Information processing apparatus, information processing method, and program

Abstract

To provide an information processor, an information processing method and a program for analyzing the action range of the user of an electronic settlement service without limiting an area to which a technology can be applied and without excessively collecting sensitive information.SOLUTION: The information management unit 140 includes an acquiring unit 142 that acquires a predetermined number of pieces of position information of one or more users from a settlement history of electronic settlement performed by the one or more users in a member store of an electronic settlement service, a calculating unit 144 that calculates a behavioral area of the one or more users based on the position information, and a providing unit 146 that provides information related to the behavioral area to terminal terminal of the member store or terminal terminal of the users. terminal.SELECTED DRAWING: Figure 4

Description

[Technical Field]

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

[0002] Conventionally, there is known a technique for analyzing the range of a user's activities based on the location information of the user of a service. For example, Patent Document 1 discloses a technique for identifying the residence points 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 station), and generating a set of identified residence points as a facility usage area. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-033812 Summary of the Invention [Problem to be solved by the invention]

[0004] The above-mentioned conventional technology identifies a user's living area (i.e., route data) and, if the identified living area overlaps with the range of a target facility, includes the user's residential location in the facility usage area of ​​the target facility. However, because the conventional technology uses the user's residential location to analyze the location information of service users, it is difficult to apply the technology to, for example, analyzing the range of activity in business districts or downtown areas. In other words, the areas to which the technology can be applied may be limited. Furthermore, as described above, the conventional technology is premised on the collection of sensitive information, such as the user's residential information, and in reality, it may be difficult for businesses to implement the technology.

[0005] The present invention has been made in consideration of these circumstances, and one of its objectives is to provide an information processing device, an information processing method, and a program that can analyze the range of behavior of users of electronic payment services without limiting the areas to which the technology can be applied and without collecting excessive sensitive information. [Means for solving the problem]

[0006] One aspect of the present invention is an information processing device that includes an acquisition unit that acquires a predetermined number of pieces of location information for one or more users from the payment history of electronic payments made by the one or more users at a member store of an electronic payment service, a calculation unit that calculates the activity area of ​​the one or more users based on the location information, and a provision unit that provides information regarding the activity area to a store terminal device of the member store or a user terminal device of the user. [Effects of the Invention]

[0007] According to one aspect of the present invention, it is possible to analyze the range of behavior of users of electronic payment services without limiting the areas to which the technology can be applied and without collecting excessive sensitive information. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram illustrating an example of a configuration for realizing an electronic payment service. [Figure 2] This is a sequence diagram (part 1) illustrating the general flow of electronic payment. [Figure 3] This is a sequence diagram (part 2) illustrating the general flow of electronic payment. [Figure 4] FIG. 2 is a configuration diagram of a payment server 100 according to the first embodiment. [Figure 5] FIG. 10 is a diagram showing an example of the contents of user information 172. [Figure 6] FIG. 10 is a diagram showing an example of the contents of affiliated store / store information 176. [Figure 7] 10 is a diagram showing an example of user location information acquired by an acquisition unit 142. FIG. [Figure 8] 10 is a diagram showing an example of the contents of confidence ellipse parameter information 178 calculated by a calculation unit 144. FIG. [Figure 9] 10 is a diagram showing an example of the contents of behavior area information 180 calculated by a calculation unit 144. FIG. [Figure 10] 10 is a diagram showing an example of analysis information 182 provided by a providing unit 146. FIG. [Figure 11] 10 is a diagram showing another example of the analysis information 182 provided by the providing unit 146. FIG. [Figure 12] 10 is a diagram showing another example of the analysis information 182 provided by the providing unit 146. FIG. [Figure 13] 10 is a diagram showing another example of the analysis information 182 provided by the providing unit 146. FIG. [Figure 14] 10 is a flowchart showing an example of the flow of processing executed by the information management unit 140. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, with reference to the drawings, embodiments of an information processing device, an information processing method, and a program according to the present invention will be described. Various devices, such as a "server," a "management device," and an "information providing device," that provide services to users and perform internal analysis, may be implemented as a group of distributed devices, and each device may be operated by a different business. Furthermore, the hardware owner (the cloud server provider) and the business that actually operates the device may also be different. An application program and a payment server work together to provide an electronic payment service. In the following description, the application program is referred to as a "payment app." An electronic payment service is a service that supports payments for the purchase of goods and services at a store. A store may be, for example, a physical store (real-world store) existing in the real world, but may also include a virtual store for e-commerce transactions. A virtual store may also be provided by an entity other than the operator of the electronic payment service. In such a case, when making a payment for a purchase at the virtual store, the user is controlled to transition to the 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 when a purchase is made at a store, processing such as payment is primarily conducted between the user and the affiliated store. Alternatively, processing such as payment may be carried out between the user and the store.

[0010] [Electronic payment service] Figure 1 shows 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, etc.

[0011] The user terminal device 10 is, for example, a portable terminal device such as a smartphone or tablet terminal. The user terminal device 10 is a computer device having at least an optical reading function, a communication function, a display function, an input acceptance function, and a program execution function. In the following description, components for realizing these functions are referred to as a camera, a communication device, a touch panel, a CPU (Central Processing Unit), 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 on the user terminal device 10 from, for example, 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 includes a so-called POS (Point of Sale) device, and the product price acquisition function and the optical reading function may be realized by the POS device. The store code image 60 is placed in the store and is a code image such as a QR code (registered trademark) printed on a paper or plastic medium. The store code image 60 may be displayed on a display placed in the store (which may be the display of a terminal device such as a smartphone).

[0013] The second store terminal device 70 is used by the operator of the affiliated store. The second store terminal device 70 is a smartphone, tablet terminal, personal computer, etc. An interface for affiliated stores 72 runs on the second store terminal device 70. The interface for affiliated stores 72 may be an app for affiliated stores or a browser. The interface for affiliated stores 72 accepts coupon settings and the like from the operator of the affiliated store and transmits them to the payment server 100. The second store terminal device 70, which is a smartphone, has the function of displaying a code image corresponding to a store code image and reading the code image displayed by the user terminal device 10 by executing the app for affiliated stores.

[0014] 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 payment information is sent from the POS device via the affiliated store server to the payment server 100. In the following explanation, this distinction will not be made and it is assumed that payment information is sent from the first store terminal device 50.

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

[0016] In the case of pattern 1 (hereinafter referred to as user scan) shown in FIG. 2, the user terminal device 10, with the payment application 20 running, decodes the store code image 60 using its 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 store identification information has been added, and is associated with an affiliated store ID, store ID, etc. in the payment server 100 (described below). The payment application 20 sends 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 below) using the affiliated store ID and store ID corresponding to the store URL, acquires the affiliated store name and store name information (S3), and sends this to the payment application 20 (S4). The user enters the payment amount into the user terminal device 10 on the screen displaying the affiliated store name and store name (S5). Then, the user terminal device 10 generates second payment information including at least the payment amount and sends it to the payment server 100 (S6). The payment server 100 makes the electronic payment based on the received second payment information (S7). The payment server 100 then sends a payment completion notice (information for displaying a payment completion screen) to the payment app 20 (S8), and the payment app 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 information on the payment amount in addition to the store URL. In this case, the step of the user inputting the payment amount is omitted, and the payment amount information is included in the first payment information and sent to the payment server 100. Information on the affiliated store name and store name may be included and displayed on the payment completion screen.

[0017] In the case of pattern 2 (hereinafter referred to as store scan) shown in FIG. 3, the payment app 20 sends a request to issue a one-time code to the payment server 100 when the payment app 20 is launched, when a payment operation is performed in the payment app 20, at the automatic update timing (e.g., every minute), and at other timings (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, generated based on the one-time code (S14). The user holds (presents) the display surface of the user terminal device 10 over the first in-store terminal device 50, and the first in-store terminal device 50 decodes the code image using its optical reading function and obtains the one-time code, etc. (S15). The first in-store terminal device 50 then 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, manually entering it, etc. Based on the received information, the payment server 100 identifies the user corresponding to the one-time code and performs electronic payment (S17). Then, the payment server 100 sends a payment completion notice to the payment application 20 (S18), and the payment application 20 displays a payment completion screen (S19).

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

[0019] [Payment server] FIG. 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, and a storage unit 170. The components other than the communication unit 110 and the storage unit 170 are implemented by, for example, a hardware processor such as a CPU executing a program (software). Some or all of these components may be implemented by hardware (including circuitry) such as a large-scale integration (LSI), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a graphics processing unit (GPU), or may be implemented by a combination of software and hardware. The program may be stored in advance in a storage device (a storage device with a non-transitory storage medium) such as a hard disk drive (HDD) or flash memory, or may be stored in a removable storage medium (a non-transitory storage medium) such as a DVD or CD-ROM, and installed in the storage device by inserting the storage medium into a drive device. The information management unit 140 further includes an acquisition unit 142, a calculation unit 144, and a provision unit 146, the functions of which will be described in detail later. The function of the information management unit 140 in the payment server 100 is an example of an "information processing device."

[0020] 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, confidence ellipse parameter information 178, behavioral area information 180, and analysis information 182.

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

[0022] The payment content providing unit 120 has, for example, a function of a web server, and provides information (content) for displaying various screens of the electronic payment service to the user terminal device 10. The 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 from the user while content is being played by the payment application 20, and transmits the above-mentioned payment information and the like to the payment server 100.

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

[0024] FIG. 5 is a diagram showing 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, a user URL, account ID, telephone number, and password, as well as associated information such as email address, user ID, name, address, date of birth, registration date, charge balance, credit card payment settings, credit card limit, credit card payment amount, available credit card payment amount, payment method settings, bank account, credit card number, charge history information, and payment history information. The user URL is used for remittance processing between users. Registration of a telephone number and password is required when registering for an electronic payment service. The account ID is issued to the user by the payment server 100, and the user ID can be set by the user (or does not have to be set). Similarly, the email address, name, address, and date of birth can be set by the user (or do not have to be set). The registration date is the date on which the user registered for the electronic payment service (the date on which the account was created). Hereinafter, the user's instance (electronic payment account) to which this information is associated will be referred to as an account.

[0025] The charge balance indicates the balance of electronic money set by the user by transferring funds to the account in advance. Transfer methods include transfers from a designated bank's ATM (Automatic Teller Machine) or from a registered bank account. The credit payment setting indicates whether the settings for electronic credit payment have been completed and is set to either "Completed" or "Not Completed." The credit payment limit is the monthly credit payment limit. The credit payment amount is the amount of credit payment already used in the current month. The available credit payment amount is the amount of credit payment available in the current month, calculated by subtracting the credit payment amount from the credit payment limit. While the figure shows only one credit payment limit, in reality, there may also be daily limits, and the lower of these may be set as the credit payment limit. Further details on credit payments will be discussed later. The payment method setting indicates whether the user will currently make electronic payments using the charge balance or by credit payment. The bank account and credit card number are information on the bank account or credit card number (account number, card number) that can be used to deposit funds into the electronic payment service. The charge history information is a history of the user's previous transfers to the electronic payment service to increase the charge balance. The payment history information is information that shows the breakdown of payments made by the user for each payment (date and time, store ID of the store where the purchase was made, payment amount, payment method, etc.).

[0026] FIG. 6 is a diagram showing an example of the contents of affiliated store / store information 176. The affiliated store / store information 176 includes, for example, a first table 176A in which an affiliated store ID, a store ID, and an address 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 a category; and a third table 176C in which a store ID is associated with a store name and location. In addition to this information, the affiliated store / store information 176 may also include information such as payment patterns. In this embodiment, the location of a store is registered, for example, as longitude and latitude information.

[0027] The information management unit 140 manages user information 172, affiliated store / store information 176, confidence ellipse parameter information 178, activity area information 180, and analysis information 182 based on information acquired from the user terminal device 10 and the second store terminal device 70. The information management unit 140 adds, edits, and deletes new records for the user information 172 and affiliated store / store information 176. Furthermore, as will be described in detail later, the calculation unit 144 calculates the confidence ellipse parameter information 178 and activity area information 180 for a user based on the longitude and latitude information of the store acquired by the acquisition unit 142 and the user's payment history information. Furthermore, the provision unit 146 provides the activity area information 180 or analysis information 182 obtained by analyzing the activity area information 180 to the user terminal device 10 or the second store terminal device 70. The activity area information 180 and analysis information 182 are examples of "information related to the activity area" in the claims.

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

[0029] The payment processing unit 130 performs electronic payments 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. The operator of the electronic payment service acts as the creditor, allowing electronic payments within the credit card payment limit and independent of the remaining balance. To receive the credit card payment service, a user may be required to obtain a credit card provided by the operator of the electronic payment service. The monthly amount used for credit card payment is settled on the following month's payment date, for example, by debit from a bank account. In this case, the payment processing unit 130 makes a provisional settlement by adding the settlement amount to the credit card payment amount and subtracting the same amount from the available credit card balance. On the closing date, the payment processing unit 130 performs the process described above to debit the current month's payment on the following month's payment date, or requests the credit card company operator to perform this process. If the settlement amount exceeds the available credit card balance at the time of provisional settlement, an error notification is returned to the payment app 20.

[0030] [User behavior area] In this way, a user can use the payment app 20 installed in the user terminal device 10 to make electronic payments at affiliated stores that are affiliated with the electronic payment service. Generally, when users make electronic payments on a daily basis, there is a certain tendency for the range of payment locations to vary for each user (for example, within 1 km of a certain station or their home). Therefore, calculating the range of such payment locations for each user as a behavioral area and providing information based on the calculated behavioral area to the user and affiliated stores contributes to improving the convenience of the payment app 20 for users and increasing sales for affiliated stores. Below, the calculation of the behavioral area and the provision of information based on the behavioral area, which are executed by the acquisition unit 142, the calculation unit 144, and the provision unit 146, will be described in detail.

[0031] FIG. 7 is a diagram illustrating an example of user location information acquired by the acquisition unit 142. The acquisition unit 142 acquires a predetermined number of recent pieces of location information for one or more users 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. More specifically, when a user makes an electronic payment, the payment history of the user information 172 for the user stores the store ID of the store where the user made the electronic payment and the date and time. Therefore, the acquisition unit 142 references the date and time stored in the payment history to acquire the store IDs where the electronic payments were made for the most recent k (k is any positive integer) electronic payments, and acquires the longitude and latitude information of the "location" corresponding to the store ID stored in the third table 176C, thereby acquiring the location information where the user made the electronic payment. In this case, the longitude and latitude information of the "location" stored in the third table 176C may be stored in advance in the payment history. In FIG. 7, as an example, the acquisition unit 142 acquires the location information of the last k=5 electronic payments from the payment history of the user information 172 related to a certain user as longitude and latitude information P j (lon j ,lat j )) (j=1~5).

[0032] Alternatively, acquisition unit 142 may acquire longitude and latitude information directly from user terminal device 10, rather than the longitude and latitude information of the store where the user made the electronic payment. More specifically, user terminal device 10 has a GNSS positioning function, and when the user makes an electronic payment using payment app 20, payment app 20 may cause user terminal device 10 to perform GNSS positioning by communicating with GNSS satellites, and transmit the measured longitude and latitude information to payment server 100, where it may be stored in the payment history of user information 172.

[0033] The calculation unit 144 calculates the activity area of ​​the user as a confidence ellipse based on the location information of the user acquired by the acquisition unit 142. First, prior to calculating the confidence ellipse, the calculation unit 144 calculates the activity area of ​​the user as a confidence ellipse based on the k pieces of acquired longitude and latitude information P jThis is to prevent irregular electronic payments that are different from the user's everyday electronic payments, such as when the user makes an electronic payment while temporarily away from home (for example, on a trip or business trip), from being calculated as part of the user's activity area.

[0034] More specifically, for example, the calculation unit 144 uses DBSCAN (Density-Based Spatial Clustering of Applications with Noise), which is a density-based clustering method for detecting outliers, as an algorithm for excluding outliers, on k pieces of longitude and latitude information P j When DBSCAN is used, multiple clusters may be identified as a result of the clustering. If multiple clusters are identified, the calculation unit 144 selects the cluster with the most longitude and latitude information P j As a result, a cluster containing relatively few longitude and latitude information P j For the sake of convenience, the cluster containing the same k pieces of longitude and latitude information P j is assumed to be obtained as a result of clustering, and is written as lon=x, lat=y.

[0035] Next, the calculation unit 144 calculates k pieces of latitude and longitude information P j (x j ,y j )) longitude and latitude mean coordinate μ=[mean(x j ),mean(y j )] is calculated, and the variance-covariance matrix Σ is calculated using the following equation (1).

[0036]

number

[0037] In formula (1), Var represents variance, and COV represents covariance. The calculation unit 144 stores the average value μ and variance-covariance matrix Σ calculated in this manner in the storage unit 170 as confidence ellipse parameter information 178. Fig. 8 is a diagram showing an example of the contents of the confidence ellipse parameter information 178 calculated by the calculation unit 144. The confidence ellipse parameter information 178 is, for example, information such as longitude and latitude average coordinates and a variance-covariance matrix associated with an account ID. The calculation unit 144 calculates k pieces of latitude and longitude information P ji (x ji ,y ji )) (j=1~k) based on the longitude-latitude average coordinate μ i and the variance-covariance matrix Σ i is calculated and stored in the storage unit 170 as confidence ellipse parameter information 178. The calculation process of the confidence ellipse parameter information 178 is an example of the "statistical process" in the claims.

[0038] After calculating the confidence ellipse parameter information 178, the calculation unit 144 calculates the activity area AS of each user based on the calculated confidence ellipse parameter information 178. More specifically, the calculation unit 144 calculates the activity area AS of each user i based on the average longitude and latitude coordinate μ i From the variance-covariance matrix Σ i The Mahalanobis distance based on the threshold n std The following two-dimensional coordinates (X', Y') are calculated as the activity area AS.

[0039] The Mahalanobis distance is calculated by dividing k pieces of latitude and longitude information P j (x j ,y j )) from the center of gravity (average longitude and latitude coordinates) is calculated taking into account its directionality (i.e., the distribution of settlement locations defined by the variance-covariance matrix Σ). As a result, even if the Euclidean distances from the center of gravity are the same, the k pieces of latitude and longitude information P j (x j ,y j)) distribution density (i.e., locations in the direction where electronic payments are more frequently made) will be calculated as closer than locations in the direction where it is lower (i.e., locations in the direction where electronic payments are less frequently made). Mahalanobis distance is more realistic than Euclidean distance in expressing the payment range (activity range) of users in electronic payment services.

[0040] More specifically, the calculation unit 144 calculates the Mahalanobis distance D by the following equation (2).

[0041]

number

[0042] In equation (2), Σ -1 is the inverse matrix of the variance-covariance matrix Σ. The calculation unit 144 calculates the Mahalanobis distance D by std A set of two-dimensional coordinates (X', Y') such that D=n std The set of two-dimensional coordinates (X', Y') that satisfy the above equation represents the confidence ellipse E) is calculated as the activity area AS. The slope θ of the calculated confidence ellipse E is calculated based on the k pieces of latitude and longitude information P j (x j ,y j )) is equal to the arctan(m) of the slope m obtained by fitting the regression equation Y=mX+c to the confidence ellipse E. In other words, the set of two-dimensional coordinates (X', Y') representing the confidence ellipse E can also be expressed by the following equation (3).

[0043]

number

[0044] In formula (3), a and b represent the major axis and minor axis of the confidence ellipse, respectively, and are calculated by the following formulas (4) and (5).

[0045]

number

[0046]

number

[0047] Here, r is k pieces of latitude and longitude information P j (x j ,y j )), and where Var is the variance, the correlation coefficient r is calculated by the following equation (6).

[0048]

number

[0049] In the above description, the calculation unit 144 is described as storing the longitude-latitude mean coordinate μ and the variance-covariance matrix Σ as the confidence ellipse parameter information 178. Alternatively, the calculation unit 144 may store the slope θ, the major axis a, and the minor axis b of the confidence ellipse E in the storage unit 170 as the confidence ellipse parameter information 178.

[0050] threshold n std is a value such as 1 (confidence interval 66.7%), 2 (confidence interval 95%), or 3 (confidence interval 99%), which is set in advance by the operator of the electronic payment service to analyze the user's behavior area AS. For example, the threshold n std When 1 (confidence interval 66.7%) is set, the Mahalanobis distance D is std The range of the two-dimensional coordinates (X', Y') is k pieces of latitude and longitude information P j (x j ,y j )) covers 66.7% of the threshold n std If the confidence interval is set to 3 (confidence interval 99%), the Mahalanobis distance D is std The range of the two-dimensional coordinates (X', Y') is k pieces of latitude and longitude information P j (x j ,y j)) covers 99% of the threshold n std If a lower confidence interval is set, this means that the user's activity area AS is set narrower, while if a higher confidence interval is set, this means that the user's activity area AS is set wider. The calculation unit 144 stores the calculated activity area AS in the storage unit 170 as activity area information 180.

[0051] 9 is a diagram showing an example of the contents of the behavior area information 180 calculated by the calculation unit 144. In FIG. 9, the symbol E1 indicates n std = 3, and the symbol E2 represents the confidence ellipse when n std = 2, and the symbol E3 represents the confidence ellipse when n std = 1. The area within the confidence ellipse E1 corresponds to the behavioral area AS1, the area within the confidence ellipse E2 corresponds to the behavioral area AS2, and the area within the confidence ellipse E3 corresponds to the behavioral area AS3.

[0052] As in the case of Figure 9, the threshold n std is set to multiple values, the calculation unit 144 calculates the threshold value n std For each, the Mahalanobis distance D is std A set of the following two-dimensional coordinates (X', Y') may be stored in the storage unit 170 as the behavior area information 180. std is set to a single value, the calculation unit 144 calculates the Mahalanobis distance D by std The following set (single set) of two-dimensional coordinates (X', Y') may be stored in the storage unit 170 as the behavior area information 180. Also, for example, the calculation unit 144 may store the confidence ellipse parameter information 178 and the set threshold n std may be stored in the storage unit 170 as the activity area information 180. In this case, the calculation unit 144 may use the confidence ellipse parameter information 178 and the threshold value n std The confidence ellipse is drawn each time using the above.

[0053] In this way, according to this embodiment, the range of a user's activities can be calculated based only on the location information of the store where the user made an electronic payment. In other words, the range of activities of users of electronic payment services can be analyzed without limiting the areas to which the technology can be applied and without collecting excessive sensitive information.

[0054] In the above description, the calculation unit 144 selects the cluster containing the largest number of longitude and latitude information Pj from among the multiple clusters and calculates a confidence ellipse for the selected cluster. However, the present invention is not limited to such a configuration. The calculation unit 144 may select all of the multiple clusters that satisfy a predetermined criterion and calculate multiple confidence ellipses for all of the selected clusters. For example, the calculation unit 144 may select, as a predetermined criterion, a cluster that ranks Xth from the top in the number of longitude and latitude information Pj (X is a positive integer equal to or greater than 2). Furthermore, for example, the calculation unit 144 may select, from the multiple clusters, a cluster that has a certain number of longitude and latitude information Pj or more within a certain period. In this way, by selecting multiple clusters and calculating multiple confidence ellipses (behavior regions) for the multiple selected clusters, it is possible to represent the diverse lifestyles of users. When multiple confidence ellipses are calculated, the confidence ellipse parameter information 178 shown in FIG. 8 stores multiple combinations of longitude and latitude average coordinates μ and variance-covariance matrices Σ for each account ID.

[0055] [Providing analytical information] The providing unit 146 provides the behavior area information 180 calculated by the calculating unit to the second store terminal device 70 or the user terminal device 10. At this time, the providing unit 146 may provide the calculated behavior area information 180 as is, or may perform a predetermined analysis process on the behavior area information 180 (which may be an analysis process for each user, or an analysis process that aggregates the behavior area information 180 related to multiple users) and then provide it to the second store terminal device 70 or the user terminal device 10 as analysis information 182.

[0056] Fig. 10 is a diagram showing an example of the analysis information 182 provided by the providing unit 146. Fig. 10 shows a case where the providing unit 146 provides the analysis information 182 to the user terminal device 10. As shown in Fig. 10, the providing unit 146 may provide each user with suggested information about electronic payment services in the form of a map MP, together with the user's current location CP, based on the user's activity area AS.

[0057] For example, the providing unit 146 refers to the payment history of the user information 172 and identifies the category of affiliated stores where the user has made electronic payments most frequently during the most recent predetermined period (e.g., one month). Next, the providing unit 146 identifies affiliated stores or shops within the identified category within the user's activity area AS that the user has not yet visited. The providing unit 146 then displays and provides information about the identified affiliated stores or shops on the map MP. In the example of FIG. 10, the providing unit 146 identifies that a certain user most frequently uses affiliated stores belonging to the "cafe" category, and causes the user terminal device 10 to display information SC1 and SC2 about affiliated stores or shops within the user's activity area AS that belong to the "cafe" category that the user has not yet visited, together with locations S1 and S2 on the map.

[0058] Note that the recommendation algorithm for users is not limited to the above, and any known recommendation algorithm may be applied. For example, the providing unit 146 may calculate the similarity between the payment history of a certain user and the payment history of other users using a trained model or collaborative filtering, and display on the user terminal device 10 information about affiliated stores or shops where other users with similar payment histories frequently make electronic payments within the user's activity area AS.

[0059] In another aspect, when multiple activity areas AS are calculated for each user, the providing unit 146 may perform an analysis process on the multiple activity areas AS to calculate feature amounts for each activity area AS and provide the calculated attributes to the second store terminal device 70 or the user terminal device 10. For example, the providing unit 146 may calculate attributes for each activity area AS, such as the latest payment time, average payment time zone, and the merchant or category of the affiliated store where the electronic payment was made, and provide the calculated attributes to the second store terminal device 70 or the user terminal device 10. In this case, the providing unit 146 may compare the latest payment times of each activity area AS to identify the activity area AS corresponding to the latest payment time and provide it to the second store terminal device 70 or the user terminal device 10 as the activity area AS active for the user. Furthermore, the providing unit 146 may track the transition of the latest payment times of multiple activity areas AS to identify a change in the user's living base (e.g., moving, etc.) and provide it to the second store terminal device 70 or the user terminal device 10. In this way, by calculating and tracking the attributes of each behavioral area AS, it is possible to grasp the characteristics of users' payment behavior more accurately.

[0060] 10, a single confidence ellipse is displayed as the user's activity range AS. In this case, the confidence interval of the displayed confidence ellipse may be preset by the operator of the electronic payment service from among the above-mentioned confidence intervals of 66.7%, 95%, and 99%, or the providing unit 146 may change the confidence interval depending on the attributes of the user providing the analysis information 182. For example, if the user's age is equal to or greater than a threshold (i.e., if the user is elderly), the providing unit 146 may display a confidence ellipse with a lower confidence interval (i.e., a narrower range). Since this method basically calculates the activity range based on the user's payment history, such consideration is unnecessary. However, for example, if the number of payment history data used to calculate the confidence ellipse is less than a predetermined value (i.e., if the number is small), the providing unit 146 may display a confidence ellipse with a confidence interval tailored to the user's attributes.

[0061] FIG. 11 is a diagram illustrating another example of the analysis information 182 provided by the providing unit 146. In FIG. 10, the providing unit 146 displays information about affiliated stores or shops included in the user's activity area AS on the user terminal device 10. On the other hand, as shown in FIG. 11, the providing unit 146 may compare the calculated user's activity area AS with pre-stored map information, and if an index value indicating the degree of correspondence between the activity area AS and a specific area is equal to or greater than a threshold, display information about affiliated stores or shops included in the specific area as analysis information 182 on the user terminal device 10. For example, in FIG. 11, if the calculated user's activity area AS covers a predetermined percentage or more of the area of ​​Shibuya Ward stored in the map information, the providing unit 146 may display information about affiliated stores or shops included in Shibuya Ward on the user terminal device 10. This allows the providing unit 146 to identify whether a certain store is included in the activity area AS simply by referring to the address information in the first table 176A, without having to perform coordinate calculations.

[0062] FIG. 12 is a diagram showing another example of the analysis information 182 provided by the providing unit 146. FIG. 12 illustrates a case in which the providing unit 146 provides the analysis information 182 to the affiliated store interface 72 of the second store terminal device 70. The providing unit 146 causes the affiliated store interface 72 to display, for example, an area A1 representing an affiliated store and its account that has logged in to the affiliated store interface 72, an area A2 representing a menu that can be operated via the affiliated store interface 72, and an area A3 displaying information related to an item selected from the menu A2. FIG. 12 illustrates an example in which the providing unit 146 displays the analysis information 182 in area A3 in response to an employee of a store belonging to the affiliated store logging in to the affiliated store interface 72 as a store account and selecting "Behavior Area Analysis" from menu A7.

[0063] For example, the providing unit 146 may identify users (hereinafter, potential customers) who have never visited the affiliated store even though their behavioral area AS includes the affiliated store, and may aggregate attributes related to the identified potential customers to provide the affiliated store-oriented analysis information 182 to the affiliated store-oriented interface 72. As an example, the providing unit 146 may aggregate the number of potential customers, gender, age (e.g., average age, etc.), and payment patterns (e.g., payment time period, category, tendency of payment unit price, etc.) based on the payment history of the potential customers, and provide the aggregated information to the affiliated store-oriented interface 72. For example, in the case of FIG. 12 , the coffee shop can recognize that potential customers tend to be middle-aged or elderly men who make electronic payments at izakayas during the nighttime, and therefore may consider operating a bar at night as a measure to increase sales.

[0064] Fig. 13 is a diagram showing another example of the analysis information 182 provided by the providing unit 146. Fig. 13 shows an example in which the providing unit 146 displays the analysis information 182 in area A3 in response to an operator of an affiliated store that operates multiple stores logging in to the affiliated store interface 72 as an affiliated store account and selecting "Behavior Area Analysis" from menu A7.

[0065] For example, the providing unit 146 defines the center (average value μ) of each user's activity area AS as the maximum activity area score, and sets the activity area score so that the greater the Mahalanobis distance from the center, the smaller the activity area score. Next, the providing unit 146 adds up the activity area scores set for each user's activity area AS. The graph obtained in this manner is a three-dimensional graph in which the added activity area scores are associated with longitude and latitude. The providing unit 146 may display such graph information, together with location information of affiliated stores, as analysis information 182 on the affiliated store interface 72. This allows affiliated stores to refer to the analysis information 182, for example, when formulating plans for opening new stores or closing existing stores. The graph information is an example of "aggregated information" in the claims.

[0066] [Processing flow] Next, the flow of processing executed by the information management unit 140 will be described with reference to Fig. 14. Fig. 14 is a flowchart showing an example of the flow of processing executed by the information management unit 140. The processing of the flowchart shown in Fig. 14 is executed repeatedly at a predetermined cycle (for example, daily or weekly).

[0067] First, the acquisition unit 142 acquires a predetermined number of pieces of user location information from the payment history of the user information 172 (step S100). Next, the calculation unit 144 applies an outlier exclusion algorithm to the acquired location information to eliminate outliers from the location information (step S102).

[0068] Next, the calculation unit 144 calculates a confidence ellipse (confidence ellipse parameters) representing the user's activity area based on the location information from which the abnormal values ​​have been removed (step S104). Next, the calculation unit 144 calculates the range of the calculated confidence ellipse as the user's activity area (step S106).

[0069] Next, the providing unit 146 performs a predetermined analysis process on the calculated behavior area and provides the result as analysis information to the user terminal device 10 or the second store terminal device 70 (step S108). This ends the processing of this flowchart.

[0070] According to the embodiment described above, location information of one or more users is obtained from the payment history of one or more users who have made electronic payments at affiliated stores of the electronic payment service, the activity area of ​​the one or more users is calculated based on the location information, and information about the activity area is provided to the affiliated store's store terminal device or the user's user terminal device. This makes it possible to analyze the activity range of users of the electronic payment service without limiting the areas to which the technology can be applied and without collecting excessive sensitive information.

[0071] The above describes the form for carrying out the present invention using an embodiment, but the present invention is not limited to such an embodiment, and various modifications and substitutions can be made within the scope that does not deviate from the gist of the present invention. [Explanation of symbols]

[0072] 10 User terminal device 20. Payment App 70 Second store terminal device 100 Payment Server 120 Payment Contents Department 130 Payment processing unit 140 Information Management Department 142 Acquisition Department 144 Calculation Unit 146 Provision Department

Claims

1. an acquisition unit that acquires a predetermined number of pieces of location information of one or more users from a payment history of the one or more users making electronic payments at affiliated stores of the electronic payment service; a calculation unit that calculates a behavioral area of ​​the one or more users based on the location information; a providing unit that provides information about the activity area to a store terminal device of the affiliated store or a user terminal device of the user, Information processing device.

2. the acquisition unit acquires, from the payment history, latitude and longitude information of the affiliated store where the one or more users made an electronic payment, as the location information; The information processing device according to claim 1 .

3. the acquiring unit acquires, from the payment history, latitude and longitude information received from the user terminal device when the one or more users made an electronic payment, as the location information; The information processing device according to claim 1 .

4. the calculation unit applies an outlier exclusion algorithm to a predetermined number of pieces of the location information, and calculates the behavioral area based on the location information that has been subjected to the outlier exclusion algorithm. The information processing device according to claim 1 .

5. the location information is longitude and latitude information of a location where the one or more users made an electronic payment; the calculation unit calculates the activity area by performing statistical processing on the longitude and latitude information. The information processing device according to claim 1 .

6. the calculation unit calculates an average value and a variance-covariance matrix of the longitude and latitude information as the statistical processing, and calculates, as the activity area, a range of coordinates in which a Mahalanobis distance from the average value based on the variance-covariance matrix is ​​equal to or less than a threshold value. The information processing device according to claim 5 .

7. The providing unit provides, as information about the behavioral area, to the user terminal device of the user, recommendation information about affiliated stores or shops of the electronic payment service included in the behavioral area. The information processing device according to claim 1 .

8. the providing unit provides, to the store terminal device of the affiliated store, information on attributes of users who are included in the activity area and have not yet visited the store of the affiliated store, as information on the activity area; The information processing device according to claim 1 .

9. the providing unit provides aggregated information that aggregates the activity areas of the plurality of users to the store terminal device of the affiliated store as information regarding the activity area; The information processing device according to claim 1 .

10. the providing unit provides, as the aggregated information, graph information obtained by scoring the activity areas of the plurality of users and adding up the scores to the store terminal device of the affiliated store. The information processing device according to claim 9 .

11. The computer Obtaining a predetermined number of pieces of location information of one or more users from a payment history of the one or more users making electronic payments at affiliated stores of the electronic payment service; Calculating a behavioral area of ​​the one or more users based on the location information; providing information about the activity area to a store terminal device of the affiliated store or a user terminal device of the user; Information processing methods.

12. On the computer, acquiring a predetermined number of pieces of location information of one or more users from a payment history of the one or more users making electronic payments at a member store of the electronic payment service; Calculating a behavioral area of ​​the one or more users based on the location information; The information about the activity area is provided to a store terminal device of the affiliated store or a user terminal device of the user. program.

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