Dynamic transaction card optimization

Patent metadata

Field	Value
Publication number	US-10210505-B2
Application number	US-201615099008-A
Country	US
Kind code	B2
Filing date	Apr 14, 2016
Priority date	Jul 23, 2013
Publication date	Feb 19, 2019
Grant date	Feb 19, 2019

Abstract

The present disclosure relates to devices and methods relating to an optimized electronic transaction card where various data inputs associated with a dynamic transaction card optimize operational configurations and/or a user experience of the dynamic transaction card to extend an energy storage life of the dynamic transaction card, promote various behaviors, and/or detect system and/or device defects. A dynamic transaction card may include a dynamic transaction card with various configuration and/or functionality that use the power components (e.g., printed circuit board (PCB), energy storage component, battery, and/or the like) of the dynamic transaction card. The configuration and/or functionality data may include, for example, sensor input, connection data, transaction data, display data, and/or the like. The configuration and/or functionality data may then be used to determine optimal configuration settings.

First claim

The invention claimed is:

1. A dynamic transaction card comprising:
a microcontroller within the dynamic transaction card;
data storage within the dynamic transaction card storing optimal configurations; wherein:
the microcontroller:
monitors a plurality of hardware components of the dynamic transaction card in order to determine functionality data and a plurality of software configurations used to derive configuration data;
transmits, via a network, the functionality data and configuration data to a processor configured to use machine learning to cluster the functionality and/or configuration data to process the functionality data and configuration data with respect to a user group segment;
receives, via the network, optimal configuration data from the processor; and
applies the optimal configurations to the dynamic transaction card.

2. The dynamic transaction card of claim 1 , wherein the processor calculates the optimal configuration data by maximizing energy storage length using the functionality data and configuration data as variables.

3. The dynamic transaction card of claim 1 , wherein the processor calculates the optimal configuration data by determining a desired user behavior, determining a probability that a particular functionality data and a particular configuration data results in the desired user behavior, and where the probability is higher than a predetermined threshold, assigning the configuration data as the optimal configuration data.

4. The dynamic transaction card of claim 1 , further comprising a sensor that monitors a plurality of hardware components of the dynamic transaction card in order to determine functionality data and a plurality of software configurations in order to determine configuration data.

5. The dynamic transaction card of claim 4 , wherein the functionality data and configuration data comprise sensor input, connection data, transaction data, and/or display data.

6. The dynamic transaction card of claim 1 , wherein the optimal configurations are calculated at the processor by determining a desired user behavior, determining a probability that a particular functionality data and a particular configuration data results in the desired user behavior, and where the probability is higher than a predetermined threshold, assigning the configuration data as the optimal configuration data.

7. The dynamic transaction card of claim 1 , wherein the dynamic transaction card and/or data storage associated with a merchant system, a backend system and/or a mobile device stores functionality data and/or configuration data.

8. The dynamic transaction card of claim 1 , wherein the optimal configurations are calculated at the processor by linear regression, logistic regression, ridge regression, lasso regression, Bayesian regression, and/or machine learning algorithms.

9. The dynamic transaction card of claim 1 , wherein the optimal configurations are calculated on a transaction card basis, a grouping of transaction card basis, and/or an entirety of transaction card basis.

10. The dynamic transaction card of claim 1 , wherein the functionality and configuration data are optimized to determine a maximum number of transaction attempts.

11. The dynamic transaction card of claim 1 , wherein the microcontroller transmits functionality data, configuration data and/or optimal configurations at a timed interval and/or on an event basis.

12. The dynamic transaction card of claim 1 , wherein the optimal configurations are pushed to the dynamic transaction card using a EuroPay-MasterCard-Visa (EMV) Issuer script.

13. The dynamic transaction card of claim 1 , wherein the microcontroller continuously monitors the functionality data, configuration data, and/or optimal configurations.

14. The dynamic transaction card of claim 1 , wherein the processor detects system and/or device defects by determining outlier data associated with dynamic transaction card data and outlier data associated with functionality and/or configuration data.

15. The dynamic transaction card of claim 14 , wherein the outlier data is transmitted via a network to the dynamic transaction card, a backend system and/or a mobile device associated with the dynamic transaction card.

16. A method, comprising:
monitoring, using a microcontroller within a dynamic transaction card, a plurality of hardware components of the dynamic transaction card in order to determine functionality data and a plurality of software configurations used to derive configuration data;
transmitting, via a network, the functionality data and configuration data to a processor;
utilizing machine learning to cluster functionality and/or configuration data to process the functionality data and configuration data with respect to a user group segment;
calculating, using a processor, optimal configuration data;
storing, in data storage within the dynamic transaction card, the optimal configurations; and
applying, using the microcontroller, the optimal configurations to the dynamic transaction card.

17. The method of claim 16 , wherein the optimal configurations are calculated at the processor by maximizing energy storage length using the functionality data and configuration data as variables.

18. The method of claim 16 , wherein the optimal configurations are calculated at the processor by determining a desired user behavior, determining a probability that a particular functionality data and a particular configuration data results in the desired user behavior, and where the probability is higher than a predetermined threshold, assigning the configuration data as the optimal configuration data.

19. The method of claim 16 , wherein the functionality data and configuration data comprise sensor input, connection data, transaction data, and/or display data.

20. The method of claim 16 , further comprising storing the functionality data and/or configuration data in the dynamic transaction card and/or data storage associated with a merchant system, a backend system and/or a mobile device.

21. The method of claim 16 , wherein the optimal configurations are calculated at the processor by linear regression, logistic regression, ridge regression, lasso regression, Bayesian regression, and/or machine learning algorithms.

22. The method of claim 16 , wherein the optimal configurations are calculated on a transaction card basis, a grouping of transaction card basis, and/or an entirety of transaction card basis.

23. The method of claim 16 , wherein the functionality and configuration data are optimized to determine a maximum number of transaction attempts.

24. The method of claim 16 , wherein the transmitting of functionality data, configuration data and/or optimal configurations are performed at a timed interval and/or on an event basis.

25. The method of claim 16 , further comprising pushing the optimal configurations to the dynamic transaction card using a EuroPay-MasterCard-Visa (EMV) Issuer script.

26. The method of claim 25 , further comprising applying the EMV Issuer script during the completion of a transaction via contacts of an EMV chip within the dynamic transaction card.

27. The method of claim 16 , further comprising continuously monitoring the functionality data, configuration data, and/or optimal configurations.

28. The method of claim 16 , further comprising detecting system and/or device defects by determining o

Assignees

• Capital One Services Llc

Inventors

• ZARAKAS James
• York Brennon

Classifications

• G06Q20/354

  Card activation or deactivation · CPC title

• G06Q20/3563

  Software being resident on card · CPC title

• G06Q20/352

  Contactless payments by cards · CPC title

• H04L67/22

  Electricity · mapped topic

• G06Q20/341 Primary

  Active cards, i.e. cards including their own processing means, e.g. including an IC or chip · CPC title