JP7671911B1 - Information processing device, information processing method, and program - Google Patents

Abstract

An information processing device, an information processing method, and a program are provided that are capable of estimating a reference position such as a user's residential location.
[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 affiliated stores of an electronic payment service; a clustering unit that clusters the predetermined number of payment histories; and an estimation unit that estimates a cluster from the one or more clusters that indicates the user's reference position by inputting cluster information regarding one or more clusters obtained by the clustering into a trained model.
[Selected Figure] Figure 1

Description

The present invention relates to an information processing device, an information processing method, and a program.

Conventionally, there is known a technology for analyzing the range of a user's activities based on the user's location information. For example, Patent Literature 1 discloses a technology for identifying the residence of a user who uses a target facility (such as a store) based on route data that starts from the user's residence and ends at a local base (such as a train station), and generating a collection of identified residence points as a facility usage area.

JP 2021-033812 A

The above-mentioned conventional technology identifies the facility usage area of a user on the assumption that the user's residence location is known. However, in the actual operation of the service, the user's residence location is not always known, and with the conventional technology, it is sometimes difficult to estimate the reference position of the user's residence location, etc.

The present invention has been made in consideration of these circumstances, and one of its objectives is to provide an information processing device, information processing method, and program that can estimate a reference position, such as a user's residential location.

One aspect of the present invention is an information processing device that includes an acquisition unit that acquires a predetermined number of payment histories in which a user makes electronic payments at affiliated stores of 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 from among the one or more clusters by inputting cluster information related to one or more clusters obtained by the clustering into a trained model.

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 position such as a user's residence location.

FIG. 1 is a diagram showing an example of a configuration for realizing an electronic payment service. FIG. 1 is a sequence diagram (part 1) illustrating an example of a general flow of electronic payment. FIG. 2 is a sequence diagram (part 2) illustrating an example of a general flow of electronic payment. FIG. 2 is a configuration diagram of a payment server 100 according to the first embodiment. FIG. 11 is a diagram showing an example of the contents of user information 172. FIG. 13 is a diagram showing an example of the contents of affiliated store/store information 176. 13 is a diagram showing an example of a user's payment history acquired by the acquisition unit 152 and clustered by the clustering unit 154. FIG. FIG. 13 is a diagram showing an example of the contents of cluster information 178 created by an estimation unit 156. 13 is a diagram for explaining a method of creating external statistics utilization information to be included in cluster information 178. FIG. A figure showing an example of the contents of estimation result information 182. A figure for explaining a method of generating a trained model 180. FIG. 11 is a diagram showing an example of notification information output by an output unit 158. 13 is a diagram showing another example of notification information output by the output unit 158. FIG. 10 is a flowchart showing an example of the flow of processing executed by an information processing unit 150.

Hereinafter, with reference to the drawings, an embodiment of an information processing 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", "management device", and "information providing device" that will appear below, may be realized by a group of distributed devices, and each device may be operated by a different business operator. In addition, the owner of the hardware of the device (the cloud server provider) and the business that actually operates it 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 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. The virtual store may include one provided by an entity other than the operator of the electronic payment service. In that case, when making a payment for shopping at a virtual store, the user 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.

[Electronic payment service]
1 is a diagram showing an example of a configuration for realizing an electronic payment service. The electronic payment service is realized mainly by a payment server 100. 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, a provider device, and the like.

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, the components for realizing these functions are referred to as a camera, a communication device, a touch panel, a CPU (Central Processing Unit), etc. In the user terminal device 10, a processor such as a CPU executes a payment app 20, which operates in cooperation with the payment server 100 to provide electronic payment services to users. The payment app 20 is installed in the user terminal device 10 from, for example, an application store, and controls the camera, communication device, touch panel, etc.

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).

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, etc. 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 equivalent to a store code image and reading a code image displayed by the user terminal device 10 by executing the app for affiliated stores.

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 to the payment server 100 via the affiliated store server. 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.

Figures 2 and 3 are sequence diagrams illustrating the general flow of electronic payment. There may be two types of electronic payment: pattern 1 and pattern 2.

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 the domain of the electronic payment service to which information capable of identifying the store is added, and is associated with the affiliated store ID, 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 store ID corresponding to the store URL, acquires the affiliated store name and 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 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 procedure in which the user inputs the payment amount is omitted, and the first payment information is transmitted to the payment server 100 with the information on the payment amount included. Information on the affiliated store name and store name may be included and displayed on the payment completion screen.

In the case of pattern 2 (hereinafter referred to as store scan) shown in FIG. 3, when the payment app 20 is started, when a payment operation is performed in the payment app 20, when the automatic update timing (for example, every minute) occurs, and at other timings, the payment app 20 sends a request to issue a one-time code to the payment server 100 (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 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 using an 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, affiliated store ID, store ID, etc., and sends it to the payment server 100 (S16). The payment amount information is acquired in advance by reading a barcode or manually entering it. Based on the received information, the payment server 100 identifies the user corresponding to the one-time code and performs electronic payment (S17). The payment server 100 then sends a payment completion notice to the payment application 20 (S18), and the payment application 20 displays a payment completion screen (S19).

Note that electronic payment may be made 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 app 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.

[Payment server]
4 is a configuration diagram of the payment server 100 according to the first embodiment. The payment server 100 includes, for example, a communication unit 110, a payment content providing unit 120, a payment processing unit 130, an information management unit 140, an information processing unit 150, 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), or a GPU (Graphics Processing Unit), or may be realized by cooperation between software and hardware. The program may be stored in advance in a storage device (a storage device having a non-transient storage medium) such as a hard disk drive (HDD) or a flash memory, or may be stored in a removable storage medium (a non-transient storage medium) such as a DVD or a CD-ROM, and installed in the storage device by mounting the storage medium in a drive device. The function of the information processing unit 150 in the payment server 100 is an example of an "information processing device" in the claims.

The storage unit 170 is a HDD, flash memory, RAM (Random Access Memory), etc. 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, payment content information 174, affiliated store/shop information 176, cluster information 178, trained model 180, and estimation result information 182.

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.

The payment content providing unit 120 has, for example, a web server function, and provides information (content) for displaying various screens of the electronic payment service to the user terminal device 10. The payment content providing unit 120 reads out necessary content from the payment 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 aforementioned payment information and the like to the payment server 100.

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.

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, a charge history information, and a 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). The email address, and the name, address, and date of birth are also 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 instance (electronic payment account) to which this information is associated will be referred to as an account.

The charge balance is information indicating the balance of electronic money that is set by the user by transferring money to the account in advance. The means of transfer include transfer from an ATM (Automatic Teller Machine) of a designated company (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 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. In the figure, only one credit payment limit is shown, but 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 credit payment will be described later. The payment method setting is setting information indicating whether the user will make electronic payment using the charge balance or 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. The charge history information is a history of the user transferring funds to the electronic payment service in advance to increase the charge balance. The 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.).

Figure 6 is a diagram showing an example of the contents of affiliated store/store information 176. 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 ID is associated with an affiliated store name, sales amount (described above), and category, and a third table 176C in which a store ID is associated with a store name. The category is, for example, information indicating the business type of the affiliated store. Categories may be assigned on a store-by-store basis rather than on an affiliated store basis. In addition to this information, affiliated store/store information 176 may also include information such as the store's location and payment pattern.

The information management unit 140 manages the user information 172 and the 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, edits, deletes, etc. for the user information 172 and the affiliated store/store information 176.

The information processing unit 150 estimates the reference location (in this embodiment, the address location) of the user 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 functions of which will be described in detail later.

[Electronic Payment]
When payment information is obtained 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 obtain 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 itself used as electronic money, 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.

The payment processing unit 130 performs electronic payment for users whose "setting information" is set to "credit card payment" as follows. Credit card 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 the creditor and allows electronic payment that is not dependent on the charge balance within the credit card payment limit. In order to receive the credit card payment service, a credit card provided by the operator of the electronic payment service may be required. The amount used in the credit card 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 card payment amount and subtracting the same amount from the available credit card 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 card payment amount at the time of provisional payment, an error notification is returned to the payment application 20.

[User address estimation]
In this way, the user can use the payment application 20 installed in the user terminal device 10 to make electronic payments at stores affiliated with the electronic payment service. When using the electronic payment service, the user must first verify his/her identity using an official identification card (such as a driver's license or an IC card), and register information such as his/her name, address, and date of birth as user information 172 in the payment server 100. However, unlike fixed information such as a date of birth, information on a reference location such as an address may change, for example, when the user moves. Therefore, it is preferable that the electronic payment service estimates the user's address from user information collected in advance (such as a payment history), and if the estimated address differs from the registered address, it is preferable that some kind of action (such as a notice to the user or monitoring by an administrator) be taken. The present invention addresses such a problem.

7 is a diagram showing an example of a user's payment history acquired by the acquisition unit 152. The acquisition unit 152 acquires a predetermined number of pieces of data from the payment history of the user information 172 obtained when the user makes an electronic payment at a member store of the electronic payment service. When the acquisition unit 152 acquires a predetermined number of pieces of payment history, it identifies the location where the payment corresponding to each payment history was made. More specifically, when a user makes an electronic payment, the store ID of the store where the user made the electronic payment and the date and time are stored in the payment history of the user information 172 related to the user. Therefore, the acquisition unit 152 can acquire the predetermined number of store IDs where the electronic payment was made by referring to the date and time stored in the payment history, and acquire the longitude and latitude information of the "location" corresponding to the store ID stored in the third table 176C, thereby identifying the location information where the user made the electronic payment. FIG. 7 shows a state in which each piece of location information of the identified payment history is mapped to map information as a point P.

Alternatively, the acquisition unit 152 may acquire the longitude and latitude information directly from the user terminal device 10, instead of the longitude and latitude information of the store where the user made the electronic payment. More specifically, the user terminal device 10 has a GNSS positioning function, and when the user makes an electronic payment using the payment app 20, the payment app 20 may cause the user terminal device 10 to perform GNSS positioning by communicating with a GNSS satellite, and transmit the measured longitude and latitude information to the payment server 100, where it may be stored in the payment history of the user information 172.

The clustering unit 154 clusters the user's payment history acquired by the acquisition unit 152 based on the 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 the location information. As a result, a predetermined number of payment histories are clustered into one or more clusters. Figure 7 shows, as an example, a state in which three clusters CL1 to CL3 are obtained as a result of clustering the payment histories by the clustering unit 154.

When clustering is performed by the clustering unit 154, the estimation unit 156 first creates cluster information 178 for the user whose address is to be estimated. The cluster information 178 is used as input information for the trained model 180 described below, and serves as a feature for estimating the user's address. Hereinafter, for convenience, the cluster indicating the user's address may be referred to as the "home cluster."

[Cluster Information]
Fig. 8 is a diagram showing an example of the contents of cluster information 178 created by the estimation unit 156. In the cluster information 178, for example, information such as a cluster ID, cluster location information, payment statistics information, inter-cluster ranking information, and external statistics usage information is associated with an account ID. The cluster ID is identification information for identifying multiple clusters obtained by the clustering unit 154 for a single user. For example, in the case of Fig. 7, clusters CL1 to CL3 are assigned different cluster IDs.

Cluster location information is information that indicates the location of each cluster. For example, the cluster location information is the central coordinates of each circular cluster expressed in terms of longitude and latitude. Alternatively, the cluster location information may include the central coordinates of each cluster, or may be regional information (such as a postal district) regarding the area with the widest overlapping range.

The payment statistics information is statistical information obtained by performing statistical processing on the payment history included in each cluster. More specifically, for example, the payment statistics information may include statistical values such as the average, median, or total of the payment amounts included in the payment history, or the average, median, or total of the payment amounts (by affiliated store, shop, or category). In particular, the payment statistics information may include statistical values such as the average, median, or total of the payment amounts of a specific category included in the payment history (for example, the category of affiliated stores that sell the user's daily necessities, such as "food" or "supermarket"). This is based on the empirical findings of the inventor of the present invention that the larger the amount used in a specific category, the more likely that the cluster is a home cluster.

Furthermore, for example, the payment statistics information may include the standard deviation of the payment times included in the payment history. In this case, the standard deviation of the payment times is obtained by, for example, expressing the payment time of each payment as a coordinate vector (cosθ, sinθ) on a unit circle where midnight is zero and 24:00 is 2π, and calculating the standard deviation of these coordinate vectors (angle statistics). This is based on the empirical findings of the inventor of the present invention that the larger the standard deviation of the payment times, the more dispersed the users' payment times are (for example, users make payments within the cluster in the morning and evening), and the more likely the cluster is to be a home cluster.

For example, the payment statistics information may also include the cosine or sinue component of the average value of the payment times included in the payment history. This is based on the inventor's empirical findings that the larger the cosine component of the average value of the payment times, and the smaller the sinue component, the more likely it is that the user is making payments at night, close to midnight or midnight, and that the cluster in question is more likely to be a home cluster.

When multiple clusters are obtained by the clustering unit 154, the inter-cluster ranking information is information indicating the order in which the multiple clusters are ranked based on a predetermined index. More specifically, for example, the inter-cluster ranking information may be information that ranks the number of payment history data items contained in each cluster (i.e., the number of payments). This is based on the empirical findings of the inventor of the present invention 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 a home cluster.

For example, the inter-cluster ranking information may be information that ranks statistical values such as the total payment amounts of the payment histories included in each cluster. This is based on the inventor's empirical findings that the higher the total payment amount of a user, the more deeply the user is involved in that cluster, and the more likely that the cluster is a home cluster.

In this way, by inputting ranking information for multiple clusters in addition to payment statistics information for a single cluster into the trained model 180 described below, the accuracy of home cluster estimation can be improved.

External statistical usage information is information obtained by processing external statistical information distributed by an external organization (e.g., a public organization such as a government agency) different from the electronic payment service. In this embodiment, the external statistical information is mesh data in which meshes into which land is subdivided are linked with identifiers indicating their uses (e.g., 3 = road, 6 = low-rise building, etc.). External statistical usage information is obtained by processing such mesh data in order to express the characteristics of each cluster.

Figure 9 is a diagram for explaining a method of creating 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 with) cluster CL. As shown in Figure 9, each mesh area M is pre-associated with its intended use. Therefore, the estimation unit 156, for example, counts the intended use of the mesh areas M included in each cluster CL, and creates the count number as external statistical usage information. In the case of Figure 9, cluster CL includes six meshes M for "low-rise buildings" and three meshes M for "roads", so the estimation unit 156 creates "low-rise buildings: 6" and "roads: 3" as external statistical usage information.

To create the external statistical usage information, the estimation unit 156 may tally up data for all uses, or may tally up data for only specific uses. For example, the estimation unit 156 may tally up data for only specific uses for which a correlation between the number of uses and the possibility of them being a home cluster has been empirically confirmed, such as the above-mentioned "low-rise buildings" and "roads," to create the external statistical usage information.

In this way, by inputting the external statistical usage information obtained by processing the external statistical information, in addition to the payment statistical information and inter-cluster ranking information created from the payment history collected by the electronic payment service, into the trained model 180 described below, the estimation accuracy of the home cluster can be improved.

Note that cluster information 178 may not include all of the cluster location information, payment statistics information, inter-cluster ranking information, and external statistics usage information described above, but may include only some of them. Furthermore, cluster information 178 may additionally include different types of data. For example, cluster information 178 may include data representing personal attributes of a user, such as the user's date of birth, occupation, and gender.

[Trained model]
When the estimation unit 156 creates cluster information 178 for the user to be estimated, it inputs the created cluster information 178 to the trained model 180 and obtains an output result as estimation result information 182. FIG. 10 is a diagram showing an example of the contents of the estimation result information 182. The estimation result information 182 is, for example, information indicating whether or not a cluster indicated by a cluster ID in relation to a combination of an account ID and a cluster ID is a home cluster. For example, in the case of FIG. 10, of the clusters indicated by cluster IDs 001 to 003, the cluster indicated by cluster ID 001 indicates that it is a home cluster, while the clusters indicated by cluster IDs 002 and 003 indicate that they are not home clusters.

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

Next, the administrator of the electronic payment service compares the cluster location information of each cluster contained in the cluster information 178 with the address information of the user, and assigns a value of 1 to the cluster whose cluster location information and address information are closest and whose distance is within a threshold, indicating that it is a home cluster, while assigning a value of 0 to other clusters, indicating that they are not home clusters. The address information of the user compared here may be the location of the user's address itself, or the center location of the postal district including the user's address. In this way, the administrator of the electronic payment service obtains the cluster information 178 as an input to be used in machine learning, and flag information related to the home cluster as an output. Next, the administrator of the electronic payment service obtains the 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.

In this embodiment, as an example, the trained model 180 is trained to output an identifier (i.e., 1) indicating a cluster that is a home cluster in response to the input of 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 a home cluster in response to the input of one or more clusters.

[Notification information]
When there is a discrepancy between the address information registered by the user in 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 is equal to or greater than a threshold), the output unit 158 outputs notification information regarding the discrepancy. In this case, the output unit 158 may output the notification information to the user terminal device 10 of the user in which the discrepancy occurred, or to the administrator terminal device of the administrator of the electronic payment service.

FIG. 12 is a diagram showing an example of notification information output by the output unit 158. FIG. 12 shows, as an example, a case where the output unit 158 outputs notification information to the user terminal device 10. For example, when a discrepancy occurs for a certain user, the output unit 158 outputs and displays a pop-up notification screen IM1 to the user terminal device 10, requesting an address update. The pop-up notification screen IM1 includes, for example, a button B1 for selecting to update the address and a button B2 for not selecting to update the address. When the user presses button B1, the payment application 20 transitions to an update screen for the user to update the address, whereas when the user presses button B2, the payment application 20 erases the pop-up notification screen IM1. This makes it possible to prompt the user to update the address registered in the electronic payment service if the user has moved, for example, and has forgotten to update the address.

In another embodiment, the output unit 158 may output to the user terminal device 10 a command value that restricts the use of the payment app 20, without being limited to notification information such as the pop-up notification screen IM1. For example, the output unit 158 may output to the user terminal device 10 a command value that restricts the use of a remittance function of the payment app 20 (e.g., a function for transferring a 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 when the deviation is equal to or greater than a first threshold and less than a second threshold, while outputting to the user terminal device 10 a command value that restricts the use of the payment app 20 when the deviation is equal to or greater than the second threshold. This makes it possible to suppress a user who maliciously intends to commit a financial crime from committing the crime.

Figure 13 is a diagram showing another example of notification information output by the output unit 158. Figure 13 shows, as an example, a case where the output unit 158 outputs notification information to an administrator terminal device of the electronic payment service. The output unit 158 outputs, for example, a list of users of the electronic payment service in which a discrepancy has occurred to the administrator terminal device. As shown in Figure 13, the output unit 158 may display the registered address, estimated address, and the degree of discrepancy between the registered address and the estimated address (such as distance) in addition to the account ID of the user in which a discrepancy has occurred. This allows the administrator of the electronic payment service to identify users of the electronic payment service who have forgotten to update their addresses or who may be committing financial crimes.

[Processing flow]
14 is a flowchart showing an example of the flow of processing executed by the information processing unit 150. First, the acquisition unit 152 acquires a predetermined number of payment histories of a user of the electronic payment service whose address is to be estimated (step S100). Next, the clustering unit 154 clusters the acquired predetermined number of payment histories (step S102).

Next, the estimation unit 156 inputs cluster information related to 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 in the electronic payment service and the estimated address (step S106). If it is determined that there is no discrepancy between the address registered in the electronic payment service and the estimated address, the information processing unit 150 ends the processing of this flowchart. On the other hand, if it is determined that there is a discrepancy between the address registered in the electronic payment service and the estimated address, the output unit 158 outputs notification information related to the user's address (step S108). This ends the processing of this flowchart.

In this embodiment, as an example, a case where the reference location is the address where the user lives has been described. However, the present invention is not limited to such a configuration, and the reference location may be another location, such as the address of the user's workplace. Even if the reference location is the address of the user's workplace, it is possible to execute processing similar to that of the above embodiment, for example, by authenticating an official document in which the user's workplace is written and using it as correct answer data for the machine learning model.

According to the embodiment described above, a predetermined number of payment histories in which a user has made electronic payments at affiliated stores of the electronic payment service are acquired, the predetermined number of payment histories are clustered, and cluster information on one or more clusters obtained by the clustering is input to a trained model, thereby estimating a cluster indicating the user's reference location from among the one or more clusters. This makes it possible to provide an information processing device, information processing method, and program that can estimate a reference location such as the user's residential location.

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 without departing from the spirit of the present invention.

10 User terminal device 20 Payment application 100 Payment server 110 Communication unit 120 Payment content providing unit 130 Payment processing unit 140 Information management unit 150 Information processing unit 152 Acquisition unit 154 Clustering unit 156 Estimation unit 158 Output unit

Claims (9)

an acquisition unit that acquires a predetermined number of payment histories of electronic payments made by a user at a member store of the electronic payment service;
A clustering unit that clusters the predetermined number of payment histories;
an estimation unit that estimates a cluster indicating a reference position of the user from among the one or more clusters by inputting cluster information regarding the one or more clusters obtained by the clustering into a trained model ;
The cluster information is a feature related to the payment behavior of the user generated for input to the trained model, and includes at least payment amount statistical information obtained by applying statistical processing to the payment amounts included in the predetermined number of payment histories, or payment time statistical information which is a standard deviation obtained by applying statistical processing to the payment times included in the predetermined number of payment histories.
Information processing device. The cluster information includes at least one of location information of the cluster, inter-cluster ranking information that ranks the one or more clusters with respect to payments included in the payment history, and external statistical utilization information obtained by processing external statistical information.
The information processing device according to claim 1 . The inter-cluster ranking information includes ranking information obtained by ranking the total value of the number of payments or the amount of payments included in the payment history among the one or more clusters.
The information processing device according to claim 2 . The external statistical information is information in which an identifier indicating a use is associated with each mesh into which land is subdivided,
The external statistical usage information is a compilation of usage applications associated with the meshes included in each of the one or more clusters.
The information processing device according to claim 2 . and an output unit that outputs notification information regarding a deviation between the reference location registered by the user in the electronic payment service and the reference location indicated by the estimated cluster when the deviation occurs.
The information processing device according to claim 1 . The output unit outputs, to a user terminal device of the user for whom it is determined that the deviation exists, notification information requesting confirmation of the reference position of the user.
The information processing device according to claim 5 . The output unit outputs a list of users for whom it is determined that there is a discrepancy to an administrator terminal device of the electronic payment service.
The information processing device according to claim 5 . The computer
Acquire a predetermined number of payment histories of electronic payments made by the user at affiliated stores of the electronic payment service;
Clustering the predetermined number of payment histories;
inputting cluster information about the one or more clusters obtained by the clustering into a trained model, thereby estimating a cluster indicating a reference position of the user from among the one or more clusters ;
The cluster information is a feature related to the payment behavior of the user generated for input to the trained model, and includes at least payment amount statistical information obtained by applying statistical processing to the payment amounts included in the predetermined number of payment histories, or payment time statistical information which is a standard deviation obtained by applying statistical processing to the payment times included in the predetermined number of payment histories.
Information processing methods. On the computer,
Acquire a predetermined number of payment histories of electronic payments made by the user at affiliated stores of the electronic payment service;
clustering the predetermined number of payment histories;
inputting cluster information regarding the one or more clusters obtained by the clustering into a trained model, thereby estimating a cluster indicating a reference position of the user from among the one or more clusters;
The cluster information is a feature related to the payment behavior of the user generated for input to the trained model, and includes at least payment amount statistical information obtained by applying statistical processing to the payment amounts included in the predetermined number of payment histories, or payment time statistical information which is a standard deviation obtained by applying statistical processing to the payment times included in the predetermined number of payment histories.
program.

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