Information processing device, information processing method, and program

Abstract

To provide an information processing device, an information processing method, and a program that can estimate a reference location such as the user's residence. [Solution] An information processing device comprising: an acquisition unit that acquires a predetermined number of payment histories of electronic payments made by a user at a merchant affiliated with an electronic payment service; a clustering unit that clusters the predetermined number of payment histories; and an estimation unit that estimates a cluster indicating the user's reference position among the one or more clusters by inputting cluster information relating to one or more clusters obtained by the clustering into a trained model.

Description

Technical Field

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

Background Art

[0002] Conventionally, a technique for analyzing the action range of a service user based on the location information of the user is known. For example, in Patent Document 1, based on route data starting from the user's residence location and ending at a regional base (such as a station), the residence location of a user who uses a target facility (such as a store) is specified, and a set of the specified residence locations is generated as a facility use area.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The above conventional technique specifies the facility use area of the user on the premise that the user's residence location is known. However, in the actual operation of a service, the user's residence location is not always known, and in the conventional technique, it may be difficult to estimate a reference position such as the user's residence location.

[0005] The present invention has been made in consideration of such circumstances, and one of its purposes is to provide an information processing apparatus, an information processing method, and a program capable of estimating a reference position such as the user's residence location.

Means for Solving the Problems

[0006] One aspect of the present invention is an information processing device comprising: an acquisition unit that acquires a predetermined number of payment histories in which a user has made an electronic payment at a merchant affiliated with an electronic payment service; a clustering unit that clusters the predetermined number of payment histories; and an estimation unit that estimates a cluster that indicates the user's reference position among the one or more clusters by inputting cluster information relating to one or more clusters obtained by the clustering into a trained model. [Effects of the Invention]

[0007] According to one aspect of the present invention, it is possible to provide an information processing device, an information processing method, and a program that can estimate a reference location such as the user's residence. [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 first 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 a user's payment history acquired by the acquisition unit 152 and clustered by the clustering unit 154. [Figure 8] This figure shows an example of the contents of the cluster information 178 created by the estimation unit 156. [Figure 9] This diagram illustrates how to create the external statistical information to be included in cluster information 178. [Figure 10] This figure shows an example of the content of estimated result information 182. [Figure 11] This is a diagram illustrating how to generate the 180 pre-trained models. [Figure 12] This figure shows an example of notification information output by the output unit 158. [Figure 13] This figure shows another example of notification information output by the output unit 158. [Figure 14] This flowchart shows an example of the processing flow performed by the information processing unit 150. [Modes for carrying out the invention]

[0009] The following describes embodiments of the information processing apparatus, information processing method, and program of the present invention with reference to the drawings. Various devices used to provide services to users or perform internal analysis, such as the "server," "management device," and "information providing device" described below, 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 the 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 a configuration for realizing an electronic payment service. The electronic payment service is implemented with a payment server 100 at its core. The payment server 100 communicates with, for example, one or more user terminal devices 10, one or more first store terminal devices 50, and one or more second store terminal devices 70 via a network NW. The network NW includes, for example, the Internet, a LAN (Local Area Network), a wireless base station, and provider equipment.

[0011] The user terminal device 10 is, for example, a portable terminal device such as a smartphone or tablet. The user terminal device 10 is a computer device having at least optical reading function, communication function, display function, input reception function, and program execution function. In the following description, the components for realizing these functions will be referred to as a camera, communication device, touch panel, CPU (Central Processing Unit), etc. In the user terminal device 10, the payment application 20 is executed by a processor such as the CPU, and it operates in cooperation with the payment server 100 to provide electronic payment services to the user. The payment application 20 is installed on the user terminal device 10, for example, from an application store, and controls the camera, communication device, touch panel, etc.

[0012] 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 may include a so-called POS (Point of Sale) device, and the product price acquisition function and 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 paper or plastic media. The store code image 60 may also be displayed on a display placed in the store (which may be the display of a terminal device such as a smartphone).

[0013] 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 the franchise store operates. The interface 72 for the franchise store may be an application for the franchise store or a browser. The interface 72 for the franchise store accepts settings of coupons by the operator of the franchise store and transmits them to the payment server 100. The second store terminal device 70 which is a smartphone has functions of 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 the franchise store.

[0014] The payment server 100 realizes electronic payment based on the 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 a franchise store server. In that case, the payment information is transmitted from the POS device to the payment server 100 via the franchise store server. In the following description, without particularly distinguishing this, it is assumed that the payment 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 payment. There may be two patterns, pattern 1 and pattern 2, in the electronic payment.

[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] Figure 4 is a configuration diagram of a payment server 100 according to the first embodiment. The payment server 100 includes, for example, a communication unit 110, a payment content provision unit 120, a payment processing unit 130, an information management unit 140, an information processing unit 150, 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), and GPU (Graphics Processing Unit), 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 a drive device. The functions of the information processing unit 150 of the payment server 100 are an example of an "information processing device" in the claims.

[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, payment content information 174, merchant / store information 176, cluster information 178, trained model 180, and estimation result information 182.

[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 payment content provision 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 payment content provision unit 120 reads the necessary content from the payment content information 174 as appropriate and provides it to the user terminal device 10. The user terminal device 10 receives various inputs from the user while the content is being played by the payment application 20 and transmits the aforementioned payment information and other data to the payment server 100.

[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] 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 by credit card, and is set to either "Completed" or "Not Completed". The credit payment limit is the monthly limit for 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] 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 where the merchant ID and store ID are associated with the store URL, a second table 176B where the merchant name, sales amount (as described above), and category are associated with the merchant ID, and a third table 176C where the store name is associated with the store ID. The category is, for example, information indicating the type of business the merchant is engaged in. The category may be assigned on a store-by-store basis rather than on a merchant-by-store basis. In addition to this information, the merchant / store information 176 may also include information such as the store's location and payment patterns.

[0027] The Information Management Unit 140 manages user information 172 and affiliated store / store information 176 based on information obtained from the user terminal device 10 and the second store terminal device 70. The Information Management Unit 140 performs operations such as adding, editing, and deleting new records for user information 172 and affiliated store / store information 176.

[0028] The information processing unit 150 estimates the user's reference location (in this embodiment, the address location) based on multiple payment histories for each user included in the user information 172. The information processing unit 150 further includes an acquisition unit 152, a clustering unit 154, an estimation unit 156, and an output unit 158, the details of which will be described later.

[0029] [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.

[0030] The payment processing unit 130 performs electronic payment as follows for users whose "settings information" is set to "credit payment". Credit payment is a payment method in cooperation with a credit card company, which is a separate entity from the electronic payment service operator. The electronic payment service operator acts as the donor and allows electronic payment within the credit limit, without relying on the charge balance. In order to use the credit payment service, users may be required to obtain a credit card provided by the electronic payment service operator. 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 credit card company operator 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.

[0031] [Estimating the user's address] In this way, users can make electronic payments at stores of merchants participating in the electronic payment service using the payment application 20 installed on the user terminal device 10. When using the electronic payment service, users must verify their identity in advance using an official identification document (such as a driver's license or IC card identification) and register information such as their name, address, and date of birth as user information 172 with the payment server 100. However, unlike fixed information such as the date of birth, information related to a reference location, such as an address, may change, for example, due to the user moving. Therefore, it is preferable for the electronic payment service to be able to estimate the user's address from user information collected in advance (for example, payment history) and take some action (for example, notifying the user or monitoring by an administrator) if the estimated address differs from the registered address. The present invention addresses such problems.

[0032] Figure 7 shows an example of a user's payment history acquired by the acquisition unit 152. The acquisition unit 152 acquires a predetermined number of recent data entries from the payment history of user information 172 obtained when a user makes an electronic payment at a merchant of the electronic payment service. Once the acquisition unit 152 has acquired the predetermined number of payment history entries, it identifies the location where the payment corresponding to each payment history was made. More specifically, when a user makes an electronic payment, the payment history of user information 172 related to that user stores the store ID and date and time of the store where the user made the electronic payment. Therefore, the acquisition unit 152 can identify the location information where the user made the electronic payment by referring to the date and time stored in the payment history, acquiring the store ID of the most recent predetermined number of electronic payments, and acquiring the longitude and latitude information of the "location" corresponding to that store ID stored in the third table 176C. Figure 7 shows the state in which each identified location information of the payment history is mapped to map information as point P.

[0033] Alternatively, the acquisition unit 152 may acquire longitude and latitude information directly from the user terminal device 10, rather than from the longitude and latitude information of the store where the user made the electronic payment. More specifically, the user terminal device 10 may be equipped with a GNSS positioning function, and when the user makes an electronic payment using the payment application 20, the payment application 20 may cause the user terminal device 10 to perform GNSS positioning by communicating with GNSS satellites, transmit the measured longitude and latitude information to the payment server 100, and store it in the payment history of the user information 172.

[0034] The clustering unit 154 clusters the user's payment history acquired by the acquisition unit 152 based on location information. First, the clustering unit 154 applies DBSCAN (Density-Based Spatial Clustering of Applications with Noise), a density-based clustering method for detecting outliers, to the location information of the payment history as an algorithm for eliminating outliers, and clusters this location information. As a result, a predetermined number of payment history entries are clustered into one or more clusters. Figure 7 shows, as an example, the result of clustering the payment history by the clustering unit 154, resulting in three clusters CL1 to CL3.

[0035] When clustering is performed by the clustering unit 154, the estimation unit 156 first creates cluster information 178 for the users whose addresses are to be estimated. The cluster information 178 is used as input information to the trained model 180, which will be described later, and serves as a feature for estimating the user's address. Hereinafter, the cluster representing the user's address may be referred to as the "home cluster" for convenience.

[0036] [Cluster Information] Figure 8 shows an example of the contents of cluster information 178 created by the estimation unit 156. Cluster information 178 associates information such as cluster ID, cluster location information, payment statistics information, inter-cluster ranking information, and external statistics usage information with an account ID. The cluster ID is identification information that identifies clusters when multiple clusters obtained by the clustering unit 154 exist for a single user. For example, in Figure 7, clusters CL1 to CL3 will each be assigned a different cluster ID.

[0037] Cluster location information indicates the location of each cluster. For example, cluster location information represents the coordinates of the center of each circular cluster, expressed in terms of longitude and latitude. Alternatively, cluster location information may include the coordinates of the center of each cluster, or it may be regional information (such as postal districts) relating to the area with the widest overlap.

[0038] Payment statistics are statistical information obtained by applying statistical processing to the payment history included in each cluster. More specifically, for example, payment statistics may include statistical values ​​such as the mean, median, and total of payment amounts included in the payment history, or the mean, median, and total of payment amounts (by merchant, store, or category). In particular, payment statistics may include statistical values ​​such as the mean, median, and total of payment amounts for specific categories included in the payment history (for example, categories of merchants that sell daily necessities for users, such as "food" or "supermarket"). This is based on the inventor's empirical finding that the larger the amount spent in a specific category, the more likely that the cluster is to be a home cluster.

[0039] Furthermore, for example, payment statistics may include the standard deviation of payment times included in the payment history. In this case, the standard deviation of payment times is obtained by representing the payment time of each payment with a coordinate vector (cosθ, sinθ) on a unit circle where 0:00 is 0 and 24:00 is 2π, and calculating the standard deviation of these coordinate vectors (angle statistics). This is based on the inventor's empirical findings that the larger the standard deviation of payment times, the more dispersed the users' payment times are (for example, users are making payments within their cluster in the morning and evening), and that such cluster is likely to be a home cluster.

[0040] Furthermore, for example, the payment statistics information may include the cosine or sine component of the average payment time included in the payment history. This is based on the inventor's empirical findings that the larger the cosine component of the average payment time, and the smaller the sine component, the more likely users are to be making payments at night, close to midnight or 12 AM, and that such clusters are likely to be home clusters.

[0041] The inter-cluster ranking information is information that indicates the ranking among multiple clusters obtained by the clustering unit 154, based on a predetermined index. More specifically, for example, the inter-cluster ranking information may be information that ranks each cluster based on the number of payment history data (i.e., the number of payments). This is based on the inventor's empirical findings that the more payments a user makes, the more frequently the user is involved with that cluster, and the more likely that the cluster is their home cluster.

[0042] Furthermore, for example, the inter-cluster ranking information may be information that ranks statistical values ​​such as the total amount of payments in the payment history included in each cluster. This is based on the inventor's empirical findings that the higher the total amount of payments made by a user, the more deeply the user is involved with that cluster, and the more likely that the cluster is a home cluster.

[0043] In this way, by inputting ranking information for multiple clusters, in addition to settlement statistics for a single cluster, into the pre-trained model 180 described later, the accuracy of home cluster estimation can be improved.

[0044] External statistical information is information obtained by processing external statistical information distributed by external organizations (e.g., public institutions such as government agencies) that are different from the electronic payment service. In this embodiment, the external statistical information is assumed to be mesh data in which identifiers indicating the intended use (e.g., 3 = road, 6 = low-rise building, etc.) are linked to meshes that subdivide land. The external statistical information is obtained by processing such mesh data in order to represent the characteristics of each cluster.

[0045] Figure 9 is a diagram illustrating how to create external statistical usage information to be included in cluster information 178. In Figure 9, the symbol M represents one or more mesh areas included in (or partially overlapping) cluster CL. As shown in Figure 9, each mesh area M is pre-associated with its intended use. Therefore, the estimation unit 156 counts the intended use of the mesh areas M included in each cluster CL, for example, and creates the count as external statistical usage information. In the case of Figure 9, cluster CL contains six mesh M for "low-rise buildings" and three mesh M for "roads," so the estimation unit 156 creates "low-rise buildings: 6" and "roads: 3" as external statistical usage information.

[0046] The estimation unit 156 may aggregate data for all usage purposes or for specific usage purposes only in order to create external statistical usage information. For example, the estimation unit 156 may aggregate data only for specific usage purposes, such as "low-rise buildings" or "roads," for which a correlation between their number and the likelihood of them being home clusters has been empirically confirmed, and create external statistical usage information.

[0047] In this way, by inputting payment statistics and inter-cluster ranking information created from payment history collected by electronic payment services, as well as external statistical utilization information obtained by processing external statistical information, into the trained model 180 described later, the accuracy of home cluster estimation can be improved.

[0048] Furthermore, cluster information 178 may not include all of the above-mentioned cluster location information, payment statistics information, inter-cluster ranking information, and external statistics usage information, but may include only some of them. In addition, cluster information 178 may include other types of data. For example, cluster information 178 may include data representing the user's personal attributes, such as the user's date of birth, occupation, and gender.

[0049] [Pre-trained model] The estimation unit 156 creates cluster information 178 for the user to be estimated, inputs the created cluster information 178 into the trained model 180, and obtains the output result as estimation result information 182. Figure 10 shows an example of the contents of estimation result information 182. Estimation result information 182 is, for example, information indicating whether the cluster indicated by a given cluster ID is a home cluster for a given combination of account ID and cluster ID. For example, in the case of Figure 10, among the clusters indicated by cluster IDs 001 to 003, the cluster indicated by cluster ID 001 is a home cluster, while the clusters indicated by cluster IDs 002 and 003 are not home clusters.

[0050] Figure 11 is a diagram illustrating the method for generating the trained model 180. First, prior to generating the trained model 180, the administrator of the electronic payment service prepares cluster information 178, which will serve as training data, for each user of the electronic payment service. Here, the users for whom cluster information 178 is prepared are those who have completed identity verification, including address information, on the electronic payment service. Furthermore, the users for whom cluster information 178 is prepared may be limited to those who have made a predetermined number of payments (e.g., 20 times) or more within a predetermined distance (e.g., 1500m) on the electronic payment service during a predetermined period (e.g., 1 month).

[0051] Next, the administrator of the electronic payment service compares the cluster location information of each cluster included in the cluster information 178 with the user's address information. The administrator assigns a value of 1 to the cluster whose location information and address information are closest and whose distance is within a threshold, indicating that it is the home cluster, while assigning a value of 0 to all other clusters, indicating that it is not the home cluster. The user's address information compared here may be the location of the user's address itself, or the central location of the postal district that includes the user's address. This yields cluster information 178 as input for machine learning and flag information regarding home clusters as output. Next, the administrator of the electronic payment service obtains a trained model 180 by having a machine learning model, such as a deep neural network (DNN), learn the correspondence between the cluster information 178 and the home cluster flag information.

[0052] In this embodiment, as an example, the trained model 180 is trained to output an identifier (i.e., 1) indicating the home cluster in response to input from one or more clusters. However, the present invention is not limited to such a configuration, and for example, the trained model 180 may be trained to output a probability value that each cluster is the home cluster in response to input from one or more clusters.

[0053] [Notification Information] The output unit 158 ​​outputs notification information regarding a discrepancy between the address information registered by the user with the electronic payment service and the location information of the cluster estimated to be the home cluster by the estimation unit 156 (more precisely, when the distance exceeds a threshold). In this case, the output unit 158 ​​may output the notification information to the user terminal device 10 of the user where the discrepancy occurred, or to the administrator terminal device of the administrator of the electronic payment service.

[0054] Figure 12 shows an example of notification information output by the output unit 158. As an example, Figure 12 shows the case where the output unit 158 ​​outputs notification information to the user terminal device 10. For example, if a discrepancy occurs for a certain user, the output unit 158 ​​outputs and displays a pop-up notification screen IM1 on the user terminal device 10 requesting an address update. The pop-up notification screen IM1 includes, for example, a button B1 to select an address update and a button B2 to not select an address update. If the user presses button B1, the payment application 20 transitions to an update screen for the user to update their address, while if the user presses button B2, the payment application 20 dismisses the pop-up notification screen IM1. This allows the application to prompt a user to update their registered address for the electronic payment service if, for example, the user has moved and forgotten to update their address.

[0055] In another embodiment, the output unit 158 ​​may output a command value to the user terminal device 10 that restricts the use of the payment application 20, rather than being limited to notification information such as the pop-up notification screen IM1. For example, the output unit 158 ​​may output a command value to the user terminal device 10 that restricts the use of the payment application 20's money transfer function (for example, the function to transfer the charge balance from one user to another user). As a stepwise control, the output unit 158 ​​may output notification information to the user terminal device 10 if the deviation is above the first threshold and below the second threshold, while outputting a command value to the user terminal device 10 that restricts the use of the payment application 20 if the deviation is above the second threshold. This makes it possible to deter a user from committing a financial crime if they intend to do so with malicious intent.

[0056] Figure 13 shows another example of notification information output by the output unit 158. As an example, Figure 13 shows a case where the output unit 158 ​​outputs notification information to the administrator terminal device of the electronic payment service. For example, the output unit 158 ​​outputs a list of users of the electronic payment service who have experienced discrepancies to the administrator terminal device. As shown in Figure 13, in addition to the account ID of the user who experienced a discrepancy, the output unit 158 ​​may also display the registered address, estimated address, and the degree of discrepancy (distance, etc.) between the registered address and the estimated address. This allows the administrator of the electronic payment service to identify users of the electronic payment service who may have forgotten to update their address or who may be engaging in financial crimes.

[0057] [Process Flow] Figure 14 is a flowchart showing an example of the processing flow performed by the information processing unit 150. First, the acquisition unit 152 acquires a predetermined number of payment histories of users of the electronic payment service whose addresses are to be estimated (step S100). Next, the clustering unit 154 clusters the acquired predetermined number of payment histories (step S102).

[0058] Next, the estimation unit 156 inputs cluster information for one or more clusters obtained by clustering into the trained model to estimate the user's address (step S104). Next, the output unit 158 ​​determines whether or not there is a discrepancy between the address registered with the electronic payment service and the estimated address (step S106). If it is determined that there is no discrepancy between the address registered with the electronic payment service and the estimated address, the information processing unit 150 terminates the processing of this flowchart. On the other hand, if it is determined that there is a discrepancy between the address registered with the electronic payment service and the estimated address, the output unit 158 ​​outputs notification information regarding the user's address (step S108). This terminates the processing of this flowchart.

[0059] In this embodiment, the case where the reference location is the user's residential address was described as an example. However, the present invention is not limited to such a configuration, and the reference location may be another location, such as the user's workplace address. Even when the reference location is the user's workplace address, the same processing as in the above embodiment can be performed, for example, by authenticating an official document that lists the user's workplace and using it as ground truth data for the machine learning model.

[0060] According to the embodiment described above, a predetermined number of payment histories of electronic payments made by a user at a merchant of an electronic payment service are acquired, the predetermined number of payment histories are clustered, and cluster information for one or more clusters obtained by clustering is input into a trained model to estimate the cluster that indicates the user's reference location from among the one or more clusters. This makes it possible to provide an information processing device, an information processing method, and a program that can estimate the reference location of a user, such as their place of residence.

[0061] 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]

[0062] 10. User terminal device 20 Payment Apps 100 Payment Servers 110 Communications Department 120 Payment Content Provision Department 130 Payment Processing Unit 140 Information Management Department 150 Information Processing Unit 152 Acquisition Department 154 Clustering section 156 Estimation Department 158 Output section

Claims

1. An acquisition unit that acquires a predetermined number of payment histories of electronic payments made by users at merchants participating in the electronic payment service, A clustering unit that clusters the predetermined number of payment histories, The system includes an estimation unit that estimates the cluster representing the user's reference position among the multiple clusters obtained by the aforementioned clustering by inputting cluster information relating to the multiple clusters into a trained model, The cluster information includes inter-cluster ranking information that ranks the multiple clusters with respect to the payments included in the payment history. The aforementioned inter-cluster ranking information includes ranking information obtained by ranking multiple clusters such that the higher the total number or amount of transactions included in the payment history, the higher the ranking. The aforementioned trained model is trained such that clusters with higher rankings are more likely to be output as clusters indicating the user's reference position. Information processing device.

2. The cluster information includes at least one of the following: location information of the cluster, settlement statistics information relating to the settlement included in the settlement history, and external statistics utilization information obtained by processing external statistics information. The information processing apparatus according to claim 1.

3. The settlement statistics include the standard deviation of the settlement times included in the settlement history. The information processing apparatus according to claim 2.

4. The aforementioned external statistical information associates identifiers indicating land use with meshes that subdivide the land, The aforementioned external statistical information is an aggregate of usage purposes associated with the mesh contained in each of the multiple clusters. The information processing apparatus according to claim 2.

5. The system further includes an output unit that outputs notification information regarding a discrepancy between a reference position registered by the user with the electronic payment service and a reference position indicated by the estimated cluster. The information processing apparatus according to claim 1.

6. The output unit outputs notification information to the user terminal device of the user in whom the discrepancy is determined to exist, requesting confirmation of the user's reference position. The information processing apparatus according to claim 5.

7. The output unit outputs a list of users for whom the discrepancy is determined to exist to the administrator terminal device of the electronic payment service. The information processing apparatus according to claim 5.

8. Computers The system acquires a predetermined number of payment histories of electronic payments made by users at merchants participating in the electronic payment service. The predetermined number of payment histories are clustered, By inputting cluster information about multiple clusters obtained through the aforementioned clustering into the trained model, the cluster representing the user's reference location is estimated from among the multiple clusters. The cluster information includes inter-cluster ranking information that ranks the multiple clusters with respect to the payments included in the payment history. The aforementioned inter-cluster ranking information includes ranking information obtained by ranking multiple clusters such that the higher the total number or amount of transactions included in the payment history, the higher the ranking. The aforementioned trained model is trained such that clusters with higher rankings are more likely to be output as clusters indicating the user's reference position. Information processing methods.

9. On the computer, The electronic payment service provider will obtain a predetermined number of payment histories of electronic payments made by users at participating merchants. The predetermined number of payment histories are clustered, By inputting cluster information about the multiple clusters obtained by the aforementioned clustering into the trained model, the model estimates the cluster that represents the user's reference location among the multiple clusters. The cluster information includes inter-cluster ranking information that ranks the multiple clusters with respect to the payments included in the payment history. The aforementioned inter-cluster ranking information includes ranking information obtained by ranking multiple clusters such that the higher the total number or amount of transactions included in the payment history, the higher the ranking. The aforementioned trained model is trained such that clusters with higher rankings are more likely to be output as clusters indicating the user's reference position. program.

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