# US 12,182,830 B2 · Method, system, and non-transitory computer-readable recording media for supporting payment

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- Number: US 12,182,830 B2
- Document ID: US12182830B2
- Status: Granted
- Kind: B2
- Application number: 18/025,549
- Filed: 2021-09-09
- Published: 2023-11-16
- Granted: 2024-12-31
- Earliest claimed: 2020-09-09
- Assignees: Bank X Inc.
- Applicants: Bank X Inc.
- Inventors: Seung Hon Kim
- Web page: https://patentgrep.com/patent/US12182830B2

## CPC

- G06Q 30/0207: Discounts or incentives, e.g. coupons or rebates
- G06K 19/06: characterised by the kind of the digital marking, e.g. shape, nature, code
- G06N 20/00: Machine learning
- G06Q 20/227: {characterised in that multiple accounts are available, e.g. to the payer}
- G06Q 20/387: {Payment using discounts or coupons (relating to marketing G06Q30/02)}
- G06Q 30/06: Buying, selling or leasing transactions
- G06Q 40/00: Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- 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
- G06K: GRAPHICAL DATA READING (image or video recognition or understanding G06V); PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A method for supporting payment is provided. The method includes the steps of: acquiring information on at least one payment means associated with a user and payment information of the at least one payment means; and determining a payment means to recommend to the user at the time of payment by referring to the result of learning performed using learning data including feature information extracted from the information on the at least one payment means and the payment information of the at least one payment means to maximize cumulative benefits according to the user's payment pattern.

## Claims

### Claim 1

A method for supporting payment, comprising:
acquiring information on at least one payment means associated with a user and payment information of the at least one payment means;
determining, using an artificial neural network based learning model and using reinforcement learning, a policy that maximizes a value of an action corresponding to a use of the at least one payment means by the user based on a state corresponding to feature information;
training the learning model using learning data including the feature information extracted from the information on the at least one payment means and the payment information of the at least one payment means by maximizing cumulative benefits according to a payment pattern of the user and by maximizing the value of the action corresponding to the use of the at least one payment means by the user based on to the state corresponding to the feature information, the value of the action being determined based on an expected value of a sum of a current reward and a future reward corresponding to the cumulative benefits according to the payment pattern of the user,
wherein the reinforcement learning is based on Q-learning for determining the policy that maximizes the value;
updating, using the Q-learning, a value of Q for a specific state-action pair by repeatedly adding a reward received for the specific state-action pair and (a maximum Q value)×(a discount rate) in an adjacent state, to determine the policy that maximizes the value of the action, the discount rate indicating a size of a benefit in a long-term perspective;
determining a payment means to recommend to the user at a time of payment based on the determined policy that maximizes the value of the action, the trained learning model, and the updated value of Q; and
providing notification of the recommended payment means to the user.

### Claim 2 (depends on 1)

The method according to claim 1, wherein neural network weights in the learning model are dynamically updated based on gradient descent, and the neural network weights are associated with the payment pattern of the user.

### Claim 3 (depends on 1)

The method according to claim 1, wherein the feature information is related to the payment pattern of the user.

### Claim 4 (depends on 1)

The method according to claim 1, comprising learning using a deep learning-based learning algorithm including an input layer, multiple hidden layers, and an output layer.

### Claim 5 (depends on 1)

The method according to claim 1, wherein a result of learning for other users associated with the user are further referred to in determining to the payment means to recommend to the user.

### Claim 6 (depends on 1)

The method according to claim 1, wherein, in determining the payment means, a payment means with a maximum value among the at least one payment means whose value corresponds to a result of the learning is recommended to the user at the time of payment according to the payment pattern of the user.

### Claim 7 (depends on 1)

The method according to claim 1, wherein the determining the payment means includes determining two or more payment means to recommend to the user, and
wherein, in determining the two or more payment means, information on the two or more payment means is provided as recommendation information, which is determined based on a result of learning for the user, a size of a benefit provided by each of the at least one payment means, and a result of learning for other users similar to the user.

### Claim 8 (depends on 1)

A non-transitory computer-readable recording media on which a computer program for executing the method according to claim 1 is recorded.

### Claim 9 (depends on 1)

The method according to claim 1, wherein the discount rate is dynamically determined based on tendencies of the user, which are identified by analyzing the information on the at least one payment means and the payment information of the at least one payment means.

### Claim 10

A system for supporting payment, comprising:
a memory configured to store computer program; and
a processor configured to execute the computer program to perform:
acquiring information about at least one payment means associated with a user and payment information of the at least one payment means;
determining, using an artificial neural network based learning model and using reinforcement learning, a policy that maximizes a value of an action corresponding to a use of the at least one payment means by the user based on a state corresponding to feature information;
training the learning model using learning data including the feature information extracted from the information about the at least one payment means and the payment information to determine a payment means by maximizing that maximizes cumulative benefits according to a payment pattern of the user and by maximizing the value of the action corresponding to the use of the at least one payment means by the user based on the state corresponding to the feature information, the value of the action being determined based on an expected value of a sum of a current reward and a future reward corresponding to the cumulative benefits according to the payment pattern of the user,
wherein the reinforcement learning is based on Q-learning for determining the policy that maximizes the value;
updating, using the Q-learning, a value of Q for a specific state-action pair by repeatedly adding a reward received for the specific state-action pair and (a maximum Q value)×(a discount rate) in an adjacent state, to determine the policy that maximizes the value of the action, the discount rate indicating a size of a benefit in a long-term perspective;
determining a payment means to recommend to the user at a time of payment based on the determined policy that maximizes the value of the action, the trained learning model, and the updated value of Q; and
providing notification of the recommended payment means to the user.

### Claim 11 (depends on 10)

The system of claim 10, wherein neural network weights in the learning model are dynamically updated based on gradient descent, and the neural network weights are associated with the payment pattern of the user.

### Claim 12 (depends on 10)

The system of claim 10, wherein the feature information is related to the payment pattern of the user.

### Claim 13 (depends on 10)

The system of claim 10, wherein the processor is configured to execute the computer program to perform learning using a deep learning-based learning algorithm including an input layer, multiple hidden layers, and an output layer to perform the learning.

### Claim 14 (depends on 10)

The system of claim 10, wherein the processor is configured to execute the computer program to determine to the payment means to recommend to the user by referring to a result of learning about other users associated with the user.

### Claim 15 (depends on 10)

The system of claim 10, wherein the processor is configured to execute the computer program to determine to the payment means to recommend to the user at the time of payment by recommending a payment means among the at least one payment means that maximizes the value.

### Claim 16 (depends on 10)

The system of claim 10, wherein the determining the payment means includes determining two or more payment means to recommend to the user, and
wherein the processor is configured to execute the computer program to provide information about the two or more payment means, including the recommended payment means based on a result of learning about the user and a size of a benefit provided by each of the at least one payment means and the recommended payment means based on a result of learning about other users who are similar to the user at or above a certain level.

### Claim 17 (depends on 10)

The system according to claim 10, wherein the discount rate is dynamically determined based on tendencies of the user, which are identified by analyzing the information on the at least one payment means and the payment information of the at least one payment means.