# US 12,182,859 B1 · Methods and apparatuses for customized credit card recommendations

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- Number: US 12,182,859 B1
- Document ID: US12182859B1
- Status: Granted
- Kind: B1
- Application number: 18/152,613
- Filed: 2023-01-10
- Granted: 2024-12-31
- Earliest claimed: 2018-11-16
- Assignees: ConsumerInfo.com, Inc.
- Applicants: ConsumerInfo.com, Inc.
- Inventors: Erik John Rehder; Elias John Tuma; Andrew Alexander Charles Scott; Rakesh Patel; Christine Marie Brueggeman
- Web page: https://patentgrep.com/patent/US12182859B1

## CPC

- G06Q 40/03: Credit; Loans; Processing thereof
- G06F 18/2113: by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
- G06N 3/09: Supervised learning
- G06Q 30/0226: Incentive systems for frequent usage, e.g. frequent flyer miles programs or point systems
- G06Q 40/03055: {using artificial intelligence, machine learning or neural networks}
- G06N 20/00: Machine learning
- 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 credit card recommendation system for recommending credit cards to a user can be based on the consumer's estimated monthly spend, estimated spend across a plurality of categories, and user credit data. The credit card recommendation system can filter credit cards based on a likelihood of approval for the user. The credit card recommendation can determine a reward valuation and an adjustment valuation by assessing user spend and the characteristics corresponding to the credit card. The credit card recommendation can train a model to score credit cards for users, and apply specific user data to the model to determine a credit card score particular to the user.

## Claims

### Claim 1

A system comprising:
a memory; and
one or more processors configured to:
obtain estimated spend by a user for each of a plurality of categories of goods or services, wherein the estimated spend comprises (1) a first estimated spend for a first category of goods or services and (2) a second estimated spend for a second category of goods or services;
identify a plurality of credit cards available from respective credit card providers;
generate a score for each credit card of at least a subset of the plurality of credit cards with respect to the user, wherein the score for an individual credit card of the plurality of credit cards is based at least in part on (a) estimated spend for at least a subset of the plurality of categories of goods or services and (b) reward data for the individual credit card;
rank at least the subset of the plurality of credit cards based on respective scores;
cause display of two or more of the at least a subset of the plurality of credit cards by a user computing device;
receive an indication of a selection of one of the two or more displayed credit cards from the user computing device; and
cause display, by the user computing device, of a composite webpage comprising content from a server of a credit card provider associated with the selected one of the two or more displayed credit cards combined with additional content generated by the system, wherein the content is displayed without redirecting the user computing device to a website of the credit card provider.

### Claim 2 (depends on 1)

The system of claim 1, wherein the one or more processors are further configured to train a model to determine a score for a credit card when provided with input associated with an individual, wherein the model is trained based on historical credit data of a plurality of individuals.

### Claim 3 (depends on 1)

The system of claim 1, wherein the one or more processors are further configured to receive a reward type preference of the user, wherein the plurality of credit cards are identified based on the reward type preference.

### Claim 4 (depends on 1)

The system of claim 1, wherein the one or more processors are further configured to:
determine a reward valuation for each of the plurality of credit cards based on the estimated spend for the plurality of categories, wherein determining the reward valuation for each individual credit card of the plurality of credit cards comprises:
receiving multipliers for each of the plurality of categories, wherein the multipliers are specific to the individual credit card;
generating category point totals for each of the plurality of categories, wherein the category point totals for an individual category are based on a multiplier corresponding to the individual category and an estimated spend corresponding to the individual category for the user; and
aggregating the category point totals to generate an estimated reward total for the individual credit card with respect to the user.

### Claim 5 (depends on 4)

The system of claim 4, wherein the one or more processors are further configured to:
normalize the aggregated category point totals for each individual credit card, wherein the normalization is based on a dollar value of a point for an individual credit card.

### Claim 6 (depends on 4)

The system of claim 4, wherein the composite webpage includes the content from the server of the credit card provider overlaid on top of the additional content generated by the system.

### Claim 7 (depends on 1)

The system of claim 1, wherein the one or more processors are further configured to:
receive user credit data, for the user, from a credit database; and
determine a valuation for each of the plurality of credit cards based on the user credit data and an estimated monthly total spend across the plurality of categories, wherein the user credit data comprises a credit score and a non-zero existing balance on an existing credit card, wherein the valuation is determined by estimating an amount of interest to be accrued on a newly acquired credit card for the user based on the user credit data of the user.

### Claim 8 (depends on 7)

The system of claim 7, wherein determining the amount of interest to be accrued on the newly acquired credit card comprises:
identifying the non-zero existing balance on the existing credit card in the user credit data;
determining an estimated payoff of the non-zero existing balance on the newly acquired credit card over a period of time based on a balance transfer of the non-zero existing balance on the existing credit card to the newly acquired credit card and the estimated monthly total spend; and
estimating the amount of interest to be accrued on the newly acquired credit card based on the estimated payoff of the non-zero existing balance and an Annual Percentage Rate (APR) for the newly acquired credit card.

### Claim 9 (depends on 1)

The system of claim 1, wherein the reward data comprises multipliers for each of the plurality of categories.

### Claim 10

A computer-implemented method comprising:
obtaining, by a computer system, estimated spend by a user for each of a plurality of categories of goods or services, wherein the estimated spend comprises (1) a first estimated spend for a first category of goods or services and (2) a second estimated spend for a second category of goods or services;
identifying, by the computer system, a plurality of credit cards;
generating, by the computer system, a score for each credit card of at least a subset of the plurality of credit cards with respect to the user, wherein the score for an individual credit card of the plurality of credit cards is based at least in part on (a) estimated spend for at least a subset of the plurality of categories of goods or services and (b) reward data for the individual credit card;
ranking, by the computer system, at least the subset of the plurality of credit cards based on respective scores;
causing display, on a user computing device, of two or more of the at least a subset of the plurality of credit cards;
receiving an indication of a selection of one of the two or more displayed credit cards from the user computing device; and
causing display, by the user computing device, of a composite webpage comprising content from a server of a credit card provider associated with the selected one of the two or more displayed credit cards combined with additional content generated by the computer system, wherein the content is displayed without redirecting the user computing device to a website of the credit card provider.

### Claim 11 (depends on 10)

The computer-implemented method of claim 10, wherein identifying the plurality of credit cards is based on filtering a set of credit cards based on historical approval rates.

### Claim 12 (depends on 11)

The computer-implemented method of claim 11, wherein identifying the plurality of credit cards is further based on user credit data.

### Claim 13 (depends on 11)

The computer-implemented method of claim 11, wherein filtering the set of credit cards is further based on an exclusion rule specific to a credit card provider associated with the corresponding credit card.

### Claim 14 (depends on 10)

The computer-implemented method of claim 10, wherein the plurality of credit cards are identified based in part on determinations that the plurality of credit cards are not currently owned by the user.

### Claim 15 (depends on 10)

The computer-implemented method of claim 10, further comprising scoring credit cards currently owned by the user.

### Claim 16 (depends on 10)

The computer-implemented method of claim 10, wherein the score for each credit card is generated using a trained model, wherein the trained model comprises at least one of: a scorecard or a machine learning model.

### Claim 17

A non-transitory computer storage medium storing computer-executable instructions that, when executed by a processor of a computing system, cause the processor to perform operations comprising:
obtaining estimated spend by a user for each of a plurality of categories of goods or services, wherein the estimated spend comprises (1) a first estimated spend for a first category of goods or services and (2) a second estimated spend for a second category of goods or services;
identifying a plurality of credit cards;
generating a score for each credit card of at least a subset of the plurality of credit cards with respect to the user, wherein the score for an individual credit card of the plurality of credit cards is based at least in part on (a) estimated spend for at least a subset of the plurality of categories of goods or services and (b) reward data for the individual credit card;
ranking at least the subset of the plurality of credit cards based on respective scores;
causing display, on a user computing device, of two or more of the at least a subset of the plurality of credit cards;
receiving an indication of a selection of one of the two or more displayed credit cards from the user computing device; and
causing display, by the user computing device, of a composite webpage comprising content from a server of a credit card provider associated with the selected one of the two or more displayed credit cards combined with additional content generated by the computing system, wherein the content is displayed without redirecting the user computing device to a website of the credit card provider.

### Claim 18 (depends on 17)

The non-transitory computer storage medium of claim 17, wherein the operations further comprise receiving a reward type preference of the user, wherein the plurality of credit cards are identified based at least in part on the reward type preference.