Automated multi-party event and transaction decisioning system

Patent metadata

Field	Value
Publication number	US-12430621-B2
Application number	US-202318517736-A
Country	US
Kind code	B2
Filing date	Nov 22, 2023
Priority date	Aug 19, 2021
Publication date	Sep 30, 2025
Grant date	Sep 30, 2025

Abstract

Methods, systems, devices, and computer-readable media for detecting multi-party events and transactions are provided. User data may be monitored to detect data associated with an event involving multiple individuals, such as by identifying transaction data associated with certain types of merchants and/or scheduling, calendar, or correspondence data indicative of an event. The data may be further analyzed to identify a date, location, and/or parties associated with the event. A multi-party event may be generated. The user data may continue to be monitored to identify transactions associated with multiple parties and occurring during a time and/or at a location of the event. At a conclusion of the event, the transactions may be aggregated and an optimal payment scheme may be determined for settlement of the transactions between the parties. In accordance with the determined payment scheme, delegation of portions of the aggregated transactions may be initiated for settlement amongst the parties.

First claim

What is claimed is:

1. A computer-implemented method comprising:
receiving, by a computing device, first data comprising a first transaction associated with a first user;
identifying, based on scanning correspondence data or calendar data on a first device associated with the first user, a location and a time associated with the first transaction, and a second user associated with the first transaction;
generating, based on the location and the time, by the computing device and using a first machine learning model, a multi-party event associated with splitting a payment associated with the first transaction between the first user and the second user, the multi-party event comprising information identifying the location and the time;
generating, by the computing device, a payment token in an encrypted format for the multi-party event, wherein the payment token comprises a URL link to a script configured to access information on a plurality of delegation requests to split the payment associated with the first transaction;
storing, in a database, the multi-party event associating the first user as a first party to the multi-party event and the second user as a second party to the multi-party event, and the payment token;
transmitting, to the first device associated with the first user, a notification indicating identification of the multi-party event;
transmitting, to a second device associated with the second user, a delegation message comprising the payment token in the encrypted format;
detecting the payment token in the encrypted format in second data associated with a second transaction by the second user;
decoding the payment token to extract the first data associated with the first transaction;
linking, based on the extracted first data, the second transaction to the first transaction; and
generating, based on the linking, a transaction summary comprising the first transaction associated with the first user and the second transaction associated with a payment, by the second user, of a portion of a value of the first transaction.

2. The computer-implemented method of claim 1 , wherein the first machine learning model is trained based on training data comprising:
pre-labeled transactions indicating whether a training transaction record is a multi-party transaction or an individual transaction,
a transaction amount of the training transaction record,
a date and time associated with the training transaction record, and
a type and a location of merchant associated with the training transaction record.

3. The computer-implemented method of claim 1 , wherein the first data comprises a merchant category associated with the first transaction, and generating the multi-party event is further based on the merchant category.

4. The computer-implemented method of claim 3 , wherein the merchant category comprises a transportation provider,
wherein the first transaction comprises a transaction for a transportation-related purchase, and
wherein the correspondence data comprises an email comprising a receipt for the transportation-related purchase.

5. The computer-implemented method of claim 1 , wherein the first data comprises a transaction amount and a merchant category associated with the first transaction, and generating the multi-party event is further based on the transaction amount exceeding a predetermined threshold amount for the merchant category.

6. The computer-implemented method of claim 1 , further comprising:
receiving, from the first device associated with the first user, an indication that the first user is located proximate to the location during a time frame comprising the time,
wherein generating the multi-party event is further based on receiving the indication that the first user is located proximate to the location during the time frame encompassing the time.

7. The computer-implemented method of claim 1 , further comprising:
receiving, based on the scanning and from the first device associated with the first user, the correspondence data comprising contact information for the second user; and
transmitting, to the second device associated with the second user and using the contact information, a request for the second device to share transactions of the second user with the multi-party event.

8. The computer-implemented method of claim 1 , further comprising:
receiving, by the computing device, additional data comprising a plurality of second transactions associated with the first user and the second user;
detecting, in the additional data, one or more transactions having a particular location and a particular time that correspond to the location and the time associated with the multi-party event; and
associating, in the database, the one or more transactions with the multi-party event.

9. The computer-implemented method of claim 8 , further comprising:
retrieving, in response to detecting the one or more transactions and from one or more of the first device associated with the first user or the second device associated with the second user, user data associated with the first user or the second user;
analyzing the user data to identify one or more third users associated with the one or more transactions; and
associating, in the database, the one or more third users as third parties to the multi-party event.

10. The computer-implemented method of claim 1 , further comprising:
determining, based on the time associated with the multi-party event, that the multi-party event has concluded;
retrieving, from the database, transactions and parties associated with the multi-party event; and
generating, based on an aggregation of transaction amounts of the transactions associated with the multi-party event, an optimized payment scheme for payment to one or more of the parties associated with the multi-party event.

11. The computer-implemented method of claim 1 , further comprising:
generating one or more delegation requests, wherein each delegation request comprises information indicating:
a party from the multi-party event identified as a payor party,
a party from the multi-party event identified as a payee party, and
an amount to be paid by the payor party to the payee party; and

for each of the one or more delegation requests, transmitting the delegation request to a corresponding payor party.

12. The computer-implemented method of claim 1 , further comprising:
storing the payment token in a standard filed of a payment transaction record as an indication that the payment transaction record is associated with a delegation request,
wherein the delegation message comprises the payment transaction record.

13. A computing device comprising:
at least one processor; and
memory storing instructions that, when executed by the at least one processor, cause the computing device to:
receive first data comprising a first transaction associated with a first user;
identify, based on scanning correspondence data or calendar data on a first device associated with the first user, a location and a time associated with the first transaction, and a second user associated with the first transaction;
generate, based on the location and the time, by the computing device and using a first machine learning model, a multi-party event associated with splitting a payment associated with the first transaction between the first user and the second user, the multi-party event comprising information identifying the location and the time;
generate a payment token in an encrypted format for the multi-party event, wherein the payment token comprises a URL link to a script configured to access information on a plurality of deleg

Assignees

• Capital One Services Llc

Inventors

• Bantanur Vijay Sahebgouda
• Balasubramanian Muralidharan
• Dallen Julie

Classifications

• G06Q20/123

  Shopping for digital content · CPC title

• G06Q20/347

  Passive cards · CPC title

• G06Q20/40155

  for triggering transactions · CPC title

• G06Q20/047 Primary

  using payment protocols involving electronic receipts · CPC title