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

Abstract

To determine users to whom service proposals can be made more efficiently. [Solution] An information processing device comprising an acquisition unit that acquires target information for multiple targets, and a processing unit that inputs each of the target information into a model, acquires a score regarding the use of a cash advance service output by the model, and determines notification targets from among the multiple targets based on the score, wherein the model is trained to output a score according to whether or not the target person has used the cash advance service associated with the input target person when target information for a target person is input.

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2024-98753 A Summary of the Invention [Problem to be solved by the invention]

[0004] In the above-mentioned device, no consideration is given to how to select a user to whom a proposal is to be made. For this reason, it is sometimes impossible to efficiently determine a target user to whom a proposal is to be made.

[0005] The present invention has been made in consideration of the above circumstances, and one of its objectives is to provide an information processing device, a learning device, an information processing method, and a program that can more efficiently determine users to whom a service (e.g., a caching service) should be proposed. [Means for solving the problem]

[0006] One aspect of the present invention is an information processing device that includes an acquisition unit that acquires target information for a plurality of targets, and a processing unit that inputs each of the target information into a model, acquires a score regarding the use of a caching service output by the model, and determines notification targets from among the plurality of targets based on the score, wherein the model is trained to output a score according to whether or not the target information for a target is input, the score corresponding to the use of the caching service associated with the input target. Effect 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 a user to whom a service (eg, a caching service) is to be proposed. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing an example of a configuration for realizing an electronic payment service. [Diagram 2] This is a sequence diagram (part 1) illustrating the general flow of electronic payment. [Diagram 3] This is a sequence diagram (part 2) illustrating the general flow of electronic payment. [Figure 4] FIG. 2 is a configuration diagram of a payment server 100 according to the embodiment. [Diagram 5] FIG. 13 is a diagram showing an example of the contents of user information 172. [Figure 6] FIG. 13 is a diagram showing an example of the contents of affiliated store / store information 176. [Figure 7] FIG. 2 is a diagram illustrating an example of a functional configuration of an information processing device 300. [Figure 8] FIG. 13 is a diagram showing an example of an additional claim. [Figure 9] FIG. 13 is a diagram showing another example of an additional claim. [Figure 10] FIG. 13 is a diagram for explaining the processing of a first model 374 and a second model 376. [Figure 11]FIG. 13 is a diagram for explaining subject information. [Figure 12] 13 is a flowchart showing an example of the flow of processing executed by information processing device 300. [Figure 13] FIG. 11 is a diagram illustrating an example of learning information. [Figure 14] FIG. 11 is a diagram illustrating an example of learning information. [Figure 15] FIG. 13 is a diagram for explaining the processing of a first model 374A and a second model 376B. [Figure 16] The score, the cost, and the integrated score obtained from these are associated with each other to form correspondence information. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, with reference to the drawings, an embodiment of an information processing device, a learning device, an information processing method, and a program of the present invention will be described. Various devices for providing services to users and performing internal analysis, such as the "server" that will be described below, may be realized by a group of distributed devices, and each device may be operated by a different business operator. Furthermore, the owner of the hardware of the device (the provider of the cloud server) and the business that actually operates the device may also be different. An application program and a payment server work together to provide an electronic payment service. In the following description, the application program is referred to as a payment application. An electronic payment service is a service that supports payments related to 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 electronic commerce. A virtual store may include a store provided by an entity different from the operator of the electronic payment service. In that case, when making a payment for shopping at a virtual store, the screen may be controlled to transition to an interface screen of the electronic payment service. In an electronic payment service, a store is treated as belonging to, for example, an affiliated store (brand), and processing such as payment when a purchase is made at a store is mainly performed between the user and the affiliated store. Alternatively, processing such as payment may be carried out between the user and the store.

[0010] [Electronic payment service] FIG. 1 is a diagram showing an example of the configuration of an electronic payment system in which an electronic payment service is realized. The electronic payment service is realized mainly by a payment server 100. The electronic payment system that realizes 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 via a network NW, for example. The network NW includes, for example, the Internet, a LAN (Local Area Network), a wireless base station, a provider device, and the like. Some or all of the functional configuration included in the electronic payment system may be distributed to multiple devices in any form, or may be integrated into any device.

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

[0012] [First store terminal device] The first store terminal device 50 is installed, for example, in a store. The first store terminal device 50 is a computer device having at least a product 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 product 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. The store code image 60 may be displayed on a display placed in the store (which may be the 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 affiliated store. The second store terminal device 70 is a smartphone, a tablet terminal, a personal computer, or the like. An interface 72 for affiliated stores runs on the second store terminal device 70. The interface 72 for affiliated stores may be an app for affiliated stores or a browser. The interface 72 for affiliated stores accepts coupon settings and the like made by the operator of the affiliated store and transmits them to the payment server 100. The second store terminal device 70, which is a smartphone, has the function of displaying a code image corresponding to a store code image and reading a code image displayed by the user terminal device 10 by executing the app for affiliated stores.

[0014] [Payment server] The payment server 100 realizes electronic payment based on payment 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 an affiliated store server, in which case the payment information is sent from the POS device via the affiliated store server to the payment server 100. In the following explanation, no distinction is made between these two and it is assumed that the payment information is sent from the first store terminal device 50.

[0015] 2 and 3 are sequence diagrams illustrating the general flow of electronic payment. There may be two patterns of electronic payment: pattern 1 and pattern 2.

[0016] In the case of pattern 1 (hereinafter referred to as user scan) shown in FIG. 2, the user terminal device 10 with the payment application 20 activated decodes the store code image 60 by the optical reading function (S1). The store code image 60 includes store URL (Uniform Resource Locator) information. This store URL is an electronic payment service domain to which store-identifying information is added, and is associated with an affiliated store ID, a store ID, etc. in the payment server 100 (described later). The payment application 20 transmits the first payment information including the store URL and the account ID to the payment server 100 (S2). The payment server 100 searches for store information (described later) from the affiliated store ID and the store ID corresponding to the store URL, acquires the affiliated store name and the store name information (S3), and transmits it to the payment application 20 (S4). The user inputs the payment amount into the user terminal device 10 on the screen on which the affiliated store name and the store name are displayed (S5). Then, the user terminal device 10 generates second payment information including at least the payment amount, and transmits 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 transmits a payment completion notice (information for displaying a payment completion screen) to the payment application 20 (S8), and the payment application 20 displays the payment completion screen (S9). Note that when the store code image 60 is displayed on a display installed in the store, the store code image 60 may include not only the store URL but also information on the payment amount. In this case, the step of the user inputting the payment amount is omitted, and the information on the payment amount is included in the first payment information and transmitted to the payment server 100. Information on the affiliated store name and the store name may be included in the payment completion screen and displayed.

[0017] In the case of pattern 2 (hereinafter referred to as store scan) shown in FIG. 3, when the payment application 20 is started, when a payment operation is performed in the payment application 20, when an automatic update timing (e.g., every minute) occurs, and at other timings, the payment application 20 transmits a request for issuing a one-time code to the payment server 100 (S11). The payment server 100 generates a one-time code (S12) and transmits it to the payment application 20 (S13). The payment application 20 displays a code image such as a QR code or a barcode generated based on the one-time code (S14). The user holds (presents) the display surface of the user terminal device 10 over the first store terminal device 50, and the first store terminal device 50 decodes the code image by an optical reading function and obtains the one-time code, etc. (S15). The first store terminal device 50 then generates payment information including the one-time code, payment amount, affiliated store ID, store ID, etc., and transmits it to the payment server 100 (S16). The payment amount information is acquired in advance by reading a barcode or manually entering it. The payment server 100 identifies the user corresponding to the one-time code based on the received information and performs electronic payment (S17). The payment server 100 then transmits a payment completion notice to the payment application 20 (S18), and the payment application 20 displays a payment completion screen (S19).

[0018] Note that electronic payment may be performed using only one of the above patterns. Furthermore, the "account ID" described in FIG. 2 may be other information (e.g., a phone number) that can be used as user identification information. Furthermore, issuance of a one-time code may be omitted in the store scan, and the payment application 20 may display a code image generated based on the user's account ID. In this case, the payment server 100 identifies the user corresponding to the account ID instead of identifying the user corresponding to the one-time code.

[0019] [Payment server functional configuration] 4 is a configuration diagram of the payment server 100. The payment server 100 includes, for example, a communication unit 110, a content providing unit 120, a payment processing unit 130, an information management unit 140, and a storage unit 170. The components other than the communication unit 110 and the storage unit 170 are realized by, for example, 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 (Large Scale Integration), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), a GPU (Graphics Processing Unit), or a SOC (System On Chip), or may be realized by cooperation between software and hardware. The program may be stored in advance in a storage device (a storage device with a non-transient storage medium) such as an HDD (Hard Disk Drive) or flash memory, or may be stored in a removable storage medium (non-transient storage medium) such as a DVD or CD-ROM, and installed in the storage device by inserting the storage medium into a drive device.

[0020] The storage unit 170 is a HDD, a flash memory, a RAM (Random Access Memory), or the like. The storage unit 170 may be a NAS (Network Attached Storage) device that the payment server 100 can access via a network. The storage unit 170 stores information such as user information 172, content information 174, and affiliated store / shop information 176. Some of this information may 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 the network NW. The communication unit 110 is, for example, a network interface card.

[0022] The content providing unit 120 has, for example, a function 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 providing unit 120 reads out 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 by the user while the content is being played by the payment application 20, and transmits the above-mentioned payment information and the like to the payment server 100. The above content may be generated by the payment application 20. In this case, the content providing unit 120 provides the payment application 20 with information required for generating the content.

[0023] The payment processing unit 130 performs payment processing based on the 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] FIG. 5 is a diagram showing an example of the contents of the user information 172. The user information 172 is an example of the registration information of a user. The user information 172 is, for example, a user URL, an account ID, a telephone number, a password, as well as information associated with an email address, a user ID, a name, an address, a date of birth, a registration date, a charge balance, a credit payment setting, a credit payment limit, a credit payment amount used, a credit payment available amount, a payment method setting, a bank account, a credit card number, charge history information, and payment history information. The user URL is used for a remittance process between users. When registering for the electronic payment service, it is necessary to register a telephone number and a password. The account ID is issued to the user by the payment server 100, and the user ID is an ID that can be set by the user at will (does not have to be set). Similarly, the email address, and the name, address, and date of birth are information that can be set by the user at will (does not have to be set). The registration date is the date on which the user registered for the electronic payment service (the date on which the account was created). Hereinafter, the user's 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 a user by transferring money to an account in advance. Means of transfer include transfer from an ATM (Automatic Teller Machine) of a designated business (bank) and transfer from a registered bank account. The credit payment setting is information indicating whether or not the setting for enabling electronic payment by credit payment using the payment application 20 has been completed, and is set to either "completed" or "not completed". The credit payment limit is the credit payment limit that can be used each month, the credit payment usage amount is the amount of credit payment already used in the current month, and the credit payment available amount is the amount of credit payment that can be used in the current month, which is calculated by subtracting the credit payment usage amount from the credit payment limit. Although only one credit payment limit is shown in the figure, in reality, there is also a daily upper limit, and the lower of these may be set as the credit payment limit. Further details of the credit payment will be described later. The payment method setting is setting information indicating whether the user will perform electronic payment using the charge balance or payment by credit payment at that time. The bank account and credit card number are information on a bank account or credit card number (account number, card number) that can be used to deposit funds into an electronic payment service. Charge history information is a history of the user's previous transfers to the electronic payment service to increase the charge balance. Payment history information is information that indicates the details of payments made by the user for each payment (date and time, store ID of the store where the purchase was made, payment amount, payment method, etc.).

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

[0027] The information management unit 140 acquires information provided by other server devices and the card server 200. The information management unit 140 manages user information 172 and affiliated store / store information 176 based on information acquired from the user terminal device 10 and the second store terminal device 70. The information management unit 140 adds new records to, edits, and deletes the user information 172 and affiliated store / store information 176.

[0028] [Electronic payment] When payment information is acquired from the user terminal device 10 or the first store terminal device 50, the payment processing unit 130 refers to the user information 172 to acquire the "payment method setting" of the user. For a user 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 managed in association with the user ID and increasing the item value of the affiliated store's sales. The item value of the affiliated store's sales is not used as electronic money itself, for example, and an amount corresponding to the item value of the sales is transferred to a bank account in a cycle according to an agreement between the affiliated store and the electronic payment service.

[0029] The payment processing unit 130 performs electronic payment for users whose "setting information" is set to "credit payment (credit payment using code information)" as follows. Credit payment is a payment method in cooperation with a credit card company, which is a separate entity from the operator of the electronic payment service, and the operator of the electronic payment service acts as a creditor and allows electronic payment that is not dependent on the charge balance within the credit payment limit. In order to receive the credit payment service, a credit card provided by the operator of the electronic payment service may be required. The amount used in the credit payment is settled on the payment date of the following month, for example, by debiting from a bank account, for one month. In this case, the payment processing unit 130 performs provisional payment by adding the payment amount to the credit payment amount and subtracting the same amount from the available credit payment amount, and when the closing date comes, it performs processing to debit the payment for the current month on the payment date of the following month as described above, or requests the operator of the credit card company to perform such processing. In addition, if the payment amount exceeds the available credit payment amount at the time of provisional payment, an error notification is returned to the payment application 20.

[0030] [Card Server] The card server 200 cooperates with the payment application 20 or the mini application 30 to provide credit card related services to the user terminal device 10. Hereinafter, it is assumed that the mini application 30 provides the credit card related services. For example, in response to the user's operation on the mini application 30, the card server 200 causes the display unit of the user terminal device 10 to display various information such as the credit card usage history and information related to the credit card usage fee.

[0031] [Information processing device] FIG. 7 is a diagram showing an example of a 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 by, for example, 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 may be realized by cooperation between software and hardware. The program may be stored in a storage device (storage device having a non-transient storage medium) such as an HDD or flash memory in advance, or may be stored in a removable storage medium (non-transient storage medium) such as a DVD or CD-ROM, and may be installed in the storage device by mounting the storage medium in a drive device.

[0032] The storage unit 370 is a HDD, a flash memory, a RAM, or the like. The storage unit 370 may be a NAS device that the information processing device 300 can access via a network. The storage unit 370 stores target person information 372 including target person information described later, a first model 374, a second model 376, credit information 378, transmission target person information 380, and the like. Information stored in the storage unit 370, such as one or both of the first model 374 and the second model 376, 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 to be distinguished from each other, they may be referred to as "models".

[0033] The acquisition unit 310 acquires target person information of a plurality of targets. The processing unit 320 inputs each of the target person information into a model, acquires a score related to the use of the caching service output by the model, and determines a notification target from among the plurality of targets based on the score. The 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 on the 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 will be described later.

[0035] [overview] The processing unit 320 of the information processing device 300 inputs the target person information of each of the multiple targets into the model, obtains the score related to the use of the caching service output by the model, and determines the notification target from among the multiple targets based on the score. The model is trained so that when the target person information of a target is input, it outputs a score according to the presence or absence of use of the caching service associated with the input target.

[0036] (Proposal using the first model) The information processing device 300 acquires target information of multiple targets who have not used a cash advance service in the past, acquires scores for the targets who have not used a cash advance service in the past by inputting the target information into the first model 374, and determines the notification targets from among the multiple targets based on the scores (see FIG. 10).

[0037] The first model 374 is a model that learns learning information that corresponds subject information of multiple subjects who have not used a cash advance service in the past with information indicating whether the subjects used the cash advance service after being notified of the suggestion to use the cash advance service, and when the subject information is input, it outputs a score indicating whether the subject will use or not use a cash advance service after being notified of the suggestion to use it.

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

[0039] 8 is a diagram showing an example of an additional claim. For example, interface screens IM1 and IM2 are provided to a user who has never used a cash advance service before. The user can use the cash advance service by using the payment application 20 or web content on interface screen IM2.

[0040] (Proposal using the second model) The information processing device 300 obtains the score of the subject who has used the cash advance service in the past by inputting the subject information into the second model 376, and determines the notification subject from among multiple subjects based on the score (see FIG. 10).

[0041] The second model 376 is a model that learns learning information that corresponds subject information of multiple subjects who have used a cash advance service in the past with information indicating whether the subjects used a cash advance service after being notified of the suggestion to use the cash advance service, and when the subject information of a subject is input, it outputs a score indicating whether the subject will use or will not use a cash advance service after being notified of the suggestion to use it.

[0042] 9 is a diagram showing an example of an additional claim. For example, interface screens IM3 and IM4 are provided to a user who has used a cash advance service in the past. The user can use the cash advance service by using the payment application 20 or web content on interface screen IM4.

[0043] 10 is a diagram for explaining 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 may be information that is partially different.

[0044] [Target information] FIG. 11 is a diagram for explaining the subject information 372. The subject information includes one or more of attribute information, contract information, usage history information, and related service information (related payment information). The subject information may be attribute information, contract information, and usage history information, or may include related service information in addition to these pieces of information. The subject information input to the first model 374 and the second model 376 may be the same information, or may be partially different information.

[0045] (Attribute information) The attribute information includes, for example, one or more of the target person's age, sex, years of residence, annual income, amount of savings, annual household income, family structure, etc. For example, the shorter the years of residence, the more single-person the family is, and the lower the savings of the target person, the more likely they are to use a cash advance service in response to an appeal.

[0046] The contract information includes one or more of the following information, for example: partner code, shopping category, shopping limit, cash advance category, cash advance limit, number of years since the contract was signed, and application channel information. The partner code is a code indicating the credit card partner. The shopping category is the payment category (revolving payment, one-time payment, three-time payment, etc.) when using an available (used) credit card. The cash advance category is a category related to the use (possibility of use) of cash advances. The shopping limit is the limit for deferred payment. The cash advance limit is the maximum amount that can be cashed. For example, the lower (or higher) the shopping limit or cash advance limit, the more likely it is that a cash advance service will be used in response to an appeal.

[0047] The application channel information is information indicating the channel through which a user who has used a cash advance service in the past applied for the cash advance service. The application channel may be, for example, application via the payment application 20 (electronic payment service), application via a website related to the electronic payment service, application via an affiliated communication service, etc. For example, subjects who applied via the payment application 20 tend to use the cash advance service.

[0048] The usage history information includes, for example, the number of times SP is used, the ad CA balance, the ad SP balance, the SP usage amount, the CA usage amount, the SP limit consumption rate, and the CA limit consumption rate. These cover a period of N months. The usage history information includes the difference between the SP from the previous month and N months ago. "SP" is shopping and indicates a one-time deferred payment settlement. CA indicates a cash advance service. "Ad" indicates a one-time payment. The usage history information may include, for example, the revolving payment balance, the number of times used, the usage amount, and the revolving limit consumption rate.

[0049] For example, subjects who use cash advance services or revolving payments tend to use cash advance services in response to appeals. For example, subjects who have a high or high number of SP usages, ad CA balance, ad SP balance, SP usage amount, CA usage amount, SP limit consumption rate, CA limit consumption rate, etc. tend to use cash advance services in response to appeals.

[0050] The related service information is, for example, the usage status of an electronic payment service and the usage status of other services. The usage status of an electronic payment service is, for example, the usage status of electronic payment of an electronic payment service. The usage status of electronic payment is, for example, the usage status of electronic payment made using payment application 20. The usage status is, for example, information on the date and time of use, information on the time period of use, information on the affiliated store used, remittance information which is the usage status of the remittance service, operation information, etc. The usage status may be the amount used for the electronic payment service, the number of times of use, the number of times a specified type of affiliated store is used in the electronic payment service, the frequency of use, etc. The related service information may be information included in user information 172.

[0051] The date and time of use is information such as whether the use was on the weekend or on a weekday. The time period of use is information indicating whether the use was late at night, early in the morning, or daytime. The affiliated store information is the type of affiliated store, the location of the affiliated store, etc. For example, subjects who use the service on a specific day of the week or during a specific time period tend to use a cash advance service in response to an appeal.

[0052] The usage status of the remittance service is the usage status of the remittance service provided by the payment application 20. The remittance service is a service that allows a target person to remit money to a user who uses the payment application 20. The usage status of the remittance service is information such as the amount, number of times, and time of remittance. For example, a target person who uses the remittance service is more likely to use a cash advance service in response to an appeal.

[0053] The operation information is information on the operation history of the subject's operation of the payment application 20. The operation information is, for example, the number of times the home screen of the payment application 20 has been displayed on the display unit and the frequency of display.

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

[0055] By using related service information, it is possible to extract targets who are more likely to repay. The target's behavior and usage status in other services are correlated with the probability of repaying. Therefore, by using related service information, it is possible to determine more appropriate targets for notification.

[0056] For example, subjects who operate payment application 20 more frequently (e.g., the number of times the home screen of payment application 20 is displayed) tend to use a cash advance service in response to an appeal.

[0057] [flowchart] 12 is a flowchart showing an example of the flow of processing executed by the information processing device 300. First, the information processing device 300 identifies new targets (S100). Next, the information processing device 300 acquires a first score for each new user (S102). Next, the information processing device 300 identifies transmission targets (S104).

[0058] Next, the information processing device 300 excludes high-risk users from among the transmission targets (S106). The information processing device 300 refers to the credit information 378 to determine whether the transmission target is a high-risk user for providing a cash advance service. The credit information 378 is, for example, information associated with information indicating the risk of the user. The credit information 378 is, for example, information indicating the risk of the user generated based on the payment history of electronic payment services, the usage status of financial services, and the like.

[0059] Next, the information processing device 300 determines the recipients of transmission, excluding high-risk users, as the recipients of the new CA quota attachment claim (S108). The determined recipients of transmission are managed as the recipient information 380. This ends the processing of one routine of this flowchart.

[0060] In the above example, the process of determining the target of the new CA quota attached claim has been described. The target of the existing CA quota attached claim may be determined by the same process as above. In this case, the information processing device 300 identifies the target of the transmission using the second model 376, and determines the users excluding high-risk users from the target of the transmission as the target of the transmission.

[0061] [Generation of the first model] The learning device 400 learns learning information in which subject information of a plurality of subjects who have not used a cash advance service in the past is associated with information indicating whether the subject has used a cash advance service after being notified of the suggestion to use the cash advance service. When the learning device 400 receives the subject information of a subject who has not used a cash advance service in the past, it generates a first model 374 that outputs a score indicating whether the subject will use or not use a cash advance service after being notified of the suggestion to use the service.

[0062] 13 is a diagram showing an example of learning information. The learning information is, for example, information in which subject information (e.g., attribute information, contract information, usage history information, and related service information) is associated with information indicating whether a new CA quota has been opened. For example, when subject information on a subject who has opened a new CA quota is input, the learning device 400 outputs a score indicating a high possibility of opening a new CA quota, and when subject information on a subject who has not opened a new CA quota is input, the learning device 400 trains a model to output a score indicating a low possibility of opening a new CA quota, thereby generating a first model 374.

[0063] As described above, the learning device 400 trains the first model 374 so as to generate the first model 374 that outputs a highly accurate first score.

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

[0065] The learning information is, for example, information in which subject information (e.g., attribute information, contract information, usage history information, and related service information) is associated with information indicating whether or not the CA quota has been used. When subject information on a subject who has used the CA quota is input, the learning device 400 outputs a score indicating a high possibility of using the CA quota, and when subject information on a subject who has not used the CA quota is input, the learning device 400 trains the model to output a score indicating a low possibility of using the CA quota, thereby generating the second model 376.

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

[0067] As described above, the information processing device 300 inputs each of the target information into the model, obtains the score regarding the use of the caching service output by the model, and determines the notification target from among multiple targets based on the score, thereby making it possible to more efficiently determine the users to whom the caching service should be proposed.

[0068] (Modification) In the above example, the recipients of the messages are determined without taking into account the proposed method. However, a model may be generated for each proposed method and used as described below.

[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 is an email, and the second proposed method is a pop-up in payment application 20.

[0070] 14 is a diagram showing 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 in which subject information of a plurality of subjects who have not used a cash advance service in the past is associated with information indicating whether the subject has used a 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 the subject information of a subject who has not used a cash advance service in the past, it generates a first model 374A that outputs a score indicating whether the subject will use or will not use a cash advance service after being notified of the proposal to use the first proposed method.

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

[0073] 15 is a diagram for explaining the processing of the first model 374A and the second model 376B. The information processing device 300 inputs the subject information to the first model 374A, and obtains the score A outputted by the first model 374A. The score A is a score indicating the probability that the user will use the cash advance service when the cash advance service is proposed by email.

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

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

[0076] The information processing device 300 determines a recipient by referring to the score for each proposed method. For example, when the proposed method is email, the information processing device 300 determines a recipient based on the score of the email. Furthermore, when a recipient has been determined, the information processing device 300 may determine a method to propose to the recipient based on the score. For example, the information processing device 300 refers to the score for each proposed method of the recipient, and proposes a caching service to the user using a proposed method with a score that is most likely to encourage the user to use the caching service.

[0077] The information processing device 300 may determine a proposal method to be proposed to a transmission target person based on the score and the cost. FIG. 16 shows correspondence information in which the score, the cost, and an integrated score obtained from these are associated with each other. The correspondence information is information generated for each user. For example, the correspondence information is information in which the proposal method, the score according to the proposal method output by the first model 374, the cost when the proposal is made using the proposal method, and the overall score are associated with each other. The overall score is, for example, a score derived by statistically processing the score and the cost. In deriving the integrated score, the score may be given more importance than the cost.

[0078] The information processing device 300 may, for example, determine a transmission target and then make a proposal to the transmission target using a proposal method with a high overall score. The information processing device 300 may determine a transmission target by referring to an integrated score. The information processing device 300 may, for example, identify the highest overall score among the overall scores for each user, determine a predetermined number of users with high integrated scores among the identified integrated scores for each user as transmission targets, and make a proposal to the determined transmission target. In this case, a proposal may be made using a proposal method that is highly effective for the transmission target (has a high integrated score). The information processing device 300 may, for example, make a proposal to the transmission target using a proposal method with the lowest cost among proposal methods with an integrated score equal to or higher than a threshold.

[0079] In the above example, a case where a plurality of first models 374 are generated has been described, but the learning device 400 may generate a second model 376B of a first proposed method and a second model 376B of a second proposed method. The number of second models 376 is not limited to two, and three or more second models 376 may be generated for each proposed method.

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

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

[0082] Information processing device 300 may use the scores output by a plurality of second models 376 to determine transmission target persons or a method for proposing transmission target persons.

[0083] According to each of the embodiments described above, the information processing device 300 inputs each of the target information into a model, obtains a score regarding the use of the caching service output by the model, and determines the notification target from among multiple targets based on the score, thereby making it possible to more efficiently determine users to whom to propose the caching service.

[0084] The above describes the form for carrying out the present invention using an embodiment, but the present invention is not limited to such an embodiment, and various modifications and substitutions can be made within the scope that does not deviate from the gist of the present invention. [Explanation of symbols]

[0085] 10 User terminal device 20. Payment App 100 Payment Server 120 Contents Provider 130 Payment processing unit 140 Information Management Department 300 Information processing device 310 Acquisition Department 320 Processing section 372 Target information 374 1st Model 376 2nd Model 400 Learning Device

Claims

1. An acquisition unit that acquires subject information of a plurality of subjects; Input each of the subject information into a model, and obtain a score regarding the use of a cash advance service output by the model; A processing unit that determines a notification target from among the plurality of targets based on the score, The model is a model trained to output a score according to whether or not a subject has used the cash advance service associated with the subject when subject information of the subject is input, The subject information includes The information includes attribute information indicating the attributes of the subject, contract information regarding the subject's deferred payment settlement, and usage history information regarding the history of the use of the deferred payment, The deferred payment is payment information regarding the use of a physical credit card, The target information further includes related payment information for an electronic payment service other than the deferred payment service; The related payment information includes one or more of information on the day of the week when the electronic payment service was used, information on the time of day, information indicating the usage status of a remittance service in the electronic payment service, information indicating the type of affiliated store that used the electronic payment service, and information indicating the operation history of a payment app used in the electronic payment service. Information processing device.

2. The acquisition unit acquires the target person information of a plurality of target people who have not used the cash advance service in the past, By inputting the subject information into the model, the score of the subject who has not used the cash advance service in the past is obtained; determining notification recipients from among the plurality of recipients based on the scores; The model is By learning learning information in which target information of the plurality of targets who have not used the cash advance service in the past is associated with information indicating whether the target has used the cash advance service after being notified of the proposal to use the cash advance service, When the subject information of the subject is input, after the notification of the suggestion of use, a score indicating whether the subject will use or not use the cash advance service is output. The information processing device according to claim 1 .

3. The acquisition unit acquires the target information of a plurality of targets who have used the cash advance service in the past, By inputting the subject information into the model, the score of the subject who has used the cash advance service in the past is obtained; determining notification recipients from among the plurality of recipients based on the scores; The model is By learning learning information in which target person information of the plurality of targets who have used the cash advance service in the past is associated with information indicating whether the target person has used the cash advance service after being notified of the proposal to use the cash advance service, When the subject information of the subject is input, after the notification of the suggestion of use, a score indicating whether the subject will use or not use the cash advance service is output. The information processing device according to claim 1 .

4. An acquisition unit that acquires target information including attribute information indicating attributes of a target person, contract information regarding the target person's deferred payment settlement, and usage history information regarding the history of use of the deferred payment, and a processing unit; The acquisition unit acquires target person information of a plurality of targets who have not used a cash advance service in the past, By inputting the subject information into a first model, a score of the subject who has not used the cash advance service in the past is obtained; The processing unit determines a first notification target person from among the plurality of targets based on the score, sending a first notification of a proposal regarding a first type of caching service corresponding to the first notification target to a terminal device of the target; The first model is By learning learning information in which target information of the plurality of targets who have not used the cash advance service in the past is associated with information indicating whether the target has used the cash advance service after being notified of the proposal to use the cash advance service, a model that, when subject information of the subject is input, outputs a score indicating whether the subject will use or not use the cash advance service after the first notification of the suggestion to use is given; The acquisition unit acquires the target information of a plurality of targets who have used the cash advance service in the past, By inputting the subject information into a second model, the score of the subject who has used the cash advance service in the past is obtained; The processing unit determines a second notification target person from among the plurality of targets based on the score, sending a second notification of a proposal regarding a second type of caching service corresponding to the second notification target to a terminal device of the target; The second model is By learning learning information in which target person information of the plurality of targets who have used the cash advance service in the past is associated with information indicating whether the target person has used the cash advance service after being notified of the proposal to use the cash advance service, When subject information of the subject is input, a score indicating whether the subject will use or not use the cash advance service after the second notification of the suggestion to use is provided is output. Information processing device.

5. The deferred payment is payment information regarding the use of a physical credit card, The target information further includes related payment information for electronic payment services other than post-payment. The information processing device according to claim 4.

6. The related payment information includes information indicating an operation history of a payment app used in the electronic payment service. The information processing device according to claim 5 .

7. The related payment information includes one or more of information on the day of the week when the electronic payment service was used, information on the time of day, information indicating the usage status of a remittance service in the electronic payment service, information indicating the type of affiliated store that used the electronic payment service, and information indicating the operation history of a payment app used in the electronic payment service. The information processing device according to claim 5 .

8. said model is prepared for each notice of proposal; the model is a model in which learning information corresponding to each of the notifications of the proposal has been learned, and when subject information of the subject is input, the model outputs a score indicating whether the subject will use or not use the cash advance service after the notification of the proposal corresponding to the learning information is given; The processing unit determines a method of notifying the notification target of the proposal by referring to a score for each notification of the proposal. The information processing device according to claim 1 .

9. acquiring learning information in which subject information of a plurality of subjects is associated with information indicating whether the subjects have used a cash advance service after receiving a notification of a proposal to use the cash advance service; generating a model trained to output a score according to whether or not the subject has used the cash advance service associated with the input subject information by using the learning information; The subject information includes The information includes attribute information indicating the attributes of the subject, contract information regarding the subject's deferred payment settlement, and usage history information regarding the history of the use of the deferred payment, The deferred payment is payment information regarding the use of a physical credit card, The target information further includes related payment information for an electronic payment service other than the deferred payment service; The related payment information includes one or more of information on the day of the week when the electronic payment service was used, information on the time of day, information indicating the usage status of a remittance service in the electronic payment service, information indicating the type of affiliated store that used the electronic payment service, and information indicating the operation history of a payment app used in the electronic payment service. Learning device.

10. The computer Obtaining subject information for multiple subjects; Input each of the subject information into a model, and obtain a score regarding the use of a cash advance service output by the model; determining notification recipients from among the plurality of recipients based on the scores; The model is a model trained to output a score according to whether or not a subject has used the cash advance service associated with the subject when subject information of the subject is input, The subject information includes The information includes attribute information indicating the attributes of the subject, contract information regarding the subject's deferred payment settlement, and usage history information regarding the history of the use of the deferred payment, The deferred payment is payment information regarding the use of a physical credit card, The target information further includes related payment information for an electronic payment service other than the deferred payment service; The related payment information includes one or more of information on the day of the week when the electronic payment service was used, information on the time of day, information indicating the usage status of a remittance service in the electronic payment service, information indicating the type of affiliated store that used the electronic payment service, and information indicating the operation history of a payment app used in the electronic payment service. Information processing methods.

11. On the computer, Acquire target information for a plurality of targets; Input each of the subject information into a model, and obtain a score regarding the use of a cash advance service output by the model; determining notification recipients from among the plurality of recipients based on the scores; The model is a model trained to output a score according to whether or not a subject has used the cash advance service associated with the subject when subject information of the subject is input, The subject information includes The information includes attribute information indicating the attributes of the subject, contract information regarding the subject's deferred payment settlement, and usage history information regarding the history of the use of the deferred payment, The deferred payment is payment information regarding the use of a physical credit card, The target information further includes related payment information for an electronic payment service other than the deferred payment service; The related payment information includes one or more of information on the day of the week when the electronic payment service was used, information on the time of day, information indicating the usage status of a remittance service in the electronic payment service, information indicating the type of affiliated store that used the electronic payment service, and information indicating the operation history of a payment app used in the electronic payment service. A program that executes the following.

12. The computer Acquire target information including attribute information indicating the attributes of the target, contract information regarding the target's deferred payment settlement, and usage history information regarding the usage history of the deferred payment; Acquire target information of a plurality of targets who have not used a cash advance service in the past; By inputting the subject information into a first model, a score of the subject who has not used the cash advance service in the past is obtained; determining a first notification target from among the plurality of targets based on the score; sending a first notification of a proposal regarding a first type of caching service corresponding to the first notification target to a terminal device of the target; The first model is By learning learning information in which target information of the plurality of targets who have not used the cash advance service in the past is associated with information indicating whether the target has used the cash advance service after being notified of the proposal to use the cash advance service, a model that, when subject information of the subject is input, outputs a score indicating whether the subject will use or not use the cash advance service after the first notification of the suggestion to use is given; Acquire target information of a plurality of targets who have used the cash advance service in the past; By inputting the subject information into a second model, the score of the subject who has used the cash advance service in the past is obtained; determining a second notification target from among the plurality of targets based on the score; sending a second notification of a proposal regarding a second type of caching service corresponding to the second notification target to a terminal device of the target; The second model is By learning learning information in which target person information of the plurality of targets who have used the cash advance service in the past is associated with information indicating whether the target person has used the cash advance service after being notified of the proposal to use the cash advance service, When subject information of the subject is input, a score indicating whether the subject will use or not use the cash advance service after the second notification of the suggestion to use is provided is output. Information processing methods.

13. On the computer, Executing a process to acquire target information including attribute information indicating the attributes of the target, contract information regarding the target's deferred payment settlement, and usage history information regarding the usage history of the deferred payment; A process of acquiring target information of a plurality of targets who have not used a cash advance service in the past; A process of acquiring a score of the subject who has not used the cash advance service in the past by inputting the subject information into a first model; A process of determining a first notification target person from among the plurality of targets based on the score; a process of notifying a terminal device of the first notification target person of a first notification proposal regarding a first type of caching service according to the first notification target person; The first model is By learning learning information in which target information of the plurality of targets who have not used the cash advance service in the past is associated with information indicating whether the target has used the cash advance service after being notified of the proposal to use the cash advance service, a model that, when subject information of the subject is input, outputs a score indicating whether the subject will use or not use the cash advance service after the first notification of the suggestion to use is given; A process of acquiring target information of a plurality of targets who have used the cash advance service in the past; A process of acquiring the score of the subject who has used the cash advance service in the past by inputting the subject information into a second model; A process of determining a second notification target person from among the plurality of targets based on the score; a process of notifying a terminal device of the second notification target person of a second notification proposal regarding a second type of caching service according to the second notification target person; The second model is By learning learning information in which target person information of the plurality of targets who have used the cash advance service in the past is associated with information indicating whether the target person has used the cash advance service after being notified of the proposal to use the cash advance service, When subject information of the subject is input, a score indicating whether the subject will use or not use the cash advance service after the second notification of the suggestion to use is provided is output. A program that executes the following.

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