Patents by Inventor Tania CRUZ MORALES
Tania CRUZ MORALES 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: 20240169324Abstract: A method for executing actions based on event data using machine learning is disclosed. The method comprises: receiving occasion data associated with a user; analyzing, using a trained machine learning model, the occasion data to identify an occasion associated with a first classification, wherein the trained machine learning model has been trained based on (i) training occasion data that includes information regarding one or more occasions associated with the training occasion data and (ii) training classification data that includes a prior classification for each of the occasions, to learn relationships between the training occasion data and the training classification data, such that the trained machine learning model is configured to use the learned relationships to identify an occasion associated with a first classification in response to input of the occasion data; determining an action based on the occasion associated with the first classification; and automatically executing the action.Type: ApplicationFiled: November 21, 2022Publication date: May 23, 2024Applicant: Capital One Services, LLCInventors: Joshua EDWARDS, Jason ZWIERZYNSKI, Abhay DONTHI, Sara Rose BRODSKY, Jennifer KWOK, Tania Cruz MORALES
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Publication number: 20240169329Abstract: A method for machine-learning based action generation, and more specifically, using machine-learning to dynamically adjust financial account payments and fees. The method may comprise: receiving user data; determining whether a trigger condition has been met; upon determining that a trigger condition has been met, generating, using a trained machine-learning model, one or more actions based on the user data associated with the user, wherein the trained machine-learning model has been trained based on (i) training user data and (ii) training action data, to learn relationships between the training user data and the training actions data, such that the trained machine-learning model is configured to use the learned relationships to generate one or more actions in response to input of the user data associated with the user; selecting a first action of the one or more actions; and automatically executing the first action.Type: ApplicationFiled: November 22, 2022Publication date: May 23, 2024Applicant: Capital One Services, LLCInventors: Jennifer KWOK, Tania Cruz MORALES, Sara Rose BRODSKY, Abhay DONTHI, Joshua EDWARDS, Jason ZWIERZYNSKI
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Publication number: 20240135381Abstract: Systems and methods for external account authentication are disclosed herein. They include receiving a call to pair the external account with a secure account, extracting external data from the external account, the external data corresponding to external account content, providing user activity data from the secure account as an input to an authentication machine learning model, providing the external data as an input to the authentication machine learning model, the authentication machine learning model configured to output a certainty level that the external account is associated with a user of the secure account based on the external data and the activity data, receiving the certainty level from the authentication machine learning model, determining that the certainty level meets a certainty threshold, and pairing the external account with the secure account based on determining that the certainty level meets the certainty threshold.Type: ApplicationFiled: October 23, 2022Publication date: April 25, 2024Applicant: Capital One Services, LLCInventors: Jennifer KWOK, Sara Rose BRODSKY, Jason ZWIERZYNSKI, Joshua EDWARDS, Abhay DONTHI, Tania Cruz MORALES
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Publication number: 20240061845Abstract: In some implementations, a system may receive interaction data associated with interactions between a user and subsets of a plurality of interaction parties. The system may store the interaction data and the as historical interaction data associated with historical interactions of the user. The system may provide the historical interaction data as input to a machine learning model, which may be trained using supervised learning and the historical interactions of the user or historical interactions of one or more other users with one or more of the plurality of interaction parties. The system may receive an output, based on applying the machine learning model to the historical interaction data, that may indicate one or more recommended interaction parties based at least in part on one or more factors, wherein the one or more recommended parties may be local entities local to a geographic location associated with the user.Type: ApplicationFiled: August 16, 2022Publication date: February 22, 2024Inventors: Dwipam KATARIYA, Muhammad UDDIN, Tania CRUZ MORALES, Julian DUQUE, Kimberly STOCKLEY
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Publication number: 20240056511Abstract: Systems and methods for detecting and repairing loss of a primary digital communication channel may include a server and a user device. The server may be configured to send a push notification to an application of the user device over a network, receive, in response to the sending of the push notification, push notification status data, apply a predictive model to determine whether the primary digital communication channel has failed based on the push notification and the push notification status data; and transmit, upon a determination that the primary digital communication channel has failed, a communication to the user over one or more alternative digital communication channels.Type: ApplicationFiled: August 15, 2022Publication date: February 15, 2024Inventors: Jason ZWIERZYNSKI, Sara Rose BRODSKY, Jennifer KWOK, Joshua EDWARDS, Abhay DONTHI, Tania CRUZ MORALES
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Publication number: 20240036928Abstract: Embodiments described herein reduce resource insufficiency of a resource source despite inconsistent resource accumulation at the resource source. For example, a request frequency may be determined to define times at which the resource source is predicted to be sufficient despite the inconsistent accumulation or influx. In one use case, with respect to a distributed computing environment having computing resource source(s)/pool(s), a requesting system may identify a machine learning model trained to generate predictions for a resource source at which inconsistent resource accumulation occurs. The system may obtain accumulation data that describes accumulation events at which resources were made available at the resource source.Type: ApplicationFiled: July 26, 2022Publication date: February 1, 2024Applicant: Capital One Services, LLCInventors: Abhay DONTHI, Tania CRUZ MORALES, Jason ZWIERZYNSKI, Joshua EDWARDS, Jennifer KWOK, Sara Rose BRODSKY
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Publication number: 20230388403Abstract: Methods and systems are disclosed herein for using one or more machine learning models to determine whether a user is expected to complete a task or action by a deadline. The one or more machine learning models may be trained and/or used to recommend a communication protocol for sending a reminder to the user such that the user is predicted to be more likely to complete an action by the action's deadline. A computing system may use the one or more machine learning models to generate a recommendation for type of reminder to send, for example, if it is predicted that the user is not expected to complete the task by the deadline. A computing system may determine the message to send, the communication protocol to use, and/or the time to send the message.Type: ApplicationFiled: August 7, 2023Publication date: November 30, 2023Applicant: Capital One Services, LLCInventors: Sara BRODSKY, Jennifer KWOK, Tania CRUZ MORALES, Joshua EDWARDS, Abhay DONTHI, Jason ZWIERZYNSKI
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Patent number: 11756035Abstract: Systems and methods for confirming and/or updating account information are disclosed. The systems and methods may calculate relocation scores based on transaction data and device location data. The relocation scores may be based on locations of the transactions and/or locations of a user device beyond a registered customer address. A relocation score can be calculated based on a quantity of transactions having transaction locations beyond a first predetermined distance from the customer address as determined by transaction location identifiers. A relocation score can be calculated based on an elapsed duration of time since a customer transaction was completed within the first predetermined distance from the customer address.Type: GrantFiled: October 27, 2021Date of Patent: September 12, 2023Assignee: CAPITAL ONE SERVICES, LLCInventors: Sara Rose Brodsky, Abhay Donthi, Joshua Edwards, Jennifer Kwok, Tania Cruz Morales, Jason Zwierzynski
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Patent number: 11750731Abstract: Methods and systems are disclosed herein for using one or more machine learning models to determine whether a user is expected to complete a task or action by a deadline. The one or more machine learning models may be trained and/or used to recommend a communication protocol for sending a reminder to the user such that the user is predicted to be more likely to complete an action by the action's deadline. A computing system may use the one or more machine learning models to generate a recommendation for type of reminder to send, for example, if it is predicted that the user is not expected to complete the task by the deadline. A computing system may determine the message to send, the communication protocol to use, and/or the time to send the message.Type: GrantFiled: May 17, 2021Date of Patent: September 5, 2023Assignee: Capital One Services, LLCInventors: Sara Brodsky, Jennifer Kwok, Tania Cruz Morales, Joshua Edwards, Abhay Donthi, Jason Zwierzynski
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Publication number: 20230186171Abstract: A method of for analyzing data using machine learning models comprising: receiving data associated with a request to add a new occasion to an electronic database, wherein: the electronic database includes a plurality of occasions; a portion of the plurality of occasions is associated with a timing value and a substance value; the electronic database is associated with a first progress value; and the data associated with the request to add the new occasion is at least partially automatically generated by a first trained machine learning model; receiving data associated with the new occasion; predicting, by a second trained machine learning model, a timing value and a substance value for the new occasion; calculating a second progress value based on the timing value and the substance value for the new occasion; and causing a graphical user interface to display a notification to add the new occasion to the electronic database.Type: ApplicationFiled: December 13, 2021Publication date: June 15, 2023Applicant: Capital One Services, LLCInventors: Gena WOMACK, Tania Cruz MORALES
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Publication number: 20230128845Abstract: Systems and methods for confirming and/or updating account information are disclosed. The systems and methods may calculate relocation scores based on transaction data and device location data. The relocation scores may be based on locations of the transactions and/or locations of a user device beyond a registered customer address. A relocation score can be calculated based on a quantity of transactions having transaction locations beyond a first predetermined distance from the customer address as determined by transaction location identifiers. A relocation score can be calculated based on an elapsed duration of time since a customer transaction was completed within the first predetermined distance from the customer address.Type: ApplicationFiled: October 27, 2021Publication date: April 27, 2023Inventors: Sara Rose Brodsky, Abhay Donthi, Joshua Edwards, Jennifer Kwok, Tania Cruz Morales, Jason Zwierzynski
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Publication number: 20220368789Abstract: Methods and systems are disclosed herein for using one or more machine learning models to determine whether a user is expected to complete a task or action by a deadline. The one or more machine learning models may be trained and/or used to recommend a communication protocol for sending a reminder to the user such that the user is predicted to be more likely to complete an action by the action's deadline. A computing system may use the one or more machine learning models to generate a recommendation for type of reminder to send, for example, if it is predicted that the user is not expected to complete the task by the deadline. A computing system may determine the message to send, the communication protocol to use, and/or the time to send the message.Type: ApplicationFiled: May 17, 2021Publication date: November 17, 2022Applicant: Capital One Services, LLCInventors: Sara BRODSKY, Jennifer KWOK, Tania CRUZ MORALES, Joshua EDWARDS, Abhay DONTHI, Jason ZWIERZYNSKI