Capital One Services, LLC

Molly Elizabeth Johnson

James Zarakas

Adam R Koeppel

Adam Vukich

Tyler Sylvain Locke

Robert T Perry

Joshua S Edwards

Colin Hart

Kaitlin J Newman

Jason Ji

Angelina Wu

Saleem Ahmed Sangi

Francisco Perezleon

Rajko Ilincic

Kevan Emmott

Daniel B Clifton

Natasha Jensen

David Kelly Wurmfeld

Kevin Patrick Kelly

Francisco PerezLeon

Mohamed Lafeer

Adam Koeppel

A fuel price determination system may receive transaction data identifying purchases, made with transaction cards and mobile transaction card applications of client devices, of fuel at fuel stations. The fuel price determination system may receive location data identifying locations associated with users of the client devices and the transaction cards, and user history data associated with prior purchases of fuel at fuel stations by the users. The fuel price determination system may process the transaction data, location data, and user history data, with a machine learning model, to determine fuel prices at the fuel stations. The fuel price determination system may determine a ranked list of particular fuel stations in a geographical area based on the fuel prices and populate, based on the location data and the ranked list, a map to identify the particular fuel stations and the fuel prices at the particular fuel stations.

Utilizing machine learning and transaction data to determine fuel prices at fuel stations

The present disclosure describes a smart card that determines a context associated with the smart card and displays information based on the determined context. The smart card may determine a location of the smart card. Based on the determined location, the smart card may retrieve a list of establishments. The list of establishments may be used to determine a context, for example, based on information related to the establishments, historical information, user information, etc. Once the context is determined, the smart card may select information to display on the smart card. By limiting the information displayed on the smart card, the smart card may limit what can be captured by bad actors, thereby improving the security of the smart cards.

Dynamically displaying contextual information on a smart card

A device receives first transaction information associated with a first transaction, and a first transaction account utilized for the first transaction and associated with a first financial institution. The device determines, based on a fraud model, that the first transaction is to be denied due to potential fraud associated with the first transaction account and receives second transaction information associated with a second transaction, and a second transaction account utilized for the second transaction and associated with a second financial institution. The device processes the first transaction information and the second transaction information, with a matching model, to determine whether the first transaction information matches the second transaction information and determines that the first transaction was incorrectly denied when the first transaction information matches the second transaction information within a predetermined threshold. The device performs one or more actions based on determining that the first transaction was incorrectly denied.

Updating a machine learning fraud model based on third party transaction information

A device may include a processor, a wireless transceiver in communication with the processor, and a non-transitory memory. The memory may store instructions that, when executed by the processor, cause the processor to perform processing. The processing may include receiving a command configured to be executed by an external user device. The external user device and the user device may be members of a network of trust. The processing may include detecting, by the wireless transceiver, a plurality of member devices of the network of trust in communication range of the device. The processing may include sending, by the wireless transceiver, the command to each of the plurality of member devices.

Remote commands using network of trust

A multi-function transaction card may receive, from shelf sensors, first data indicating that items have been removed from shelves by a customer of a merchant, and may receive, from shopping cart sensors, second data indicating that the items have been placed in a shopping cart. The multi-function transaction card may receive, based on the first data and the second data, item data identifying the items placed in the shopping cart, wherein the item data is received from price tags of the items. The multi-function transaction card may store the item data, and may provide, to a transaction terminal of the merchant, the item data identifying the items and customer data identifying the customer. The multi-function transaction card may receive, from the transaction terminal, transaction data identifying a transaction that charges the multi-function transaction card for a total cost of the items.

Utilizing a multi-function transaction card to capture item data and enable expedited checkout for purchased items

A fraud detection platform may receive transaction data relating to a transaction conducted by a user with a transaction card. The fraud detection platform may receive, from a biometric sensor of the transaction card, biometric data relating to one or more biometric characteristics of the user during the transaction. The fraud detection platform may receive, from an accelerometer of the transaction card, card movement data relating to a measure of shaking of the transaction card by the user during the transaction. The fraud detection platform may process the transaction data, the biometric data, and the card movement data, with a fraud detection model, to determine a fraud score associated with the transaction. The fraud detection platform may perform one or more actions based on the fraud score.

Utilizing card movement data to identify fraudulent transactions

An EMV enabled terminal may communicate transaction related information to a user via a standard user interface of the terminal, for example, within a user interface displayed on a display screen of the terminal. The transaction related information may be spoken out loud to the user by the terminal. However, users with visual, audio, or other physical impairments may be unable to access the display of the terminal, view the displayed information, or hear the spoken information. Systems and methods disclosed herein communicate transaction related information via an alternate user interface. Instead of relying on the standard user interface related communications, information about the status of a transaction or transaction related information may be determined based on monitoring and evaluating the communications between an EMV secure element of a dynamic transaction card and the terminal via a contact connection.

EMV secure chip communications

A device may receive, from a client device of a customer, item data identifying a price of an item and customer data identifying the customer, where the item data may be received by a transaction card from a price tag of the item. The device may receive price data identifying prices associated with multiple items and other data identifying locations, availabilities, and terms of the multiple items, and may process the item data, the price data, and the other data, with a machine learning model, to identify an optimal price for the item. The device may provide, to the client device, data identifying the optimal price and data identifying a merchant associated with the optimal price, and may receive transaction data identifying the item, the optimal price, and the merchant when the customer purchases the item. The device may perform actions based on the transaction data.

Utilizing machine learning and a smart transaction card to automatically identify optimal prices and rebates for items during in-person shopping

A transaction card includes a near-field communication (NFC) component, a security component, a wireless component, one or more memories, and one or more processors communicatively coupled to the one or more memories. The device receives a signal from a user device attempting to access a secure application, and energizes the NFC component based on the signal received from the user device. The device causes the security component to generate an encrypted code based on the NFC component being energized, and provides, via the security component, the encrypted code to the wireless component. The device provides, via the wireless component, the encrypted code to the user device to permit the user device to utilize the encrypted code as authentication for accessing the secure application.

Utilizing a transaction card to provide secondary authentication for accessing a secure application with a user device

A transaction card may power on the transaction card using electric current induced from an interaction of the transaction card with an electromagnetic field. The transaction card may establish a communication with a device. The communication may indicate that the transaction card has powered. The transaction card may receive, from the device, a set of instructions to configure a set of applets on the transaction card after notifying the device that the transaction card has powered on. The set of applets to be configured may be related to completing one or more different transactions. The set of applets to be configured may be different than another set of applets already configured on the transaction card. The transaction card may configure the set of applets on the transaction card according to the set of instructions after receiving the set of instructions.

Configuring a set of applets on a battery-less transaction card

A device receives a transaction request concerning a transaction and a transaction card from a transaction terminal, and determines, based on the transaction request, information concerning the transaction card. The device sends, based on the transaction request and to a user device, a query concerning possession of the transaction card by a user of the user device, where the query includes an instruction for the user to confirm possession of the transaction card via a component of the user device. The device receives, from the user device and after sending the query to the user device, a response concerning possession of the transaction card by the user, and determines, based on the response and the information concerning the transaction card, whether the user has possession of the transaction card. The device performs, based on determining whether the user has possession of the transaction card, at least one action.

Determining whether a user has possession of a transaction card and/or whether the user is authorized to possess the transaction card

A controller of a transaction card may cause the transaction card to be in a first state. The transaction card may include a battery and at least one other component. In the first state, the battery does not provide power to the at least one other component of the transaction card. The transaction card may receive a signal from a near-field communication (NFC) device and may cause the transaction card to be in a second state based on receiving the signal. In the second state, the battery provides power to the at least one other component of the transaction card. The transaction card may perform a transaction based on the transaction card being in the second state.

Systems and methods for providing power to a multi-purpose transaction card

A device may receive, from client devices of users, user data identifying the users, client device data identifying the client devices, and transaction card data identifying transaction cards, and may receive transaction account data identifying transaction accounts. The device may process the user data, the client device data, the transaction card data, and the transaction account data, with a machine learning model, to determine trust scores for the transaction cards, and may identify trusted transaction cards based on the trust scores. The device may receive, from trusted client devices associated with the trusted transaction cards, location data identifying locations of the trusted client devices and communication data indicating communications between the trusted transaction cards and the trusted client devices. The device may generate a card mapping for the trusted transaction cards based on the location data and the communication data, and may perform actions based on the card mapping.

Utilizing machine learning and trusted transaction card locations to generate a geographical map of the trusted transaction cards

Disclosed herein are system, method, and apparatus for detecting a skimmer via a vibration sensor on a transaction card. The method includes receiving, at a server, from a user equipment, vibration information recorded by a transaction card in response to an execution of a transaction at a point-of-service (POS) terminal of a plurality of POS terminals using the transaction card. The vibration information may be recorded by a microphone or an accelerometer on the transaction card while the transaction card is swiped through the POS terminal or inserted into or removed from the POS terminal. The method includes determining, at the server, a state of the POS terminal based on the received vibration information and in response to a determination of the state of the POS being compromised, sending to one or more stakeholders of the transaction a warning message containing the POS terminal information including the POS terminal's state.

Detecting a skimmer via a vibration sensor

A device may obtain, from a client device associated with a customer and based on the customer joining a rewards program, item data identifying an item, of a plurality of items, placed in a shopping cart and customer data identifying the customer, obtain rewards data identifying rewards associated with the plurality of items, process the item data, the rewards data, and the customer data, with a machine learning model, to identify a reward based on the rewards data associated with the item, verify a completed purchase of the item based on data indicating an intent of the customer to purchase the item and transaction data, provide, to the client device, data identifying the identified reward based on verifying the completed purchase of the item, and retrain the machine learning model based on the identified reward.

Growing community of inventors

Alexandria, VA, United States of America

Molly Elizabeth Johnson