Patents by Inventor Keegan Hines
Keegan Hines 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: 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
-
Publication number: 20210182907Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for targeting bid and position for a keyword. For instance, the method may include obtaining information about the keyword, the information about the keyword including observations of value with respect to position for the keyword. The method may further include applying a Gaussian Process Model on the observations to obtain a prediction function and associated uncertainties, the prediction function and the associated uncertainties relating positions to expected values; applying a Thompson sampling reinforcement learning model on the expected values and the positions to obtain a target position; and applying a bid model to the target position to obtain bid information for the keyword. The method may also include transmitting a bid message to a search engine, the bid message including the bid information.Type: ApplicationFiled: February 26, 2021Publication date: June 17, 2021Applicant: Capital One Services, LLCInventors: James O. H. MONTGOMERY, Athanassios KINTSAKIS, Keegan HINES
-
Publication number: 20210174258Abstract: A method for monitoring performance of a ML system includes receiving a data stream via a processor and generating a first plurality of metrics based on the data stream. The processor also generates input data based on the data stream, and sends the input data to a machine learning (ML) model for generation of intermediate output and model output based on the input data. The processor also generates a second plurality of metrics based on the intermediate output, and a third plurality of metrics based on the model output. An alert is generated based on at least one of the first plurality of metrics, the second plurality of metrics, or the third plurality of metrics, and a signal representing the alert is sent for display to a user via an interface.Type: ApplicationFiled: December 9, 2020Publication date: June 10, 2021Inventors: Adam WENCHEL, John DICKERSON, Priscilla ALEXANDER, Elizabeth O'SULLIVAN, Keegan HINES
-
Patent number: 11023767Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for localization and recognition of text from images. For instance, a first method may include: receiving an image; processing the image through a convolutional backbone to obtain feature maps(s); processing the feature maps through a region of interest (RoI) network to obtain RoIs; filtering the RoIs through a filtering block to obtain final RoIs; and processing the final RoIs through a text recognition stack to obtain predicted character sequences for the final RoIs. A second method may include: constructing a text localization and recognition neural network (TLaRNN); obtaining training data; training the TLaRNN on the training data; and storing trained weights of the TLaRNN. The constructing the TLaRNN may include: connecting a convolutional backbone to a region of interest (RoI) network; connecting the RoI network to a filtering block; and connecting the filtering block to a text recognition network.Type: GrantFiled: April 24, 2020Date of Patent: June 1, 2021Assignee: CAPITAL ONE SERVICES, LLCInventors: Mohammad Reza Sarshogh, Keegan Hines
-
Patent number: 10937058Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for targeting bid and position for a keyword. For instance, the method may include obtaining information about the keyword, the information about the keyword including observations of value with respect to position for the keyword. The method may further include applying a Gaussian Process Model on the observations to obtain a prediction function and associated uncertainties, the prediction function and the associated uncertainties relating positions to expected values; applying a Thompson sampling reinforcement learning model on the expected values and the positions to obtain a target position; and applying a bid model to the target position to obtain bid information for the keyword. The method may also include transmitting a bid message to a search engine, the bid message including the bid information.Type: GrantFiled: July 2, 2019Date of Patent: March 2, 2021Assignee: CAPITAL ONE SERVICES, LLCInventors: James O. H. Montgomery, Athanassios Kintsakis, Keegan Hines
-
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
-
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
-
Publication number: 20210004868Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for targeting bid and position for a keyword. For instance, the method may include obtaining information about the keyword, the information about the keyword including observations of value with respect to position for the keyword. The method may further include applying a Gaussian Process Model on the observations to obtain a prediction function and associated uncertainties, the prediction function and the associated uncertainties relating positions to expected values; applying a Thompson sampling reinforcement learning model on the expected values and the positions to obtain a target position; and applying a bid model to the target position to obtain bid information for the keyword. The method may also include transmitting a bid message to a search engine, the bid message including the bid information.Type: ApplicationFiled: July 2, 2019Publication date: January 7, 2021Applicant: Capital One Services, LLCInventors: James O. H. MONTGOMERY, Athanassios KINTSAKIS, Keegan HINES
-
Publication number: 20200349437Abstract: Systems, methods, and computer program products to provide neural embeddings of transaction data. A network graph of transaction data based on a plurality of transactions may be received. The network graph of transaction data may define relationships between the transactions, each transaction associated with at least a merchant and an account. A neural network may be trained based on training data comprising a plurality of positive entity pairs and a plurality of negative entity pairs. An embedding function may then encode transaction data for a first new transaction. An embeddings layer of the neural network may determine a vector for the first new transaction based on the encoded transaction data for the first new transaction. A similarity between the vectors for the transactions may be determined. The first new transaction may be determined to be related to the second transaction based on the similarity.Type: ApplicationFiled: July 22, 2020Publication date: November 5, 2020Applicant: Capital One Services, LLCInventors: Christopher BRUSS, Keegan HINES
-
Patent number: 10789530Abstract: Systems, methods, and computer program products to provide neural embeddings of transaction data. A network graph of transaction data based on a plurality of transactions may be received. The network graph of transaction data may define relationships between the transactions, each transaction associated with at least a merchant and an account. A neural network may be trained based on training data comprising a plurality of positive entity pairs and a plurality of negative entity pairs. An embedding function may then encode transaction data for a first new transaction. An embeddings layer of the neural network may determine a vector for the first new transaction based on the encoded transaction data for the first new transaction. A similarity between the vectors for the transactions may be determined. The first new transaction may be determined to be related to the second transaction based on the similarity.Type: GrantFiled: January 14, 2019Date of Patent: September 29, 2020Assignee: Capital One Services, LLCInventors: Christopher Bruss, Keegan Hines
-
Publication number: 20200250459Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for localization and recognition of text from images. For instance, a first method may include: receiving an image; processing the image through a convolutional backbone to obtain feature maps(s); processing the feature maps through a region of interest (Rol) network to obtain Rols; filtering the Rols through a filtering block to obtain final Rols; and processing the final Rols through a text recognition stack to obtain predicted character sequences for the final Rols. A second method may include: constructing a text localization and recognition neural network (TLaRNN); obtaining training data; training the TLaRNN on the training data; and storing trained weights of the TLaRNN. The constructing the TLaRNN may include: connecting a convolutional backbone to a region of interest (Rol) network; connecting the Rol network to a filtering block; and connecting the filtering block to a text recognition network.Type: ApplicationFiled: April 24, 2020Publication date: August 6, 2020Applicant: CAPITAL ONE SERVICES, LLCInventors: Mohammad Reza SARSHOGH, Keegan HINES
-
Publication number: 20200226460Abstract: Systems, methods, and computer program products to provide neural embeddings of transaction data. A network graph of transaction data based on a plurality of transactions may be received. The network graph of transaction data may define relationships between the transactions, each transaction associated with at least a merchant and an account. A neural network may be trained based on training data comprising a plurality of positive entity pairs and a plurality of negative entity pairs. An embedding function may then encode transaction data for a first new transaction. An embeddings layer of the neural network may determine a vector for the first new transaction based on the encoded transaction data for the first new transaction. A similarity between the vectors for the transactions may be determined. The first new transaction may be determined to be related to the second transaction based on the similarity.Type: ApplicationFiled: January 14, 2019Publication date: July 16, 2020Applicant: Capital One Services, LLCInventors: Christopher BRUSS, Keegan HINES
-
Patent number: 10671878Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for localization and recognition of text from images. For instance, a first method may include: receiving an image; processing the image through a convolutional backbone to obtain feature maps(s); processing the feature maps through a region of interest (RoI) network to obtain RoIs; filtering the RoIs through a filtering block to obtain final RoIs; and processing the final RoIs through a text recognition stack to obtain predicted character sequences for the final RoIs. A second method may include: constructing a text localization and recognition neural network (TLaRNN); obtaining training data; training the TLaRNN on the training data; and storing trained weights of the TLaRNN. The constructing the TLaRNN may include: connecting a convolutional backbone to a region of interest (RoI) network; connecting the RoI network to a filtering block; and connecting the filtering block to a text recognition network.Type: GrantFiled: June 28, 2019Date of Patent: June 2, 2020Assignee: Capital One Services, LLCInventors: Mohammad Reza Sarshogh, Keegan Hines
-
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
-
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