Victor Bobo

William Victor Bobo 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).

  • Analytics and machine learning framework for actionable intelligence from clinical and omics data
    Patent number: 11869633
    Abstract: The present disclosure provides methods for accurately predicting the dynamics of symptom response to drugs or other interventions for the treatment of major depressive disorder or other psychological conditions. These methods can allow for a shortening of the time period necessary for the evaluation of a drug or other therapeutic intervention. These predictive methods are based on measured and/or self-reported symptom severity measures at two or more points in time. These measures are then discretized into symptom classes (e.g., low, moderate, severe) and the symptom classes are then applied to the predictive model to predict the progression of symptoms and/or the effectiveness of a drug or other therapeutic intervention.
    Type: Grant
    Filed: December 14, 2018
    Date of Patent: January 9, 2024
    Assignee: The Board of Trustees of the University of Illinois
    Inventors: Ravishankar Krishnan Iyer, Arjun Prasanna Athreya, Richard Merle Weinshilboum, Liewei Wang, William Victor Bobo, Mark Andrew Frye
  • ANALYTICS AND MACHINE LEARNING FRAMEWORK FOR ACTIONABLE INTELLIGENCE FROM CLINICAL AND OMICS DATA
    Publication number: 20190189247
    Abstract: The present disclosure provides methods for accurately predicting the dynamics of symptom response to drugs or other interventions for the treatment of major depressive disorder or other psychological conditions. These methods can allow for a shortening of the time period necessary for the evaluation of a drug or other therapeutic intervention. These predictive methods are based on measured and/or self-reported symptom severity measures at two or more points in time. These measures are then discretized into symptom classes (e.g., low, moderate, severe) and the symptom classes are then applied to the predictive model to predict the progression of symptoms and/or the effectiveness of a drug or other therapeutic intervention.
    Type: Application
    Filed: December 14, 2018
    Publication date: June 20, 2019
    Applicants: The Board of Trustees of the University of Illinois, Mayo Foundation for Medical Education and Research
    Inventors: Ravishankar K. Iyer, Arjun Prasanna Athreya, Richard Merle Weinshilboum, Liewei Wang, William Victor Bobo, Mark Andrew Frye