Patents by Inventor Daniel BARCKLOW

Daniel BARCKLOW 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).

  • Determining quality of machine learning model output
    Patent number: 12585998
    Abstract: In some aspects, a computing system may generate uninformative features that may be added to a dataset of real features to use as a baseline for determining the quality of an explanation of model output. The uninformative features may be features that do not correlate with what a model is tasked with predicting (e.g., the uninformative features may be random values), and the real features may be informative and correlate with what the model is tasked with predicting (e.g., variables of a dataset sample). A machine learning model may be trained on a dataset that includes both the real features and the uninformative features. The computing system may generate feature attributions for model output, which may include feature attributions for the uninformative features and the real features in the dataset.
    Type: Grant
    Filed: February 17, 2023
    Date of Patent: March 24, 2026
    Assignee: Capital One Services, LLC
    Inventors: Samuel Sharpe, Brian Barr, Isha Hameed, Justin Au-Yeung, Areal Tal, Daniel Barcklow
  • DETERMINING QUALITY OF MACHINE LEARNING MODEL OUTPUT
    Publication number: 20240281700
    Abstract: In some aspects, a computing system may generate uninformative features that may be added to a dataset of real features to use as a baseline for determining the quality of an explanation of model output. The uninformative features may be features that do not correlate with what a model is tasked with predicting (e.g., the uninformative features may be random values), and the real features may be informative and correlate with what the model is tasked with predicting (e.g., variables of a dataset sample). A machine learning model may be trained on a dataset that includes both the real features and the uninformative features. The computing system may generate feature attributions for model output, which may include feature attributions for the uninformative features and the real features in the dataset.
    Type: Application
    Filed: February 17, 2023
    Publication date: August 22, 2024
    Applicant: Capital One Services, LLC
    Inventors: Samuel SHARPE, Brian BARR, Isha HAMEED, Justin AU-YEUNG, Areal TAL, Daniel BARCKLOW
  • UNCERTAINTY ESTIMATION USING UNINFORMATIVE FEATURES
    Publication number: 20240281701
    Abstract: In some aspects, a computing system may generate uninformative features that may be added to a dataset of real features to use as a baseline for determining the quality of an explanation of model output. The uninformative features may be features that do not correlate with what a model is tasked with predicting (e.g., the uninformative features may be random values), and the real features may be informative and correlate with what the model is tasked with predicting (e.g., variables of a dataset sample). A machine learning model may be trained on a dataset that includes both the real features and the uninformative features. The computing system may generate feature attributions for model output, which may include feature attributions for the uninformative features and the real features in the dataset.
    Type: Application
    Filed: February 17, 2023
    Publication date: August 22, 2024
    Applicant: Capital One Services, LLC
    Inventors: Samuel SHARPE, Brian BARR, Isha HAMEED, Justin AU-YEUNG, Areal TAL, Daniel BARCKLOW