Patents by Inventor Robin Astrid Epp NEUFELD

Robin Astrid Epp NEUFELD 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).

  • Publication number: 20230334119
    Abstract: Disclosed are a system, apparatus and techniques for evaluating a dataset to confirm that the data in the dataset satisfies a data quality metric. A machine learning engine or the like may evaluate text strings within the dataset may be of arbitrary length and encoded according to an encoding standard. Data vectors of a preset length may be generated from the evaluated text strings using various techniques. Each data vector may be representative of the content of the text string and a category may be assigned to the respective data vector. The category assigned to each data vectors may be evaluated with respect to other data vectors in the dataset to determine compliance with a quality metric. In the case that a number of data vectors fail to meet a predetermined quality metric, an alert may be generated to mitigate any system errors that may result from unsatisfactory data quality.
    Type: Application
    Filed: June 16, 2023
    Publication date: October 19, 2023
    Applicant: Capital One Services, LLC
    Inventor: Robin Astrid Epp NEUFELD
  • Patent number: 11748448
    Abstract: Disclosed are a system, apparatus and techniques for evaluating a dataset to confirm that the data in the dataset satisfies a data quality metric. A machine learning engine or the like may evaluate text strings within the dataset may be of arbitrary length and encoded according to an encoding standard. Data vectors of a preset length may be generated from the evaluated text strings using various techniques. Each data vector may be representative of the content of the text string and a category may be assigned to the respective data vector. The category assigned to each data vectors may be evaluated with respect to other data vectors in the dataset to determine compliance with a quality metric. In the case that a number of data vectors fail to meet a predetermined quality metric, an alert may be generated to mitigate any system errors that may result from unsatisfactory data quality.
    Type: Grant
    Filed: October 10, 2022
    Date of Patent: September 5, 2023
    Assignee: Capital One Services, LLC
    Inventor: Robin Astrid Epp Neufeld
  • Publication number: 20230133247
    Abstract: Disclosed are a system, apparatus and techniques for evaluating a dataset to confirm that the data in the dataset satisfies a data quality metric. A machine learning engine or the like may evaluate text strings within the dataset may be of arbitrary length and encoded according to an encoding standard. Data vectors of a preset length may be generated from the evaluated text strings using various techniques. Each data vector may be representative of the content of the text string and a category may be assigned to the respective data vector. The category assigned to each data vectors may be evaluated with respect to other data vectors in the dataset to determine compliance with a quality metric. In the case that a number of data vectors fail to meet a predetermined quality metric, an alert may be generated to mitigate any system errors that may result from unsatisfactory data quality.
    Type: Application
    Filed: October 10, 2022
    Publication date: May 4, 2023
    Applicant: Capital One Services, LLC
    Inventor: Robin Astrid Epp NEUFELD
  • Patent number: 11475252
    Abstract: Disclosed are a system, apparatus and techniques for evaluating a dataset to confirm that the data in the dataset satisfies a data quality metric. A machine learning engine or the like may evaluate text strings within the dataset may be of arbitrary length and encoded according to an encoding standard. Data vectors of a preset length may be generated from the evaluated text strings using various techniques. Each data vector may be representative of the content of the text string and a category may be assigned to the respective data vector. The category assigned to each data vectors may be evaluated with respect to other data vectors in the dataset to determine compliance with a quality metric. In the case that a number of data vectors fail to meet a predetermined quality metric, an alert may be generated to mitigate any system errors that may result from unsatisfactory data quality.
    Type: Grant
    Filed: October 15, 2019
    Date of Patent: October 18, 2022
    Inventor: Robin Astrid Epp Neufeld
  • Patent number: 11048984
    Abstract: Disclosed are a system, apparatus and techniques for evaluating a dataset to confirm that the data in the dataset satisfies a data quality metric. A machine learning engine or the like may evaluate text strings within the dataset may be of arbitrary length and encoded according to an encoding standard. Data vectors of a preset length may be generated from the evaluated text strings using various techniques. Each data vector may be representative of the content of the text string and a category may be assigned to the respective data vector. The category assigned to each data vectors may be evaluated with respect to other data vectors in the dataset to determine compliance with a quality metric. In the case that a number of data vectors fail to meet a predetermined quality metric, an alert may be generated to mitigate any system errors that may result from unsatisfactory data quality.
    Type: Grant
    Filed: May 8, 2019
    Date of Patent: June 29, 2021
    Assignee: Capital One Services, LLC
    Inventor: Robin Astrid Epp Neufeld
  • Publication number: 20200356823
    Abstract: Disclosed are a system, apparatus and techniques for evaluating a dataset to confirm that the data in the dataset satisfies a data quality metric. A machine learning engine or the like may evaluate text strings within the dataset may be of arbitrary length and encoded according to an encoding standard. Data vectors of a preset length may be generated from the evaluated text strings using various techniques. Each data vector may be representative of the content of the text string and a category may be assigned to the respective data vector. The category assigned to each data vectors may be evaluated with respect to other data vectors in the dataset to determine compliance with a quality metric. In the case that a number of data vectors fail to meet a predetermined quality metric, an alert may be generated to mitigate any system errors that may result from unsatisfactory data quality.
    Type: Application
    Filed: May 8, 2019
    Publication date: November 12, 2020
    Applicant: Capital One Services, LLC
    Inventor: Robin Astrid Epp NEUFELD
  • Publication number: 20200356824
    Abstract: Disclosed are a system, apparatus and techniques for evaluating a dataset to confirm that the data in the dataset satisfies a data quality metric. A machine learning engine or the like may evaluate text strings within the dataset may be of arbitrary length and encoded according to an encoding standard. Data vectors of a preset length may be generated from the evaluated text strings using various techniques. Each data vector may be representative of the content of the text string and a category may be assigned to the respective data vector. The category assigned to each data vectors may be evaluated with respect to other data vectors in the dataset to determine compliance with a quality metric. In the case that a number of data vectors fail to meet a predetermined quality metric, an alert may be generated to mitigate any system errors that may result from unsatisfactory data quality.
    Type: Application
    Filed: October 15, 2019
    Publication date: November 12, 2020
    Applicant: Capital One Services, LLC
    Inventor: Robin Astrid Epp NEUFELD