Patents by Inventor Clay HOPF

Clay HOPF 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: 10745263
    Abstract: A liquid container refill management system including a machine learning algorithm and method of training the same, the system and method making use of noninvasive tank-in-tank measuring techniques. The system can comprise of a container fill level indicator. The container fill level indicator can be capable of detecting a vibration response signal on the outer surface of a container, wherein the system is capable of transmitting the response signal to a remote data processor for processing using a trained machine learning algorithm. The trained machine learning algorithm can be trained by the process of selecting model inputs and outputs to define an internal structure of the machine learning algorithm, applying a collection of input and output data samples to train the machine learning algorithm, and verifying the accuracy of the machine learning algorithm by applying input data samples and comparing received output values with expected output values.
    Type: Grant
    Filed: October 3, 2017
    Date of Patent: August 18, 2020
    Assignee: Sonicu, LLC
    Inventors: Kent Eldon Crouse, Jason Young, Clay Hopf
  • Publication number: 20180044159
    Abstract: A liquid container refill management system including a machine learning algorithm and method of training the same, the system and method making use of noninvasive tank-in-tank measuring techniques. The system can comprise of a container fill level indicator. The container fill level indicator can be capable of detecting a vibration response signal on the outer surface of a container, wherein the system is capable of transmitting the response signal to a remote data processor for processing using a trained machine learning algorithm. The trained machine learning algorithm can be trained by the process of selecting model inputs and outputs to define an internal structure of the machine learning algorithm, applying a collection of input and output data samples to train the machine learning algorithm, and verifying the accuracy of the machine learning algorithm by applying input data samples and comparing received output values with expected output values.
    Type: Application
    Filed: October 3, 2017
    Publication date: February 15, 2018
    Inventors: Kent Eldon CROUSE, Jason YOUNG, Clay HOPF