IP Library › Granted Patent US 11,538,116
Granted Patent B2
US 11,538,116 · App. 17/222,609 · Granted Dec 27, 2022

Life event bank ledger

Inventors: Abdelkader M'Hamed Benkreira (Brooklyn, NY); Michael Mossoba (Great Falls, VA); Joshua Edwards (Philadelphia, PA)
Assignee: Capital One Services, LLC
G06Q40/12G06F16/21G06N20/00
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 11,538,116
App. No.
17/222,609
Granted
Dec 27, 2022
Kind
B2
Abstract

A system and method for generating a ledger is disclosed herein. A computing system receives, from one or more third party vendors, a plurality of transactions associated with a user. The computing system parses the plurality of transactions to identify one or more parameters associated with each transaction of the plurality of transactions. The computing system groups the one or more transactions into one or more clusters based on the identified one or more parameters. The computing system associates a life event to each cluster of the one or more clusters. The computing system interfaces with a client device associated with the user to confirm an associated life event. Upon receiving a confirmation from the user regarding the associated life event, the computing system generates a ledger. The ledger includes the life event and the one or more transactions associated therewith.

Claims (75)

1. A method, comprising:

anonymizing, by a computing system, a plurality of transactions associated with a plurality of users;

generating, by the computing system, a prediction model to identify groups of related transactions by:

training, by the computing system, the prediction model to identify for a respective user, based on the anonymized plurality of transactions, a subset of the respective user's transactions that are related to each other;

testing, by the computing system, the prediction model to identify a success ratio of the training, wherein the success ratio corresponds to a threshold level of accuracy; and

retraining and retesting, by the computing system, the prediction model until the prediction model groups the subset of transactions in accordance with the threshold level of accuracy;

retrieving, by the computing system, a set of target transactions associated with a target user;

grouping, by the computing system and based on the prediction model identifying relatedness to each other, one or more transactions of the set of target transactions into a cluster; and

associating, by the computing system, a life event to the cluster of the one or more transactions;

generating, by the computing system, an interactive graphical user interface comprising an indication of the life event and corresponding cluster associated with the life event; and

causing, by the computing system, a client device associated with the target user to display the interactive graphical user interface.

2. The method of claim 1 , further comprising:

interfacing, by the computing system, with the client device associated with the target user to confirm the life event.

3. The method of claim 2 , wherein interfacing, by the computing system, with the client device associated with the target user to confirm the life event, comprises:

activating an interactive agent configured to interact with the client device;

generating, by the interactive agent, a confirmation message to be transmitted to the client device, the confirmation message seeking to confirm the life event; and

sending, by the interactive agent, the confirmation message to the client device.

4. The method of claim 3 , wherein sending, by the interactive agent, the confirmation message to confirm the life event, comprises:

sending, by the interactive agent, the confirmation message as a text message to a text message application executing on the client device.

5. The method of claim 1 , further comprising:

generating, by the computing system, a plurality of synthetic transactions configured to mimic real transactions.

6. The method of claim 5 , further comprising:

further training, by the computing system, the prediction model to identify for the respective user, based on the plurality of synthetic transactions, a subset of the respective user's transactions that are related to each other.

7. The method of claim 1 , further comprising:

receiving, by the computing system from the client device, an indication from the target user regarding a modification to the life event; and

re-assessing, by the computing system, the one or more transactions associated with the cluster to generate a new cluster.

8. A method, comprising:

identifying, by a computing system, a plurality of transactions associated with a plurality of users;

anonymizing, by the computing system, the plurality of transactions;

generating, by the computing system, a plurality of synthetic transactions configured to mimic real transactions;

generating, by the computing system, a prediction model to identify groups of related transactions by:

training, by the computing system, the prediction model to identify, based on the plurality of transactions and the plurality of synthetic transactions, a subset of transactions related to each other;

testing, by the computing system, the prediction model to identify a success ratio of the training, wherein the success ratio corresponds to a threshold level of accuracy; and

retraining and retesting, by the computing system, the prediction model until the prediction model groups transactions in accordance with the threshold level of accuracy;

retrieving, by the computing system, a set of target transactions associated with a target user;

grouping, by the computing system via the prediction model, one or more transactions of the set of target transactions into a cluster; and

associating, by the computing system, a life event to the cluster of the one or more transactions.

9. The method of claim 8 , further comprising:

interfacing, by the computing system, with a client device associated with the target user to confirm the cluster.

10. The method of claim 9 , wherein interfacing, by the computing system, with the client device associated with the target user to confirm the cluster, comprises:

activating an interactive agent configured to interact with the client device;

generating, by the interactive agent, a confirmation message to be transmitted to the client device, the confirmation message seeking to confirm the cluster; and

sending, by the interactive agent, the confirmation message to the client device.

11. The method of claim 10 , wherein sending, by the interactive agent, the confirmation message to confirm the cluster, comprises:

sending, by the interactive agent, the confirmation message as a text message to a text message application executing on the client device.

12. The method of claim 8 , further comprising:

generating, by the computing system, a plurality of synthetic transactions configured to mimic real transactions.

13. The method of claim 12 , further comprising:

further training, by the computing system, the prediction model to identify for the respective user, based on the plurality of synthetic transactions, a subset of the respective user's transactions that are related to each other.

14. The method of claim 8 , further comprising:

receiving, by the computing system from a client device, an indication from the target user regarding a modification to the life event; and

re-assessing, by the computing system, the one or more transactions associated with the cluster to generate a new cluster.

15. A system, comprising:

a processor in communication with one or more client devices associated with one or more users; and

a memory having programming instructions stored thereon, which, when executed by the processor, performs operations comprising:

anonymizing a plurality of transactions associated with a plurality of users;

generating a prediction model to identify groups of related transactions by:

training the prediction model to identify for a respective user, based on the anonymized plurality of transactions, a subset of the respective user's transactions that are related to each other;

testing the prediction model to identify a success ratio of the training, wherein the success ratio corresponds to a threshold level of accuracy; and

retraining and retesting the prediction model until the prediction model groups transactions in accordance with the threshold level of accuracy;

retrieving a set of target transactions associated with a target user;

grouping, based on the prediction model identifying relatedness to each other, one or more transactions of the set of target transactions into a cluster; and

associating a life event to the cluster.

16. The system of claim 15 , wherein the operations further comprise:

interfacing with a client device associated with the target user to confirm the cluster.

17. The system of claim 16 , wherein interfacing with the client device associated with the target user to confirm the cluster, comprises:

activating an interactive agent configured to interact with the client device;

generating, by the interactive agent, a confirmation message to be transmitted to the client device, the confirmation message seeking to confirm the cluster; and

sending, by the interactive agent, the confirmation message to the client device.

18. The system of claim 17 , wherein sending, by the interactive agent, the confirmation message to confirm the cluster, comprises:

sending, by the interactive agent, the confirmation message as a text message to a text message application executing on the client device.

19. The system of claim 15 , wherein the operations further comprise:

generating a plurality of synthetic transactions configured to mimic real transactions.

20. The system of claim 19 , further comprising:

further training the prediction model to identify for the respective user, based on the plurality of synthetic transactions, a subset of the respective user's transactions that are related to each other.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 5, 2021
From: BENKREIRA, ABDELKADER M'HAMED; MOSSOBA, MICHAEL; EDWARDS, JOSHUA
To: CAPITAL ONE SERVICES LLC
Reel/Frame 055827/0315 →
Continuity (2)
Continuation 16703305 · Dec 4, 2019
Related Publication 20210224923A1 · Jul 22, 2021
Cited By (2)
US 12,641,178 US 12,750,443