Patents by Inventor Christopher Bayan Bruss
Christopher Bayan Bruss 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).
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Publication number: 20240119303Abstract: In some aspects, a computing system may use a surrogate machine learning model to detect whether a production or other machine learning model has a tendency to generate different output depending on which subpopulation a particular sample belongs to. The surrogate machine learning model may be trained using features/outputs that are not included in the data used by the production model. For example, by using demographic information in lieu of the original labels of a dataset that was used to train a production model, a surrogate model may be used to detect whether the production model is able to discern one or more characteristics associated with but not present in a sample using other features of the dataset. Output of the surrogate machine learning model may be clustered to detect whether certain subpopulations are treated differently by the production model.Type: ApplicationFiled: October 11, 2022Publication date: April 11, 2024Applicant: Capital One Services, LLCInventors: Samuel SHARPE, Christopher Bayan BRUSS, Brian BARR, Justin AU-YEUNG
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Publication number: 20240112072Abstract: In some aspects, a computing system may aggregating multiple counterfactual samples so that machine learning explanations can be generated for sub-populations. In addition, methods and systems described herein use machine learning and counterfactual samples to determine text to use in an explanation for a model's prediction. A computing system may also train machine learning models to not only determine whether a request to perform an action should be accepted, but also to generate output that is consistent with output generated by previous machine learning models. Further, a computing system may generate counterfactual samples based on user preferences. A computing system may obtain preferences and then apply a penalty or adjustment parameter such that when a counterfactual sample is created, the computing system is forced to change one or more features indicated by the preferences to create the counterfactual sample.Type: ApplicationFiled: September 30, 2022Publication date: April 4, 2024Applicant: Capital One Services, LLCInventors: Samuel SHARPE, Christopher Bayan BRUSS, Brian BARR
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Publication number: 20240112052Abstract: In some aspects, a computing system may aggregating multiple counterfactual samples so that machine learning explanations can be generated for sub-populations. In addition, methods and systems described herein use machine learning and counterfactual samples to determine text to use in an explanation for a model's prediction. A computing system may also train machine learning models to not only determine whether a request to perform an action should be accepted, but also to generate output that is consistent with output generated by previous machine learning models. Further, a computing system may generate counterfactual samples based on user preferences. A computing system may obtain preferences and then apply a penalty or adjustment parameter such that when a counterfactual sample is created, the computing system is forced to change one or more features indicated by the preferences to create the counterfactual sample.Type: ApplicationFiled: September 30, 2022Publication date: April 4, 2024Applicant: Capital One Services, LLCInventors: Brian BARR, Samuel SHARPE, Christopher Bayan BRUSS
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Publication number: 20240112092Abstract: In some aspects, a computing system may aggregating multiple counterfactual samples so that machine learning explanations can be generated for sub-populations. In addition, methods and systems described herein use machine learning and counterfactual samples to determine text to use in an explanation for a model's prediction. A computing system may also train machine learning models to not only determine whether a request to perform an action should be accepted, but also to generate output that is consistent with output generated by previous machine learning models. Further, a computing system may generate counterfactual samples based on user preferences. A computing system may obtain preferences and then apply a penalty or adjustment parameter such that when a counterfactual sample is created, the computing system is forced to change one or more features indicated by the preferences to create the counterfactual sample.Type: ApplicationFiled: September 30, 2022Publication date: April 4, 2024Applicant: Capital One Services, LLCInventors: Samuel SHARPE, Christopher Bayan BRUSS, Brian BARR
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Patent number: 11948379Abstract: A system including at least one processor; and at least one memory having stored thereon computer program code that, when executed by the at least one processor, controls the at least one processor to: receive an email addressed to a user; separate the email into a plurality of email components; analyze, using respective machine-learning techniques, each of the plurality of email components; feed the analysis of each of the plurality of email components into a stacked ensemble analyzer; and based on an output of the stacked ensemble analyzer, determine whether the email is malicious.Type: GrantFiled: October 12, 2020Date of Patent: April 2, 2024Assignee: CAPITAL ONE SERVICES, LLCInventors: Christopher Bayan Bruss, Stephen Fletcher, Lei Yu, Jakob Kressel
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Publication number: 20240037427Abstract: A computing system may generate a first set of importance metrics (e.g., scores or values) for a model. The importance metrics may be generated using an explainable artificial intelligence technique, and an individual importance metric may indicate how influential a corresponding feature is for a decision made by a model. The computing system may determine an important feature and create a modified dataset by removing the important feature from the dataset. The computing system may train the model on the modified dataset and evaluate the performance of the model to determine the effect of removing the feature (e.g., which may indicate how important the feature is to output generated by the model). This process may be repeated for additional features and additional performance metrics may be obtained.Type: ApplicationFiled: July 26, 2022Publication date: February 1, 2024Applicant: Capital One Services, LLCInventors: Samuel SHARPE, Christopher Bayan BRUSS, Brian BARR, Sahil VERMA, Jocelyn HUANG
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Publication number: 20230351788Abstract: Methods and systems disclosed herein may quantify a representation of a type of input an image analysis system should expect. The image analysis system may be trained on the type of input the image analysis system should expect using a first image stream. A first model of the type of input that the image analysis system should expect may be built from the first image stream. After the first model is built, a second image, or a second image stream, may be compared to the first model to determine a difference between the second image, or second image stream, and the first image stream. When the difference is greater than or equal to a threshold, a drift may be detected and steps may be taken to determine the cause of the drift.Type: ApplicationFiled: May 19, 2023Publication date: November 2, 2023Inventors: Keegan Hines, Christopher Bayan Bruss
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Patent number: 11790369Abstract: 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: GrantFiled: September 3, 2020Date of Patent: October 17, 2023Assignee: Capital One Services, LLCInventors: Jason Wittenbach, James O. H. Montgomery, Christopher Bayan Bruss
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Patent number: 11694457Abstract: Methods and systems disclosed herein may quantify a representation of a type of input an image analysis system should expect. The image analysis system may be trained on the type of input the image analysis system should expect using a first image stream. A first model of the type of input that the image analysis system should expect may be built from the first image stream. After the first model is built, a second image, or a second image stream, may be compared to the first model to determine a difference between the second image, or second image stream, and the first image stream. When the difference is greater than or equal to a threshold, a drift may be detected and steps may be taken to determine the cause of the drift.Type: GrantFiled: June 24, 2022Date of Patent: July 4, 2023Assignee: Capital One Services, LLCInventors: Keegan Hines, Christopher Bayan Bruss
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Publication number: 20220318562Abstract: Methods and systems disclosed herein may quantify a representation of a type of input an image analysis system should expect. The image analysis system may be trained on the type of input the image analysis system should expect using a first image stream. A first model of the type of input that the image analysis system should expect may be built from the first image stream. After the first model is built, a second image, or a second image stream, may be compared to the first model to determine a difference between the second image, or second image stream, and the first image stream. When the difference is greater than or equal to a threshold, a drift may be detected and steps may be taken to determine the cause of the drift.Type: ApplicationFiled: June 24, 2022Publication date: October 6, 2022Inventors: Keegan Hines, Christopher Bayan Bruss
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Patent number: 11386286Abstract: Methods and systems disclosed herein may quantify a representation of a type of input an image analysis system should expect. The image analysis system may be trained on the type of input the image analysis system should expect using a first image stream. A first model of the type of input that the image analysis system should expect may be built from the first image stream. After the first model is built, a second image, or a second image stream, may be compared to the first model to determine a difference between the second image, or second image stream, and the first image stream. When the difference is greater than or equal to a threshold, a drift may be detected and steps may be taken to determine the cause of the drift.Type: GrantFiled: April 7, 2020Date of Patent: July 12, 2022Assignee: Capital One Services, LLCInventors: Keegan Hines, Christopher Bayan Bruss
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Publication number: 20220067737Abstract: 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: ApplicationFiled: September 3, 2020Publication date: March 3, 2022Applicant: Capital One Services, LLCInventors: Jason Wittenbach, James O.H. Montgomery, Christopher Bayan Bruss
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Publication number: 20220019836Abstract: Methods and systems disclosed herein may quantify the content and nature of first streaming data to detect when the typical composition of the first streaming data changes. Quantifying the content and nature of the first streaming data may begin by generating a baseline representation of the content of the first streaming data as represented by a first matrix. Once generated, the first matrix may be used as a control against subsequently received data streams. In this regard, a second matrix may be generated from second streaming data and compared to the first matrix to determine the differences between the first streaming data and the second streaming data. Once a difference is determined, the difference may be compared to a threshold value and, when the difference exceeds the threshold value, an administrator may be notified and corrective action taken.Type: ApplicationFiled: September 1, 2021Publication date: January 20, 2022Applicant: Capital One Services, LLCInventors: Keegan Hines, Christopher Bayan Bruss
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Patent number: 11138458Abstract: Methods and systems disclosed herein may quantify the content and nature of a first stream of text to detect when the typical composition of the first stream of text changes. Quantifying the content and nature of the first stream of text may begin by generating a baseline representation of the content of the first stream of text as represented by a first matrix. Once generated, the first matrix may be used as a control against subsequently received sequences of text. In this regard, a second matrix may be generated from a second sequence of text and compared to the first matrix to determine the differences between the first sequence of text and the second sequence of text. Once a difference is determined, the difference may be compared to a threshold value and, when the difference exceeds the threshold value, an administrator may be notified and corrective action taken.Type: GrantFiled: January 16, 2020Date of Patent: October 5, 2021Assignee: Capital One Services, LLCInventors: Keegan Hines, Christopher Bayan Bruss
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Publication number: 20210112095Abstract: A system including at least one processor; and at least one memory having stored thereon computer program code that, when executed by the at least one processor, controls the at least one processor to: receive an email addressed to a user; separate the email into a plurality of email components; analyze, using respective machine-learning techniques, each of the plurality of email components; feed the analysis of each of the plurality of email components into a stacked ensemble analyzer; and based on an output of the stacked ensemble analyzer, determine whether the email is malicious.Type: ApplicationFiled: October 12, 2020Publication date: April 15, 2021Inventors: Christopher Bayan Bruss, Stephen Fletcher, Lei Yu, Jakob Kressel
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Publication number: 20210019559Abstract: Methods and systems disclosed herein may quantify a representation of a type of input an image analysis system should expect. The image analysis system may be trained on the type of input the image analysis system should expect using a first image stream. A first model of the type of input that the image analysis system should expect may be built from the first image stream. After the first model is built, a second image, or a second image stream, may be compared to the first model to determine a difference between the second image, or second image stream, and the first image stream. When the difference is greater than or equal to a threshold, a drift may be detected and steps may be taken to determine the cause of the drift.Type: ApplicationFiled: April 7, 2020Publication date: January 21, 2021Applicant: Capital One Services, LLCInventors: Keegan Hines, Christopher Bayan Bruss
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Publication number: 20210019546Abstract: Methods and systems disclosed herein may quantify the content and nature of a first stream of text to detect when the typical composition of the first stream of text changes. Quantifying the content and nature of the first stream of text may begin by generating a baseline representation of the content of the first stream of text as represented by a first matrix. Once generated, the first matrix may be used as a control against subsequently received sequences of text. In this regard, a second matrix may be generated from a second sequence of text and compared to the first matrix to determine the differences between the first sequence of text and the second sequence of text. Once a difference is determined, the difference may be compared to a threshold value and, when the difference exceeds the threshold value, an administrator may be notified and corrective action taken.Type: ApplicationFiled: January 16, 2020Publication date: January 21, 2021Inventors: Keegan Hines, Christopher Bayan Bruss
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Patent number: 10805347Abstract: A system including at least one processor; and at least one memory having stored thereon computer program code that, when executed by the at least one processor, controls the at least one processor to: receive an email addressed to a user; separate the email into a plurality of email components; analyze, using respective machine-learning techniques, each of the plurality of email components; feed the analysis of each of the plurality of email components into a stacked ensemble analyzer; and based on an output of the stacked ensemble analyzer, determine whether the email is malicious.Type: GrantFiled: July 10, 2019Date of Patent: October 13, 2020Assignee: CAPITAL ONE SERVICES, LLCInventors: Christopher Bayan Bruss, Stephen Fletcher, Lei Yu, Jakob Kressel
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Patent number: 10657416Abstract: Methods and systems disclosed herein may quantify a representation of a type of input an image analysis system should expect. The image analysis system may be trained on the type of input the image analysis system should expect using a first image stream. A first model of the type of input that the image analysis system should expect may be built from the first image stream. After the first model is built, a second image, or a second image stream, may be compared to the first model to determine a difference between the second image, or second image stream, and the first image stream. When the difference is greater than or equal to a threshold, a drift may be detected and steps may be taken to determine the cause of the drift.Type: GrantFiled: July 17, 2019Date of Patent: May 19, 2020Assignee: Capital One Services, LLCInventors: Keegan Hines, Christopher Bayan Bruss
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Patent number: 10579894Abstract: Methods and systems disclosed herein may quantify the content and nature of a first stream of text to detect when the typical composition of the first stream of text changes. Quantifying the content and nature of the first stream of text may begin by generating a baseline representation of the content of the first stream of text as represented by a first matrix. Once generated, the first matrix may be used as a control against subsequently received sequences of text. In this regard, a second matrix may be generated from a second sequence of text and compared to the first matrix to determine the differences between the first sequence of text and the second sequence of text. Once a difference is determined, the difference may be compared to a threshold value and, when the difference exceeds the threshold value, an administrator may be notified and corrective action taken.Type: GrantFiled: July 17, 2019Date of Patent: March 3, 2020Assignee: Capital One Service, LLCInventors: Keegan Hines, Christopher Bayan Bruss