Preemptive transaction analysis

Patent metadata

Field	Value
Publication number	US-11915282-B2
Application number	US-202217664438-A
Country	US
Kind code	B2
Filing date	May 23, 2022
Priority date	Jan 7, 2019
Publication date	Feb 27, 2024
Grant date	Feb 27, 2024

Abstract

A method may include receiving activity data associated with a user, wherein the activity data relates to online activity involving a product type, identifying the product type associated with the activity data, and predicting, based on the activity data, that the user is likely to purchase a product of the product type. The method may include generating, based on predicting that the user is likely interested in purchasing the product of the product type, an annotation to indicate that a potential transaction to purchase the product is forthcoming, and storing the annotation in a profile associated with an account of the user. The method may include detecting a transaction to purchase the product, wherein the transaction involves a payment from the account, and performing an action associated with a fraud analysis of the transaction based on the annotation.

First claim

What is claimed is:

1. A method, comprising:
processing, by a device, unstructured data, associated with activity related to a user, to generate activity data associated with the activity,
wherein the unstructured data is received from at least one application operating on another device;

predicting, by the device, based on a machine learning model that is trained to predict that the user is likely to purchase a particular type of product based on the activity data, that the user is likely to purchase the particular type of product;
generating, by the device and based on predicting that the user is likely to purchase the particular type of product, a note associated with a potential transaction to purchase the particular type of product,
wherein the note associated with the potential transaction is removed after a threshold time period;

detecting, by the device, a transaction indicative of a purchase of a product associated with the particular type of product;
performing, by the device and based on the note associated with the potential transaction, a particular set of processes from one or more fraud analysis processes associated with the transaction; and
sending, by the device and based on performing the particular set of processes, an authorization associated with the transaction.

2. The method of claim 1 , wherein the activity data indicates activity associated with:
capturing an image of the particular type of product,
sending a message identifying the particular type or product,
accessing offline media associated with the particular type of product,
purchasing related products associated with the particular type of product,
adding the product to a cart, or
traveling to a location of a merchant that sells the particular type of product.

3. The method of claim 1 , further comprising:
analyzing the activity data using one or more of:
an image processing technique,
a text processing technique, or
a code processing technique; and

determining, based on analyzing the activity data, that the activity data is associated with the particular type of product.

4. The method of claim 1 , further comprising:
determining that the transaction is associated with the product based on online activity associated with the transaction.

5. The method of claim 1 , wherein the unstructured data is received based on providing configuration data enabling installation of the application.

6. The method of claim 1 , wherein the threshold time period is determined based on an expiration model that is trained with historical information associated with the activity data.

7. The method of claim 1 , wherein the activity data is associated with at least one of:
location information associated with the user,
camera data, or
radio frequency identification data.

8. A device, comprising:
one or more memories; and
one or more processors, coupled to the one or more memories, configured to:
process unstructured data, associated with activity related to a user, to generate activity data associated with the activity,
wherein the unstructured data is received from at least one application operating on another device;

predict, based on a machine learning model that is trained to predict that the user is likely to purchase a particular type of product using the activity data, that the user is likely to purchase the particular type of product;
generate, based on predicting that the user is likely to purchase the particular type of product, a note associated with a potential transaction to purchase the particular type of product,
wherein the note associated with the potential transaction is removed after a threshold time period;

detect a transaction indicative of a purchase of a product associated with the particular type of product;
perform, based on the note associated with the potential transaction, a particular set of processes from one or more fraud analysis processes associated with the transaction; and
send, based on performing the particular set of processes, an authorization associated with the transaction.

9. The device of claim 8 , wherein the activity data indicates activity associated with:
capturing an image of the particular type of product,
sending a message identifying the particular type or product,
accessing offline media associated with the particular type of product,
purchasing related products associated with the particular type of product,
adding the product to a cart, or
traveling to a location of a merchant that sells the particular type of product.

10. The device of claim 8 , wherein the one or more processors are further configured to:
analyze the activity data using one or more of:
an image processing technique,
a text processing technique, or
a code processing technique; and

determine, based on analyzing the activity data, that the activity data is associated with the particular type of product.

11. The device of claim 8 , wherein the one or more processors are further configured to:
determine that the transaction is associated with the product based on online activity associated with the transaction.

12. The device of claim 8 , wherein the unstructured data is received based on providing configuration data enabling installation of the application.

13. The device of claim 8 , wherein the threshold time period is determined based on an expiration model that is trained based on historical information associated with the activity data.

14. The device of claim 8 , wherein the activity data is associated with at least one of:
location information associated with the user,
camera data, or
radio frequency identification data.

15. A non-transitory computer-readable medium storing instructions, the instructions comprising:
one or more instructions that, when executed by one or more processors, cause the one or more processors to:
process unstructured data, associated with activity related to a user, to generate activity data associated with the activity,
wherein the unstructured data is received from at least one application operating on a device;

predict, based on a machine learning model that is trained to predict that the user is likely to purchase a particular type of product using the activity data, that the user is likely to purchase the particular type of product;
generate, based on predicting that the user is likely to purchase the particular type of product, a note associated with a potential transaction to purchase the particular type of product,
wherein the note associated with the potential transaction is removed after a threshold time period;

detect a transaction indicative of a purchase of a product associated with the particular type of product;
perform, based on the note associated with the potential transaction, a particular set of processes from one or more fraud analysis processes associated with the transaction; and
send, based on performing the particular set of processes, an authorization associated with the transaction.

16. The non-transitory computer-readable medium of claim 15 , wherein the activity data indicates activity associated with:
capturing an image of the particular type of product,
sending a message identifying the particular type or product,
accessing offline media associated with the particular type of product,
purchasing related products associated with the particular type of product,
adding the product to a cart, or
traveling to a location of a merchant that sells the particular type of product.

17. The non-transitory computer-readable medium of cl

Assignees

• Capital One Services Llc

Inventors

• Mossoba Michael
• Edwards Joshua
• Benkreira Abdelkadar M'Hamed

Classifications

• G06Q30/0624

  using guided investigation, e.g. hierarchical browsing of shopping information · CPC title

• G06Q30/0601 Primary

  Electronic shopping [e-shopping] · CPC title

• G06Q20/40

  Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists · CPC title

• G06Q20/401

  Transaction verification · CPC title

• G06Q20/4016

  involving fraud or risk level assessment in transaction processing · CPC title