# US 11,443,360 B2 · Systems and methods for casual spending recommendations to modify customer spending

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 11,443,360 B2
- Document ID: US11443360B2
- Status: Granted
- Kind: B2
- Application number: 17/087,133
- Filed: 2020-11-02
- Published: 2021-02-18
- Granted: 2022-09-13
- Earliest claimed: 2019-07-08
- Assignees: Capital One Services, LLC
- Applicants: Capital One Services, LLC
- Inventors: Austin Walters; Jeremy Goodsitt; Fardin Abdi Taghi Abad
- Web page: https://patentgrep.com/patent/US11443360B2

## CPC

- G06Q 30/0631: {Recommending goods or services}
- G06F 16/9024: {Graphs; Linked lists (G06F16/9027 takes precedence)}
- G06N 5/046: Forward inferencing; Production systems
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 20/00: Machine learning
- G06N 20/10: using kernel methods, e.g. support vector machines [SVM]
- G06Q 30/0202: Market predictions or forecasting for commercial activities
- G06Q 30/0241: Advertisements
- G06Q 30/0252: {based on events or environment, e.g. weather or festivals}
- G06Q 40/02: Banking, e.g. interest calculation or account maintenance (credit or loans G06Q40/03)
- G06Q 40/06: Asset management; Financial planning or analysis
- G06Q 40/08: Insurance
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A system for providing spending recommendations to a user. The system may include at least one memory unit storing instructions and at least one processor configured to execute the instructions to perform operations. The operations may include receiving first user shopping data based on a plurality of user shopping purchases over a first time period; determining, based on a statistical model, at least one of a temporal or a geographic trigger of the user shopping purchases; displaying a message to the user indicating the trigger; adding a rule to the statistical model based on user input; and displaying a f personalized spending recommendation, based on the rule.

## Claims

### Claim 1

A system for providing spending recommendations to a user, the system comprising:
at least one memory unit storing instructions; and
at least one processor configured to execute the instructions to perform operations comprising:
maintaining an account card service for an account associated with a first user;
receiving first user shopping data based on a plurality of user shopping purchases over a first time period;
determining, based on a Bayesian model, at least one of a temporal or a geographic trigger of the user shopping purchases by extrapolating rate of change data from a graph corresponding to the Bayesian model and determining an effect on user spending patterns based on the extrapolated rate of change data;
generating for displaying, on a user device, a message to the user indicating the trigger, wherein the message comprises a suggestion for a user spending recommendation based on the effect on the user spending patterns;
adding a spending recommendation rule to the Bayesian model based on user input; and
generating for displaying, on the user device, a personalized spending recommendation, based on the rule.

### Claim 2 (depends on 1)

The system of claim 1, wherein the operations further comprise categorizing the user shopping data by at least one of purchase quantity, purchase price, time of purchase, or total amount spent.

### Claim 3 (depends on 1)

The system of claim 1, wherein the graph is a directed graph or a bar graph.

### Claim 4 (depends on 3)

The system of claim 3, wherein the operations further comprise:
temporarily freezing a credit line of an account card associated with the account card service based upon at least the trigger; and
generating for displaying, on the user device, a message that informs the user of an ability to override a temporary freezing of the credit line.

### Claim 5 (depends on 4)

The system of claim 4, wherein the receiving second user shopping data and user behavior over a second time period.

### Claim 6 (depends on 5)

The system of claim 5, wherein the operations further comprise
modifying the rule based on the second user shopping data and user behavior.

### Claim 7 (depends on 1)

The system of claim 1, wherein the operations further comprise:
monitoring the spending patterns of the user; and
updating the personalized spending recommendation.

### Claim 8

A computer-implemented method for providing spending recommendations to a user, the method comprising:
maintaining an account card service for an account associated with a first user;
receiving first user shopping data based on a plurality of user shopping purchases over a first time period;
determining, based on a Bayesian model, at least one of a temporal or a geographic trigger of the user shopping purchases by extrapolating rate of change data from a graph corresponding to the Bayesian model and determining an effect on user spending patterns based on the extrapolated rate of change data;
generating for display, on a user device, a message to the user indicating the trigger, wherein the message comprises a suggestion for a user spending recommendation based on the effect on the user spending patterns;
adding a spending recommendation rule to the Bayesian model based on user input; and
generating for display, on the user device, a personalized spending recommendation, based on the rule.

### Claim 9 (depends on 8)

The method of claim 8, further comprising categorizing the user shopping data by at least one of purchase quantity, purchase price, time of purchase, or total amount spent.

### Claim 10 (depends on 8)

The method of claim 8, further comprising temporarily freezing a credit line of an account card associated with the account card service based upon at least the trigger.

### Claim 11 (depends on 10)

The method of claim 10, wherein
the graph is a directed graph or a bar graph.

### Claim 12 (depends on 10)

The method of claim 10, further comprising:
generating for display, on the user device, a message that informs the user of an ability to override a temporary freezing of the credit line; and
receiving second user shopping data and user behavior over a second time period.

### Claim 13 (depends on 12)

The method of claim 12, further comprising
modifying the rule based on the second user shopping data and user behavior.

### Claim 14 (depends on 13)

The method of claim 13, further comprising:
monitoring the spending patterns of the user; and
updating the personalized spending recommendation.

### Claim 15

A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations comprising:
maintaining an account card service for an account associated with a first user;
receiving first user shopping data based on a plurality of user shopping purchases over a first time period;
determining, based on a Bayesian model, at least one of a temporal or a geographic trigger of the user shopping purchases by extrapolating rate of change data from a graph corresponding to the Bayesian model and determining an effect on user spending patterns based on the extrapolated rate of change data;
generating for display, on a user device, a message to the user indicating the trigger, wherein the message comprises a suggestion for a user spending recommendation based on the effect on the user spending patterns;
adding a spending recommendation rule to the Bayesian model based on user input; and
generating for display, on the user device, a personalized spending recommendation, based on the rule.

### Claim 16 (depends on 15)

The computer-readable medium of claim 15, wherein the operations further comprise categorizing the user shopping data by at least one of purchase quantity, purchase price, time of purchase, or total amount spent.

### Claim 17 (depends on 15)

The computer-readable medium of claim 15, wherein the graph is a directed graph or a bar graph.

### Claim 18 (depends on 15)

The computer-readable medium of claim 15, wherein the operations further comprise:
temporarily freezing a credit line of an account card associated with the account card service based upon at least the trigger; and
generating for display, on the user device, a message that informs the user of an ability to override a temporary freezing of the credit line.

### Claim 19 (depends on 18)

The computer-readable medium of claim 18, wherein the operations further comprise receiving second user shopping data and user behavior over a second time period.

### Claim 20 (depends on 19)

The computer-readable medium of claim 19, wherein the operations further comprise modifying the rule based on the second user shopping data and user behavior.