Capital One Services, LLC

Other

Sierra Nevada Corporation

Joshua S Edwards

Michael Bakr Mossoba

Abdelkader M'Hamed Benkreira

Tyler Maiman

Adam Vukich

Samuel Rapowitz

Viraj Chaudhary

David Septimus

David Gabriele

Jenny Melendez

Vu Nguyen

Ljubica Chatman

Lin Ni Lisa Cheng

Jonatan Yucra Rodriguez

Abdelkader Benkreira

Shabnam Kousha

Jason Ji

Mykhaylo Bulgakov

Eric Loucks

Alexandra Colevas

George Bergeron

Lukiih Cuan

Asher Smith-Rose

Kevin Grant Osborn

Andrea Montealegre

Salik Shah

Alexander Lin

Daniel Miller

Colin Hart

Max Miracolo

Abdelkadar M'Hamed Benkreira

Bryant Alan Yee

Daniel John Marsch

Vyjayanthi Vadrevu

Kaitlin J Newman

Daniel E Miller

Phoebe Atkins

Carlos Eduardo Rodriguez

Molly Elizabeth Johnson

Angelina Wu

Mia Rodriguez

Cruz Vargas

Jiawei Zhao

Rajko Ilincic

Latika Gulati

Aysu Ezen Can

Jennifer Kwok

Tania Cruz Morales

Francisco Perezleon

Carrie Norman

Guadalupe Bonilla

Meredith Leigh Critzer

Feng Qiu

Sarah Jane Cunningham

Kyle Rankin Johnson

William Carroll

Gaurang J Bhatt

Abhay Donthi

Kathryn Tikoian

Abdelkader M'hamed Benkreira

Brian McClanahan

Brian Lee

Vahid Khanagha

Jason Zwierzynski

Sara Rose Brodsky

Mohammad Shami

Mark Rothbaum

Bogdan Nedanov

Conor Anstett

Daniel Herrington

Sophie Bermudez

Austen Novis

Jacob Learned

Michael Saia

Jerry Wagner

James Zarakas

Kevan Emmott

Matthew Carson

Dan Lin

Eric Medin

Abdelkadar M′Hamed Benkreira

Sossena Negussie

Francisco PerezLeon

Jonathan Bodner

Sarvani Kare

Purva Shanker

Shaun Webb

Louis Buell

Nathan Wolfe

Peter Fischer

Haytham Yaghi

Jesse Emery

Marcelo Jabif Epsztejn

Samuel Yip

Alexander Mireles

Andrew Feldkamp

Nelson Rodriguez

Noel Lyles

Lauren Fox

A card for storing both emergency information and payment information is disclosed. The card can be used by an emergency responder to retrieve emergency information for the user. The emergency information can enable the emergency responder to better render aid to the user. The card can have one or more processors, a sensor, and a memory in communication with the one or more processors and storing an application thereon. The application can cause the card to receive a request from a user device (e.g., from an emergency medical technician, or EMT) to access emergency information stored in the memory, transmit a security challenge to the user device, authenticate a received response to the security challenge from the user device, and transmit the emergency information to the user device.

Devices and methods for providing emergency information using a payment card

Transaction card

A system may include processor(s), and memory in communication with the processor(s) and storing instructions configured to cause the system to correct ASR errors. The system may receive a transcription comprising transcribed word(s) and may determine whether the transcribed word(s) exceed associated predefined confidence level(s). Responsive to determining a transcribed word does not exceed a predefined confidence level, the system may generate a predicted word. The system may calculate a distance between numerical representations of the transcribed word and the predicted word and may determine whether the distance exceeds a predefined threshold. Responsive to determining the distance exceeds the predefined threshold, the system may determine whether at least one red flag word of a list of red flag words corresponds to a context of the transcription, and, responsive to making that determination, may classify the transcription as associated with a first category.

Systems and methods for correcting automatic speech recognition errors

A fraud detection system may identify a suspected fraudulent transaction based on one or more criteria. The transaction may be associated with a user. Based on identifying a suspected fraudulent transaction, the fraud detection system may receive augmented reality data from an augmented reality device of the user. The fraud detection system may determine, based on the augmented reality data, whether the transaction is fraudulent, and may take an action based on the determination.

Using augmented reality data as part of a fraud detection process

A device may receive customer data, and store the customer data in a secure memory device of the device. The device may implement a backup mode in which the device utilizes the customer data to authorize requests based on determining that the device lacks a network connection. The device may receive, when the device is in the backup mode, a request for currency that includes authentication information. The device, while in the backup mode, may authorize the request for currency based on comparing the authentication information and the customer data. The device may dispense currency based on a result of comparing the authentication information and the customer data. The device may store transaction data associated with dispensing of the currency to permit the transaction data to be transmitted when the network connection is restored.

Automated teller machines (ATMs) having offline functionality

In some implementations, a device may obtain event data relating to events involving one or more entities and one or more individuals, where the one or more entities are associated with entity categories. The device may determine, based on origin locations of the one or more individuals and locations of the one or more entities, distances between the origin locations of the one or more individuals and the locations of the one or more entities, the distances indicating travel distances of the one or more individuals to perform exchanges in the entity categories associated with the one or more entities. The device may determine, based on the event data and the distances, that a geographic area is associated with an opportunity for an entity category. The device may transmit a notification indicating the opportunity for the entity category in the geographic area.

Machine learning model prediction of an opportunity for an entity category in a geographic area

Systems, methods, and apparatuses are described herein for performing sentiment analysis on electronic communications relating to one or more image-based communications methods, such as emoji. Message data may be received. The message data may correspond to a message that is intended to be sent but has not yet been sent to an application. Using a first machine learning model, one or more subsets of the plurality of emoji may be determined. The one or more subsets of the plurality of emoji may comprise one or more different types and quantities of emoji, and may each correspond to the same or a different sentiment. Using a second machine learning model, one or more emojis may be selected from the one or more subsets. The one or more emojis selected may correspond to responses to the message.

Response prediction for electronic communications

Disclosed herein are systems and methods for determining whether an automated telephone call has been answered by a particular user, a different person, or a voicemail service. In response to determining that the call has been answered by the user, a different person, or a voicemail service, the systems and methods can output different respective messages, initiate messaging to the user via alternate communication paths, or perform other actions.

Systems for identifying the answering party of an automated voice call

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.

Systems and methods for external account authentication

Methods and systems are described herein for moving access tokens for validating network operations between user accounts to prevent malicious or nonaligned usage. The system may determine that a user is associated with a first user account and a second user account, retrieve parameters associated with the first and second user accounts, determine that an access token is available for migration, update a binding associated with the access token from the first to the second user account, and, in response to receiving a network operation request, may process the request using the second user account.

Systems and methods for validating network operations between user accounts through access tokens

Disclosed herein are system, method, and computer program product embodiments for generating an alert to a user for a change between weather conditions. An application can be operated by a processor to perform operations include determining a first weather condition at a geographic location at a first time when a user of the device enters a structure located within the geographic location. In addition, the operations can include, based on information received from the server, predicting a second weather condition at the geographic location at a second time while the user remains in the structure. Afterwards, the operations can include alerting the user for a change between the first and second weather conditions.

Alert a user of a weather change

A device may obtain a notification that a transaction occurred, where the transaction is associated with a financial account of a user, and a merchant. The device may determine that the merchant is associated with providing confirmation messages for transactions, and may process, based on determining that the merchant is associated with providing confirmation messages, one or more messages in a messaging account associated with the user to determine whether the messaging account includes a confirmation message associated with the transaction. The device may determine that the messaging account does not include any confirmation message associated with the transaction, may determine a likelihood that the transaction was unauthorized based on the messaging account not including a confirmation message, and may perform an action associated with the financial account of the user based on the likelihood that the transaction was unauthorized.

Fraud detection based on an analysis of messages in a messaging account

Methods, systems, and apparatuses are described herein for improving the security of personal information by preventing attempts at gleaning personal information from authentication questions. A computing device may receive a request for access to an account associated with a user. The request may comprise candidate authentication information. Based on comparing the candidate authentication information with the account data, the computing device may generate a synthetic authentication question. The synthetic authentication question may be generated as if the candidate authentication information is valid. A response to the synthetic authentication question may be received, and the request for access to the account may be denied.

Preventing unauthorized access to personal data during authentication processes

In some embodiments, the present disclosure provides an exemplary method that may include steps of obtaining, a permission from each user to monitor a plurality of activities; continually receiving monitoring data of the plurality of activities; identifying a plurality of related incoming interaction sessions within the predetermined period of time; automatically verifying at least one common session parameter; utilizing a machine learning algorithm to determine at least one transaction; determining a plurality of fraudulent transactional rules; automatically adjusting the plurality of fraudulent transactional rules; utilizing a natural language processing algorithm to automatically generate at least one interaction notification; receiving a response to the interactive notification; and automatically updating the database of known session interaction parameters based on the response to the interactive notification.

Computer-based systems configured to generate an interactive communication for a plurality of the repetitive interaction sessions and methods of use thereof

In some embodiments, the present disclosure provides an exemplary method that may include steps of monitoring, a conversation script between a call center agent and a customer; utilizing, a speech-to-text deep machine learning model to transcribe the audio call to text; utilizing, a natural language processing deep machine learning model to map intent mappings of the audio call; utilizing, a similarity measurement model to determine a semantic similarity between predefined intent mappings and the intent mappings of the call audio text; determining, an error based on the semantic similarity in the intent mapping call audio text; determining a training session based on the error.

Computer-based systems configured to monitor a communication session and generate a training session and methods of use thereof

Growing community of inventors

McLean, VA, United States of America

Joshua S Edwards