Information processing device, learning device, information processing method, and program

Abstract

To determine which users will be most efficiently targeted for service proposals. [Solution] An information processing device comprising: an acquisition unit that acquires target information of multiple target persons; and a processing unit that inputs each of the target person information into a model, acquires a score related to the use of the cash advance service output by the model, and determines a notification target from among the multiple target persons based on the score, wherein the model is a model that has been trained to output a score according to whether or not the cash advance service associated with the input target person has been used when the target person information of the target person is input, and the target information includes attribute information indicating the attributes of the target person, contract information related to the deferred payment settlement of the target person, and usage history information related to the history of the use of the deferred payment settlement.

Description

Technical Field

[0001] The present invention relates to an information processing apparatus, a learning apparatus, an information processing method, and a program.

Background Art

[0002] Conventionally, an information processing apparatus is known to include a limit amount estimation unit that estimates a borrowing limit amount of a user based on the price of a target product and the estimated annual income of the user, and a provision unit that presents the estimated borrowing limit amount to the user and makes a borrowing proposal (see Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the above apparatus, how to select the users to whom the proposal is to be made has not been considered. For this reason, it may not be possible to efficiently determine the target persons to whom the proposal is to be made.

[0005] The present invention has been made in consideration of such circumstances, and one of its purposes is to provide an information processing apparatus , a learning apparatus, an information processing method, and a program that can more efficiently determine users to whom a service (for example, a cashing service) is to be proposed.

Means for Solving the Problems

[0006] One aspect of the present invention is an information processing device comprising: an acquisition unit that acquires target information of a plurality of target persons; and a processing unit that inputs each of the target person information into a model, acquires a score related to the use of the cash advance service output by the model, and determines a notification target from among the plurality of target persons based on the score, wherein the model is a model that, when target person information of a target person is input, outputs a score according to whether or not the cash advance service associated with the input target person has been used, and the target information is an information processing device that includes attribute information indicating the attributes of the target person, contract information related to the deferred payment settlement of the target person, and usage history information related to the history of the use of the deferred payment settlement. [Effects of the Invention]

[0007] According to one aspect of the present invention, it is possible to provide an information processing device, a learning device, an information processing method, and a program that can more efficiently determine users to whom services (e.g., cash advance services) are offered. [Brief explanation of the drawing]

[0008] [Figure 1] This diagram shows an example of a configuration for implementing an electronic payment service. [Figure 2] This is a sequence diagram (part 1) illustrating the general flow of electronic payments. [Figure 3] This is a sequence diagram (part 2) illustrating the general flow of electronic payments. [Figure 4] This is a configuration diagram of the payment server 100 according to the embodiment. [Figure 5] This figure shows an example of the contents of user information 172. [Figure 6] This figure shows an example of the contents of merchant / store information 176. [Figure 7] This figure shows an example of the functional configuration of the information processing device 300. [Figure 8] This figure shows an example of an ancillary appeal. [Figure 9] This figure shows another example of an ancillary claim. [Figure 10] This is a diagram illustrating the processing of the first model 374 and the second model 376. [Figure 11] This is a diagram to explain the target information. [Figure 12] This flowchart shows an example of the processing flow executed by the information processing device 300. [Figure 13] This figure shows an example of learning information. [Figure 14] This figure shows an example of learning information. [Figure 15] This is a diagram illustrating the processing of the first model 374A and the second model 376B. [Figure 16] This is correspondence information where the score, cost, and the integrated score derived from them are linked. [Modes for carrying out the invention]

[0009] The following describes embodiments of the information processing device, learning device, information processing method, and program of the present invention with reference to the drawings. Various devices such as "servers" that appear below, which provide services to users or perform internal analysis, may be implemented by a distributed group of devices, and the operators of each device may be different. Furthermore, the owner of the hardware of the devices (the provider of the cloud server) and the operator that actually operates them may also be different. The application program and the payment server work together to provide an electronic payment service. In the following description, the application program will be referred to as a payment app. The electronic payment service is a service that supports payment for the purchase of goods and services at a store. A store is, for example, a physical store (real store) that exists in the real world, but may also include a virtual store for e-commerce. A virtual store may include one provided by an entity different from the operator of the electronic payment service. In that case, when settling a purchase at a virtual store, the user may be directed to the interface screen of the electronic payment service. In the electronic payment service, stores are treated as belonging to, for example, affiliated merchants (brands), and processing such as payment when a purchase is made at a store is mainly carried out between the user and the affiliated merchant. Alternatively, payment and other processing may be conducted between the user and the store.

[0010] [Electronic payment service] Figure 1 shows an example of the configuration of an electronic payment system in which an electronic payment service is implemented. The electronic payment service is implemented with a payment server 100 at its center. The electronic payment system that implements the electronic payment service includes, for example, one or more user terminal devices 10, one or more first store terminal devices 50, one or more second store terminal devices 70, a payment server 100, a card server 200, an information processing device 300, and a learning device 400. These devices communicate with each other, for example, via a network NW. The network NW includes, for example, the Internet, a LAN (Local Area Network), a wireless base station, provider equipment, etc. Some or all of the functional configuration included in the electronic payment system may be distributed across multiple devices in any form, or may be integrated into any device.

[0011] [User terminal device] The user terminal device 10 is a portable terminal device such as a smartphone or a tablet terminal, for example. The user terminal device 10 is a computer device having at least an optical reading function, a communication function, a display function, an input reception function, and a program execution function. In the following description, the configurations for realizing these functions are respectively referred to as a camera, a communication device, a touch panel, a CPU (Central Processing Unit), etc. In the user terminal device 10, the settlement application 20 is executed by a processor such as a CPU, and it operates to provide an electronic payment service to the user in cooperation with the settlement server 100. The settlement application 20 is installed in the user terminal device 10 from an application store, for example, and controls a camera, a communication device, a touch panel, etc. A mini application 30 that operates within the settlement application 20 is installed in the user terminal device 10. The mini application 30 provides services related to credit cards to the user in cooperation with the card server 200, for example.

[0012] [First store terminal device] The first store terminal device 50 is installed in a store, for example. The first store terminal device 50 is a computer device having at least a commodity price acquisition function, an optical reading function, a program execution function, and a communication function. The first store terminal device 50 includes a so-called POS (Point of Sale) device, and the commodity price acquisition function and the optical reading function may be realized by the POS device. The store code image 60 is placed in the store and is a code image such as a QR code (registered trademark) printed on a paper or plastic medium. Note that the store code image 60 may be displayed by a display placed in the store (which may be a display of a terminal device such as a smartphone).

[0013] [Second store terminal device] The second store terminal device 70 is used by the operator of the franchise store. The second store terminal device 70 is a smartphone, a tablet terminal, a personal computer, or the like. In the second store terminal device 70, an interface 72 for franchise stores operates. The interface 72 for franchise stores may be an application for franchise stores or a browser. The interface 72 for franchise stores accepts settings of coupons, etc. by the operator of the franchise store and transmits them to the settlement server 100. The second store terminal device 70 which is a smartphone has functions such as displaying a code image corresponding to the store code image or reading the code image displayed by the user terminal device 10 by executing an application for franchise stores.

[0014] [Settlement Server] The settlement server 100 realizes electronic settlement based on the settlement information received from the user terminal device 10 or the first store terminal device 50. The first store terminal device 50 may include a POS device and a franchise store server. In that case, the settlement information is transmitted from the POS device to the settlement server 100 via the franchise store server. In the following description, without particularly distinguishing this, it is assumed that the settlement information is transmitted from the first store terminal device 50.

[0015] FIG. 2 and FIG. 3 are sequence diagrams illustrating a rough flow of electronic settlement. There may be two patterns, pattern 1 and pattern 2, in electronic settlement.

[0016] In the case of Pattern 1 shown in Figure 2 (hereinafter referred to as User Scan), the user terminal device 10, with the payment application 20 running, decodes the store code image 60 using its optical reading function (S1). The store code image 60 contains information about the store URL (Uniform Resource Locator). This store URL is an electronic payment service domain to which information that can identify the store has been added, and is associated with the merchant ID and store ID, etc., at the payment server 100 (described later). The payment application 20 sends the first payment information, including the store URL and account ID, to the payment server 100 (S2). The payment server 100 searches for store information (described later) from the merchant ID and store ID corresponding to the store URL, obtains the merchant name and store name information (S3), and sends it to the payment application 20 (S4). The user enters the payment amount into the user terminal device 10 on the screen where the merchant name and store name are displayed (S5). The user terminal device 10 then generates second payment information, including at least the payment amount, and sends it to the payment server 100 (S6). The payment server 100 performs electronic payment based on the received second payment information (S7). The payment server 100 then sends a payment completion notification (information for displaying the payment completion screen) to the payment application 20 (S8), and the payment application 20 displays the payment completion screen (S9). If the store code image 60 is displayed on a display placed in the store, the store code image 60 may include payment amount information as well as the store URL. In this case, the procedure for the user to enter the payment amount is omitted, and the payment amount information is included in the first payment information and sent to the payment server 100. Merchant name and store name information may be included and displayed on the payment completion screen.

[0017] In the case of Pattern 2 shown in Figure 3 (hereinafter referred to as Store Scan), when the payment app 20 is launched, when a payment operation is performed in the payment app 20, when it is time for an automatic update (for example, every minute), and at other times, the payment app 20 sends a request to the payment server 100 to issue a one-time code (S11). The payment server 100 generates a one-time code (S12) and sends it to the payment app 20 (S13). The payment app 20 displays a code image such as a QR code or barcode that was generated based on the one-time code (S14). The user holds the display surface of the user terminal device 10 over the first store terminal device 50 (presents it), and the first store terminal device 50 decodes the code image using its optical reading function and obtains the one-time code, etc. (S15). Then, the first store terminal device 50 generates payment information including the one-time code, payment amount, merchant ID, store ID, etc., and sends it to the payment server 100 (S16). The payment amount information is obtained in advance by barcode scanning or manual input. Based on the received information, the payment server 100 identifies the user corresponding to the one-time code and performs the electronic payment (S17). The payment server 100 then sends a payment completion notification to the payment app 20 (S18), and the payment app 20 displays a payment completion screen (S19).

[0018] Furthermore, electronic payment may be performed using only one of the above patterns. Also, the "account ID" explained in Figure 2 may be other information that can be used as user identification information (for example, a phone number). In addition, the issuance of a one-time code may be omitted during store scanning, and the payment app 20 may display a code image generated based on the user's account ID. In that case, the payment server 100 will identify the user corresponding to the account ID instead of identifying the user corresponding to the one-time code.

[0019] [Payment Server Functional Configuration] Figure 4 is a diagram of the configuration of the payment server 100. The payment server 100 includes, for example, a communication unit 110, a content provision unit 120, a payment processing unit 130, an information management unit 140, and a storage unit 170. Components other than the communication unit 110 and the storage unit 170 are realized, for example, by a hardware processor such as a CPU executing a program (software). Some or all of these components may be realized by hardware (including circuitry) such as LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), GPU (Graphics Processing Unit), and SOC (System On Chip), or by the cooperation of software and hardware. The program may be stored in advance on a storage device such as an HDD (Hard Disk Drive) or flash memory (a storage device equipped with a non-transient storage medium), or it may be stored on a removable storage medium such as a DVD or CD-ROM (a non-transient storage medium) and installed on the storage device when the storage medium is inserted into the drive device.

[0020] The storage unit 170 can be an HDD, flash memory, RAM (Random Access Memory), etc. The storage unit 170 may also be a NAS (Network Attached Storage) device that the payment server 100 can access via the network. The storage unit 170 stores information such as user information 172, content information 174, and merchant / store information 176. Some of this information may also be stored in the storage unit of the user terminal device 10. Details of each piece of information will be described later.

[0021] The communication unit 110 is a communication interface for connecting to a network NW. The communication unit 110 is, for example, a network interface card.

[0022] The content provider unit 120, for example, has the functionality of a web server and provides information (content) for displaying various screens of the electronic payment service to the user terminal device 10. The content provider unit 120 reads the necessary content from the content information 174 as appropriate and provides it to the user terminal device 10. The user terminal device 10 accepts various inputs from the user while the content is being played by the payment application 20 and transmits the aforementioned payment information to the payment server 100. The above content may also be generated by the payment application 20. In this case, the content provider unit 120 provides the payment application 20 with the information necessary for generating the content.

[0023] The payment processing unit 130 performs payment processing based on payment information transmitted by the user terminal device 10 or the first store terminal device 50. The payment processing unit 130 performs payment processing while referring to the user information 172.

[0024] [User information] Figure 5 shows an example of the contents of User Information 172. User Information 172 is an example of user registration information. User Information 172 includes, for example, user URL, account ID, telephone number, password, as well as information such as email address, user ID, name, address, date of birth, registration date, charge balance, credit payment settings, credit payment limit, credit payment amount, available credit payment amount, payment method settings, bank account, credit card number, charge history information, and payment history information. The user URL is used for money transfer processing between users. When registering for a new electronic payment service, registration of a telephone number and password is mandatory. The account ID is issued to the user by the payment server 100, and the user ID is an ID that the user can set at will (or does not have to set). Similarly, the email address and name, address, and date of birth are also information that the user can set at will (or does not have to set). The registration date is the date the user registered for the electronic payment service (the date the account was created). Hereafter, the user instance (electronic payment account) to which this information is associated will be referred to as an account.

[0025] The charge balance is information indicating the balance of electronic money set by the user by sending money to their account in advance. Methods of sending money include sending from an ATM (Automatic Teller Machine) of a designated provider (bank) and sending from a registered bank account. The credit payment setting indicates whether or not the user has completed the settings to enable electronic payments via credit card using the payment app 20, and is set to either "Completed" or "Not Completed". The credit payment limit is the monthly limit for available credit payments, the credit payment amount is the amount already used for credit payments in the current month, and the available credit payment amount is the amount available for credit payments in the current month, calculated by subtracting the credit payment amount from the credit payment limit. While the diagram shows only one credit payment limit, in reality there are also daily limits, and the lower of these may be set as the credit payment limit. Further details on credit payments will be described later. The payment method setting indicates whether the user will use electronic payment with the charge balance or payment by credit card at that time. The bank account and credit card number information, respectively, refers to the bank account or credit card number (account number, card number) to which funds can be deposited into the electronic payment service. The charge history information is a record of when the user has previously sent money to the electronic payment service to increase the charge balance. The payment history information shows the details of each payment made by the user (date and time, store ID of the store where the purchase was made, payment amount, payment method, etc.).

[0026] [Franchise / Store Information] Figure 6 shows an example of the contents of the merchant / store information 176. The merchant / store information 176 includes, for example, a first table 176A in which the merchant ID and store ID are associated with the store URL, a second table 176B in which the merchant name and sales amount (as described above) are associated with the merchant ID, and a third table 176C in which the store name is associated with the store ID. In addition to this information, the merchant / store information 176 may also include information such as the merchant or store category, the store's location, and payment patterns.

[0027] The information management unit 140 acquires information provided by other server devices and the card server 200. Based on the information acquired from the user terminal device 10 and the second store terminal device 70, the information management unit 140 manages user information 172 and merchant / store information 176. The information management unit 140 performs operations such as adding, editing, and deleting new records for user information 172 and merchant / store information 176.

[0028] [Electronic payment] When the payment processing unit 130 obtains payment information from the user terminal device 10 or the first store terminal device 50, it refers to the user information 172 to obtain the user's "payment method setting". For users whose "payment method setting" is set to "charge balance", the payment processing unit 130 performs electronic payment as follows: For example, the payment processing unit 130 performs electronic payment by decreasing the charge balance, which is managed in association with the user ID, and increasing the value of the merchant's sales proceeds item. For example, the value of the merchant's sales proceeds item is not used as electronic money itself, but rather the amount corresponding to the value of the sales proceeds item is transferred to the bank account in a cycle according to the agreement between the merchant and the electronic payment service.

[0029] The payment processing unit 130 performs electronic payment as follows for users whose "settings information" is set to "credit payment (credit payment using code information)". Credit payment is a payment method that is carried out in cooperation with a credit card company, which is a separate entity from the operator of the electronic payment service. The operator of the electronic payment service acts as the donor and allows electronic payment that does not depend on the charge balance within the credit payment limit. In order to use the credit payment service, it may be required to obtain a credit card provided by the operator of the electronic payment service. The amount used by credit payment is settled in a lump sum for the month on the payment date of the following month, for example, by withdrawal from a bank account. In this case, the payment processing unit 130 performs a provisional settlement by adding the settlement amount to the amount used by credit payment and subtracting the same amount from the available credit payment limit. When the closing date arrives, it processes the payment for the current month to be withdrawn on the payment date of the following month as described above, or requests the operator of the credit card company to perform the said process. If the settlement amount exceeds the available credit payment limit at the time of provisional settlement, an error notification is sent back to the payment app 20.

[0030] [Card Server] The card server 200 works in conjunction with the payment application 20 or mini-application 30 to provide credit card-related services to the user terminal device 10. Hereinafter, the mini-application 30 will be the one providing the credit card-related services. For example, the card server 200 displays various information, such as credit card usage history and information regarding credit card usage charges, on the display unit of the user terminal device 10 in response to the user's operation of the mini-application 30.

[0031] [Information Processing Device] Figure 7 shows an example of the functional configuration of the information processing device 300. The information processing device 300 includes, for example, an acquisition unit 310, a processing unit 320, and a storage unit 370. The components of the acquisition unit 310 and the processing unit 320 are realized, for example, by a hardware processor such as a CPU executing a program (software). Some or all of these components may be realized by hardware (including circuitry) such as an LSI, ASIC, FPGA, GPU, or SOC, or by the cooperation of software and hardware. The program may be stored in advance in a storage device such as an HDD or flash memory (a storage device with a non-transient storage medium), or it may be stored in a removable storage medium such as a DVD or CD-ROM (a non-transient storage medium) and installed in the storage device when the storage medium is mounted on a drive device.

[0032] The storage unit 370 may be an HDD, flash memory, RAM, etc. The storage unit 370 may also be a NAS device accessible by the information processing device 300 via a network. The storage unit 370 stores target information 372, which includes target information described later, as well as the first model 374, the second model 376, credit information 378, and recipient information 380. The information stored in the storage unit 370, such as the first model 374 and the second model 376, or both, may be stored in a device different from the information processing device 300. Hereinafter, when the first model 374 and the second model 376 are not distinguished, they may be referred to as "model".

[0033] The acquisition unit 310 acquires information on multiple individuals. The processing unit 320 inputs each individual's information into a model, obtains the score related to the use of the caching service output by the model, and determines which individuals will be notified based on the score. Details of the processing of the acquisition unit 310 and the processing unit 320 will be described later.

[0034] [Learning device] The learning device 400 trains the model with learning information. The learning device 400 generates, for example, a first model 374 and a second model 376, and provides the generated first model 374 and second model 376 to the information processing device 300. Details of the learning process will be described later.

[0035] [overview] The processing unit 320 of the information processing device 300 inputs the subject information of multiple subjects into a model, obtains a score related to the use of the caching service output by the model, and determines which subjects to notify from among the multiple subjects based on the score. The model is a model that has been trained to output a score according to whether or not the subject has used the caching service associated with the input subject when the subject information of a subject is input.

[0036] (Proposal using the first model) The information processing device 300 acquires information on multiple individuals who have not previously used the cash advance service, inputs this information into the first model 374 to obtain a score for each individual who has not previously used the cash advance service, and determines who will be notified from among the multiple individuals based on the score (see Figure 10).

[0037] Model 1, 374, learns from training information that associates the subject information of multiple subjects who have not previously used the cash advance service with information indicating whether or not the subject used the cash advance service after being notified of a proposal to use the service. When subject information of a subject is input, the model outputs a score indicating whether or not the subject will use the cash advance service after being notified of a proposal to use the service.

[0038] The information processing device 300 makes an additional appeal for a new cash advance service to the user determined using the first model 374. The additional appeal may be made by email, as a pop-up in the payment app 20 (or mini-app 30), or by other means. Other methods include telephone or postal mail. In the following explanation, the additional appeal will be described as being made by email.

[0039] Figure 8 shows an example of supplementary appeals. For example, users who have never used a cash advance service before are provided with interface screens IM1 and IM2. Users can use the cash advance service by using the payment app 20 or web content on interface screen IM2.

[0040] (Proposal using the second model) The information processing device 300 obtains the scores of individuals who have previously used the caching service by inputting the target person information into the second model 376, and determines who will be notified from among multiple individuals based on the scores (see Figure 10).

[0041] Model 376, the second model, learns from data that associates the information of multiple individuals who have previously used the cash advance service with information indicating whether or not the individual used the cash advance service after being notified of a suggestion to use it. When the individual's information is input, the model outputs a score indicating whether or not the individual used the cash advance service after being notified of a suggestion to use it.

[0042] Figure 9 shows an example of supplementary appeals. For example, users who have previously used the cash advance service are provided with interface screens IM3 and IM4. Users can use the cash advance service by using the payment app 20 or web content on interface screen IM4.

[0043] Figure 10 is a diagram illustrating the processing of the first model 374 and the second model 376. As described above, when subject information is input to the first model 374 and the second model 376, the first model 374 outputs a first score and the second model 376 outputs a second score. The subject information input to the first model 374 and the subject information input to the second model may be the same information, or they may be partially different information.

[0044] [Target Information] Figure 11 is a diagram illustrating the target information 372. The target information includes one or more of the following: attribute information, contract information, usage history information, and related service information (related payment information). The target information may be attribute information, contract information, and usage history information, or it may include related service information in addition to these. The target information entered into the first model 374 and the second model 376 may be the same information, or it may be partially different information.

[0045] (Attribute information) Attribute information includes one or more pieces of information such as the target person's age, gender, length of residence, annual income, savings amount, household income, and family structure. For example, the shorter the length of residence, the more single-person households, and the lower the savings, the more likely the target person is to use cash advance services in response to the appeal.

[0046] Contract information includes one or more pieces of information, such as partner code, shopping category, shopping limit, cash advance category, cash advance limit, contract duration, and application channel information. The partner code is a code that identifies the credit card's partner. The shopping category is the payment category (e.g., revolving credit, single payment, three installments) used when using the available (currently used) credit card. The cash advance category is the category related to the use (or availability) of cash advances. The shopping limit is the limit for deferred payment. The cash advance limit is the limit for cash advances. For example, the lower (or higher) the shopping limit or cash advance limit, the more likely the customer is to use the cash advance service in response to the offer.

[0047] Application channel information indicates the channels through which users who have previously used the cash advance service applied for the service. Application channels include, for example, applications via payment app 20 (electronic payment service), applications via websites related to electronic payment services, and applications via affiliated communication services. For instance, individuals who applied via payment app 20 tend to use the cash advance service more frequently.

[0048] Usage history information includes, for example, the number of SP usages, AdCA balance, AdSP balance, SP usage amount, CA usage amount, SP limit utilization rate, and CA limit utilization rate. This information covers a period of N months. The usage history information includes the difference between the previous month and N months prior for SP. "SP" stands for Shopping and indicates a one-time deferred payment. CA stands for Cash Advance Service. "Ad" stands for One-Time Payment. The usage history information may also include, for example, the balance, number of usages, usage amount, and revolving credit limit utilization rate for revolving payments.

[0049] For example, individuals who use cash advance services or revolving credit are more likely to use cash advance services in response to an appeal. Similarly, individuals with a high or high number of SP usages, ad CA balance, ad SP balance, SP usage amount, CA usage amount, SP limit utilization rate, and CA limit utilization rate are more likely to use cash advance services in response to an appeal.

[0050] Related service information may include, for example, the usage status of electronic payment services or the usage status of other services. Electronic payment service usage status may include, for example, the usage status of electronic payments made using the electronic payment service. Electronic payment usage status may include, for example, the usage status of electronic payments made using payment app 20. Usage status may also include, for example, information on the date and time of use, information on the time of use, information on the merchant used, remittance information (such as the usage status of remittance services), and operation information. Usage status may also include the amount used for electronic payment services, the number of times used, the number of times used for a specified type of merchant in the electronic payment service, and the frequency of use. Related service information may also include information included in user information 172.

[0051] The usage date and time information includes whether the usage occurred on a weekend or a weekday. The usage time indicates whether the usage occurred late at night, early in the morning, or during the day. The merchant information includes the type of merchant and its location. For example, target users whose usage dates and times fall on specific days of the week or within specific time slots are more likely to use the cash advance service in response to the appeal.

[0052] The usage status of the remittance service refers to the usage status of the remittance service provided by Payment App 20. The remittance service allows users of Payment App 20 to send money to other users. The usage status includes information such as the amount, frequency, and time of remittances. For example, users who frequently use the remittance service tend to use cash advance services in response to appeals.

[0053] Operation information refers to the user's operation history using the payment app 20. Examples of operation information include the number of times the payment app 20's home screen was displayed and how frequently it was displayed.

[0054] Related service information may include, in addition to (or instead of) the information described above, the information included in user information 172. Related service information may further include user information managed by services provided by the electronic payment service or services provided within the electronic payment service.

[0055] By utilizing related service information, it is possible to identify individuals who are more likely to repay their debts. A person's behavior and usage of other services correlate with the probability of repayment. Therefore, using related service information allows for the determination of more appropriate recipients of notifications.

[0056] For example, the more frequently a target user interacts with payment app 20 (e.g., the more often they display the home screen of payment app 20), the more likely they are to use the cash advance service in response to the appeal.

[0057] [flowchart] Figure 12 is a flowchart illustrating an example of the processing flow performed by the information processing device 300. First, the information processing device 300 identifies new target individuals (S100). Next, the information processing device 300 obtains the first score for each new user (S102). Then, the information processing device 300 identifies recipients (S104).

[0058] Next, the information processing device 300 excludes high-risk users from the recipients (S106). The information processing device 300 refers to the credit information 378 to determine whether the recipient is high-risk as a user of the cash advance service. The credit information 378 is, for example, information associated with information indicating the user's risk. The credit information 378 is, for example, information indicating the user's risk that is generated based on the settlement history of electronic payment services or the usage status of financial services.

[0059] Next, the information processing device 300 determines the target recipients for the new CA (Call for Advertisement) supplement, excluding high-risk users (S108). The determined target recipients are managed as target recipient information 380. This completes the processing of one routine in this flowchart.

[0060] The above example describes the process for determining the target recipients of a new CA (Call for Advertisement) supplementary appeal. The target recipients of an existing CA supplementary appeal may also be determined using the same process as above. In this case, the information processing device 300 uses the second model 376 to identify the target recipients and determines the target recipients to be those users who have been excluded from the target recipients who are at high risk.

[0061] [Generating the first model] The learning device 400 learns learning information that associates the subject information of multiple subjects who have not previously used the cash advance service with information indicating whether or not the subject used the cash advance service after being notified of a proposal to use the cash advance service. When the learning device 400 receives subject information of a subject who has not previously used the cash advance service, it generates a first model 374 that outputs a score indicating whether or not the subject used the cash advance service after being notified of a proposal to use the service.

[0062] Figure 13 shows an example of learning information. The learning information is, for example, information that associates target information (e.g., attribute information, contract information, usage history information, and related service information) with information indicating whether or not a new CA slot has been opened. The learning device 400 generates the first model 374 by training the model so that, for example, when target information of a person who has opened a new CA slot is input, it outputs a score indicating a high probability of opening a new CA slot, and when target information of a person who has not opened a new CA slot is input, it outputs a score indicating a low probability of opening a new CA slot.

[0063] As described above, the learning device 400 trains the first model 374 to generate a first model 374 that outputs a first score with high accuracy.

[0064] [Generating the second model] The learning device 400 learns learning information that associates the subject information of multiple subjects who have used the cash advance service in the past with information indicating whether or not the subject used the cash advance service after being notified of a proposal to use the cash advance service. When the learning device 400 receives subject information of subjects who have used the cash advance service in the past, it generates a second model 376 that outputs a score indicating whether or not the subject used the cash advance service after being notified of a proposal to use the service.

[0065] The learning information is, for example, information that associates target information (e.g., attribute information, contract information, usage history information, and related service information) with information indicating whether or not a CA (Computer Advisor) slot was used. The learning device 400, for example, generates a second model 376 by training the model so that when target information that has used a CA slot is input, it outputs a score indicating a high probability of using a CA slot, and when target information that has not used a CA slot is input, it outputs a score indicating a low probability of using a CA slot.

[0066] As described above, the learning device 400 trains the second model 376 to generate a second model 376 that outputs a highly accurate second score.

[0067] As described above, the information processing device 300 inputs each of the target person's information into the model, obtains the score related to the use of the cash advance service output by the model, and determines which users to notify from among multiple target persons based on the score, thereby enabling it to more efficiently determine which users to propose the cash advance service to.

[0068] (modified version) In the example above, the recipients were determined without considering the proposed method, but a model may be generated for each proposed method, as shown below, and this model may be used.

[0069] The learning device 400 generates a first model 374A of the first proposed method and a first model 374B of the second proposed method. Not limited to two first models 374, three or more first models 374 may be generated for each proposed method. Hereinafter, the first proposed method will be an email, and the second proposed method will be a pop-up in the payment application 20.

[0070] Figure 14 shows an example of learning information. The learning device 400 generates the first model 374A by training the model with learning information A for the first model 374A. The learning device 400 generates the first model 374B by training the model with learning information B for the first model 374B.

[0071] The learning device 400 learns learning information that associates the subject information of multiple subjects who have not previously used the cash advance service with information indicating whether or not the subject used the cash advance service after being notified of the proposal to use the cash advance service of the first proposed method. When the learning device 400 receives subject information of a subject who has not previously used the cash advance service, it generates a first model 374A that outputs a score indicating whether or not the subject will use the cash advance service after being notified of the proposal to use the first proposed method.

[0072] The learning device 400 learns learning information that associates the subject information of multiple subjects who have not previously used the cash advance service with information indicating whether or not the subject used the cash advance service after being notified of the proposal to use the cash advance service of the second proposed method. When the learning device 400 receives subject information of a subject who has not previously used the cash advance service, it generates a first model 374B that outputs a score indicating whether or not the subject will use the cash advance service after being notified of the proposal to use the second proposed method.

[0073] Figure 15 is a diagram illustrating the processing of the first model 374A and the second model 376B. The information processing device 300 inputs the subject information into the first model 374A and obtains the score A output by the first model 374A. Score A is a score that indicates the probability that the user will use the cash advance service if the cash advance service is offered via email.

[0074] The information processing device 300 inputs the subject information into the first model 374B and obtains the score B output by the first model 374B. Score B is a score that indicates the probability that the user will use the cash advance service when the cash advance service is offered in a pop-up.

[0075] As described above, the information processing device 300 uses multiple first models 374 to obtain a score for each proposed method.

[0076] The information processing device 300 determines the recipients by referring to the score for each proposed method. For example, if the proposed method is email, the information processing device 300 determines the recipients based on the email score. Alternatively, if the recipients have been determined, the information processing device 300 may determine the proposed method to each recipient based on the score. For example, the information processing device 300 refers to the score for each proposed method for each recipient and proposes the cash advance service to the user using the proposed method with the score that is most likely to result in the use of the cash advance service.

[0077] The information processing device 300 may determine a proposed method to propose to the recipient based on the score and cost. Figure 16 shows correspondence information where the score, cost, and the integrated score obtained from them are associated. Correspondence information is generated for each user. Correspondence information is, for example, information where the proposed method, the score corresponding to the proposed method output by the first model 374, the cost when proposed using the proposed method, and the overall score are associated. The overall score is, for example, a score derived by statistically processing the score and cost. In deriving the integrated score, the score may be given more weight than the cost.

[0078] The information processing device 300 may, for example, determine the recipients and then make a proposal to them using the proposal method with the highest overall score. The information processing device 300 may also determine the recipients by referring to the integrated score. The information processing device 300 may, for example, identify the highest overall score among the overall scores of each user, determine a predetermined number of users with high integrated scores among the identified integrated scores of each user as recipients, and then make a proposal to the determined recipients. In this case, the proposal may be made using the proposal method that is most effective for the recipients (high integrated score). The information processing device 300 may, for example, make a proposal to the recipients using the proposal method with the lowest cost among proposal methods whose integrated score is above a threshold.

[0079] In the example above, we described an example in which multiple first models 374 are generated, but the learning device 400 may also generate a second model 376B of the first proposed method and a second model 376B of the second proposed method. Not limited to two second models 376, three or more second models 376 may be generated for each proposed method.

[0080] The learning device 400 learns learning information that associates the subject information of multiple subjects who have used the cash advance service in the past with information indicating whether or not the subject used the cash advance service after being notified of the proposal to use the cash advance service of the first proposed method. When the learning device 400 receives subject information of subjects who have used the cash advance service in the past, it generates a second model 376A (not shown) that outputs a score indicating whether or not the subject used the cash advance service after being notified of the proposal to use the first proposed method.

[0081] The learning device 400 learns learning information that associates the subject information of multiple subjects who have used the cash advance service in the past with information indicating whether or not the subject used the cash advance service after being notified of the proposal to use the cash advance service of the second proposed method. When the learning device 400 receives subject information of subjects who have used the cash advance service in the past, it generates a second model 376B (not shown) that outputs a score indicating whether or not the subject used the cash advance service after being notified of the proposal to use the second proposed method.

[0082] The information processing device 300 may use the scores output by multiple second models 376 to determine the recipients of the transmission or to determine the method of proposal for the recipients.

[0083] According to each embodiment described above, the information processing device 300 inputs each of the target person's information into a model, obtains a score related to the use of the cash advance service output by the model, and determines the target person to be notified from among multiple target persons based on the score, thereby enabling it to more efficiently determine the user to whom the cash advance service will be offered.

[0084] Although embodiments for carrying out the present invention have been described above using examples, the present invention is not limited in any way to these embodiments, and various modifications and substitutions can be made without departing from the spirit of the present invention. [Explanation of Symbols]

[0085] 10. User terminal device 20 Payment Apps 100 Payment Servers 120 Content Provision Department 130 Payment Processing Unit 140 Information Management Department 300 Information Processing Devices 310 Acquisition Department 320 Processing Unit 372 Target Information 374 First Model 376 Second Model 400 Learning Devices

Claims

1. An acquisition unit that acquires subject information for multiple subjects, Each of the aforementioned subject information is input into the model, and the score regarding the use of the cash advance service output by the model is obtained. The system includes a processing unit that determines the recipients of the notification from among the multiple recipients based on the score, The aforementioned model is a model that, upon inputting the subject information of a subject, outputs a score corresponding to whether or not the subject has used the cash advance service associated with the input subject. The aforementioned information on the target persons is: This includes attribute information indicating the attributes of the subject, contract information regarding the subject's deferred payment service, and usage history information regarding the history of the deferred payment service. Information processing device.

2. The aforementioned information on the target person further includes related payment information in electronic payment services other than deferred payment services. The information processing apparatus according to claim 1.

3. The aforementioned related payment information includes one or more of the following: information on the day of the week the electronic payment service was used, information on the time of day the electronic payment service was used, information indicating the usage status of the remittance service in the electronic payment service, information indicating the type of merchant using the electronic payment service, and information indicating the operation history of the payment application used in the electronic payment service. The information processing apparatus according to claim 2.

4. The acquisition unit acquires the information of multiple individuals who have not previously used the cash advance service. By inputting the subject information into the model, the score of the subject who has not previously used the cash advance service is obtained. Based on the aforementioned score, the individuals to be notified are selected from among the multiple individuals, The aforementioned model, By learning learning information that associates the subject information of the multiple subjects who have not previously used the cash advance service with information indicating whether or not the subject used the cash advance service after being notified of a proposal to use the cash advance service, This model, upon inputting the subject information of the subject, outputs a score indicating whether the subject will use or not use the cash advance service after being notified of the proposed use. The information processing apparatus according to any one of claims 1 to 3.

5. The acquisition unit acquires the information of multiple individuals who have previously used the cash advance service. By inputting the subject information into the model, the score of the subject who has previously used the cash advance service is obtained. Based on the aforementioned score, the individuals to be notified are selected from among the multiple individuals, The aforementioned model, By learning learning information that associates the subject information of the multiple subjects who have used the cash advance service in the past with information indicating whether or not the subject used the cash advance service after being notified of a proposal to use the cash advance service, This model, upon inputting the subject information of the subject, outputs a score indicating whether the subject will use or not use the cash advance service after being notified of the proposed use. The information processing apparatus according to any one of claims 1 to 3.

6. The aforementioned model is prepared for each notification of a proposal, The aforementioned model is a model that has learned learning information corresponding to each of the notifications of the aforementioned proposal, and when the subject information of the subject is input, the model outputs a score indicating whether the subject will use or not use the caching service after the notification of the aforementioned proposal corresponding to the learning information has been sent. The processing unit determines the method of notifying the recipient of the proposal by referring to the score for each notification of the proposal. The information processing apparatus according to any one of claims 1 to 3.

7. Learning information is obtained in which the subject information of multiple subjects and information indicating whether or not the subject used the cash advance service after being notified of a proposal to use the cash advance service are associated. Using the aforementioned learning information, a model is generated that, when the subject information of the subject is input, outputs a score corresponding to whether or not the subject has used the caching service associated with the input subject. The aforementioned information on the target persons is: This includes attribute information indicating the attributes of the subject, contract information regarding the subject's deferred payment service, and usage history information regarding the history of the deferred payment service. Learning device.

8. Computers Obtain subject information from multiple subjects, Each of the aforementioned subject information is input into the model, and the score regarding the use of the cash advance service output by the model is obtained. Based on the aforementioned score, the individuals to be notified are selected from among the multiple individuals, The aforementioned model is a model that, upon inputting the subject information of a subject, outputs a score corresponding to whether or not the subject has used the cash advance service associated with the input subject. The aforementioned information on the target persons is: This includes attribute information indicating the attributes of the subject, contract information regarding the subject's deferred payment service, and usage history information regarding the history of the deferred payment service. Information processing methods.

9. On the computer, Obtain subject information from multiple subjects, Each of the aforementioned subject information is input into the model, and the model outputs a score regarding the use of the cash advance service. Based on the aforementioned score, the individuals to be notified are selected from among the multiple individuals. The aforementioned model is a model that, upon inputting the subject information of a subject, outputs a score corresponding to whether or not the subject has used the cash advance service associated with the input subject. The aforementioned information on the target persons is: This includes attribute information indicating the attributes of the subject, contract information regarding the subject's deferred payment service, and usage history information regarding the history of the deferred payment service. program.

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