Patents by Inventor Philip Remmele

Philip Remmele 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).

  • Systems and methods for improving the interpretability and transparency of machine learning models
    Patent number: 11537934
    Abstract: Embodiments herein provide for a machine learning algorithm that generates models that are more interpretable and transparent than existing machine learning approaches. These embodiments identify, at a record level, the effect of individual input variables on the machine learning model. To provide those improvements, a reason code generator assigns monotonic relationships to a series of input variables, which are then incorporated into the machine learning algorithm as metadata. In some embodiments, the reason code generator creates records based on the monotonic relationships, which are used by the machine learning algorithm to generate predicted values. The reason code generator compares an original predicted value from the machine learning model to the predicted values from the machine learning model.
    Type: Grant
    Filed: September 20, 2018
    Date of Patent: December 27, 2022
    Assignee: Bluestem Brands, Inc.
    Inventors: Marick Sinay, Damien Benveniste, Peng Jiang, Philip Remmele, Junqing Wu, Mike Zhang
  • SYSTEMS AND METHODS FOR IMPROVING THE INTERPRETABILITY AND TRANSPARENCY OF MACHINE LEARNING MODELS
    Publication number: 20200097439
    Abstract: Embodiments herein provide for a machine learning algorithm that generates models that are more interpretable and transparent than existing machine learning approaches. These embodiments identify, at a record level, the effect of individual input variables on the machine learning model. To provide those improvements, a reason code generator assigns monotonic relationships to a series of input variables, which are then incorporated into the machine learning algorithm as metadata. In some embodiments, the reason code generator creates records based on the monotonic relationships, which are used by the machine learning algorithm to generate predicted values. The reason code generator compares an original predicted value from the machine learning model to the predicted values from the machine learning model.
    Type: Application
    Filed: September 20, 2018
    Publication date: March 26, 2020
    Inventors: Marick Sinay, Damien Benveniste, Peng Jiang, Philip Remmele, Junqing Wu, Mike Zhang