JP2023133167A - Information processing device, information processing method and information processing program - Google Patents

Abstract

To appropriately predict a future trend in a real store.SOLUTION: An information processing device includes an acquisition part for acquiring store information relating to each real store included in a store group, and a learning part when the store information acquired by the acquisition part is input, learning a learning model that outputs future information relating to a future trend in the store group.SELECTED DRAWING: Figure 1

Description

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

2. Description of the Related Art Conventionally, there is a technology that predicts future sales of an online store opening in an electronic shopping mall based on information about the store. For example, in this technique, funds are provided to the store by purchasing from the store a receivable corresponding to the predicted future sales amount (for example, see Patent Document 1).

Patent No. 6836255

However, there is room for improvement in the prior art. For example, although conventional technology predicts future sales for online stores, it is difficult to apply this to offline physical stores. Therefore, it is desired to appropriately predict future trends in physical stores.

The present invention has been made in view of the above, and an object of the present invention is to provide an information processing device, an information processing method, and an information processing program that can appropriately predict future trends in physical stores.

In order to solve the above-mentioned problems and achieve the purpose, an information processing device according to the present invention includes an acquisition unit that acquires store information regarding each physical store included in a store group, and a store information processing unit that acquires store information regarding each physical store included in a store group; and a learning section that learns a learning model that outputs future information regarding future trends of the store group when information is input.

According to the present invention, future trends in physical stores can be appropriately predicted.

FIG. 1 is a diagram illustrating an example of information processing according to an embodiment. FIG. 2A is a diagram illustrating an example of an output result by the learning model according to the embodiment. FIG. 2B is a diagram illustrating an example of an output result by the learning model according to the embodiment. FIG. 2C is a diagram illustrating an example of an output result by the learning model according to the embodiment. FIG. 3 is a block diagram illustrating a configuration example of an information processing apparatus according to an embodiment. FIG. 4 is a diagram illustrating an example of a store information database according to the embodiment. FIG. 5 is a diagram illustrating an example of the member store information database according to the embodiment. FIG. 6 is a diagram illustrating an example of the area information database according to the embodiment. FIG. 7 is a diagram illustrating an example of a model database according to the embodiment. FIG. 8 is a diagram illustrating an example of a factoring database according to the embodiment. FIG. 9 is a diagram illustrating an example of a factoring application screen according to the embodiment. FIG. 10 is a diagram illustrating an example of a factoring application screen according to the embodiment. FIG. 11 is a flowchart illustrating an example of information processing according to the embodiment. FIG. 12 is a hardware configuration diagram showing an example of a computer that implements the functions of the information processing device according to the embodiment.

DESCRIPTION OF THE PREFERRED EMBODIMENTS An information processing apparatus, an information processing method, and an embodiment of an information processing program (hereinafter referred to as "embodiments") according to the present application will be described in detail below with reference to the drawings. Note that the information processing apparatus, information processing method, and information processing program according to the present application are not limited to this embodiment.

[1. Embodiment]
Information processing realized by the information processing apparatus and the like of this embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating an example of information processing according to an embodiment. Note that in FIG. 1, it is assumed that the information processing apparatus 10 according to the present embodiment realizes information processing according to the embodiment.

As shown in FIG. 1, the information processing system 1 according to the embodiment includes an information processing device 10, a user terminal 50, a payment server 100, a store terminal 200, and a business terminal 300. The information processing device 10, the user terminal 50, the payment server 100, the store terminal 200, and the business terminal 300 are connected to each other via a network so that they can communicate with each other by wire or wirelessly. The network is, for example, a WAN (Wide Area Network) such as the Internet. Note that the information processing system 1 shown in FIG. 1 may include a plurality of information processing apparatuses 10, user terminals 50, payment servers 100, store terminals 200, and business terminals 300.

The information processing device 10 shown in FIG. 1 is an information processing device that executes information processing according to the embodiment, and is realized by a server device, a cloud system, or the like. For example, the information processing device 10 may buy a receivable from a member store of an electronic payment service based on information regarding an electronic payment service related to electronic payment using the user terminal 50 provided by the payment server 100. Provide factoring services that provide funds.

For example, the payment server 100 manages the accounts of providers (business operators) of transaction objects and users to whom transaction objects are provided, and transfers electronic money between accounts in accordance with payment requests from users. By doing so, various payments can be realized. Note that electronic money may be, for example, points or currency used independently by various companies, or may be currency provided by a country such as Japanese yen or dollars that can be electronically transacted. good.

In addition, the payment server 100 transmits profit information regarding profits (coupons, etc.) that are given to users in the electronic payment service and can be used in the electronic payment service to the profit provider (in other words, plans, settings, etc. of coupons). , the person who invests (provides) the coupon funds) and manages it in the storage unit of its own device.

Further, the payment server 100 manages area information including information on stores (member stores of electronic payment services) etc. located in each area in its own storage unit. For example, the information processing device 10 provides identification information (area ID) for identifying each area, location information (location, range, etc.) for each area, road information within each area, route information within each area, Stores area information including the store where it is located. Note that the area may be appropriately set to have an arbitrary size range. For example, the area may be divided into meshes of similar size based on latitude and longitude. That is, the area may be an area divided by a regional mesh. In this case, a regional mesh code may be used for the area ID. Note that the above is an example, and the area may be set based on various information other than the regional mesh. For example, the area may be set based on administrative divisions such as "town", "ward", "city", and "prefecture". Further, the area may be set for each facility such as a store, or may be set for each street, shopping district, etc.

The user terminal 50 shown in FIG. 1 is an information processing device used by a user U. The user terminal 50 is realized by, for example, a smartphone, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), or the like.

The store terminal 200 shown in FIG. 1 is an information processing device used by a store that provides transactions to users. The store terminal 200 is realized by, for example, a POS (Point of Sales) terminal, a smartphone, a tablet terminal, a notebook PC, a desktop PC, a mobile phone, a PDA, or the like. Further, the store terminal 200 displays information distributed by the payment server 100 using a web browser or an application. Note that the example shown in FIG. 1 shows a case where the store terminal 200 is a POS terminal.

Note that, when the user terminal 50 and the store terminal 200 receive control information for realizing predetermined information processing from the information processing device 10, the user terminal 50 and the store terminal 200 realize the information processing according to the control information. Here, the control information is, for example, a script language such as JavaScript (registered trademark), a style sheet language such as CSS (Cascading Style Sheets), a programming language such as Java (registered trademark), or a mark such as HTML (HyperText Markup Language). It is written in an application language, etc. Note that the predetermined application itself distributed from the payment server 100 may be regarded as the control information.

The business terminal 300 shown in FIG. 1 is, for example, an information processing device used by a business operator that operates each store. The business terminal 300 is realized by a smartphone, a tablet terminal, a notebook PC, a desktop PC, a mobile phone, a PDA, or the like. Further, the business terminal 300 displays information distributed by the information processing device 10 and the payment server 100 using a web browser or an application. Note that the example shown in FIG. 1 shows a case where the business terminal 300 is a notebook PC.

[1-1. About payment using user terminal 50]
Here, an example of payment (electronic payment) using the user terminal 50 will be described prior to the provision processing performed by the information processing device 10. In the following explanation, user U uses a two-dimensional code (QR code (registered trademark)) placed at store A that indicates store identification information to identify store A. Although an example in which payment is made using the user terminal 50 will be described, the embodiment is not limited to this. An example of a payment described below can be applied to a case where any user makes a payment at any store using any user terminal 50. Further, the store identification information may be not only a QR code but also a bar code, a predetermined mark, a number, etc.

For example, when user U makes a payment for the use or purchase of payment objects (transaction objects) such as various products and services at store A, user U uses the payment application pre-installed on the user terminal 50. to start. Then, the user U photographs the store identification information installed at the store A via the payment application. In such a case, the user terminal 50 displays a screen for inputting the price to be paid, and accepts input of the payment amount from the user U or the clerk at the store A. The user terminal 50 then stores the user identification information that identifies the user U and the store identification information (or information indicated by the store identification information, that is, information indicating the store A (or the business operator of the store A) (for example, , store ID) and the payment amount are transmitted to the information processing device 10.

In such a case, the information processing device 10 transfers the amount of electronic money indicated by the payment amount from the account of the user U indicated by the user identification information to the account of the store A indicated by the store identification information. The information processing device 10 then sends a notification to the user terminal 50 that the payment has been completed. In such a case, the user terminal 50 notifies that the payment has been made using electronic money by displaying a screen or outputting a predetermined sound indicating that the payment has been completed.

Note that the payment using the user terminal 50 is not limited to the above-described processing. For example, the payment using the user terminal 50 may be made using the store terminal 200 installed in the store A. For example, the user terminal 50 displays user identification information for identifying the user U on the screen. In such a case, the store terminal 200 installed in the store A reads the user identification information displayed on the user terminal 50, and reads the user identification information (or the information indicated by the user identification information, that is, the user Payment information indicating information indicating U (for example, user ID), the payment amount, and information identifying store A is transmitted to the payment server 100. In such a case, the information processing device 10 The amount of electronic money indicated by the payment amount is transferred from the account of user U indicated by the information to the account of store A, and a screen indicating that the payment has been completed is displayed on the store terminal 200 or user terminal 50 of store A. The fact that the payment has been made may be notified by outputting a predetermined sound.

In addition, the payment using the user terminal 50 is not limited to the process of transferring electronic money from the account to which the user U has previously charged electronic money to the store A's account, but also uses a credit card registered in advance by the user U, for example. Payment may be made using In such a case, for example, the user terminal 50 transfers the electronic money of the payment amount to the store A's account, and requests the payment amount from the operating company (card company) of the credit card of the user U. You may.

[1-2. About information processing according to embodiment]
The information processing device 10 according to the embodiment provides a factoring service to a business operator of each store based on the payment history of electronic payment services performed at each store. Here, the factoring service is a service that provides funds to businesses by purchasing a portion of the store's future sales as receivables.

In order to provide such factoring services, it is necessary to determine receivable purchase conditions (hereinafter referred to as factoring conditions) according to the future trends of stores.

Therefore, the information processing device 10 according to the embodiment learns a learning model (hereinafter referred to as a model) that estimates future information indicating future trends of each store, and determines factoring conditions based on the estimation results of the model. In the following, a case will be described in which a factoring service is provided to a business that operates chain stores (for example, an operating headquarters), and a model for predicting future trends for each chain store is learned. Note that model learning is not limited to each chain store, but may be set to any arbitrary unit such as each store or store category.

For example, as shown in FIG. 1, in the information processing system 1 according to the embodiment, when a user U purchases a product (or service) at a store A using an electronic payment service, payment information is notified to the payment server 100. (Step S1). The payment server 100 aggregates the payment information notified from each store, and notifies the information processing device 10 of the transaction information of the electronic payment service at each store (step S2). For example, the transaction information includes information regarding the transaction volume of electronic payment services at each store, transaction date and time, users who used each store, and purchased products.

The information processing device 10 stores the transaction information in the store information database and performs model learning using chain information obtained by aggregating store information stored in the store information database for each chain store as learning data (step S3).

Here, store information includes category information regarding the store category of each store, transaction information of electronic payment services at each store, geographic information regarding location, user information regarding users, business hours information regarding business hours, etc. Contains various information regarding the store. Furthermore, chain information is information that aggregates these store information for each chain store.

For example, as shown in FIG. 1, the information processing device 10 learns models M1 to M3 (hereinafter also referred to as model M) based on chain information. The model M1 shown in FIG. 1 is a model that estimates a score considering the group characteristics of each chain store.

For example, the information processing device 10 uses, as learning data, store number information regarding an increase or decrease in the number of member stores affiliated with a chain store (hereinafter also referred to as chain stores), information regarding the transaction history of electronic payment services at each chain store, etc. Model M1 is trained. That is, the model M1 is a model that estimates an increase or decrease in the number of chain stores as future information regarding chain stores from the transaction history of electronic payment services at each chain store.

For example, as shown in FIG. 2A, model M1 is a model that estimates trends in the number of chain stores. Note that FIG. 2A is a diagram showing an example of an output result by the learning model according to the embodiment. For example, the model M1 estimates the number of stores (number of chain stores) on multiple time axes such as medium term or long term, as shown by the broken line in FIG. 2A.

In this case, the model M1 may be composed of a plurality of models depending on the time axis for estimating the model M1. That is, the model M1 may include a model that estimates the number of stores on a medium-term time axis and a model that estimates the number of stores on a long-term time axis.

Note that the model M1 may be a model that estimates future usage of the electronic payment service in the entire chain store as future information. The model M1 may be a model that estimates the withdrawal probability of a member store providing an electronic payment service at a chain store, and may be a model for estimating the withdrawal probability of a member store providing an electronic payment service at a chain store, such as the future transaction volume of the electronic payment service at the chain store or the change in the number of users using the service. It may also be a model that estimates information. Note that withdrawal refers to a store withdrawing from electronic payment services due to store closure or other reasons.

The model M2 shown in FIG. 1 is a model that estimates information regarding future usage patterns of electronic payment services as future information, taking into consideration area characteristics. For example, model M2 is a model learned for each area using store information for each area as learning data.

Here, since weather, demographic trends, local industry, etc. differ depending on the area, it is assumed that needs, business strategies, rival stores, etc. will differ depending on the area. Therefore, the information processing device 10 visualizes these characteristics for each area by preparing learning data for each area and generating the model M2.

In the learning stage of the model M2, the information processing device 10 uses information regarding the increase/decrease in the number of chain stores in each area, information regarding the transaction history of electronic payment services, etc. as learning data, and learns the model M2 for each area.

For example, as shown in FIG. 2B, model M2 is a model that outputs information indicating the withdrawal probability for each area in a chain store. Note that FIG. 2B is a diagram showing an example of an output result by the learning model according to the embodiment. Figure 2B shows an example where the area is divided into seven regions such as Hokkaido, Tohoku, Kanto, Chubu, Kinki, Chugoku, and Kyushu. It may also be classified. Furthermore, regarding the setting of areas, it is not a requirement that they be adjacent areas, such as setting multiple areas with similar demographic trends as the same area.

Model M3 shown in FIG. 1 is a model that estimates information regarding future usage patterns of electronic payment services as future information, taking into consideration the business hours characteristics of each chain store. For example, the information processing device 10 uses business hours information regarding the business hours of each chain store and information regarding the transaction history of the electronic payment service as learning data to perform learning of the model M3.

For example, the information processing device 10 learns the model M3 using learning data such as the total business hours per month of the entire chain store, the number of employees, the transaction amount (monthly sales) of the electronic payment service, and the like.

For example, as shown in FIG. 2C, model M3 is a model that has learned the relationship between withdrawal probability and business hours (monthly sales/total business hours). Note that FIG. 2B is a diagram showing an example of an output result by the learning model according to the embodiment. Although FIG. 2C shows the relationship between withdrawal probability and business hours in two dimensions, this is for convenience of explanation and is not limited to this.

By learning the relationship between business hours and the probability of withdrawal, the information processing device 10 can, for example, visualize the relationship between business hours and the probability of withdrawal.

Next, as shown in FIG. 1, when the information processing device 10 receives a factoring application from the business terminal 300 (step S4), it inputs chain information of the corresponding business for each model M1 to M3. By doing so, a withdrawal score is calculated (step S5).

For example, the information processing device 10 uses model M1 to calculate a score related to an increase or decrease in the number of chain stores operated by a business operator as a withdrawal score, and uses model M2 to calculate a withdrawal score for each area. do. Furthermore, the information processing device 10 uses the model M3 to calculate a withdrawal score according to business hours.

Here, the withdrawal score is, for example, a score regarding an increase or decrease in the number of chain stores in the entire chain stores. For example, a higher exit score suggests that the number of chain stores is more likely to decrease, and a lower exit score suggests that the number of chain stores is less likely to decrease or increases.

For example, if the number of chain stores increases, the amount spent on electronic payment services across chain stores is expected to increase. Therefore, the withdrawal score is an index for predicting changes in the usage amount of electronic payment services at chain stores, and can also be regarded as a value that quantifies the risk of default on receivables. Specifically, it can be considered that the lower the withdrawal score, the lower the risk of defaulting on the receivables, and the higher the withdrawal score, the higher the risk of defaulting on the receivables.

Then, the information processing device 10 determines factoring conditions according to the withdrawal score calculated using the models M1 to M3 (step S6). For example, the information processing device 10 determines more advantageous factoring conditions for a business with a lower withdrawal score, that is, a business with a lower possibility of withdrawal and a lower risk of debt default.

For example, the factoring conditions include conditions such as the upper limit for purchase of receivables, collection mode, etc., and the information processing device 10 determines these conditions according to the withdrawal score. For example, the information processing device 10 determines the factoring condition such that the lower the withdrawal score, the higher the upper limit purchase amount of the receivables.

Thereafter, the information processing device 10 provides funds to the business operator by making a contract with the business operator and purchasing receivables from the business operator based on the determined factoring conditions (step S7).

In this way, the information processing device 10 according to the embodiment learns the models M1 to M3 based on each store information. Therefore, according to the information processing device 10 according to the embodiment, future trends in physical stores can be appropriately predicted.

Further, in the information processing device 10 according to the embodiment, by determining the factoring conditions based on the output results of the models M1 to M3, it is possible to determine appropriate factoring conditions according to the future trends of the physical store.

Note that the purpose of using the models M1 to M3 is not limited to determining factoring conditions, but can also be used, for example, to avoid withdrawal from electronic payment services or for business activities related to new transactions. For example, chain stores or chain stores with high exit scores can be used for marketing electronic payment services, such as strengthening sales activities.

[1-3. About the learning process according to the embodiment]
For example, the above learning data includes the chain information of each chain store, which is associated with a label (also called "correct information") indicating the increase/decrease in the number of chain stores and the transaction volume of electronic payment services. Contains data.

Further, for example, as the correct answer information, "1" is assigned when the number of chain stores has decreased, and "0" is assigned when the number of chain stores has not decreased. In addition, as correct answer information, if the number of chain stores has decreased within a year from time X, "1" will be assigned to the chain information up to a certain point (time point X); If it has not decreased within the range, "0" may be assigned. In this case, the information processing device 10 uses a model that estimates (predicts) the probability (possibility) that the number of chain stores will decrease within a desired period (for example, one month, six months, five years, etc.), not just one year. can be generated.

For example, the information processing device 10 uses backpropagation (error backpropagation method) so that the score output by the model M approaches the correct information (label) associated with the chain information of the chain store input to the model M. Learning processing is performed using methods such as For example, when the correct answer information is "1", the information processing device 10 causes the score output by the model M to approach "1" when the chain information of the chain store to which the correct answer information is assigned is input. Then, the learning process is performed. Further, for example, when the correct answer information is "0", the score output by the model M approaches "0" when the chain information of the chain to which the correct answer information is assigned is input. The learning process is performed as follows.

For example, the information processing device 10 adjusts the value of a weight (i.e., connection coefficient) that is taken into consideration when transmitting a value between nodes through a learning process. In this way, the information processing device 10 learns the model M through processing such as backpropagation that corrects the parameters (connection coefficients) so that the error between the output of the model M and the correct information corresponding to the input is reduced. . For example, the information processing device 10 generates the model M by performing processing such as backpropagation to minimize a predetermined loss function. Thereby, the information processing device 10 can perform a learning process for learning the parameters of the model M.

Note that the model learning method is not limited to the method described above, and any known technique can be applied. Note that each model may be generated using various conventional techniques related to machine learning as appropriate. For example, the model may be generated using a technique related to supervised machine learning such as SVM (Support Vector Machine). Further, for example, the model may be generated using a technique related to unsupervised machine learning. For example, the model may be generated using deep learning technology. For example, the model may be generated using various deep learning techniques such as DNN (Deep Neural Network), RNN (Recurrent Neural Network), and CNN (Convolutional Neural Network). Note that the above description regarding model generation is merely an example, and model generation may be performed using a learning method appropriately selected depending on obtainable information and the like. That is, if the information processing device 10 can learn the model M so as to output a score corresponding to the correct information when chain information included in the learning data is input, then by what method can the information processing device 10 learn the model M so as to output a score corresponding to the correct answer information? You may also generate it.

[2-1. Configuration example of information processing device]
Next, a configuration example of the information processing device 10 will be described using FIG. 3. FIG. 2 is a block diagram showing a configuration example of the information processing device 10 according to the embodiment. As shown in FIG. 2, the information processing device 10 includes a communication section 110, a storage section 120, and a control section 130.

The communication unit 110 is realized by, for example, a NIC (Network Interface Card). The communication unit 110 is connected to the network N by wire or wirelessly, and transmits and receives information to and from the payment server 100, the business terminal 300, and the like.

The storage unit 120 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. As shown in FIG. 3, the storage unit 120 includes a store information database 121, an affiliated store information database 122, a geographic information database 123, a model database 124, and a factoring database 125.

The store information database 121 is a database that stores information regarding each store that uses electronic payment services. FIG. 4 is a diagram showing an example of information stored in the store information database 121 according to the embodiment.

As shown in FIG. 4, the store information database 121 includes "store ID", "affiliated store ID", "store opening date", "closing date", "category ID", "sales", "transaction amount", " Information on items such as "address," "business hours," and "number of employees" are stored in association with each other.

"Store ID" is an identifier for identifying a store. For example, the store ID in the electronic payment service is used as the store ID. The "affiliated store ID" is an identifier for identifying a chain store to which the store identified by the corresponding store ID is affiliated.

For example, if they are affiliated with the same chain store, they are given the same affiliated store ID. Note that even if stores are affiliated with the same chain store, different IDs may be assigned depending on the contract type of each store. That is, the "affiliated store ID" may include an identifier for identifying a directly managed store or a franchise store.

The "store opening date" is the date when the store identified by the corresponding store ID opened, and the "closing date" is the date when the store identified by the corresponding store ID closed (store closing). ” and “closing date” may be read as the date of membership of the electronic payment service and the date of withdrawal of the store from the electronic payment service, respectively.

"Category ID" is an identifier for identifying the store category of the store identified by the corresponding store ID. For example, the category ID is determined based on the type of industry and the products (services) handled. Note that multiple types of category IDs may be assigned to one store.

"Sales" is the sales amount per month (i.e., monthly sales) of the store identified by the corresponding store ID, and "Transaction amount" is the electronic payment at the store identified by the corresponding store ID. This is the handling amount of services.

"Address information" is information regarding the address of the store identified by the corresponding store ID. For example, the "address information" may include information regarding the location of the store, the store area, the presence or absence of a parking lot, and the like.

"Business hours" is information regarding the business hours of the store identified by the corresponding store ID, and "Number of employees" is information regarding the number of employees at the store identified by the corresponding store ID. Note that the number of employees may be information regarding the total number of employees working at the corresponding store, or may include the number of employees working at one time. Further, the number of employees may include information regarding the number of employees for each type of employment (full-time employees, part-time employees, etc.).

Returning to the explanation of FIG. 3, the affiliated store information database 122 will be explained. The affiliated store information database 122 is a database that stores information regarding affiliated stores. FIG. 5 is a diagram showing an example of information stored in the affiliated store information database 122 according to the embodiment.

As shown in FIG. 5, the member store information database 122 associates information such as "member store ID," "number of stores," "total business hours," "number of employees," and "monthly sales" with each other. Remember.

The "affiliated store ID" is an identifier for identifying a chain store, and the "number of stores" is the number of stores of the chain store identified by the corresponding affiliate store ID. The "total business hours" is the sum of the business hours per month of the entire chain store identified by the corresponding member store ID, and the "number of employees" is the number of employees of the entire chain store.

Moreover, "monthly sales" is the sales for one month of the entire chain store identified by the corresponding member store ID. Note that the monthly sales may be the sum total of the transaction volume of electronic payment services of the entire chain store.

Returning to the explanation of FIG. 3, the geographic information database 123 will be explained. The geographic information database 123 is a database that stores geographic information. FIG. 6 is a diagram showing an example of information stored in the geographic information database 123 according to the embodiment.

As shown in FIG. 6, for example, the geographic information database 123 stores information on items such as "area ID," "population information," and "store information" in association with each other. "Area ID" is an identifier for identifying each area.

"Population information" is information regarding the population in the area identified by the corresponding area ID. Population information includes information on demographic statistics (population by age, gender), statistical information on annual income and wages in the area, information on the flow of people in the area by time, information on users of electronic payment services, etc.

"Store information" is information regarding a store opened in the area identified by the corresponding area ID. For example, as shown in FIG. 6, the store information is information regarding stores corresponding to each store category, such as category 1 and category 2. The category here is, for example, a store category, and the store information stores information regarding stores belonging to similar or the same store category. Note that the store category may be such that each store is divided by industry or by products handled.

Note that, for example, the category may be set in units of chain stores, and in this case, the store information stores information regarding chain stores of the same chain store. In this case, for example, store information may include information indicating the geographical distribution of chain stores (how each chain store is distributed in the map information), and the rent (or land price) of each chain store. It may also include the following.

Returning to the explanation of FIG. 3, the model database 124 will be explained. The model database 124 is a database that stores models. FIG. 7 is a diagram showing an example of information stored in the model database 124 according to the embodiment.

As shown in FIG. 7, the model database 124 stores information on items such as "model ID", "application", "model data", and "learning data" in association with each other. "Model ID" is an identifier for identifying each model.

“Application” indicates the use of the model identified by the corresponding model ID. The example shown in FIG. 7 shows a case where the usage includes "group characteristics", "area characteristics (AR1 to AR3)", and "business hours characteristics".

"Model data" is the data body of the model identified by the corresponding model ID, and "learning data" is the learning model for generating the model identified by the corresponding model ID.

Returning to the explanation of FIG. 3, the factoring database 125 will be explained. The factoring database 125 stores various information regarding factoring. FIG. 8 is a diagram showing an example of information stored in the factoring database 125 according to the embodiment.

As shown in FIG. 8, the factoring database 125 stores information on items such as "affiliated store ID," "score information," and "factoring information" in association with each other. “Affiliate store ID” is an identifier for identifying a chain store.

"Score information" is the score for the chain store identified by the corresponding member store ID. "Factoring information" indicates whether factoring of the corresponding chain store is being used. For example, a chain store whose "factoring information" is "-" (hyphen) indicates that it is a store that does not use factoring.

A chain store whose "factoring information" is factoring information #2 indicates that it is a chain store that is using factoring, and provides specific information regarding factoring, such as the amount of factoring and its collection method (collection period, collection interval, etc.). Information is stored.

Returning to the explanation of FIG. 3, the control unit 130 will be explained. The control unit 130 is a controller, and various programs stored in a storage device inside the information processing device 10 use a RAM as a work area by, for example, a CPU (Central Processing Unit) or an MPU (Micro Processing Unit). This is achieved by executing as . Further, the control unit 130 is realized by, for example, an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The control unit 130 according to the embodiment includes an acquisition unit 131, a learning unit 132, an estimation unit 133, and a determination unit 134, and realizes or executes information processing functions and operations described below.

The acquisition unit 131 acquires store information regarding each physical store and stores it in the store information database 121. For example, the acquisition unit 131 acquires transaction information regarding electronic payment service transactions at a physical store from the payment server 100. For example, the acquisition unit 131 may acquire sales information regarding sales at a physical store. Note that the sales information is information regarding store sales including cash, credit cards, and other payment methods in addition to the transaction volume of the payment service provided by the payment server 100. For example, the acquisition unit 131 acquires sales information through a questionnaire sent to each physical store or chain store headquarters. Note that the acquisition unit 131 may estimate the sales information from the transaction history of the electronic payment service at each physical store. Further, the sales information may be, for example, monthly sales or one month's profit (monthly sales minus expenses).

The acquisition unit 131 also acquires business hours information and employee information as store information regarding each store. The acquisition unit 131 acquires business hours information and employee information, for example, by using a web page related to each physical store (such as the store's homepage) or by conducting a questionnaire on each store. Note that the acquisition unit 131 may estimate the business hours information from the time of use of the electronic payment service. For example, the acquisition unit 131 may estimate the business hours of each store based on the time when the electronic payment service is used and the time when the electronic payment service is not used.

Further, the acquisition unit 131 may estimate which user is an employee based on the position information of each user terminal 50, and then estimate the employee information. For example, the acquisition unit 131 may aggregate the typical length of stay at each physical store, and then estimate the user who has a longer length of stay than the typical length of stay as an employee. Note that the acquisition unit 131 acquires employees at each store from information regarding each user's workplace based on, for example, the electronic payment service provided by the payment server 100 or the registered information of a portal site linked to the electronic payment service. may be specified.

The acquisition unit 131 also acquires area information regarding each area. The area information includes, for example, demographic trends (population by generation), the number of users of electronic payment services, information regarding the floating population (flow of people) in each area, and the like.

Further, for example, the area information may include information regarding land prices, rent market prices (land prices), and salary market prices for each industry in each area. For example, the acquisition unit 131 acquires this area information from an external server (not shown).

The learning unit 132 learns the models M1 to M3 based on the various information acquired by the acquisition unit 131. As described above, the model M1 is a model that estimates future trends of chain stores by considering group characteristics of each chain store.

For example, in the learning stage of the model M1, the learning unit 132 learns, as learning data, transaction history regarding the transaction volume of the electronic payment service of each chain store affiliated with the chain store, and data regarding the number of stores of the chain store.

For example, the learning unit 132 performs learning of the model M1 using chain information obtained by aggregating store information regarding chain stores for each chain store as learning data.

For example, the learning data includes data that associates the chain information of each chain store with a label (also called "correct information") that indicates the increase or decrease in the number of member stores of the chain store or the transaction volume of electronic payment services. It will be done.

Further, for example, as the correct answer information, "1" is assigned when the number of chain stores has decreased, and "0" is assigned when the number of chain stores has not decreased. For example, when the correct answer information is "1", the learning unit 132 sets the score output by the model M1 to approach "1" when the chain information of the chain store to which the correct answer information is assigned is input. , performs learning processing. Further, for example, when the correct answer information is "0", the score output by the model M1 is set to "0" when the chain information of the chain store to which the correct answer information is assigned is input. Learning processing is performed to get closer. The learning unit 132 may learn the model M1 by setting the correct answer information to "1" when the number of chain stores has increased, and "0" when the number of chain stores has not increased.

In addition, in the learning stage of the model M1, the learning unit 132 sets "1" if the transaction volume of electronic payment services in the entire chain store has decreased (or increased) as the correct answer information; The model M1 may be trained by setting the value to "0" if there is no increase (increase).

Note that here, the store group is defined as a chain store unit, but the store group is not limited to this, and the store group may be defined as a store category (for example, convenience store, drug store, etc.), and each store's customer demographic, customer unit price, etc. An arbitrary store group may be created depending on the situation. That is, a store group may be created from a plurality of physical stores with similar customer unit prices or similar customer demographics, and the model M1 may be trained.

Furthermore, the learning unit 132 performs learning for each area regarding the model M2 that outputs future information in consideration of area characteristics. That is, the model M2 is generated for each area.

In this case, the learning data includes data obtained by dividing the chain information used in the learning data of the model M1 by area, data regarding the market rent (or land price) in the corresponding area, data regarding the population, and the like.

At this time, as the learning data, the number of physical stores having similar store categories to the target store (chain store) in the learning target area and the sales amount of each of these stores may be used.

In other words, the model M2 may be trained, for example, taking into consideration the demand of the entire store category in the relevant area. To give a more specific example, for example, in the Osaka area, demand for takoyaki and okonomiyaki is higher than in other areas, and the number of stores and sales are said to be higher than in other areas.

Model M2 makes it possible to visualize the characteristics of an area based on the entire store category, number of stores, and sales in the area. In other words, the learning unit 132 learns the model M2 based not only on information related to chain stores in the relevant area, but also on the entire store category or store information of the same industry, to appropriately determine the characteristics of the target area. can be reflected.

Furthermore, the learning unit 132 learns a model M3 that outputs future information in consideration of business hours characteristics. In this case, the learning data includes, for example, information on business hours per month for the entire chain store, data on the actual increase/decrease in the number of chain stores, and data on the monthly transaction volume of electronic payment services. .

Here, the business hours information includes information regarding the total business hours per month at each store, working hours per employee, and business hours mode. Note that the business hours include, for example, 24-hour operation, late-night operation, early-morning operation, and holidays.

The learning data for model M3 is generated based on business hours information and electronic payment service transaction information, and includes, for example, sales per hour (monthly sales/total business hours), sales per employee ( It includes data on monthly sales/number of employees), unit labor costs, and transaction volume of electronic payment services.

For example, the learning unit 132 associates a label with correct answer information "1" to data in which the number of chain stores has decreased, and a label with correct answer information "0" to data in which the number of chain stores has not decreased, and creates model M3. Learn.

In addition, the learning unit 132 sets the correct answer information "1" to the data of stores where the transaction volume of electronic payment services has increased (or decreased), and gives the correct answer information of "1" to the data of stores where the transaction volume of electronic payment services has not increased (or decreased). The model M3 may be trained using learning data associated with information "0".

In addition, in the above example, we have explained the case where models M1 to M3 are trained using the trading volume of electronic payment services as learning data, but separately, model M1 is trained using trading volume (monthly sales) of other than electronic payment services as learning data. ~M3 learning may be performed.

The estimation unit 133 uses the model M learned by the learning unit 132 to estimate future trends of physical stores. For example, the learning unit 132 estimates the future information of the target chain store by inputting the chain information of the target chain store into the model M1. Note that the future information here includes, for example, the withdrawal score (probability of withdrawal) of a chain store, but also information regarding the increase or decrease in the number of chain stores in the chain store, the increase or decrease in transaction volume of electronic payment services for the chain store as a whole, etc. It may be.

Furthermore, the estimating unit 133 estimates future information regarding the manner in which the electronic payment service is used in the chain stores in each area by inputting the chain information of the chain stores in the target area into the target model M2.

For example, the future information here is the withdrawal score of chain stores in the corresponding area, but it also includes information about the increase or decrease in the number of chain stores in each area, the increase or decrease in the transaction volume of electronic payment services for the chain stores as a whole, etc. It may be.

Furthermore, the estimating unit 133 estimates future information regarding the usage mode of the electronic payment service at the chain store by inputting chain information including business hours information regarding the business hours of the target chain store into the model M3. For example, model M3 is a model that outputs a withdrawal score for a chain store from business hours information, but a model that outputs a score related to the transaction amount of future electronic payment services at a chain store, or a model that outputs a score related to the future number of stores of a chain store. It may be a model that estimates .

For example, the business hours information here includes various information related to business hours, such as the total business hours per month at the target chain store (the sum of the business hours of each chain store), and the number of hours per employee. This includes the number of working hours per month (total business hours/number of employees), labor unit price per employee (monthly sales/number of employees), etc.

Note that the estimating unit 133 may estimate future information about each chain store at a predetermined period, or may estimate future information about the chain store upon receiving an application for factoring from the chain store.

The determining unit 134 determines factoring conditions based on the estimation result by the estimating unit 133. For example, the determining unit 134 determines factoring conditions for the chain store based on the estimation result by the estimating unit 133 and preset factoring rules. For example, the factoring conditions include the purchase amount of receivables, collection methods, fees, etc.

Note that the determining unit 134 may determine the factoring conditions, for example, based on a model learned using the factoring conditions when receivables are actually purchased and the collection status thereof as learning data.

For example, such a model uses score information regarding chain stores and factoring conditions when receivables are actually purchased, and learns that correct information is when receivables can be collected or when receivables cannot be collected. It can be generated using data.

Here, an example of a procedure for applying for factoring will be described using FIGS. 9 and 10. 9 and 10 are diagrams showing an example of a factoring application screen. In the example shown in Figure 9, on the screen where the applicable conditions (i.e., factoring conditions) are displayed, an input field is also displayed for the business operator to input the amount required this time and the repayment rate from the monthly launch. Ru.

For example, a business operator can apply for the purchase of a receivable up to the maximum monetization amount presented by the factoring conditions, and can also set the repayment rate for the receivable.

When the business operator inputs the amount and repayment rate shown in FIG. 9 and selects the next button, the screen changes to the screen shown in FIG. 10. On the screen shown in Figure 10, information related to confirmation of the application contents and a button to move to the terms of use are displayed.The business operator can confirm the application contents, accept the terms of use, and then select the application button. Factoring is established based on the application details.

For example, when factoring is established, the determining unit 134 stores the application details in the factoring database 125 (see FIG. 3) and notifies the payment server 100 of information regarding the repayment rate. Thereby, the payment server 100 can recover funds for the receivables by subtracting an amount corresponding to the repayment rate from the business's monthly sales based on the repayment rate.

[3. About the processing procedure of information processing according to the embodiment]
Next, an example of a processing procedure executed by the information processing device 10 according to the embodiment will be described using FIG. 11. FIG. 11 is a flowchart illustrating an example of information processing according to the embodiment.

As shown in FIG. 11, the information processing device 10 first acquires information regarding the store (step S101), and performs learning of learning models (models M1 to M3) based on the information regarding the store (step S102). .

Subsequently, the information processing device 10 determines whether factoring has been accepted through access from the business terminal 300, for example (step S103). When the information processing device 10 receives factoring from the business terminal 300 (step S103; Yes), the process proceeds to step S104, and when it does not accept factoring (step S103; No), the process returns to step S101.

Subsequently, the information processing device 10 uses the learning models (models M1 to M3) to calculate the score of the business operator (step S104), and determines factoring conditions based on the score (step S105). The information processing device 10 then ends the process.

[4. Modified example]
Incidentally, in the above-described embodiment, the case has been described with reference to a physical store, but the present invention is not limited to this. For example, stores that provide services that combine offline sales and online sales may be targeted. For example, in this case, for online sales, the model M is trained using, for example, the sales history of various EC sites as learning data. In addition, for example, in such a case, the model M may be trained for offline sales and online sales, and the model M for offline sales and the model M for online sales may be trained respectively. You may also choose to learn.

[5. effect〕
The information processing device 10 according to the embodiment includes an acquisition unit 131 that acquires store information regarding each physical store included in a store group, and when inputting the store information acquired by the acquisition unit 131, determines future trends of the store group. and a learning unit 132 that learns a learning model that outputs future information regarding. Therefore, according to the information processing device 10 according to the embodiment, future trends in physical stores can be appropriately predicted.

Furthermore, the learning unit 132 according to the embodiment learns a learning model that outputs the future information for each chain store as a store group. Therefore, according to the information processing device 10 according to the embodiment, future trends of chain stores can be appropriately predicted.

Further, the learning unit 132 according to the embodiment learns a learning model using information regarding sales of each physical store as learning data. Therefore, according to the information processing device 10 according to the embodiment, sales of a store group can be appropriately predicted.

Further, the learning unit 132 according to the embodiment learns a learning model using information regarding the transaction volume of payment services performed at each physical store as learning data. Therefore, according to the information processing device 10 according to the embodiment, it is possible to appropriately predict the transaction volume of the electronic payment service in the store group.

Further, the learning unit 132 according to the embodiment learns a learning model that outputs future information regarding usage of payment services in a store group. Therefore, it is possible to appropriately predict how the electronic payment service will be used in the store group.

Further, the learning unit 132 according to the embodiment learns a learning model that outputs future information regarding transaction volume of payment services in a store group. Therefore, according to the information processing device 10 according to the embodiment, it is possible to appropriately predict the transaction volume of the electronic payment service in the store group.

Further, the learning unit 132 according to the embodiment learns a learning model that outputs a withdrawal score for withdrawing from a payment service as future information. Therefore, according to the information processing device 10 according to the embodiment, various services can be provided that take the withdrawal score into consideration.

Furthermore, the learning unit 132 according to the embodiment learns a learning model for estimating future information regarding future changes in the number of physical stores in the store group. Therefore, the information processing device 10 can provide various services that take into consideration changes in the number of stores.

The information processing device 10 according to the embodiment also includes a determining unit 134 that determines purchase conditions regarding the purchase of receivables of the store group based on future information output from the learning model learned by the learning unit 132. Therefore, according to the information processing device 10 according to the embodiment, it is possible to appropriately determine the purchase conditions of the receivable.

Further, the learning unit 132 according to the embodiment learns a learning model that outputs the probability that a store group's receivables will become uncollectible as future information, and the determining unit 134 determines that the lower the probability that the receivables of a store group will become uncollectible, the more likely the store group will be Determine advantageous purchase conditions. Therefore, according to the information processing device 10 according to the embodiment, it is possible to appropriately determine the purchase conditions of the receivable.

Further, the information processing method according to the embodiment is an information processing method executed by a computer, and includes an acquisition step of acquiring store information regarding each physical store included in a store group, and the store information acquired by the acquisition step. and a learning step of learning a learning model that outputs future information regarding future trends of the store group when the store group is input. Therefore, according to the information processing method according to the embodiment, future trends in physical stores can be appropriately predicted.

In addition, the information processing program according to the embodiment includes an acquisition procedure for acquiring store information regarding each physical store included in a store group, and, when store information acquired by the acquisition procedure is input, information regarding future trends of the store group. A computer is caused to execute a learning procedure for learning a learning model that outputs future information. Therefore, according to the information processing program according to the embodiment, future trends in physical stores can be appropriately predicted.

[6. Hardware configuration]
Further, the information processing apparatus 10 according to the embodiments described above is realized by, for example, a computer 1000 having a configuration as shown in FIG. 12. FIG. 12 is a hardware configuration diagram showing an example of a computer that implements the functions of the information processing device 10 according to the embodiment. Computer 1000 has CPU 1100, RAM 1200, ROM 1300, HDD 1400, communication interface (I/F) 1500, input/output interface (I/F) 1600, and media interface (I/F) 1700.

CPU 1100 operates based on a program stored in ROM 1300 or HDD 1400, and controls each section. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started, programs depending on the hardware of the computer 1000, and the like.

The HDD 1400 stores programs executed by the CPU 1100, data used by the programs, and the like. Communication interface 1500 receives data from other devices via network (communication network) N and sends it to CPU 1100, and sends data generated by CPU 1100 to other devices via network N.

The CPU 1100 connects output devices such as displays and printers, and input devices such as keyboards and mice (in FIG. 12, output devices and input devices are collectively referred to as "input/output devices") via an input/output interface 1600. ). CPU 1100 obtains data from an input device via input/output interface 1600. Further, CPU 1100 outputs the generated data to an output device via input/output interface 1600.

Media interface 1700 reads programs or data stored in recording medium 1800 and provides them to CPU 1100 via RAM 1200. CPU 1100 loads this program from recording medium 1800 onto RAM 1200 via media interface 1700, and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or a PD (Phase change rewritable disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. etc.

For example, when the computer 1000 functions as the information processing device 10 according to the embodiment, the CPU 1100 of the computer 1000 realizes the functions of the control unit 40 by executing a program loaded onto the RAM 1200. The CPU 1100 of the computer 1000 reads these programs from the recording medium 1800 and executes them, but as another example, these programs may be acquired from another device via the network N.

Some of the embodiments of the present application have been described above in detail based on the drawings, but these are merely examples, and various modifications and variations may be made based on the knowledge of those skilled in the art, including the embodiments described in the disclosure section of the invention. It is possible to carry out the invention in other forms with modifications.

[7. others〕
Furthermore, among the processes described in the above embodiments and modified examples, all or part of the processes described as being performed automatically can be performed manually, or may be described as being performed manually. All or part of this processing can also be performed automatically using known methods. In addition, information including the processing procedures, specific names, and various data and parameters shown in the above documents and drawings may be changed arbitrarily, unless otherwise specified. For example, the various information shown in each figure is not limited to the illustrated information.

Furthermore, each component of each device shown in the drawings is functionally conceptual, and does not necessarily need to be physically configured as shown in the drawings. In other words, the specific form of distributing and integrating each device is not limited to what is shown in the diagram, and all or part of the devices can be functionally or physically distributed or integrated in arbitrary units depending on various loads and usage conditions. Can be integrated and configured.

Furthermore, the above-described embodiments and modifications can be combined as appropriate within a range that does not conflict with the processing contents.

Further, the above-mentioned "section, module, unit" can be read as "means", "circuit", etc. For example, the acquisition unit can be read as an acquisition means or an acquisition circuit.

1 Information processing system 10 Information processing device 50 User terminal 100 Payment server 121 Store information database 122 Member store information database 123 Geographic information database 124 Model database 125 Factoring database 130 Control section 131 Acquisition section 132 Learning section 133 Estimation section 134 Determination section 200 Store terminal 300 Business terminal M model (an example of a learning model)

Claims (12)

an acquisition unit that acquires store information regarding each physical store included in the store group;
An information processing device comprising: a learning section that learns a learning model that outputs future information regarding future trends of the store group when the store information acquired by the acquisition section is input. The learning department is
The information processing device according to claim 1, wherein the learning model is configured to output the future information for each chain store as the store group. The learning department is
The information processing device according to claim 1 or 2, wherein the learning model is trained using information regarding sales of each of the physical stores as learning data. The learning department is
4. The information processing apparatus according to claim 1, wherein the learning model is trained using information regarding transaction volume of payment services performed at each of the physical stores as learning data. The learning department is
The information processing device according to claim 4, wherein the learning model is trained to output the future information regarding usage of the payment service in the store group. The learning department is
The information processing device according to claim 5, wherein the learning model is trained to output the future information regarding the transaction volume of the payment service in the store group. The learning department is
The information processing device according to claim 5 or 6, wherein the learning model is trained to output a withdrawal score for withdrawing from the payment service as the future information. The learning department is
The information processing device according to any one of claims 1 to 7, wherein the learning model is trained to estimate the future information regarding a future change in the number of physical stores in the store group. Any one of claims 1 to 8, further comprising a determining unit that determines purchase conditions regarding purchase of receivables of the store group based on the future information output from the learning model learned by the learning unit. The information processing device according to one of the above. The learning department is
learning the learning model that outputs the probability that the receivables of the store group will become uncollectible as the future information;
The determining unit is
The information processing device according to claim 9, wherein the purchase condition is determined to be more advantageous to the store group as the probability that the collection becomes impossible is lower. An information processing method performed by a computer, the method comprising:
an acquisition step of acquiring store information regarding each physical store included in the store group;
and a learning step of learning a learning model that outputs future information regarding future trends of the store group when the store information acquired in the acquisition step is input. an acquisition procedure for acquiring store information regarding each physical store included in the store group;
an information processing program that causes a computer to execute a learning procedure for learning a learning model that outputs future information regarding future trends of the store group when the store information acquired by the acquisition procedure is input; .

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