Granted Patent B2

US 12,430,621 · App. 18/517,736 · Granted Sep 30, 2025

Inventors: Vijay Sahebgouda Bantanur (Gaithersburg, MD); Muralidharan Balasubramanian (Gaithersburg, MD); Julie Dallen (Vienna, VA)

Assignee: Capital One Services, LLC

G06Q20/047G06Q20/123G06Q20/347G06Q20/40155

Abstract

Claims (102)

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 delegation requests to split the payment associated with the first transaction;

store, 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;

transmit, to the first device associated with the first user, a notification indicating identification of the multi-party event;

transmit, to a second device associated with the second user, a delegation message comprising the payment token in the encrypted format;

detect the payment token in the encrypted format in second data associated with a second transaction by the second user;

decode the payment token to extract the first data associated with the first transaction;

link, based on the extracted first data, the second transaction to the first transaction; and

generate, 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.

14. The computing device of claim 13 , wherein the instructions, when executed by the at least one processor, further cause the computing device to:

train the first machine learning model 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.

15. The computing device of claim 13 , wherein the first data comprises a transaction amount and a merchant category associated with the first transaction, and wherein the instructions, when executed by the at least one processor, further cause the computing device to:

generate the multi-party event further based on the transaction amount exceeding a predetermined threshold amount for the merchant category.

16. The computing device of claim 13 , wherein the instructions, when executed by the at least one processor, further cause the computing device to:

receive additional data comprising a plurality of second transactions associated with the first user and the second user;

detect, 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

associate, in the database, the one or more transactions with the multi-party event.

17. The computing device of claim 16 , wherein the instructions, when executed by the at least one processor, further cause the computing device to:

retrieve, 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;

analyze the user data to identify one or more third users associated with the one or more transactions; and

associate, in the database, the one or more third users as third parties to the multi-party event.

18. The computing device of claim 13 , wherein the instructions, when executed by the at least one processor, further cause the computing device to:

generate 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.

19. A non-transitory, computer-readable medium storing instructions that, when executed by at least one processor, cause:

receiving 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 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 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.

20. The non-transitory, computer-readable medium of claim 19 , wherein the instructions, when executed by the at least one processor, further cause:

training the first machine learning model 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.

Assignments (1)

Continuity (2)

References Cited (28)