Total settlement amount estimation device and total settlement amount estimation method

Abstract

A device according to the present invention comprises: an acquisition unit for acquiring in-house settlement data for individual estimation units; a total settlement amount calculation unit whereby a total settlement amount is calculated on the basis of the in-house settlement data acquired by the acquisition unit, a correction rate α that is set in advance for each of the estimation units and is for estimating an entire total settlement amount including the settlement amounts of in-house settlement non-member stores from the settlement amounts of in-house settlement member stores, and a correction rate β for estimating the settlement amounts of the in-house settlement member stores including non-in-house settlements from in-house settlement data in the in-house settlement member stores; and an output unit for outputting the total settlement amount.

Description

Total Settlement Amount Estimation Device and Total Settlement Amount Estimation Method

[0006]

[0001] One aspect of the present disclosure relates to a device and a method.

[0002] Patent Document 1 discloses an aggregation device that aggregates the settlement amounts of transactions by region and outputs region-specific settlement amount information indicating the settlement amounts of a predetermined region to a user's terminal.

[0003] Japanese Patent Application Laid-Open No. 2020-17169

[0004] By calculating the settlement amount for each area, applications to EBPM (Evidence-based Policy Making) in local governments are expected. Here, since the settlement data is owned by each business company, it is difficult to integrate them to create overall data. Therefore, it is required to create statistical information that estimates (extrapolates) the settlement amount for the entire area from some of the settlement data.

[0005] If it is assumed that a user takes a specific behavior pattern (for example, "settles only at a specific settlement franchise store and uses only a specific settlement at the store"), the total settlement amount (including other settlement amounts) can be calculated from the specific settlement amount. However, in reality, since the user's settlement behavior pattern changes depending on the area where the settlement is made, the user who made the settlement, the store where the settlement was made, etc., it is inappropriate to make an assumption about the user's behavior pattern. Thus, conventionally, it has been difficult to accurately calculate the total settlement amount from some of the settlement amounts. <00000,11>

[0006] One aspect of the present disclosure has been made in view of the above circumstances, and aims to accurately calculate the total settlement amount.

[0007] The apparatus relating to one aspect of this disclosure comprises: an acquisition unit that acquires obtainable settlement data for each estimation unit; a total settlement amount calculation unit that calculates the total settlement amount based on the obtainable settlement data acquired by the acquisition unit and a correction rate for estimating the total settlement amount for each estimation unit, including the amount of settlements that cannot be acquired at payment merchants and the amount of settlements at non-payment merchants, from the obtainable settlement data at payment merchants; and an output unit that outputs the total settlement amount.

[0008] In the device relating to one aspect of this disclosure, the total settlement amount (the total settlement amount including both merchants and non-merchants) is calculated for each estimated unit based on the acquired available settlement data. Specifically, based on a correction factor, the settlement amount of merchants, including unacquirable settlement amounts at merchants, is calculated from the available settlement data, and the total settlement amount, including settlement amounts at non-merchants, is calculated from the merchant settlement amount. In this way, by using a correction factor and considering the ratio of available and unacquirable settlements, as well as the ratio of settlements occurring at merchants and non-merchants, the total settlement amount is calculated from the available settlement data for each estimated unit, making it possible to calculate the total settlement amount with high accuracy from a portion of the settlement data.

[0009] According to one aspect of this disclosure, the total settlement amount can be calculated accurately.

[0010] Figure 1 is a diagram illustrating the overview of the augmented estimation system according to this embodiment. Figure 2 is a diagram illustrating the device configuration of the estimation device included in the augmented estimation system. Figure 3 is a diagram illustrating user information. Figure 4 is a diagram illustrating store information. Figure 5 is a flowchart illustrating the total settlement amount calculation process. Figure 6 is a table showing the total settlement amount table. Figure 7 is a diagram illustrating the correction rate α and correction rate β. Figure 8 is a flowchart illustrating the correction rate α calculation process. Figure 9 is a table showing the correction rate α table. Figure 10 is a table showing the behavior pattern table. Figure 11 is a flowchart illustrating the correction rate β calculation process. Figure 12 is a diagram illustrating the calculation of the number of merchants using the company's payment system for which payment methods with a higher priority than the company's payment system are unavailable. Figure 13 is a diagram illustrating the calculation of the correction rate β. Figure 14 is a table showing the correction rate β table. Figure 15 is a flowchart illustrating the visitor count calculation process. Figure 16 is a table showing the visitor count table and the location augmentation coefficient table. Figure 17 is a flowchart illustrating the unit price calculation process. Figure 18 is a table showing the unit price table. Figure 19 is a table showing various output information. Figure 20 is a diagram illustrating the total settlement amount calculation process related to the modified example. Figure 21 is a flowchart of the total settlement amount calculation process related to the modified example. Figure 22 is a diagram showing an example of the hardware configuration of the estimation device.

[0011] The embodiments will be described in detail below with reference to the drawings. In the description, the same elements or elements having the same function will be denoted by the same reference numeral, and redundant descriptions will be omitted.

[0012] Figure 1 is a diagram illustrating the overview of the expanded estimation system according to this embodiment. The expanded estimation system according to this embodiment is operated by, for example, a business that provides payment services or a business that receives payment data from such a business, and is a system that derives the total payment amount, including non-company payments, by performing expanded estimation based on the company's own payment data (payment data obtained when the payment services of a business that provides payment services are used) (acquirable payment data). The expanded estimation system derives the total payment amount for each layer (estimation unit) set based on at least one of the following predetermined areas, times, user information (information related to users), and store information (information related to stores) from the company's own payment data. As shown in Figure 1(a), in stratified statistics using the company's own payment data, statistics related to payments such as the total payment amount, total number of payments, number of unique users, and average payment amount are calculated for each layer. In this embodiment, the company's own payment data is described as an example of "acquirable payment data," but "acquirable payment data" is not limited to the company's own payment data, and any payment data (not the total payment amount) that can be obtained by some means is acceptable.

[0013] In the expanded estimation system according to this embodiment, as shown in Figure 1(b), the total settlement amount, including non-company settlements, is calculated by multiplying the company's settlement data by correction rates α and β. Details of correction rates α and β will be described later.

[0014] Figure 2 shows the configuration of the estimation device 10 included in the expanded estimation system. The estimation device 10 may consist of a single server or multiple servers that can communicate with each other. The estimation device 10 includes an input unit 11 (acquisition unit), a total settlement amount calculation unit 12, a visitor count calculation unit 13, a unit price calculation unit 14, and a result output unit 15 (output unit). As shown in Figure 2, the processing of each functional unit of the estimation device 10 is executed layer by layer (estimation unit).

[0015] The input unit 11 acquires the company's own payment data (acquirable payment data) for each layer. A layer is an estimation unit set based on at least one of the following: area, time, user information (information related to the user), or store information (information related to the store). The area may be selected from any spatial granularity, such as mesh code or administrative division (prefecture, city, town, or village). The time may be selected from any temporal granularity, such as yearly, monthly, daily, or hourly units.

[0016] Figure 3 is a diagram illustrating user information. As shown in Figure 3, user information may include information such as age, gender, place of residence, tourism status (whether or not the user is a tourist), accommodation status (whether or not the user is a hotel guest), visits to specific POIs (whether or not the user has visited a hot spring, amusement park, stadium, etc.), mode of transportation, household status (whether or not the user is married, etc.), travel style (whether or not the user is traveling alone or with family, etc.), and whether or not the user is a loyal user of the company. Such user information may be obtained from, for example, attribute information, payment information, location information, or survey information.

[0017] Figure 4 is a diagram illustrating store information. As shown in Figure 4, store information may include information such as the type of business, size, location, brand, management system (franchise or not, etc.), and business hours. Multiple types of layers are pre-set, for example, "Area: Tokyo, Time: This month, User information: Female in her 40s, Store information: Restaurant". The input unit 11 acquires the company's payment data for each pre-set layer.

[0018] The proprietary payment data is payment data obtained when the proprietary payment service is used at merchants that accept the proprietary payment service. The proprietary payment data includes, at a minimum, information that uniquely identifies a payment (payment ID), information about the store where the payment was made (store name, store location, etc.), the date and time the payment was made, and the payment amount, all of which are associated with each other. The input unit 11 extracts and obtains only the proprietary payment data that matches the conditions of a pre-set layer.

[0019] The input unit 11 may further acquire location information for each user of the company's services (specific services). Each user of the company's services may include all users whose location information can be acquired by the company. For example, if the business operator is a telecommunications carrier and also a provider of payment services, each user of the company's services may include not only users of payment services but also each user receiving the company's telecommunications services. Here, the example of a company's services is given as an example of a "specific service," but "specific services" are not limited to the company's services.

[0020] Returning to Figure 2, the total settlement amount calculation unit 12 calculates the total settlement amount based on the company's own settlement data acquired by the input unit 11 and a correction rate for each layer that estimates the total settlement amount, including unobtainable settlement amounts at company-affiliated merchants and settlement amounts at non-affiliated merchants, from the company's own settlement data at company-affiliated merchants. Specifically, the total settlement amount calculation unit 12 calculates the total settlement amount based on the company's own settlement data acquired by the input unit 11 and a correction rate α for each layer that estimates the total settlement amount, including settlement amounts at non-affiliated merchants, from the settlement amounts at company-affiliated merchants, and a correction rate β for estimating the settlement amount at company-affiliated merchants, including non-company settlement amounts (unobtainable settlement amounts), from the company's own settlement data at company-affiliated merchants. In this embodiment, an example using correction rates α and β is described, but a single correction rate that takes into account the contents of correction rates α and β may be used.

[0021] Figure 5 is a flowchart illustrating the process for calculating the total settlement amount. As shown in Figure 5, first, the total settlement amount calculation unit 12 extracts the in-house settlement data for the target layer from among multiple in-house settlement data and calculates the total amount by summing the settlement amounts of the in-house settlement data for the target layer (Step S1). Next, the total settlement amount calculation unit 12 calculates the correction rate α for the target layer based on user information, time information, store information, area, etc. (details will be described later) (Step S2). Next, the total settlement amount calculation unit 12 calculates the correction rate β for the target layer based on user information, time information, store information, area, etc. (details will be described later) (Step S3). Finally, the total settlement amount calculation unit 12 calculates the total settlement amount for the target layer (expanded estimate including non-in-house settlements) by multiplying the total amount of in-house settlement data for the target layer by the calculated correction rates α and β.

[0022] Figure 6 is a table showing the total settlement amount table. The total settlement amount table stores the total settlement amount for each layer calculated by the total settlement amount calculation unit 12. In the total settlement amount table shown in Figure 6, the total amount of company payments, correction rate α, correction rate β, and total settlement amount (expanded estimate) are associated with each layer determined by city / ward / town / village (area), month (time), gender (user information), age group (user information), and business type (store information). Note that in the example shown in Figure 6, only information for two layers ("Layer L1" and "Layer L2") is shown, but in an actual total settlement amount table, there is information for as many layers as there are layers (number of combinations of area, time, user information, and store information).

[0023] Figure 7 illustrates the correction rates α and β. As shown in Figure 7, stores where payments are made can be broadly divided into those that accept our company's payment system and those that do not. The correction rate α is a value used to estimate the total number of payments, including those at non-accepting stores, from payments at merchants that accept our company's payment system. Furthermore, not all payments at merchants that accept our company's payment system are necessarily made using our company's system; payments at merchants that accept our company's payment system may include payments made using our company's system, payments made using other companies' systems, cash payments, etc. The correction rate β is a value used to estimate the total number of payments, including those at non-accepting stores, from payments made using our company's payment system to those at merchants that accept our company's payment system.

[0024] Figure 8 is a flowchart showing the correction rate α calculation process. The total settlement amount calculation unit 12 executes the α calculation process to calculate the correction rate α. As shown in Figure 8, first, the total settlement amount calculation unit 12 calculates the "percentage m of store selection behavior pattern A in all payments" for the target layer, based on the area, time information, user information, and store information of that layer. Here, store selection behavior pattern A is the behavior pattern of "always selecting from our own payment affiliated stores". Let's assume there is another store selection behavior pattern, behavior pattern B, "randomly selecting a store".

[0025] The total settlement amount calculation unit 12 may calculate "the percentage m of store selection behavior pattern A in all payments" based on a predetermined rule base derived from information related to the stratum. For example, if the area is a tourist destination, the total settlement amount calculation unit 12 may set m = 0 so that behavior pattern B accounts for 100%. Alternatively, if the area is not a tourist destination and the segment (gender, age, etc.) frequently uses the company's own payment system, the total settlement amount calculation unit 12 may set m = 1 so that behavior pattern A accounts for 100%. The total settlement amount calculation unit 12 sets m to a value between 0 and 1.

[0026] The total settlement amount calculation unit 12 may calculate "the proportion m of store selection behavior pattern A in all settlements" based on a model that estimates the proportion m constructed based on data regarding whether or not the company's payment merchants are selected in settlements for some users (for example, data obtained from questionnaires). In this case, for example, data that associates user information, store information, area, and time information with "whether or not behavior pattern A is selected" corresponding to this information may be used as training data, and a model that predicts probability values ​​such as a logistic regression model may be constructed. Then, by applying this model to the user information, store information, area, and time information corresponding to each layer, "the overall probability of A (=m)" in each layer may be estimated.

[0027] Next, the total settlement amount calculation unit 12 calculates the "percentage of stores δ, which is the percentage of all stores that accept the company's own payment system" for the target tier based on the area information, time information, and store information for that tier (step S12). Specifically, the total settlement amount calculation unit 12 obtains the total number of stores and the number of company-accepted payment system merchants corresponding to the area information (cities, towns, etc.), time information, and store information (restaurants, etc.), and calculates the percentage δ of all stores that accept the company's own payment system merchants. Note that the total number of stores may be obtained from public data, for example, and the number of company-accepted payment system merchants may be obtained from company data, for example.

[0028] Next, the total settlement amount calculation unit 12 calculates a correction rate α based on the calculated percentages m and δ (step S13). Specifically, the total settlement amount calculation unit 12 calculates the correction rate α for the target group based on the following equation (1), where N is the number of settlements in the group, m is the percentage of store selection behavior pattern A in all settlements in the group, and δ is the percentage of the group where the company's own payment merchants are present in all stores in the group. Correction rate α = (total number of settlements in the group) / (number of settlements occurring at the company's own payment merchants in the group) = N / {mN + (1 - m)Nδ} = 1 / (m + (1 - m)δ) ... (1)

[0029] Figure 9 is a table showing the correction rate α table. The correction rate α table is a table that stores the correction rate α for each layer calculated by the total settlement amount calculation unit 12, and the information related to the calculation of the correction rate α. In the correction rate α table shown in Figure 9, the percentage m, the percentage δ, and the correction rate α are associated with each layer determined by city / ward / town / village (area), month (time), gender (user information), age group (user information), and business type (store information). Note that in the example shown in Figure 9, only information for two layers is shown, but in an actual correction rate α table, there is information for as many layers as there are layers (number of combinations of area, time, user information, and store information).

[0030] Figure 10 is a table showing the behavior pattern table. The behavior pattern table stores, for example, the store selection behavior pattern at the time of payment for each layer (here, behavior pattern A "always select from our own payment affiliated stores" and behavior pattern B "randomly select a store"), the probability of the pattern occurring, the number of payments generated from that behavior pattern, and the number of payments generated from that behavior pattern (at our own payment affiliated stores). The total payment amount calculation unit 12 may calculate the correction rate α by referring to such a behavior pattern table.

[0031] Figure 11 is a flowchart showing the correction rate β calculation process. The total settlement amount calculation unit 12 performs a β calculation process to calculate the correction rate β. As shown in Figure 11, first, the total settlement amount calculation unit 12 calculates "the number of affiliated merchants of the company in that layer K" and "information on whether affiliated merchants of other companies are affiliated with the company's affiliated merchants in that layer" from area, time information and store information in the layer to be calculated (step S21).

[0032] Next, the total payment amount calculation unit 12 defines the "priority of payment methods for the relevant segment" based on area, time information, user information, and store information for that segment (step S22). The total payment amount calculation unit 12 may define the priority based, for example, on the user segment (gender, age, etc.) or the results of a user survey (share rate of payment service usage, etc.) related to that segment. Alternatively, the total payment amount calculation unit 12 may define the priority based on a model that estimates the payment usage priority, which is constructed based on the payment usage priority for some users.

[0033] Next, the total settlement amount calculation unit 12 calculates the number of merchants that accept the company's payment method and the number of merchants that accept other companies' payment methods that have a higher priority than the company's payment method, based on the priority of the payment methods for that layer and the information on whether or not other companies' payment methods are accepted at the merchants that accept the company's payment method for that layer (step S23).

[0034] Figure 12 illustrates the calculation of the number of merchants that accept our company's payment system and for whom payment methods with a higher priority than our own payment system are unavailable (P). As shown in Figure 12(a), let's assume that in a certain segment of users, the top three payment methods are: 1st place: Payment Method C, 2nd place: Our company's payment method A, and 3rd place: Payment Method B. As shown in Figure 12(b), let's assume that the payment methods accepted at each store are as follows: Store 1 accepts only Payment Method A, Store 2 accepts both Payment Method A and Payment Method B, Store 3 accepts both Payment Method A and Payment Method C, and Store 4 accepts Payment Method A, Payment Method B, and Payment Method C. In this case, as shown in Figure 12(c), since Store 1 accepts only Payment Method A, the payment method used will be Payment Method A regardless of priority. Also, since Store 2 accepts both Payment Method A and Payment Method B, the higher-priority Payment Method A will be the payment method used. Also, since Stores 3 and 4 accept Payment Method C, which has the highest priority, Payment Method C will be the payment method used. In this example, the number of merchants that accept our payment system and have a higher priority than our own payment system (in this case, payment system C) is "2".

[0035] Returning to Figure 11, the total settlement amount calculation unit 12 then calculates the correction rate β for the target layer based on the following equation (2), using the number of merchants accepting the company's payment system K and the number of merchants accepting the company's payment system P for which payment methods with a higher priority than the company's payment system are unavailable (step S24). The following equation (2) is set based on the assumptions that "in a given layer, the stores belonging to that layer are homogeneous, so the selection of merchants accepting the company's payment system in a given layer is done randomly, and the amount spent at each store is also the same, and the settlement amount is proportional to the number of merchants", and "a settlement using the company's payment system at a merchant accepting the company's payment system occurs only when that store does not accept payment methods with a higher priority than the company's payment system". Correction rate β = (Total settlement amount at merchants accepting the company's payment system) / (Settlement amount using the company's payment system at merchants accepting the company's payment system) = (Number of merchants accepting the company's payment system) / Number of merchants accepting the company's payment system P for which payment methods with a higher priority than the company's payment system are unavailable = K / P ... (2)

[0036] In the example shown in Figure 12 above, we explained the case where the user priority in the target layer is uniquely determined. However, as shown in Figures 13(a) to (c), it is also possible that there are multiple user priority levels in the target layer. In this layer, let's assume that the probability of a user with the priority level shown in Figure 13(a) is 30%, the probability of a user with the priority level shown in Figure 13(b) is 50%, and the probability of a user with the priority level shown in Figure 13(c) is 20%. Such probabilities are defined in any way from area, time information, user information, and store information. Then, as shown in Figure 13(d), let's assume that the payment method availability at each store is as follows: Store 1 only accepts payment method A, Store 2 accepts payment methods A and B, Store 3 accepts payment methods A and C, and Store 4 accepts payment methods A, B, and C. In this case, K / P is calculated for each user priority level, and finally, a weighted average is calculated using the aforementioned probabilities to obtain the final correction rate β.

[0037] Specifically, for users with the priority order shown in Figure 13(a), K / P is calculated as 4 / 2 = 2, as shown in Figure 13(e); for users with the priority order shown in Figure 13(b), K / P is calculated as 4 / 4 = 1, as shown in Figure 13(f); and for users with the priority order shown in Figure 13(c), K / P is calculated as 4 / 1 = 4, as shown in Figure 13(g). Finally, a weighted average is calculated for each using the probability of existence, and 0.3 × 2 + 0.5 × 1 + 0.2 × 4 = 1.9 is calculated as the value of the correction factor β.

[0038] Figure 14 is a table showing the correction rate β table. The correction rate β table is a table that stores the correction rate β for each layer calculated by the total settlement amount calculation unit 12, and the information related to the calculation of the correction rate β. In the correction rate β table shown in Figure 14, the number of merchants that accept the company's payment system K, the number of merchants that accept the company's payment system P for which payment methods with a higher priority than the company's payment system are unavailable, and the correction rate β are associated for each layer determined by city / ward / town / village (area), month (time), gender (user information), age group (user information), and business type (store information). Note that in the example shown in Figure 14, only information for two layers is shown, but in an actual correction rate β table, there is information for as many layers as there are layers (number of combinations of area, time, user information, and store information).

[0039] Returning to Figure 2, the visitor count calculation unit 13 estimates the total number of visitors for each layer. Based on the location information of each user (each user of the company's service) acquired by the input unit 11, the visitor count calculation unit 13 identifies the number of visitors related to the company's service for each layer, and estimates the total number of visitors by multiplying this number of visitors by a location expansion coefficient corresponding to the number of users of the company's service.

[0040] Figure 15 is a flowchart showing the visitor count calculation process. As shown in Figure 15, first, the visitor count calculation unit 13 identifies the number of visitors to the company's service in the target layer from the location information of each user (each user of the company's service) acquired by the input unit 11 (step S31). Next, the visitor count calculation unit 13 calculates a location expansion coefficient from user information (for example, three types: gender, age group, and place of residence) in that layer (step S32). Information on gender, age group, and place of residence may be acquired from public data. The location expansion coefficient is calculated, for example, by dividing the population according to the conditions of gender, age group, and place of residence by the number of users of the company's service according to the conditions of gender, age group, and place of residence. Next, the visitor count calculation unit 13 calculates the total number of visitors in that layer (an expanded estimate of the number of visitors) by multiplying the number of visitors to the company's service calculated in step S31 by the location expansion coefficient calculated in step S32 (step S33).

[0041] FIG. 16(a) is a table showing the number of visitors table, and FIG. 16(b) is a table showing the position expansion coefficient table. As shown in FIG. 16(a), the number of visitors table is a table that stores the total number of visitors for each layer (estimated expanded value of the number of visitors) calculated by the number of visitors calculation unit 13. In the number of visitors table shown in FIG. 16(a), for each layer determined by municipality (area), month (time), gender (user information), and age group (user information), the number of visitors related to the company's own service, the position expansion coefficient, and the total number of visitors, which is the estimated expanded value, are associated. In the example shown in FIG. 16(a), only the information of one layer is shown, but in the actual number of visitors table, there is information for the number of existing layers.

[0042] As shown in FIG. 16(b), the position expansion coefficient table is a table that stores the position expansion coefficient for each layer. In the position expansion coefficient table shown in FIG. 16(b), for each layer determined by gender (user information) and age group (user information), the number of company's own service users, the population, and the position expansion coefficient are associated. In the example shown in FIG. 16(b), only the information of one layer is shown, but in the actual position expansion coefficient table, there is information for the number of existing layers.

[0043] Returning to FIG. 2, the unit price calculation unit 14 calculates the consumption unit price per user by dividing the total settlement amount calculated by the total settlement amount calculation unit 12 by the total number of visitors calculated by the number of visitors calculation unit 13.

[0044] FIG. 17 is a flowchart showing the unit price calculation process. As shown in FIG. 17, the unit price calculation unit 14 first matches the table related to the total settlement amount (total settlement amount table) calculated by the total settlement amount calculation unit 12 and the table related to the total number of visitors (number of visitors table) calculated by the number of visitors calculation unit 13 using the information related to the layer (for example, area, time, user information) as a key (step S41). Then, the total settlement amount calculation unit 12 calculates the consumption unit price per user by dividing the total settlement amount by the total number of visitors in each layer (step S42).

[0045] Figure 18 is a table showing the unit price table. As shown in Figure 18, the unit price table is a table that stores the unit price for each layer (consumption unit price per user) calculated by the unit price calculation unit 14. In the unit price table shown in Figure 18, for each layer determined by the municipality (area), month (time), gender (user information), age group (user information), and business type (store information), the total settlement amount, the total number of visitors, and the consumption unit price per user are associated. Note that since the total number of visitors is not calculated for each store information, the same value may be used even if the store information is different as shown in Figure 18.

[0046] Returning to Figure 2, the result output unit 15 outputs the total settlement amount calculated by the total settlement amount calculation unit 12. The result output unit 15 may output, together with the total settlement amount, the total number of visitors calculated by the visitor number calculation unit 13 and the consumption unit price per user calculated by the unit price calculation unit 14.

[0047] Figure 19 is a table showing various output information output from the result output unit 15. In the output information shown in Figure 19(a), for each layer determined by the municipality (area), month (time), gender (user information), age group (user information), and business type (store information), the information in which the total settlement amount, the total number of visitors, and the consumption unit price per user are associated is the output information.

[0048] [[ID=​​In the example shown in Figure 19(b), the output information is re-aggregated excluding user attributes (gender in this example). In the example shown in Figure 19(c), the output information is re-aggregated excluding store attributes (business type in this example). In the example shown in Figure 19(d), the output information is re-aggregated excluding both user attributes (gender in this example) and store attributes (business type in this example).

[0050] Next, the operation and effects of the estimation device 10 according to this embodiment will be described.

[0051] The estimation device 10 according to this embodiment includes an input unit 11 that acquires the company's own payment data for each layer, a total payment amount calculation unit 12 that calculates the total payment amount based on the company's own payment data acquired by the input unit 11 and a correction rate α, which is set in advance for each layer, for estimating the total payment amount including the payment amount of non-company payment merchants from the payment amount of company-affiliated merchants, and a correction rate β, which is set in advance for each layer, for estimating the payment amount of company-affiliated merchants including non-company payment from the company's own payment data at company-affiliated merchants, and a result output unit 15 that outputs the total payment amount.

[0052] In the estimation device 10 according to this embodiment, the total settlement amount (the total settlement amount including both merchants that accept the company's payment system and those that do not) is calculated for each layer based on the acquired company payment data. Specifically, the settlement amount for merchants that accept the company's payment system, including those that do not, is calculated from the company's payment data based on a correction rate β, and the total settlement amount is calculated from the settlement amount of merchants that accept the company's payment system based on a correction rate α. In this way, by using preset correction rates α and β, the ratio of company payments to non-company payments and the ratio of payments made at merchants that accept the company's payment system to payments made at non-company payments are taken into consideration, and the total settlement amount is calculated from the company's payment data for each estimation unit, thereby enabling the calculation of the total settlement amount from the company's payment data with high accuracy.

[0053] The total settlement amount calculation unit 12 may perform an α calculation process to calculate a correction rate α. In the α calculation process, for each layer, it may calculate the proportion m of behavioral patterns that select the company's own payment merchants in all transactions, and the proportion δ of the number of company's own payment merchants in all stores. Based on proportions m and δ, the correction rate α may be calculated. With such a configuration, it is possible to calculate a correction rate α with high accuracy for estimating the total settlement amount, which includes the settlement amount of non-company-affiliated stores, from the settlement amount of company's own payment merchants.

[0054] The total settlement amount calculation unit 12 may calculate the percentage m in the α calculation process based on a predetermined rule-based model that uses information related to layers or data on whether or not the company's own payment merchants are selected in settlements for some users. With such a configuration, the correction rate α can be calculated with high accuracy and ease.

[0055] The total settlement amount calculation unit 12 executes a β calculation process to calculate a correction rate β. In the β calculation process, it defines the priority of payment methods for each layer, and based on this priority and information indicating whether non-company payment methods are available at the company's payment merchants, it calculates the number P of company's payment merchants for which payment methods with a higher priority than the company's payment are unavailable. The correction rate β may then be calculated based on the total number K of company's payment merchants and the number P. With this configuration, it is possible to calculate a correction rate β with high accuracy for estimating the settlement amount of company's payment merchants, including non-company payment methods, from the company's payment data at the company's payment merchants.

[0056] The total settlement amount calculation unit 12 may define priorities in the β calculation process based on a predetermined rule base based on information related to the estimation unit or on a model for estimating payment usage priorities constructed based on payment usage priorities for some users. With such a configuration, priorities can be defined with high accuracy and ease, and consequently, the correction rate β can be calculated with high accuracy and ease.

[0057] The layers may be defined based on at least one of the following: area, time, user-related information, or store-related information. With such a configuration, it is possible to appropriately define estimation units that have a certain degree of commonality.

[0058] The estimation device 10 further includes a visitor count calculation unit 13 that estimates the total number of visitors for each layer, the input unit 11 further acquires location information for each user of the company's service, and the visitor count calculation unit 13 identifies the number of visitors related to the company's service for each layer based on the location information for each user acquired by the input unit 11, and estimates the total number of visitors by multiplying the number of visitors by a location expansion coefficient corresponding to the number of users of the company's service. With such a configuration, the total number of visitors can be appropriately estimated from information on the company's service.

[0059] The estimation device 10 may further include a unit price calculation unit 14 that calculates the average spending per user by dividing the total settlement amount by the total number of visitors. With such a configuration, the average spending per user can be appropriately calculated from the total settlement amount and the total number of visitors.

[0060] As mentioned above, the total settlement amount is basically calculated for each layer, but the correction rate, expanded total settlement amount, unit price, etc. may be calculated for only some layers, and the total settlement amount, etc. may be calculated for other layers using those calculation results. That is, for example, as shown in Figure 20, if there are two layers, Lx (first estimation group) and Ly (second estimation group), the total settlement amount calculation unit 12 may calculate the total settlement amount for layer Ly by multiplying the total number of visitors calculated by the visitor count calculation unit 13 for layer Ly by the unit price calculation unit 14 for layer Lx (first estimation group).

[0061] Figure 21 is a flowchart showing the total settlement amount calculation process related to the modified example described above. As shown in Figure 21, the visitor count table calculated by the visitor count calculation unit 13 for layer Ly is joined with the unit price table calculated by the unit price calculation unit 14 for layer Lx, using spatial granularity and user information as keys (step S51). In this case, the information used as keys is limited to items that overlap between layer Lx and layer Ly. Subsequently, for each layer such as layer Ly, the total settlement amount is calculated by multiplying the total number of visitors in layer Ly by the consumption unit price calculated for layer Lx (step S52). This method of calculating the total settlement amount is effective when it is not possible to calculate appropriate correction rates α and β for all layers.

[0062] The apparatus and method of this disclosure have the following configurations.

[0063] [1] A device comprising: an acquisition unit that acquires available settlement data for each estimation unit; a total settlement amount calculation unit that calculates the total settlement amount based on the available settlement data acquired by the acquisition unit and a correction rate for estimating the total settlement amount for each estimation unit, including the unacquirable settlement amount at the payment merchant and the settlement amount at the non-payment merchant, from the settlement data at the payment merchant; and an output unit that outputs the total settlement amount.

[0064] [2] The apparatus according to [1], wherein the total settlement amount calculation unit calculates the total settlement amount based on the obtainable settlement data obtained by the acquisition unit, a correction rate α for estimating the total settlement amount for each estimation unit, including the settlement amount of non-payment merchants from the settlement amount of payment merchants, and a correction rate β for estimating the settlement amount of payment merchants, including the unobtainable settlement amount, from the obtainable settlement data at payment merchants, and performs an α calculation process to calculate the correction rate α, and in the α calculation process, for each estimation unit, calculates the proportion m of behavior patterns that select payment merchants in all transactions and the proportion δ of the number of payment merchants in all stores, and calculates the correction rate α based on the proportion m and the proportion δ.

[0065] [3] The apparatus according to [2], wherein the total settlement amount calculation unit calculates the ratio m in the α calculation process based on a predetermined rule base based on information relating to the estimation unit or a model for estimating the ratio m constructed based on data relating to whether or not a payment merchant is selected in a settlement for a portion of users.

[0066] [4] The apparatus according to any one of [1] to [3], wherein the total settlement amount calculation unit calculates the total settlement amount based on the obtainable settlement data obtained by the acquisition unit, a correction rate α for estimating the total settlement amount for each estimation unit, including the settlement amount of non-payment merchants from the settlement amount of payment merchants, and a correction rate β for estimating the settlement amount of payment merchants, including the unobtainable settlement amount, from the obtainable settlement data at the payment merchants, executes a β calculation process to calculate the correction rate β, and in the β calculation process, defines the priority of payment methods for each estimation unit, and calculates the number P of payment merchants for which payment methods with a higher priority than the payment methods related to the obtainable settlement data are unavailable, based on the priority and information indicating whether payment related to the unobtainable settlement amount at the payment merchants is available, and calculates the correction rate β based on the total number of payment merchants K and the number P.

[0067] [5] The apparatus according to [4], wherein the total settlement amount calculation unit defines the priority in the β calculation process based on a predetermined rule base based on information relating to the estimation unit or a model for estimating a payment usage priority constructed based on a payment usage priority for some users.

[0068] [6] The estimation unit is set based on at least one of the following: area, time, user information, and store information. (Device described in any one of items [1] to [5])

[0069] [7] The apparatus according to any one of [1] to [6], further comprising a visitor count calculation unit that estimates the total number of visitors for each estimation unit, the acquisition unit further acquires location information of each user of a specific service, the visitor count calculation unit identifies the number of visitors related to the specific service for each estimation unit based on the location information of each user acquired by the acquisition unit, and estimates the total number of visitors by multiplying the number of visitors by a location expansion coefficient corresponding to the number of users of the specific service.

[0070] [8] The apparatus according to [7], further comprising a unit price calculation unit that calculates the average spending per user by dividing the total settlement amount by the total number of visitors.

[0071] [9] The apparatus according to [8], wherein the estimation units include a first estimation group and a second estimation group that are different from each other, and the total settlement amount calculation unit calculates the total settlement amount for the second estimation group by multiplying the total number of visitors calculated by the visitor number calculation unit with respect to the second estimation group by the unit price calculation unit with respect to the first estimation group.

[0072]

[10] A method performed by the apparatus, comprising: acquiring obtainable settlement data for each estimation unit; calculating the total settlement amount based on the acquired obtainable settlement data and a correction rate for estimating the total settlement amount, including the amount of settlements that cannot be acquired at the payment merchant and the amount of settlements at the non-payment merchant, from the obtainable settlement data at the payment merchant for each estimation unit; and outputting the calculated total settlement amount.

[0073] The block diagram used in the description of the above embodiment shows functional units. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized using one device that is physically or logically coupled, or it may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wired or wireless connections). A functional block may be realized by combining the one or more devices with software.

[0074] Functions include, but are not limited to, judgment, decision, determination, calculation, calculation, processing, derivation, investigation, exploration, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, assumption, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating (mapping), and assigning. For example, a functional block (configuration part) that enables transmission is called a transmitting unit or transmitter. In all cases, as mentioned above, the method of implementation is not particularly limited.

[0075] For example, the estimation device 10 that constitutes the device in one embodiment of the present disclosure may function as a computer that processes the control method of the present disclosure. Figure 22 is a diagram showing an example of the hardware configuration of the estimation device 10 according to this embodiment. The estimation device 10 described above may be physically configured as a computer device including a processor 1001, memory 1002, storage 1003, communication device 1004, input device 1005, output device 1006, bus 1007, etc. Note that the device 10 may be configured as a computer device including at least one processor such as a CPU or GPU, may be configured as a computer device including multiple processors, or may be configured to include multiple computer devices.

[0076] In the following explanation, the term "device" can be replaced with "circuit," "device," "unit," etc. The hardware configuration of the estimation device 10 may include one or more of the devices shown in the figure, or it may be configured to omit some of the devices.

[0077] Each function in the estimation device 10 is realized by loading predetermined software (programs) onto hardware such as the processor 1001 and memory 1002, which causes the processor 1001 to perform calculations, control communication by the communication device 1004, and control at least one of data reading and writing in the memory 1002 and storage 1003.

[0078] The processor 1001 controls the entire computer, for example, by running an operating system. The processor 1001 may be composed of a central processing unit (CPU) that includes interfaces with peripheral devices, control devices, arithmetic units, registers, etc. For example, the input unit 11 described above may be implemented by the processor 1001.

[0079] Furthermore, the processor 1001 reads programs (program code), software modules, data, etc., from at least one of the storage 1003 and the communication device 1004 into the memory 1002 and executes various processes accordingly. The program used is one that causes the computer to execute at least a part of the operations described in the above embodiment. For example, the total settlement amount calculation unit 12 may be stored in the memory 1002 and implemented by a control program that runs on the processor 1001, and other functional blocks may be implemented similarly. The above-described various processes have been explained as being executed by one processor 1001, but they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The program may also be transmitted from the network via a telecommunications line.

[0080] The memory 1002 is a computer-readable recording medium and may consist of at least one of the following: ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), RAM (Random Access Memory), etc. The memory 1002 may also be called a register, cache, main memory, etc. The memory 1002 can store executable programs (program code), software modules, etc., for implementing a control method according to one embodiment of the present disclosure.

[0081] The storage 1003 is a computer-readable recording medium and may consist of at least one of the following: an optical disc such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disc, a digital multipurpose disc, a Blu-ray® disc), a smart card, flash memory (e.g., a card, a stick, a key drive), a floppy® disk, a magnetic strip, etc. The storage 1003 may also be called an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, server, or other suitable medium including at least one of memory 1002 and storage 1003.

[0082] The communication device 1004 is hardware (transceiver / receiver device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as a network device, network controller, network card, communication module, etc. The communication device 1004 may be configured to include high-frequency switches, duplexers, filters, frequency synthesizers, etc., in order to implement at least one of frequency division duplex (FDD) and time division duplex (TDD). For example, the input unit 11 described above may be implemented by the communication device 1004.

[0083] The input device 1005 is an input device that accepts input from an external source (e.g., a keyboard, mouse, microphone, switch, button, sensor, etc.). The output device 1006 is an output device that outputs to an external source (e.g., a display, speaker, LED lamp, etc.). The input device 1005 and the output device 1006 may be configured as an integrated unit (e.g., a touch panel).

[0084] Furthermore, each device, such as the processor 1001 and memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or different buses may be configured for each device.

[0085] Furthermore, the RAG system 20 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), and an FPGA (Field Programmable Gate Array), and some or all of each functional block may be realized by such hardware. For example, the processor 1001 may be implemented using at least one of these hardware components.

[0086] Information notification is not limited to the embodiments described herein and may be carried out by other means. For example, information notification may be carried out by physical layer signaling (e.g., DCI (Downlink Control Information), UCI (Uplink Control Information)), upper layer signaling (e.g., RRC (Radio Resource Control) signaling, MAC (Medium Access Control) signaling, broadcast information (MIB (Master Information Block), SIB (System Information Block))), other signals, or combinations thereof. RRC signaling may also be called RRC messages, and may be, for example, RRC Connection Setup messages, RRC Connection Reconfiguration messages, etc.

[0087] The processing procedures, sequences, flowcharts, etc., of each aspect / embodiment described in this disclosure may be reordered, provided they do not contradict each other. For example, the methods described in this disclosure present various step elements using exemplary order and are not limited to the specific order presented.

[0088] Input and output information may be stored in a specific location (e.g., memory) or managed using a management table. Input and output information may be overwritten, updated, or appended to. Output information may be deleted. Input information may be transmitted to other devices.

[0089] The determination may be made by a value represented by one bit (0 or 1), by a boolean value (true or false), or by a numerical comparison (for example, a comparison with a predetermined value).

[0090] Each aspect / embodiment described in this disclosure may be used individually, in combination, or switched between as needed during implementation. Furthermore, notification of specific information (e.g., notification that "X is") is not limited to explicit notification, but may also be implicit (e.g., by not providing such notification).

[0091] Although the present disclosure has been described in detail above, it will be clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the intent and scope of the present disclosure as defined by the claims. Accordingly, the descriptions in the present disclosure are illustrative and not intended to be restrictive in any way.

[0092] Software should be broadly interpreted to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, functions, and so on, whether they are called software, firmware, middleware, microcode, hardware description languages, or by any other name.

[0093] Furthermore, software, instructions, information, etc., may be transmitted and received via a transmission medium. For example, if software is transmitted from a website, server, or other remote source using at least one of wired technologies (such as coaxial cable, fiber optic cable, twisted pair, or digital subscriber line (DSL)) and wireless technologies (such as infrared or microwave), then at least one of these wired and wireless technologies is included in the definition of a transmission medium.

[0094] The information, signals, etc. described in this disclosure may be represented using any of the various different techniques. For example, the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0095] In addition, terms used in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, at least one of the channel and symbol may be a signal (signaling). Also, a signal may be a message. Furthermore, a component carrier (CC) may be called a carrier frequency, cell, frequency carrier, etc.

[0096] Furthermore, the information, parameters, etc., described in this disclosure may be expressed using absolute values, relative values ​​from a given value, or other corresponding information. For example, wireless resources may be indicated by an index.

[0097] The names used for the parameters described above are not restrictive in any way. Furthermore, the formulas and other expressions using these parameters may differ from those expressly disclosed in this disclosure. Various channels (e.g., PUCCH, PDCCH, etc.) and information elements can be identified by any suitable name, and therefore, the various names assigned to these various channels and information elements are not restrictive in any way.

[0098] In this disclosure, terms such as "Mobile Station (MS)," "user terminal," "User Equipment (UE)," and "terminal" may be used interchangeably.

[0099] A mobile station may also be referred to by those skilled in the art as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other appropriate term.

[0100] As used in this disclosure, the terms “determining” and “determining” may encompass a wide variety of actions. “Determining” may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, or inquiring (e.g., searching in a table, database, or other data structure), or ascertaining. “Determining” may also include, for example, receiving (e.g., receiving information), transmitting (e.g., sending information), inputting, outputting, or accessing (e.g., accessing data in memory). Furthermore, "judgment" and "decision" can include considering something as having been "judged" or "decided" after resolving, selecting, choosing, establishing, comparing, etc. In other words, "judgment" and "decision" can include considering something as having been "judged" or "decided" after some action. Also, "judgment (decision)" can be reinterpreted as "assuming," "expecting," or "considering."

[0101] The terms “connected,” “coupled,” or any variation thereof, mean any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are “connected” or “coupled” with each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, “connection” may be reinterpreted as “access.” As used in this disclosure, two elements may be considered to be “connected” or “coupled” with each other using at least one of one or more wires, cables, and printed electrical connections, and, in some non-limiting and non-exclusive examples, electromagnetic energy having wavelengths in the radio frequency domain, microwave domain, and optical (both visible and invisible) domain.

[0102] In this disclosure, the phrase "based on" does not mean "based solely on" unless otherwise specified. In other words, the phrase "based on" means both "based solely on" and "based at least on."

[0103] Any reference to elements using designations such as “first,” “second,” etc., as used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Accordingly, references to first and second elements do not imply that only two elements may be employed, or that the first element must precede the second element in any way.

[0104] Where the terms “include,” “including,” and their variations are used in this disclosure, these terms are intended to be inclusive, as is the term “comprising.” Furthermore, the term “or” as used in this disclosure is not intended to be exclusive OR.

[0105] In this disclosure, if articles are added by translation, such as a, an, and the in English, this disclosure may include the fact that the noun following these articles is plural.

[0106] In this disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "combine" may be interpreted similarly to "different."

[0107] 10... Estimation device (device), 11... Input unit (acquisition unit), 12... Total settlement amount calculation unit, 13... Visitor count calculation unit, 14... Unit price calculation unit, 15... Result output unit (output unit).

Claims

1. A device comprising: an acquisition unit that acquires available settlement data for each estimation unit; a total settlement amount calculation unit that calculates the total settlement amount based on the available settlement data acquired by the acquisition unit and a correction rate for estimating the total settlement amount, including the unacquirable settlement amount at the payment merchant and the settlement amount at the non-payment merchant, from the available settlement data at the payment merchant for each estimation unit; and an output unit that outputs the total settlement amount.

2. The apparatus according to claim 1, wherein the total settlement amount calculation unit calculates the total settlement amount based on the obtainable settlement data obtained by the acquisition unit, a correction rate α for estimating the total settlement amount for each estimation unit, including the settlement amount of non-payment merchants from the settlement amount of payment merchants, and a correction rate β for estimating the settlement amount of payment merchants, including the unobtainable settlement amount, from the obtainable settlement data at payment merchants, and performs an α calculation process to calculate the correction rate α, and in the α calculation process, for each estimation unit, calculates the proportion m of behavioral patterns that select payment merchants in all transactions and the proportion δ of the number of payment merchants in all stores, and calculates the correction rate α based on the proportion m and the proportion δ.

3. The apparatus according to claim 2, wherein the total settlement amount calculation unit calculates the ratio m in the α calculation process based on a predetermined rule base based on information relating to the estimation unit or a model for estimating the ratio m constructed based on data relating to whether or not a payment merchant is selected in a settlement for a portion of users.

4. The apparatus according to claim 1, wherein the total settlement amount calculation unit calculates the total settlement amount based on the obtainable settlement data obtained by the acquisition unit, a correction rate α for estimating the total settlement amount for each estimation unit, including the settlement amount of non-payment merchants from the settlement amount of payment merchants, and a correction rate β for estimating the settlement amount of payment merchants, including the unobtainable settlement amount, from the obtainable settlement data at the payment merchants, executes a β calculation process to calculate the correction rate β, and in the β calculation process, defines the priority of payment methods for each estimation unit, and calculates the number of payment merchants for which payment methods with a higher priority than the payment methods related to the obtainable settlement data are unavailable, based on the priority and information indicating whether payment related to the unobtainable settlement amount at the payment merchants, and calculates the correction rate β based on the total number of payment merchants K and the number P.

5. The apparatus according to claim 4, wherein the total settlement amount calculation unit defines the priority in the β calculation process based on a predetermined rule base based on information relating to the estimation unit or a model for estimating a payment usage priority constructed based on a payment usage priority for some users.

6. The apparatus according to claim 1, wherein the estimation unit is set based on at least one of the following: area, time, user information, and store information.

7. The apparatus according to any one of claims 1 to 6, further comprising a visitor count calculation unit that estimates the total number of visitors for each estimation unit, the acquisition unit further acquires location information of each user of a specific service, the visitor count calculation unit identifies the number of visitors related to the specific service for each estimation unit based on the location information of each user acquired by the acquisition unit, and estimates the total number of visitors by multiplying the number of visitors by a location expansion coefficient corresponding to the number of users of the specific service.

8. The apparatus according to claim 7, further comprising a unit price calculation unit that calculates the average spending per user by dividing the total settlement amount by the total number of visitors.

9. The apparatus according to claim 8, wherein the estimation units include a first estimation group and a second estimation group that are different from each other, and the total settlement amount calculation unit calculates the total settlement amount for the second estimation group by multiplying the total number of visitors calculated by the visitor number calculation unit with respect to the second estimation group by the unit price calculation unit with respect to the first estimation group.

10. A method performed by a device, comprising: acquiring obtainable settlement data for each estimation unit; calculating the total settlement amount based on the acquired obtainable settlement data and a correction rate for estimating the total settlement amount, including the amount of unobtainable settlements at the payment merchant and the amount of settlements at non-payment merchants, from the obtainable settlement data at the payment merchant for each estimation unit; and outputting the calculated total settlement amount.

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