Dynamic custom automated teller machine (ATM) payload

Patent metadata

Field	Value
Publication number	US-12400199-B2
Application number	US-202418418414-A
Country	US
Kind code	B2
Filing date	Jan 22, 2024
Priority date	Jan 22, 2024
Publication date	Aug 26, 2025
Grant date	Aug 26, 2025

Abstract

Systems, methods, and apparatus are provided for implementing a dynamically customized ATM payload. An ATM may extract a user identifier and home region during a session. The ATM may be a node on a distributed network and securely publish data to a distributed hash table. Extracted data may trigger smart contract logic including a transaction rule associated with the user and a transmission rule restricting network switches for payload delivery outside the home region. AI algorithms may generate a custom payload including the transaction rule and an interface adapted for the user home region. The payload may be pushed to the ATM via a set of spine-leaf switches based on the transmission rule. The payload may be deployed at the ATM during the session to modify a default ATM interface and a default ATM transaction rule. The ATM may purge the payload from memory when the session is terminated.

First claim

What is claimed is:

1. A method for adapting an ATM by generating a custom payload during an ATM session, the ATM comprising a node on a distributed network, the method comprising:
at an ATM session, extracting a user identifier and a user home region, the ATM configured to maintain extracted data in a cryptographically secured hash chain signed with a private key, the distributed network comprising a distributed hash table configured to validate the hash chain;
based on an extracted user identifier, triggering smart contract logic comprising a transaction rule associated with the user;
using generative artificial intelligence, assembling a custom payload comprising the transaction rule and an interface configured for the user home region;
delivering the custom payload to the ATM via a set of spine-leaf switches, the set of switches determined based at least in part on the smart contract logic;
deploying the custom payload at the ATM, the deploying comprising adapting a default ATM interface and default ATM transaction rule; and
purging the custom payload from ATM memory in response to termination of the ATM session.

2. The method of claim 1 , the generative AI configured to assemble the custom payload based on data extracted from the distributed hash table.

3. The method of claim 1 , the generative AI configured to assemble the custom payload based on data extracted from multiple distributed hash tables, each associated with a different entity.

4. The method of claim 1 , further comprising extracting a user preference, the custom payload interface configured based on the user preference.

5. The method of claim 1 , wherein the distributed network uses a Holochain® framework.

6. The method of claim 1 , further comprising determining, using a mobile device application, whether a geographic location of the user is within a predetermined distance from a geographic location of the ATM before deploying the custom payload on the ATM.

7. The method of claim 1 , further comprising verifying a user location via an automated chat in a mobile device application before deploying the custom payload on the ATM.

8. The method of claim 1 , the interface configured for the home region comprising a language different from a language of the default ATM interface.

9. The method of claim 1 , the interface configured for the home region comprising a set of transaction options different from a set of transaction options at the default ATM interface.

10. One or more non-transitory computer-readable media storing computer-executable instructions which, when executed by a processor on a computer system, perform a method for generating a custom payload during an ATM session, the ATM comprising a node on a distributed network, the method comprising:
at the ATM:
extracting a user identifier and a user home region, the ATM configured to maintain extracted data in a cryptographically secured hash chain signed with a private key, the distributed network comprising a distributed hash table configured to validate the hash chain;
deploying a custom payload modifying a default ATM interface and a default ATM transaction rule; and
purging the custom payload from ATM memory in response to termination of the ATM session;

at the distributed network:
based on an extracted user identifier, triggering smart contract logic comprising:
a transaction rule associated with the user; and
a transmission rule restricting network switches for payload delivery outside the user home region;

using artificial intelligence (AI), generating a custom payload comprising the transaction rule and an interface configured for the user home region; and
delivering the custom payload via a set of spine-leaf switches, the set of switches determined based at least in part on the transmission rule.

11. The media of claim 10 , the AI configured to generate the custom payload based on data extracted from the distributed hash table.

12. The media of claim 10 , further comprising extracting a user preference, the custom payload comprising an interface configured based on the user preference.

13. The media of claim 10 , wherein the distributed network uses a Holochain® framework.

14. The media of claim 10 , the interface configured for the home region comprising a language different from a language of the default ATM interface.

15. The media of claim 10 , the interface configured for the home region comprising a set of transaction options different from a set of transaction options at the default ATM interface.

16. A system for generating and deploying a custom payload during an ATM session, the ATM comprising a node on a distributed network, the system comprising:
an ATM configured to:
extract a user identifier and a user home region, the ATM maintaining extracted data in a cryptographically secured hash chain signed with a private key, the distributed network comprising a distributed hash table configured to validate the hash chain;
deploy a custom payload modifying a default ATM interface and a default ATM transaction rule; and
purge the custom payload from ATM memory in response to termination of the ATM session;

an ATM network configured to:
based on an extracted user identifier, trigger smart contract logic comprising:
a transaction rule associated with the user; and
a transmission rule restricting network switches for payload delivery outside the home region;

using one or more artificial intelligence (AI) algorithms, generate a custom payload comprising the transaction rule and an interface configured for the user home region; and
deliver the custom payload via a set of spine-leaf switches, the set of switches determined based at least in part on the transmission rule.

17. The system of claim 16 , the AI algorithms configured to generate the custom payload based on data extracted from the distributed hash table.

18. The system of claim 16 , the AI algorithms configured to assemble the custom payload based on data extracted from multiple distributed hash tables, each associated with a different entity.

19. The system of claim 16 , the ATM configured to extract metadata comprising a user preference, the custom payload interface configured based on the user preference.

20. The system of claim 16 , wherein the distributed network uses a Holochain® framework.

21. The system of claim 16 , further comprising a mobile device application, the mobile device application configured to determine whether a geographic location of the user is within a predetermined distance from a geographic location of the ATM before deploying the custom payload on the ATM.

22. The system of claim 16 , further comprising a mobile device application, the mobile device application configured to verify a user location via an automated chat in the mobile device application before deploying the custom payload on the ATM.

Assignees

• Bank Of America

Inventors

• Garg Saurabh
• Sethia Maneesh Kumar
• Singh Shailendra
• Gowni Ajay Kumar

Classifications

• H04L9/3247

  involving digital signatures · CPC title

• H04L9/50

  using hash chains, e.g. blockchains or hash trees · CPC title

• G06N3/047

  Probabilistic or stochastic networks · CPC title

• G06N3/088

  Non-supervised learning, e.g. competitive learning · CPC title

• G06N3/094

  Adversarial learning · CPC title