Patents by Inventor Jason Wittenbach

Jason Wittenbach 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: 12175529
    Abstract: Methods and systems are disclosed for generating recommendations for causes of computer alerts that are automatically detected by a machine learning algorithm are described. For example, the system may receive a first feature vector with an unknown alert status, wherein the first feature vector represents values corresponding to a plurality of computer states in a first computer system. The system may input the first feature vector into an artificial neural network, wherein the artificial neural network is trained to detect a known alert status based on a set of training data comprising labeled feature vectors corresponding to the known alert status, and wherein the artificial neural network is trained to detect conditional expectations the plurality of computer states in an inputted feature vector.
    Type: Grant
    Filed: October 18, 2023
    Date of Patent: December 24, 2024
    Assignee: Capital One Services, LLC
    Inventors: Jason Wittenbach, Samuel Sharpe
  • Publication number: 20240046350
    Abstract: Methods and systems are disclosed for generating recommendations for causes of computer alerts that are automatically detected by a machine learning algorithm are described. For example, the system may receive a first feature vector with an unknown alert status, wherein the first feature vector represents values corresponding to a plurality of computer states in a first computer system. The system may input the first feature vector into an artificial neural network, wherein the artificial neural network is trained to detect a known alert status based on a set of training data comprising labeled feature vectors corresponding to the known alert status, and wherein the artificial neural network is trained to detect conditional expectations the plurality of computer states in an inputted feature vector.
    Type: Application
    Filed: October 18, 2023
    Publication date: February 8, 2024
    Applicant: Capital One Services, LLC
    Inventors: Jason WITTENBACH, Samuel SHARPE
  • Patent number: 11798074
    Abstract: Methods and systems are disclosed for generating recommendations for causes of computer alerts that are automatically detected by a machine learning algorithm are described. For example, the system may receive a first feature vector with an unknown alert status, wherein the first feature vector represents values corresponding to a plurality of computer states in a first computer system. The system may input the first feature vector into an artificial neural network, wherein the artificial neural network is trained to detect a known alert status based on a set of training data comprising labeled feature vectors corresponding to the known alert status, and wherein the artificial neural network is trained to detect conditional expectations the plurality of computer states in an inputted feature vector.
    Type: Grant
    Filed: February 18, 2021
    Date of Patent: October 24, 2023
    Assignee: Capital One Services, LLC
    Inventors: Jason Wittenbach, Samuel Sharpe
  • Patent number: 11790369
    Abstract: Systems and methods are disclosed herein for improving machine learning of a data set. In one example, the method may include training a predictive model on an initial data set comprising labeled data, wherein the training is performed in an active learning system. The method may further include generating a set of parameters based on the training and introducing an unlabeled data set into the predictive model. According to some embodiments, the method may further include applying the set of parameters to the unlabeled data set, generating a set of predictions associated with the applied set of parameters and calculating a first uncertainty score and a second uncertainty score associated with the generated set of predictions. Moreover, the method may also include modifying the data set based on the first uncertainty score, and modifying the predictive model based on the second uncertainty score.
    Type: Grant
    Filed: September 3, 2020
    Date of Patent: October 17, 2023
    Assignee: Capital One Services, LLC
    Inventors: Jason Wittenbach, James O. H. Montgomery, Christopher Bayan Bruss
  • Publication number: 20220261889
    Abstract: Methods and systems are disclosed for generating recommendations for causes of computer alerts that are automatically detected by a machine learning algorithm are described. For example, the system may receive a first feature vector with an unknown alert status, wherein the first feature vector represents values corresponding to a plurality of computer states in a first computer system. The system may input the first feature vector into an artificial neural network, wherein the artificial neural network is trained to detect a known alert status based on a set of training data comprising labeled feature vectors corresponding to the known alert status, and wherein the artificial neural network is trained to detect conditional expectations the plurality of computer states in an inputted feature vector.
    Type: Application
    Filed: February 18, 2021
    Publication date: August 18, 2022
    Applicant: Capital One Services, LLC
    Inventors: Jason WITTENBACH, Samuel SHARPE
  • Publication number: 20220207352
    Abstract: Methods and systems are described herein for generating recommendations for counterfactual explanations to computer alerts that are automatically detected by a machine learning algorithm. The methods and systems use an artificial neural network architecture that trains a hybrid classifier and autoencoder. For example, one model (or artificial neural network), which is a classifier, is trained to make predictions. A second model (or artificial neural network), which is an autoencoder, is trained to reconstruct its inputs. As the second model is trained to reconstruct its inputs means, the second model is implicitly trained to determine what in-sample data looks like. By combining these networks and train them jointly, the system generates predictions (e.g., counterfactual explanations) that are in-sample.
    Type: Application
    Filed: December 30, 2020
    Publication date: June 30, 2022
    Applicant: Capital One Services, LLC
    Inventors: Brian BARR, Jason WITTENBACH
  • Publication number: 20220207353
    Abstract: Methods and systems are described herein for generating recommendations for counterfactual explanations to computer alerts that are automatically detected by a machine learning algorithm. The methods and systems use an artificial neural network architecture that trains a hybrid classifier and autoencoder. For example, one model (or artificial neural network), which is a classifier, is trained to make predictions. A second model (or artificial neural network), which is an autoencoder, is trained to reconstruct its inputs. As the second model is trained to reconstruct its inputs means, the second model is implicitly trained to determine what in-sample data looks like. By combining these networks and train them jointly, the system generates predictions (e.g., counterfactual explanations) that are in-sample.
    Type: Application
    Filed: December 30, 2020
    Publication date: June 30, 2022
    Applicant: Capital One Services, LLC
    Inventors: Brian BARR, Jason WITTENBACH
  • Publication number: 20220067737
    Abstract: Systems and methods are disclosed herein for improving machine learning of a data set. In one example, the method may include training a predictive model on an initial data set comprising labeled data, wherein the training is performed in an active learning system. The method may further include generating a set of parameters based on the training and introducing an unlabeled data set into the predictive model. According to some embodiments, the method may further include applying the set of parameters to the unlabeled data set, generating a set of predictions associated with the applied set of parameters and calculating a first uncertainty score and a second uncertainty score associated with the generated set of predictions. Moreover, the method may also include modifying the data set based on the first uncertainty score, and modifying the predictive model based on the second uncertainty score.
    Type: Application
    Filed: September 3, 2020
    Publication date: March 3, 2022
    Applicant: Capital One Services, LLC
    Inventors: Jason Wittenbach, James O.H. Montgomery, Christopher Bayan Bruss