Patents by Inventor Daniel Rimm

Daniel Rimm 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).

  • PREDICTING PERSISTENCE OR REDUCTION IN USER INTERACTIONS ACROSS SESSIONS USING MACHINE LEARNING MODELS AND EVENT DATA
    Publication number: 20260179744
    Abstract: Provided herein are systems and methods for methods for detecting expected reductions of interactions across sessions. A computing system can identify event data identifying interactions by a user with a first session for digital therapeutic content to address a condition. The computing system can apply the event data to a machine learning (ML) model. The computing system can generate a likelihood of the user interacting with a second session for digital therapeutic content based on applying the event data to the ML model. The computing system can detect an expected reduction in interactions by the user with the second session, responsive to the likelihood of the user not satisfying a threshold. The computing system can provide an output for the user based on detecting the expected reduction in interactions. In this manner, the efficacy of the medication that user is concurrently taking to address the condition may be improved.
    Type: Application
    Filed: February 10, 2026
    Publication date: June 25, 2026
    Inventors: Sudheer GUTTIKONDA, Gunther HAVEL, William MORSE, Daniel RIMM
  • Predicting persistence or reduction in user interactions across sessions using machine learning models and event data
    Patent number: 12580058
    Abstract: Provided herein are systems and methods for methods for detecting expected reductions of interactions across sessions. A computing system can identify event data identifying interactions by a user with a first session for digital therapeutic content to address a condition. The computing system can apply the event data to a machine learning (ML) model. The computing system can generate a likelihood of the user interacting with a second session for digital therapeutic content based on applying the event data to the ML model. The computing system can detect an expected reduction in interactions by the user with the second session, responsive to the likelihood of the user not satisfying a threshold. The computing system can provide an output for the user based on detecting the expected reduction in interactions. In this manner, the efficacy of the medication that user is concurrently taking to address the condition may be improved.
    Type: Grant
    Filed: April 2, 2024
    Date of Patent: March 17, 2026
    Assignee: Click Therapeutics, Inc.
    Inventors: Sudheer Guttikonda, Gunther Havel, William Morse, Daniel Rimm
  • PREDICTING PERSISTENCE OF REDUCTION IN USER INTERACTIONS ACROSS SESSIONS USING MACHINE LEARNING MODELS AND EVENT DATA
    Publication number: 20250308659
    Abstract: Provided herein are systems and methods for methods for detecting expected reductions of interactions across sessions. A computing system can identify event data identifying interactions by a user with a first session for digital therapeutic content to address a condition. The computing system can apply the event data to a machine learning (ML) model. The computing system can generate a likelihood of the user interacting with a second session for digital therapeutic content based on applying the event data to the ML model. The computing system can detect an expected reduction in interactions by the user with the second session, responsive to the likelihood of the user not satisfying a threshold. The computing system can provide an output for the user based on detecting the expected reduction in interactions. In this manner, the efficacy of the medication that user is concurrently taking to address the condition may be improved.
    Type: Application
    Filed: April 2, 2024
    Publication date: October 2, 2025
    Applicant: Click Therapeutics, Inc.
    Inventors: Sudheer Guttikonda, Gunther Havel, William Morse, Daniel Rimm