JP6836255B2 - Computer systems, methods, and programs for funding retailers in e-commerce - Google Patents

Description

The present invention relates to computer systems, methods, and programs for funding retailers in electronic commerce.

A store in electronic commerce (also referred to as an "net shop" in this specification) can conduct commerce on a network. An online shop is easier to open than a physical store because it allows you to conduct business transactions without having a physical store. However, in the conventional e-shopping street method, which aims to attract customers with the high name of the operator who operates the e-shopping street, when opening an online shop, the operator of the online shop opens a large amount of money to the operator who operates the e-shopping street. It is necessary to pay a fee (for example, Patent Document 1).

JP-A-2002-74133

I tried to open an online shop, but if the product does not sell, I will be in the red as much as I paid the opening fee. In addition, working capital such as product purchase costs is also required to operate an online shop. Under these circumstances, opening and operating an online shop is a high hurdle, especially for individual business owners.

The inventor of the present invention considers that it is important to lower the hurdles for opening and operating an online shop in order to encourage more people (particularly individual managers) to open an online shop. It was.

The present invention has been made in view of such circumstances, and an object of the present invention is to provide a computer system, a method, and a program for funding a store in an electronic commerce.

In one embodiment of the present invention, a computer system for providing funds to a store in electronic commerce is provided, and the computer system is a receiving means for receiving information about the store in electronic commerce and the store. Proposals for predicting means for predicting the future credit amount of the dealer and provision conditions for providing funds according to the predicted future credit amount to the terminal device of the dealer based on the information regarding A transmission means for transmitting is provided.

In one embodiment, the condition includes a provision amount, which is the amount provided in the funding, and a recovery rate indicating the rate at which the provision amount is to be recovered.

In one embodiment, the terms further include a fee for the funding.

In one embodiment, the condition varies at least according to the future claim amount, and the computer system further comprises a determinant for determining the condition based on the predicted future claim amount.

In one embodiment, the information about the store includes at least one of the store's performance, the behavior of the consumer who purchased at the store, and the attributes of the products handled by the store, and the determination means is , The condition is determined based on at least one of the performance of the store, the behavior of the consumer who purchased at the store, and the attributes of the products handled by the store.

In one embodiment, the information about the store includes the performance of the store, the behavior of the consumer who purchased at the store, and the attributes of the products handled by the store, and the determination means is the sale. The conditions are further determined based on the store's performance, the behavior of the consumer who purchased at the store, and the attributes of the products handled by the store.

In one embodiment, the receiving means further receives a request for funding from the terminal device of the store, the predicting means predicts the future credit amount of the store at predetermined periods, and the determining means , The conditions are determined based on the future credit amount of the dealer predicted when the request for funding is received.

In one embodiment, the information about the store includes at least one of the store's performance, the behavior of the consumer who purchased at the store, and the attributes of the products handled by the store, and the predictive means , The future credit amount of the store is predicted based on at least one of the performance of the store, the behavior of the consumer who purchased at the store, and the attributes of the products handled by the store.

In one embodiment, the computer system further comprises a storage means for storing information about the store, and the receiving means receives information about the store stored in the storage means.

In one embodiment, the funding is the purchase of at least a portion of the predicted future receivables.

In one embodiment, the computer system is a payment means for making a payment in an electronic commerce of the store and a payment means for paying the sales proceeds obtained by the payment to the store. It is further provided with a payment means that collects the collection rate of the acquired sales and pays the rest to the store.

In one embodiment of the invention, a method for funding a store in an electronic commerce is provided, the method being based on receiving information about the store in the electronic commerce and the information about the store. Includes predicting the future credit amount of the dealer and transmitting a proposal of conditions for providing funds to provide funds according to the predicted future credit amount to the terminal device of the dealer.

In one embodiment of the invention, a program for funding a retailer in an electronic commerce is provided, the program is executed in a computer system including a processor unit, and when the program is executed, the electronic commerce To receive information about the dealer in Japan, to predict the future credit amount of the dealer based on the information about the dealer, and to provide funds according to the predicted future credit amount. The processor unit is made to perform a process including transmitting the proposal of the condition to the terminal device of the store.

In one embodiment of the present invention, a system for providing funds to a store in electronic commerce is provided, the system includes a server device and a terminal device of the store, and the server device is used in electronic commerce. Receiving information about the dealer, predicting the future credit amount of the dealer based on the information about the dealer, and providing funds according to the predicted future credit amount. The proposal of the condition is configured to be transmitted to the terminal device of the store, and the terminal device of the store receives the proposal from the server device and accepts the proposal. Is configured to transmit to the server device, and the server device provides funding to the dealer in response to receiving the consent of the proposal and receiving the consent. It is further configured to do and do.

In one embodiment of the invention, a computer system for providing funding to a retailer in an electronic commerce is provided, the computer system funding the retailer in accordance with funding conditions, including the amount provided and the recovery rate. An execution means for executing the provision, the provision amount is an amount provided in the funding, and the recovery rate indicates the execution means and consumption indicating the rate at which the provision amount is collected. The collection rate of the receiving means for receiving the settlement request in the electronic commerce of the store from the terminal device of the person, the payment means for making the payment in the electronic commerce of the store, and the sales amount acquired by the settlement. It is provided with a payment means for collecting and paying the rest to the store.

In one embodiment of the invention, a method for providing funding to a retailer in electronic commerce is provided, the method providing funding to the retailer in accordance with funding conditions, including the amount provided and the recovery rate. To execute, the offer amount is the amount provided in the funding, and the recovery rate indicates the rate at which the offer amount is to be recovered, and the consumer terminal device. Receiving a settlement request in the electronic commerce of the retailer from, making a settlement in the electronic commerce of the retailer, collecting the collection rate portion of the sales proceeds obtained by the settlement, and the rest Includes paying to the retailer.

In one embodiment of the invention, a program for funding a retailer in an electronic commerce is provided, the program is executed in a computer system including a processor unit, and when the program is executed, the amount provided. And the recovery rate are to execute the funding to the dealer in accordance with the conditions of funding, the provision amount is the amount provided in the funding, and the recovery rate is the provision amount. Indicates the rate of collection, receiving a settlement request in the electronic commerce of the store from the terminal device of the consumer, making a payment in the electronic commerce of the store, and making the payment. Of the sales proceeds acquired by the above, the processor unit is made to perform a process including collecting the collection rate and paying the rest to the store.

According to the present invention, it is possible to provide a computer system, a method, and a program for funding a store in an electronic commerce.

The figure which outlines the flow in the new service for the operation support of an online shop. The figure which shows an example of the structure of the computer system 100 for providing the fund to the store in the electronic commerce. The figure which shows an example of the structure of the computer system 100 for achieving the function for funding a store in an electronic commerce. FIG. 5 shows an example of the configuration of another embodiment (computer system 100') of the computer system 100 for achieving the function of funding a store in electronic commerce. FIG. 5 shows an example of the configuration of yet another embodiment (computer system 1000) of the computer system 100 for achieving the function of funding a store in electronic commerce. The figure which shows an example of the structure of the neural network 1210 which can be used by a prediction means 121. The figure which shows an example of the structure of the information about the net shop stored in the database part 200. The figure which shows an example of the structure of the information about the consumer registered as a member stored in the database part 200. The figure which shows an example of the processing in the computer system 100 for providing the fund to the store in the electronic commerce. The figure which shows an example of the process in the computer system 1000'for funding a store in an electronic commerce.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.

1. 1. New service for supporting the operation of an online shop The inventor of the present invention has developed a new service for supporting the operation of an online shop. The service is to provide working capital to an online shop by purchasing future sales (also referred to as "future receivables" in the present specification) that have not yet occurred in the electronic commerce of the online shop. The operator of the online shop operates the online shop with the working capital, and when the actual sales are made, at least a part of the actual sales is paid as the purchased sales. With this service, the operator of the online shop can raise working capital before the sales are made, and even the operator without a source can operate the online shop. Furthermore, it is possible to pay for the purchased sales only when the actual sales are made, and it is possible to reduce the risk when the sales are not made.

FIG. 1 is a diagram schematically showing an example of a flow in a new service for supporting the operation of an online shop. FIG. 1 describes a flow between the online shop management company 10, the online shop 20, and the consumer 30.

The online shop management company 10 supports the operation of the online shop. The net shop 20 is a net shop opened on a network site (for example, an internet site) managed by the net shop management company 10. The online shop management company 10 may be composed of, for example, a single company or a plurality of companies. When composed of a plurality of companies, for example, the net shop management company 10 can be composed of a company that provides an online shop creation service and a company that provides a financial service. The consumer 30 accesses the network site managed by the net shop management company 10 via a network browser (for example, a Web browser) or through a local application to browse, order, and purchase the products of the net shop 20. be able to. In addition, the consumer 30 can evaluate the net shop 20 on the network site managed by the net shop management company 10. For example, the consumer 30 can evaluate the online shop or the products for sale in a graded and / or descriptive manner.

The online shop 20 that does not have sufficient working capital sends a request for funding to the online shop management company 10 in order to use this service. When the net shop management company 10 receives this request, in step S1, the net shop management company 10 predicts the future sales of the net shop 20 and proposes the purchase of the future sales to the net shop 20. For example, when the online shop management company 10 is composed of a plurality of companies as described above, the company that provides financial services among the plurality of companies constituting the online shop management company 10 is the future of the online shop 20. It is possible to predict the sales of the company and propose the purchase of the future sales to the online shop 20.

In this proposal, for example, the purchase amount indicating how much future sales will be purchased, and the payment rate indicating the ratio of the purchased sales to the actual sales (also referred to as "recovery rate" in the present specification). Includes). The proposal may include a payment period that indicates how long the purchased sales will be redeemed.

For example, if the online shop management company 10 predicts that the sales of the online shop 20 for the next month will be 10 million yen, the online shop management company 10 will purchase 1 million yen from the online shop 20. We propose to buy the sales for the next month with a payment rate of 10%.

Next, in step S2, the net shop 20 accepts the purchase proposal and sells the future sales to the net shop management company 10. For example, as described above, when the online shop management company 10 is composed of a plurality of companies, future sales are sold to a company that provides financial services among the plurality of companies constituting the online shop management company 10. be able to.

Then, in step S3, the net shop management company 10 pays the purchase amount of future sales to the net shop 20. For example, when the online shop management company 10 is composed of a plurality of companies as described above, the company that provides financial services among the plurality of companies constituting the online shop management company 10 purchases future sales. The amount can be paid to the online shop 20.

For example, in the specific example described above, the online shop management company 10 pays 1 million yen to the online shop 20. At this time, for example, the net shop 20 may be paid the amount after deducting the commission. The fee is a fee paid from the net shop 20 to the net shop management company 10 in order to use this service.

The operator of the net shop 20 can operate the net shop 20 with the paid working capital. The consumer 30 can browse the products of the online shop 20 via the network and purchase any products he / she likes.

When the consumer 30 purchases a product from the online shop 20, in step S4, the online shop 20 provides the product to the consumer 30, and in step S5, the consumer 30 pays the product price to the online shop management company 10. Pay to.

When the consumer 30 pays the product price, in step S6, the net shop management company 10 pays the paid product price to the net shop 20. At this time, the net shop management company 10 may pay the product price to the net shop 20 and then collect the amount obtained by multiplying the product price by the payment rate (payment rate) from the net shop 20. It is preferable that the online shop management company 10 deducts the amount obtained by multiplying the paid product price by the payment rate (payment rate) and pays the remaining amount to the online shop 20. This is because it is less troublesome for the online shop management company 10 to collect the payment rate first and then pay the rest to the online shop 20, and there is less risk of missing the collection. The future sales of the net shop 20 purchased by the net shop management company 10 will be redeemed at the collected payment rate. Steps S4 to S6 are repeated until all future sales purchased by the online shop management company 10 are redeemed, and each time the consumer 30 pays the product price, the online shop management company 10 pays the payment rate. Is deducted and the remaining amount is paid to the online shop 20. When all future sales purchased by the net shop management company 10 are redeemed, the net shop management company 10 will pay the paid product price to the net shop 20 as it is.

For example, in the specific example described above, if the consumer 30 purchases a product of 10,000 yen each, the online shop management company 10 multiplies 10,000 yen by a payment rate of 10% (that is, 1,000). (Yen) is deducted and the remaining amount (that is, 9,000 yen is paid to the online shop 20. In this way, 1,000 yen out of the purchase amount of 1 million yen is redeemed for each sale of the product. Steps S4 to S6 are repeated until the entire purchase amount of 1 million yen is redeemed (that is, until 1,000 products are purchased).

According to the above-mentioned flow, the operator of the online shop 20 can raise working capital before sales are made, and can operate the online shop 20 even if he / she does not have a source. Furthermore, since the purchased sales are redeemed only when the sales are made, the risk when the sales are not made can be reduced.

Instead of reducing the risk of the online shop 20, the online shop management company 10 bears the risk, and the online shop management company 10 appropriately sets the purchase conditions (for example, lowers the purchase amount). High risk can be covered by increasing the payment rate, increasing the fee, etc.). It is preferable that the online shop management company 10 sets appropriate purchase conditions (for example, purchase amount, payment rate, commission) for each online shop 20. In order to set appropriate purchase conditions, it is preferable that the net shop management company 10 accurately predicts the future sales of the net shop 20.

In the above example, the net shop 20 sends a request for funding to the net shop management company 10, and the flow is started by the net shop management company 10 receiving this request, but the trigger for the start is Not limited to this. For example, the flow may be initiated without receiving a funding request. For example, regardless of the request for funding, the future sales of the online shop 20 are predicted in step S1 at random, at predetermined intervals, or at predetermined timings, and the future sales are purchased. You may propose to the net shop 20. The predetermined period may be any period such as 1 minute, 1 hour, 12 hours, 1 day, 1 week, January, June, 1 year and the like. The predetermined timing is, for example, when the sales of the online shop 20 exceed the predetermined amount (for example, 1 million yen), when the duration of the online shop 20 exceeds the predetermined number of years (for example, one year), the consumer 30 It may be when the online shop 20 is evaluated from.

In the above example, we explained that we will buy future sales, but the purchase is an example, and in this new service to support the operation of the online shop, it is possible to fund the store by any method. it can. For example, the retailer may be funded by financing future sales as collateral. Purchasing is preferable to financing for new services to support the operation of online shops.

The new service for supporting the operation of this online shop can be realized by, for example, a computer system for funding a store in an electronic commerce described below.

2. 2. Configuration of Computer System for Providing Funds to Dealers in Electronic Commerce FIG. 2 shows an example of the configuration of a computer system 100 for providing funds to retailers in electronic commerce.

The computer system 100 is connected to the database unit 200. Further, the computer system 100 is connected to at least one retail terminal device 300 and at least one consumer terminal device 400 via a network 500.

The network 500 can be any kind of network. The network 500 may be, for example, the Internet or a LAN. The network 500 may be a wired network or a wireless network.

An example of the computer system 100 is, but is not limited to, a computer (for example, a server device) installed in the net shop management company 10 shown in FIG. An example of the store terminal device 300 is, but is not limited to, a computer (for example, a terminal device) installed in the net shop 20 shown in FIG. An example of the consumer terminal device 400 is, but is not limited to, a computer (for example, a terminal device) owned by the consumer 30 shown in FIG. Here, the computer (server device or terminal device) can be any type of computer. For example, the terminal device can be any type of terminal device such as a smartphone, tablet, personal computer, smart glasses and the like.

The computer system 100 may have, for example, the ability to create and manage an online shop. The computer system 100 can have, for example, any known configuration for the function of creating and managing an online shop.

For example, the computer system 100 requests the creation of an online shop from the store terminal device 300 via the network 500 and the information required for creating the online shop (for example, operator information (name, address, age, gender, etc.). It is configured to receive (account information, SNS account information, etc.), handling product information), and respond to these to create an online shop. Further, the computer system 100 is configured so that the operator of the online shop can operate the online shop by using the store terminal device 300.

For example, the computer system 100 requests information for membership registration of a net shop site from a consumer terminal device 400 via a network 500 and information required for membership registration of a net shop site (for example, consumer information (user name, user name,). It is configured to receive the password, address, account information, SNS account information, etc.)) and respond to these to register the consumer as a member. Further, the computer system 100 is configured so that a consumer registered as a member can browse, order, and purchase products on a net shop managed by the computer system 100 using the consumer terminal device 400. ..

It should be noted that the membership registration of the consumer is not essential, and the computer system 100 is configured so that the consumer can browse, order, and purchase products on the online shop managed by the computer system 100 without registering as a member. May be done. In this case, for example, the computer system 100 may be configured to allow consumers to enter personal information such as an address, telephone number, and email address to perform a payment procedure each time they make a purchase. However, it may be configured to allow consumers to log in to another payment service and make payments.

The computer system 100 may have, in addition to, the function of creating and managing an online shop, or, in place of the function of creating and managing an online shop, a function of funding a store in electronic commerce.

FIG. 3A shows an example of the configuration of the computer system 100 for achieving the function of funding a store in electronic commerce.

The computer system 100 includes a receiving means 110, a processor unit 120, a transmitting means 130, and a memory unit 140.

The receiving means 110 is configured to be capable of receiving data from outside the computer system 100. The receiving means 110 may receive data from, for example, the store terminal device 300 or the consumer terminal device 400 via the network 500, or a storage medium (for example, a USB memory, an optical disk, etc.) connected to the computer system 100. ) Or data may be received from the database unit 200. The receiving means 110 may receive data from an external service such as SNS via the network 500. The data received by the receiving means 110 is passed to the processor unit 120 for subsequent processing.

The receiving means 110 receives, for example, information about the net shop stored in the database unit 200. The information about the online shop can be, for example, sales information of the online shop. The receiving means 110 may also receive, for example, information about the consumer stored in the database unit 200. The receiving means 110 may, for example, receive a request for funding from the store terminal device 300 via the network 500. The receiving means 110 may, for example, receive the acceptance of the proposal of the funding condition from the store terminal device 300 via the network 500.

The receiving means 110 may, for example, receive an order for a product and a product settlement request from the consumer terminal device 400 to the online shop via the network 500. The product settlement request may include, for example, consumer information (for example, user ID, etc.), online shop information (for example, store ID, etc.), product information (for example, product code, price), and is used in electronic commerce of an online shop. It contains a lot of information. The computer system 100 can store a large amount of information in the electronic commerce of the online shop in the database unit 200 by grasping the product settlement request for all the managed online shops, and various kinds of information can be stored as big data. It can be used for various purposes. The various uses include, but are not limited to, for example, forecasting the amount of future claims by the forecasting means 121 described later, and determining the conditions for funding by the determining means 122 described below.

The processor unit 120 controls the operation of the entire computer system 100. The processor 120 reads a program stored in the memory unit 140 and executes the program. This makes it possible for the computer system 100 to function as a device that performs a desired step. The processor unit 120 may be implemented by a single processor or may be implemented by a plurality of processors. The data processed by the processor unit 120 is passed to the transmission means 130.

The processor unit 120 includes at least the prediction means 121.

The prediction means 121 is configured to predict how much the future receivables of the net shop will be, that is, the future receivable amount, based on the information about the net shop received by the receiving means 110. The predicting means 121 may predict the future claim amount based on the information about the consumer received by the receiving means 110 in addition to the information about the net shop received by the receiving means 110.

In one embodiment, the forecasting means 121 can predict future receivables, for example, by regression analysis. Regression analysis is, for example, simple regression analysis. For example, it is possible to predict the future amount of receivables by performing a simple regression analysis based on the past sales amount and obtaining the regression equation. Regression analysis can be, for example, multiple regression analysis. Multiple regression analysis is preferable to simple regression analysis because it can predict future receivables with higher accuracy. For example, in addition to past sales, information about the online shop (for example, the performance of the online shop (date of opening of the online shop, duration of the online shop, etc.), behavior of consumers who purchased at the online shop (for example) , Average rating of the online shop by consumers, number of views of the online shop by consumers, repeat rate (ratio of the number of consumers who purchased multiple times at the online shop to the number of consumers who purchased at the online shop) The amount of future receivables can be predicted by performing a multiple regression analysis and obtaining a regression equation based on at least one of the above. For example, by using the results of the online shop for the multiple regression analysis, each online shop can be predicted. It is possible to predict the amount of future receivables in consideration of circumstances. This leads to highly accurate prediction for each online shop. For example, by using the behavior of consumers purchased at the online shop for multiple regression analysis, the net It is possible to predict the amount of future claims in consideration of the consumer's tendency toward the shop, which also leads to highly accurate prediction for each net shop. The prediction means 121 can be acquired by the computer system 100 in the management of the net shop. All information can be used to predict future claims. For example, the repeat rate determines the identity of the consumer who purchased it based on the member ID if the consumer is registered as a member. If the consumer is not registered as a member, the repeat rate determines the identity of the consumer who purchased, for example, based on the ID of another payment service used at the time of payment. It may be derived by determining the identity of the consumer who purchased the product based on the terminal information of the consumer who purchased the product via the local application. However, it may be derived by determining the identity of the consumer who purchased the product based on the personal information (for example, address, telephone number, email address, etc.) entered at the time of purchasing the product.

In another embodiment, the predictor 121 can predict the future bond amount, for example, by machine learning. The prediction means 121 can predict the future bond amount by machine learning using a neural network, for example.

FIG. 4 shows an example of the structure of the neural network 1210 that can be used by the prediction means 121.

The neural network 1210 has an input layer, at least one hidden layer, and an output layer. The number of nodes in the input layer of the neural network 1210 corresponds to the number of dimensions of the input data. The number of nodes in the output layer of the neural network 1210 corresponds to the number of dimensions of the output data. Here, since the output data is the amount of future claims, the number of nodes in the output layer is 1. The hidden layer of the neural network 300 can include any number of nodes.

The neural network 1210 is a trained neural network that has been pre-learned using the data received by the receiving means 110. The learning process is a process of calculating the weighting coefficient of each node in the hidden layer of the neural network 1210 using the past data. In the learning process, for example, the sales amount within a specific period in the past is used as output teacher data, and information about the online shop before the specific period (for example, the actual results of the online shop (sales history, opening date of the online shop)). , The duration of the online shop, the average evaluation value of the online shop, etc.), the behavior of the consumer who purchased at the online shop (for example, the login history of the consumer, the number of views of the online shop by the consumer, the repeat rate (at the online shop) The ratio of the number of consumers who purchased multiple times at the online shop to the number of consumers who purchased), the attributes of the products handled by the online shop (for example, whether it is fashion, grocery, miscellaneous goods, etc.), operation User attributes (address, age, login history, SNS usage information, etc.), consumer attributes (age, SNS usage information, etc.) are used as input teacher data, and data from multiple online shops for multiple specific periods can be used. It is used to calculate the weighting coefficient of each node in the hidden layer of the neural network 1210. The predictor 121 can use any information that the computer system 100 can obtain in the management of the net shop as teacher data.

For example, assuming that a specific period is January, the set of (input teacher data, output teacher data) is (information about online shops from the past January to the past two months, current sales amount), (past February). ~ Information about online shops in the past 3 months, sales amount from January to the past 2 months), (Information about online shops from the past March to the past 4 months, sales amount from the past February to the past 3 months) ... And so on.

When information about the net shop from the present to the past January is input to the input layer of such a trained neural network 1210, the sales amount after one month can be output to the output layer.

For example, by using the information about the online shop as the input data, it is possible to predict the future loan amount in consideration of the circumstances of each online shop. This leads to highly accurate prediction for each online shop. In particular, by using the past sales, the date of opening the online shop, and the duration of the online shop as input data as the achievements of the online shop, it is sustainable whether the achievements of the online shop are transient or not. You will be able to make predictions that take into account whether it is a thing. For example, by using the behavior of consumers who purchased at an online shop as input data, it is possible to predict the amount of future claims in consideration of the consumer's tendency toward the online shop, which is also highly accurate for each online shop. It leads to prediction. In particular, by using the repeat rate as input data, it becomes possible to make a prediction considering whether or not the consumer is satisfied with the online shop.

In the above example, the specific period is January, but the specific period can be any period such as 1 minute, 1 hour, 12 hours, 1 day, 1 week, January, June, 1 year, etc. possible.

With reference to FIG. 3A again, the transmitting means 130 is configured to be capable of transmitting data to the outside of the computer system 100. The transmission means 130 transmits the data passed from the processor unit 120. The transmission means 130 may transmit data to, for example, the store terminal device 300 or the consumer terminal device 400 via the network 500, or a storage medium (for example, a USB memory, an optical disk, etc.) connected to the computer system 100. ) Or the data may be transmitted to the database unit 200. The transmission means 130 may transmit the order of the product received from the consumer terminal device 400 to the store terminal device 300.

The transmission means 130 may, for example, transmit to the store terminal device 300 a proposal of funding conditions for providing funds according to the amount of future claims predicted by the prediction means 121 of the processor unit 120.

Financing conditions may include, for example, funding amounts, recovery rates, and fees. The amount provided is the amount provided in funding, for example, a fixed percentage of the amount of future claims (eg 10%, 20%, 30%, 40%, 50%, etc.). It may be a fixed amount (for example, 70,000 yen, 140,000 yen, 210,000 yen, etc.). In another embodiment described below, the offer amount may be an amount determined by the determination means 122. The recovery rate indicates the rate at which the donation amount is recovered, and can be a fixed rate (for example, 10 to 50%, for example, 10%, 20%, 30%, 40%, 50%, etc.). In another embodiment described below, the recovery rate may be a rate determined by the determining means 122. The fee may be a fixed ratio (for example, 10 to 50%, for example, 10%, 20%, 30%, 40%, 50%, etc.) to the future claim amount or the provided amount, or a fixed amount (for example). For example, it may be 1,000 yen / month, 5,000 yen / month, 10,000 yen / month, etc.). In another embodiment described below, the fee may be an amount determined by the determining means 122.

The memory unit 140 stores a program for executing processing in the computer system 100, data required for executing the program, and the like. The memory unit 140 stores, for example, a program for providing funds to a store in electronic commerce (for example, a program for realizing the processes shown in FIGS. 6 and 7 described later). An application that implements an arbitrary function may be stored in the memory unit 140. For example, a program for the function of creating and managing an online shop may be stored. Here, it does not matter how the program is stored in the memory unit 140. For example, the program may be pre-installed in the memory unit 140. Alternatively, the program may be installed in the memory unit 140 by being downloaded via the network 500. The memory unit 140 may be implemented by any storage means.

The processor unit 120 may further include an execution means for executing the funding in response to the acceptance of the funding conditions. As the execution means, the online shop can be funded by any known method. The funds may be provided, for example, by wire transfer, electronic money, virtual currency, or cash.

In the example shown in FIG. 3A, each component of the computer system 100 is provided in the computer system 100, but the present invention is not limited thereto. It is also possible that any of the components of the computer system 100 may be provided externally or remotely to the computer system 100. For example, when each of the processor unit 120 and the memory unit 140 is composed of separate hardware components, each hardware component may be connected via an arbitrary network. At this time, the type of network does not matter. Each hardware component may be connected via a LAN, may be wirelessly connected, or may be connected by wire, for example. The computer system 100 is not limited to a specific hardware configuration. For example, it is also within the scope of the present invention that the processor unit 120 is configured by an analog circuit instead of a digital circuit. The configuration of the computer system 100 is not limited to the above-mentioned one as long as the function can be realized.

FIG. 3B shows an example of the configuration of another embodiment (computer system 100') of the computer system 100 for achieving the function of funding a store in electronic commerce. In FIG. 3B, the same elements as those shown in FIG. 3A are assigned the same reference numbers, and the description thereof will be omitted here. The embodiment shown in FIG. 3B is an embodiment in which the processor unit 120'is further provided with the determination means 122. The processor unit 120'may have the same configuration as the processor unit 120, except that the determination means 122 is provided.

The determining means 122 is configured to determine the terms of funding based on the amount of future receivables predicted by the predicting means 121. The terms of funding will vary, at least, depending on the amount of future claims. The determining means 122 may predict the conditions of funding based on, for example, the amount of future receivables predicted by the predicting means 121 and the information about the online shop received by the receiving means 110. At this time, the conditions for funding will change depending on the amount of future claims and information about the online shop. Financing conditions may include, for example, funding amounts, recovery rates, and fees.

The offer amount can be, for example, a fixed amount (for example, 70,000 yen, 140,000 yen, 210,000 yen, etc.), and the determining means 122 decides which fixed amount the offer amount should be based on the predicted future loan amount. May be decided. For example, if the future loan amount is less than 1 million yen, the offer amount is 70,000 yen, if the future loan amount is less than 5 million yen, the offer amount is 140,000 yen, and if the future loan amount is 5 million yen or more. The amount provided can be set to 210,000 yen.

The offer amount may be, for example, a predetermined ratio to the future claim amount, and the determining means 122 may determine the offer amount by varying the predetermined ratio based on the predicted future claim amount. The method of fluctuation does not matter. For example, the predetermined ratio may be continuously changed based on a specific formula with the future claim amount as a variable, or the predetermined ratio may be changed discretely. For example, if the future loan amount is less than 1 million yen, the predetermined ratio is 10%, if the future loan amount is less than 5 million yen, the predetermined ratio is 15%, and if the future loan amount is 5 million yen or more, the predetermined ratio is specified. The ratio can be set to 20%.

The determination means 122 may determine the recovery rate based on the predicted future loan amount. For example, the recovery rate may be determined based on a specific formula with the future loan amount as a variable, or the recovery rate may be determined discretely. For example, if the future loan amount is less than 1 million yen, the collection rate is 10%, if the future loan amount is less than 5 million yen, the collection rate is 15%, and if the future loan amount is 5 million yen or more, the collection rate is set to 15%. The rate can be set to 20%.

The fee may be, for example, a predetermined ratio to the provided amount, and the determining means 122 may determine the provided amount by varying the predetermined ratio based on the predicted future claim amount. The method of fluctuation does not matter. For example, the predetermined ratio may be continuously changed based on a specific formula with the provided amount as a variable, or the predetermined ratio may be changed discretely. For example, if the offer amount is less than 100,000 yen, the predetermined rate is 10%, if the offer amount is less than 500,000 yen, the predetermined rate is 15%, and if the offer amount is 500,000 yen or more, the predetermined rate is 20. It can be set to%.

In another embodiment, the determining means 122 can predict funding conditions, for example, by machine learning. The prediction means 121 can predict the conditions of funding by, for example, machine learning using a neural network.

The determining means 122 can determine the funding conditions by machine learning using a neural network similar to the neural network 1210 shown in FIG. 4, for example. The neural network is a trained neural network that has been pre-learned using the data received by the receiving means 110. The learning process is a process of calculating the weighting coefficient of each node in the hidden layer of the neural network using the past data. In the learning process, for example, the sales amount within a specific period is used as input teacher data, and the ideal funding condition for the sales amount is used as output teacher data, and a neural network is used using data of a plurality of specific periods. Calculate the weighting coefficient for each node in the hidden layer of 1210. Alternatively, in the learning process, for example, the amount of sales within a specific period and information about the online shop before the specific period (for example, the actual results of the online shop (sales history, the date of opening the online shop, the continuation of the online shop) Period, average rating of the online shop, etc.), behavior of consumers who purchased at the online shop (for example, consumer login history, number of views of the online shop by consumers, repeat rate (number of consumers who purchased at the online shop) (Ratio of the number of consumers who purchased multiple times at the online shop to)), the attributes of the products handled by the online shop (for example, whether it is fashion, grocery, miscellaneous goods, etc.), the attributes of the operator ( Outputs teacher data for inputting address, age, login history, SNS usage information, etc.), consumer attributes (age, SNS usage information, etc.), and ideal funding conditions for information about sales and online shops. As the teacher data, a plurality of data are used to calculate the weight coefficient of each node in the hidden layer of the neural network 1210.

For example, when the information of an online shop that was opened more than 10 years ago or has a long duration of the online shop is used as input teacher data, it is used for output so as to give preferential treatment to the conditions of funding. Teacher data can be set. Such online shops have been in business for many years and are likely to continue to generate sales in the future, so even with preferential funding conditions, the risk of failing to recover the funding provided is low. Is. For example, when the information of an online shop having a high average evaluation value of an online shop or a high repeat rate is used as input teacher data, the output teacher data can be set so as to give preferential treatment to the conditions of funding. Such online shops are popular with consumers and are likely to continue to generate sales in the future, so even if the funding conditions are favored, the risk of failing to recover the funding provided is low. Because.

The determination means 122 can use any information that can be acquired by the computer system 100'in the management of the net shop as teacher data.

For example, if the sales amount before a specific period is input to the input layer of such a trained neural network, the conditions for funding may be output to the output layer. Alternatively, if information about the sales amount before a specific period and the online shop before a specific period is input to the input layer of the trained neural network, the conditions for funding may be output to the output layer. Here, the specific period may be any period such as 1 minute, 1 hour, 12 hours, 1 day, 1 week, January, June, 1 year and the like.

For example, by using information about an online shop as input data, it is possible to determine the conditions for funding in consideration of the circumstances of each online shop. In particular, as an achievement of the online shop, by using the opening date of the online shop, the duration of the online shop, etc. as input data, funding is provided in consideration of the growth potential of the online shop, which cannot be known only from the past sales amount. Conditions can be determined. For example, by using the behavior of a consumer purchased at an online shop as input data, it is possible to determine the conditions for funding in consideration of the consumer's tendency toward the online shop. In particular, by using the repeat rate as input data, it is possible to determine the conditions for funding in consideration of the growth potential of the online shop, which cannot be determined from the past sales amount alone.

As described above, the funding conditions determined by the determination means 122 can be suitable conditions for the online shop. This makes it possible to provide funding under the conditions of funding with as little risk as possible.

In the example shown in FIG. 3B, the processor unit 120 separately includes the prediction means 121 and the determination means 122, and the determination means 122 determines the conditions for funding based on the future loan amount predicted by the prediction means 121. However, the present invention is not limited to this. For example, the forecasting means 121 and the determining means 122 may have a single configuration, and the conditions for funding may be determined based on the received information about the online shop without using the forecasting of the future bond amount. .. This can be achieved, for example, by machine learning using the neural network described above.

For example, information about a net shop in a specific period is used as input teacher data, and ideal funding conditions for the information about the net shop are used as output teacher data, and data of a plurality of specific periods are used as data of a neural network. Learning processing is performed by calculating the weighting coefficient of each node in the hidden layer. When information about an online shop for a specific period is input to the input layer of a trained neural network, funding conditions can be output to the output layer.

In the example shown in FIG. 3B, each component of the computer system 100'is provided in the computer system 100', but the present invention is not limited thereto. It is also possible that any of the components of the computer system 100'is provided externally or remotely to the computer system 100'. For example, when the processor unit 120'and the memory unit 140 are each composed of separate hardware components, each hardware component may be connected via an arbitrary network. At this time, the type of network does not matter. Each hardware component may be connected via a LAN, may be wirelessly connected, or may be connected by wire, for example. The computer system 100'is not limited to a specific hardware configuration. For example, it is also within the scope of the present invention that the processor unit 120'is configured by an analog circuit instead of a digital circuit. The configuration of the computer system 100'is not limited to the above-mentioned one as long as the function can be realized.

FIG. 3C shows an example of the configuration of yet another embodiment (computer system 1000) of the computer system 100 for achieving the function of funding a store in electronic commerce. In FIG. 3C, the same elements as those shown in FIGS. 3A or 3B are assigned the same reference numbers, and description thereof will be omitted here. The embodiment shown in FIG. 3C is an embodiment in which the computer system 1000 includes a computer system 100 or a computer system 100'and a computer system 100'', for example, a server device (computer) that provides funds to a net shop. It corresponds to a system including a system 100 or a computer system 100') and a server device (computer system 100'') that acts as a payment agent for an online shop. The computer system 100 ″ may have the same configuration as the computer system 100 except that the processor unit 120 ″ includes a payment means 123 and a payment means 124 instead of the prediction means 121.

The payment means 123 is configured to make a payment in response to the product payment request received by the receiving means 110. The payment means 123 can make a payment by any known method. The settlement may be, for example, a credit card settlement, an account transfer settlement, an electronic money settlement, a virtual currency settlement, or the like. By the settlement, the settlement means 123 can acquire the sales amount from the consumer.

The payment means 124 is configured to pay the sales proceeds obtained by the settlement to the online shop. The payment to the online shop may be, for example, payment by wire transfer, but may be, for example, payment by electronic money, payment by virtual currency, payment by cash, or the like.

If the online shop is funded by the computer system 100 or 100', the payment instrument 124 collects the sales amount obtained by the settlement multiplied by the collection rate and pays the rest to the online shop. May be good. The payment means 124 collects the amount obtained by multiplying the sales amount obtained by the settlement by the collection rate and pays the rest to the online shop every time there is a settlement until the amount provided to the online shop is collected. can do. At this time, the payment means 124 may, for example, collect the amount obtained by multiplying the sales amount acquired by the settlement by the collection rate, deduct the commission amount, and then pay the rest to the online shop. Thereby, the computer system 100 ″ can recover the fee in addition to the funds provided by the computer system 100 or 100 ″.

In the example shown in FIG. 3C, each component of the computer system 100 ″ is provided in the computer system 100 ″, but the present invention is not limited thereto. It is also possible that any of the components of the computer system 100 ″ can be provided externally or remotely to the computer system 100 ″. For example, when each of the processor unit 120 ″ and the memory unit 140 is composed of separate hardware components, each hardware component may be connected via an arbitrary network. At this time, the type of network does not matter. Each hardware component may be connected via a LAN, may be wirelessly connected, or may be connected by wire, for example. The computer system 100 ″ is not limited to a particular hardware configuration. For example, it is also within the scope of the present invention that the processor unit 120 ″ is configured by an analog circuit instead of a digital circuit. The configuration of the computer system 100 ″ is not limited to that described above as long as the function can be realized.

In the example shown in FIG. 3C, the computer system 100 or the computer system 100 ′ and the computer system 100 ″ are depicted as separate computer systems, but the present invention is not limited thereto. It is also possible that the computer system 100 or the computer system 100'and the computer system 100' are configured as a single computer system.

In the above example, each component of the processor 120, the processor 120', or the processor 120' is provided in the same processor, but the present invention is not limited thereto. A configuration in which each component of the processor 120, the processor 120 ′, or the processor 120 ″ is distributed to a plurality of processor units is also within the scope of the present invention.

Referencing FIG. 2 again, a database unit is connected to the computer system 100. The database unit 200 may store, for example, information about the created online shop. Information about the online shop includes, for example, operator information (name, address, age, gender, account information, SNS account information, etc.), store information (opening date, product information), sales information (for example, when). , Information on which product was sold to which user and at what price). The database unit 200 may also store, for example, information about a consumer registered as a member. Information about consumers registered as members includes, for example, consumer information (user name, password, address, account information, SNS account information, etc.), login history information, product purchase history information (for example, when and at which online shop). , Information on which product was purchased and how much).

The database unit 200 may store, for example, information indicating the future sales of the net shop predicted by the computer system 100. Further, in the database unit 200, for example, the predicted future sales purchase amount, payment rate, and commission may be stored in association with each online shop.

In the example shown in FIG. 2, the database unit 200 is provided outside the computer system 100, but the present invention is not limited thereto. It is also possible to provide the database unit 200 inside the computer system 100. At this time, the database unit 200 may be implemented by the same storage means as the storage means on which the memory unit 130 is mounted, or may be implemented by a storage means different from the storage means on which the memory unit 130 is mounted. In any case, the database unit 200 is configured as a storage unit for the computer system 100. The configuration of the database unit 200 is not limited to a specific hardware configuration. For example, the database unit 200 may be composed of a single hardware component or may be composed of a plurality of hardware components. For example, the database unit 200 may be configured as an external hard disk device of the computer system 100, or may be configured as a storage on the cloud connected via a network.

FIG. 5A shows an example of the configuration of information about the online shop stored in the database unit 200.

A store ID is assigned to each store, and information about the online shop can be stored in the database unit 200 in association with the store ID. Information about the online shop includes, but is not limited to, operator information, store information, and sales information.

FIG. 5B shows an example of the configuration of information about a member-registered consumer stored in the database unit 200.

A member ID is assigned to each consumer registered as a member, and information about the consumer registered as a member can be stored in the database unit 200 in association with the member ID. Information about consumers registered as members includes, but is not limited to, consumer information, login history information, and product purchase history information.

3. 3. Processing by a Computer System for Providing Funds to a Dealer in Electronic Commerce FIG. 6 shows an example of processing in a computer system 100 for providing funds to a retailer in electronic commerce. In the example shown in FIG. 6, the process 600 for providing funding to the online shop will be described.

In step S601, the receiving means 110 of the computer system 100 receives information about the online shop. The information about the net shop is stored in the database unit 200, for example, and the receiving means 110 receives the information about the net shop from the database unit 200. Information about the net shop may be received from outside the computer system 100 via the network 500, for example.

The information about the online shop can be, for example, sales information of the online shop. Information about the online shop includes, for example, the results of the online shop (sales history, date of opening the online shop, duration of the online shop, average evaluation value of the online shop, etc.), behavior of consumers who purchased from the online shop (for example). , Consumer login history, number of online shop views by consumers, repeat rate (ratio of the number of consumers who purchased multiple times at the online shop to the number of consumers who purchased at the online shop), products handled by the online shop Attributes (for example, fashion, groceries, miscellaneous goods, etc.), operator attributes (address, age, login history, SNS usage information, etc.), consumer attributes (age, SNS usage, etc.) It may be at least one of (information, etc.).

The receiving means 110 passes the received information about the net shop to the processor unit 120, and the processor unit 120 receives the information about the net shop.

When the processor unit 120 receives the information about the net shop, in step S602, the prediction means 121 of the processor unit 120 predicts the future credit amount of the net shop based on the information about the net shop.

The forecasting means 121 predicts the future bond amount by performing regression analysis based on, for example, information about an online shop. The prediction means 121 predicts the future bond amount by using, for example, a learned neural network. When using the trained neural network, the future bond amount is output to the output layer by inputting the information about the net shop to the input layer of the neural network.

When the future bond amount is predicted, in step S603, the processor unit 120 proposes the funding conditions for providing the fund according to the predicted future bond amount via the transmission means 130 to the terminal device of the net shop. It is transmitted to (dealer terminal device 300).

Financing conditions include, for example, funding amounts, recovery rates, and fees. The funding conditions can be, for example, conditions that are uniquely determined from the projected future bond amount. The amount to be provided is, for example, a fixed percentage (for example, 10 to 50%, for example, 10%, 20%, 30%, 40%, 50%, etc.) uniquely determined from the predicted future loan amount. It may be an amount multiplied by, or a fixed amount (for example, 70,000 yen, 140,000 yen, 210,000 yen, etc.) uniquely determined from the predicted future loan amount. The recovery rate can be, for example, a fixed percentage uniquely determined from the predicted future bond amount (eg, 10-50%, eg, 10%, 20%, 30%, 40%, 50%, etc.). The fee is, for example, a fixed percentage (for example, 10 to 50%, for example, 10%, 20%, 30%, 40%, 50%, etc.) uniquely determined from the predicted future loan amount as the future loan amount. It may be the amount multiplied, or at a fixed amount (for example, 1,000 yen / month, 5,000 yen / month, 10,000 yen / month, etc.) that is uniquely determined from the predicted future loan amount. There may be.

When the dealer terminal device 300 that has received the proposal of the funding conditions accepts the funding conditions, the funding is executed. At this time, for example, the funds may be provided in an amount obtained by subtracting the commission from the provided amount.

The computer system 100'described above with reference to FIG. 3B can perform the process 600 in the same manner as the computer system 100, but in the process by the computer system 100'', the process S602 between steps S602 and S603 May be included.

In step S6025, the determination means 122 of the processor unit 120 determines the conditions for funding based on the amount of future receivables predicted in step S602. Financing conditions include, for example, funding amounts, recovery rates, and fees.

The determining means 122 can predict the conditions of funding based on, for example, the amount of future claims predicted in step S602 and the information about the online shop received by the receiving means 110. The information about the online shop can be, for example, at least one of the performance of the online shop, the behavior of the consumer who purchased the online shop, and the attributes of the products handled by the online shop.

The determination means 122 can predict the funding conditions using, for example, a trained neural network. When the trained neural network is used, the funding conditions are output to the output layer by inputting the future bond amount predicted in step S602 to the input layer of the neural network. Alternatively, by inputting the information on the future loan amount and the net shop predicted in step S602 into the input layer of the neural network, the conditions for funding are output to the output layer.

At this time, the transmission means 130 transmits the funding conditions determined by the determination means 122 to the terminal device of the net shop (dealer terminal device 300).

In the process 600, the timing at which each step of the process 600 is executed does not matter. Each step of the process 600 can be executed at an arbitrary timing.

For example, when a request for funding is received from the store terminal device 300, steps S601 to S603 may be executed in response to the request.

For example, steps S601 to S603 may be executed randomly, at predetermined intervals, or at predetermined timings without receiving a request for funding from the store terminal device 300. The predetermined period may be any period such as 1 minute, 1 hour, 12 hours, 1 day, 1 week, January, June, 1 year and the like. The predetermined timing is, for example, when the sales of the online shop exceed the predetermined amount (for example, 1 million yen), when the continuation period of the store exceeds the predetermined number of years (for example, one year), from the consumer to the online shop. It can be when there is an evaluation.

For example, when steps S601 to S602 are executed at predetermined intervals and a request for funding is received from the store terminal device 300, step S603 or step S603 or based on the amount of future claims predicted at that time is used. Step S6025 and step S603 may be executed, or when steps S601 to S6025 are executed at predetermined intervals and a request for funding is received from the store terminal device 300, the determination is made at that time. Step S603 may be performed based on the funding conditions provided. The predetermined period may be any period such as 1 minute, 1 hour, 12 hours, 1 day, 1 week, January, June, 1 year and the like. By setting the predetermined period to, for example, one month or a shorter period, for example, one week, one day, one hour, etc., the future loan amount of the online shop is predicted in "real time", and the current state of the online shop is predicted. It will be possible to present the conditions for funding that better reflect the status of.

For example, when steps S601 to S602 are executed each time a consumer orders an item in an online shop and a request for funding is received from the store terminal device 300, the future claim predicted at that time is executed. Based on the amount, steps S603, or steps S6025 and S603 may be executed, or steps S601 to S6025 are executed every time a consumer orders an item at an online shop, and a store is used. When a request for funding is received from the terminal device 300, step S603 may be executed based on the funding conditions determined at that time. This makes it possible to predict the future debt amount of the online shop in "real time" and present the conditions of funding that more closely reflect the current state of the online shop.

In one embodiment, when the processor unit 120 receives the request for funding, the processor unit 120 may determine whether or not the online shop that requested the funding is qualified to provide the funding. Good. A sufficient eligibility to provide funds may be, for example, an online shop that has a transaction record of 6 months or more under the control of the computer system 100. Sufficient eligibility to fund may be, for example, an average monthly sales of 1 million or more over the last three months. By determining whether or not you are qualified to provide funds, you can eliminate in advance online shops that may fail to collect the provided funds, and risk failing to collect the provided funds. Can be reduced.

After the terms of funding have been accepted and the funding has been executed, the computer system 1000'can recover the funding provided, for example, by process 700 shown in FIG.

FIG. 7 shows an example of processing in the computer system 1000'for funding a store in electronic commerce. In the example shown in FIG. 7, the process 700 for collecting the funds provided to the online shop will be described. The process 700 is, for example, a process for recovering the funded funds in accordance with the funding conditions presented by the above-mentioned process 600. In the following description, the terms of funding shall include at least the amount provided and the recovery rate.

When a consumer purchases a product at an online shop, the consumer terminal device 300 sends a product payment request to the computer system 1000 together with the product order. Then, in step S701, the receiving means 110 of the computer system 1000 receives the product settlement request. The product settlement request may include, for example, consumer information (for example, user ID, etc.), online shop information (for example, store ID, etc.), and product information (for example, product code, price).

The receiving means 110 passes the received product settlement request to the processor unit 120, and the processor unit 120 receives information about the net shop.

When the processor unit 120 receives the product settlement request, in step S702, the settlement means 123 of the processor unit 120 makes a settlement. By the settlement, the settlement means 123 can acquire the sales amount of the product.

In step S703, the payment means 124 of the processor unit 120 collects the collection rate portion of the sales proceeds acquired by the settlement, and pays the rest to the online shop. The recovery amount is the amount obtained by multiplying the sales amount obtained by settlement by the recovery rate. The payment means 124 may, for example, collect the collection rate portion, deduct the commission portion, and then pay the rest to the online shop.

In step S704, the payment instrument 123 compares the accumulated recovery rate with the offer amount. If the accumulated recovery rate is less than the provided amount, the process 700 is not completed and the process returns to step S701 because all the provided amounts provided to the online shop have not been collected. If the accumulated collection rate exceeds the provided amount, all the provided amount provided to the online shop can be collected, so the processing 700 is completed, and in the subsequent settlement, all the acquired sales will be transferred to the online shop. You can pay.

In the above-described example with reference to FIGS. 6 and 7, it has been explained that the processing of each step shown in FIGS. 6 and 7 is realized by the program stored in the processor unit 120 and the memory unit 140. , The present invention is not limited to this. At least one of the processes of each step shown in FIGS. 6 and 7 may be realized by a hardware configuration such as a control circuit.

The present invention is not limited to the above-described embodiments. It is understood that the invention should be construed only by the claims. It will be understood by those skilled in the art that from the description of specific preferred embodiments of the present invention, an equivalent range can be implemented based on the description of the present invention and common general technical knowledge.

The present invention is useful as providing computer systems, methods, and programs for funding retailers in electronic commerce.

10 Net shop management company 20 Net shop 30 Consumer 100, 100', 1000 Computer system 200 Database department 300 Dealer terminal device 400 Consumer terminal device 500 Network

Claims (24)

A computer system for funding retailers in e-commerce.
Receiving means for receiving information about retailers in e-commerce,
A forecasting means for predicting the future loan amount of the dealer based on the information about the dealer, and
A computer system comprising a transmission means for transmitting a proposal of funding conditions for providing funds according to the predicted future receivable amount to the terminal device of the store. Further provided with a management means for creating a store in the electronic commerce and managing the created store.
The receiving means receives information about the store from the management means, and the information about the store is at least one information acquired in the creation of the store and at least one acquired in the management of the store. and one of the information only including,
The forecasting means predicts the future credit amount of the store based on at least one information acquired in the creation of the store and at least one information acquired in the management of the store. Item 1. The computer system according to item 1. The computer system according to claim 1 or 2, wherein the condition includes an amount of money provided in the funding and a recovery rate indicating the rate at which the amount of money is collected. The computer system of claim 3, wherein the conditions further include a fee for the funding. The conditions vary at least according to the amount of future claims.
The computer system according to any one of claims 1 to 4, further comprising a determination means for determining the conditions based on the predicted future claim amount. The information about the store includes at least one of the store's performance, the behavior of the consumer who purchased at the store, and the attributes of the products handled by the store.
The determination means further determines the conditions based on the performance of the store, the behavior of the consumer who purchased at the store, and at least one of the attributes of the products handled by the store. 5. The computer system according to 5. The information about the store includes the performance of the store, the behavior of the consumer who purchased at the store, and the attributes of the products handled by the store.
The determination means according to claim 5, wherein the determination means determines the conditions based on the results of the store, the behavior of the consumer who purchased at the store, and the attributes of the products handled by the store. Computer system. The receiving means further receives a request for funding from the terminal device of the store.
The forecasting means predicts the future claim amount of the dealer at predetermined periods, and
The computer system according to any one of claims 5 to 7, wherein the determination means determines the conditions based on the amount of future claims of the dealer predicted when the request for funding is received. .. The information about the store includes at least one of the store's performance, the behavior of the consumer who purchased at the store, and the attributes of the products handled by the store.
The forecasting means predicts the future credit amount of the retailer based on at least one of the performance of the retailer, the behavior of the consumer who purchased at the retailer, and the attributes of the products handled by the retailer. The computer system according to any one of claims 1 to 8. The item according to any one of claims 1 to 9, further comprising a storage means for storing information about the store, and receiving the information about the store stored in the storage means. Computer system. The computer system according to any one of claims 1 to 10, wherein the funding is the purchase of at least a portion of the predicted future claim amount. A payment method for making payments in the electronic commerce of the store,
A payment means that pays the sales amount acquired by the settlement to the store, and the payment means collects the collection rate of the acquired sales amount and pays the rest to the store. The computer system according to any one of claims 1 to 11, further comprising. A method for funding a store in an electronic commerce, said method being performed in a computer system including a processor unit, said method.
When the processor unit receives information about a store in electronic commerce,
The processor unit predicts the future credit amount of the dealer based on the information about the dealer.
A method comprising the processor unit transmitting a proposal for funding conditions to provide funding according to the predicted future claim amount to the terminal device of the dealer. The processor unit further includes creating a store in the electronic commerce and managing the created store.
The reception includes receiving information about the store from the management means, and the information about the store includes at least one information acquired in the creation of the store and the management of the store. see contains at least one information is acquired,
The prediction is to predict the future claim amount of the store based on at least one information acquired in the creation of the store and at least one information acquired in the management of the store. including method of claim 13. A program for providing funds to a dealer in electronic commerce, the program is executed in a computer system including a processor unit, and when the program is executed, the program is executed.
Receiving information about retailers in e-commerce and
Forecasting the future credit amount of the dealer based on the information about the dealer,
A program that causes the processor unit to perform processing including transmitting a proposal of funding conditions for providing funds according to the predicted future receivable amount to the terminal device of the store. The process further comprises creating a store in the e-commerce and managing the created store.
The reception includes receiving information about the store from the management means, and the information about the store includes at least one information acquired in the creation of the store and the management of the store. see contains at least one information is acquired,
The prediction is to predict the future claim amount of the store based on at least one information acquired in the creation of the store and at least one information acquired in the management of the store. the containing, program of claim 15. A system for providing funds to a store in an electronic commerce, the system including a server device and a terminal device of the store.
The server device
Receiving information about the retailer in e-commerce
Forecasting the future credit amount of the dealer based on the information about the dealer,
It is configured to send a proposal for funding conditions to the terminal device of the retailer to provide funding according to the predicted future claim amount.
The terminal device of the store is
Receiving the proposal from the server device and
It is configured to send the acceptance of the proposal to the server device.
The server device
Receiving acceptance of the proposal and
A system further configured to fund the store in response to receiving the consent. The server device is further configured to create a store in the electronic commerce and manage the created store.
The reception includes receiving information about the store from the management means, and the information about the store includes at least one information acquired in the creation of the store and the management of the store. see contains at least one information is acquired,
The prediction is to predict the future credit amount of the store based on at least one information acquired in the creation of the store and at least one information acquired in the management of the store. containing system of claim 17. A computer system for funding retailers in e-commerce.
Receiving means for receiving information about retailers in e-commerce,
A forecasting means for predicting the future loan amount of the store based on the information about the store in the electronic commerce, and
An execution means for executing financing to the dealer in accordance with the funding conditions for providing funds according to the predicted future receivable amount, the conditions including the provision amount and the recovery rate, and the provision. The amount is the amount provided in the funding, and the recovery rate indicates the means at which the provision amount is to be recovered.
A receiving means for receiving a payment request in the electronic commerce of the store from the terminal device of the consumer, and
A payment method for making payments in the electronic commerce of the store,
A computer system including a payment means that collects the collection rate of the sales proceeds obtained by the settlement and pays the rest to the store. Further provided with a management means for creating a store in the electronic commerce and managing the created store.
The receiving means receives information about the store from the management means, and the information about the store is at least one information acquired in the creation of the store and at least one acquired in the management of the store. and one of the information only including,
The forecasting means predicts the future credit amount of the store based on at least one information acquired in the creation of the store and at least one information acquired in the management of the store. Item 19. The computer system according to item 19. A method for funding a store in an electronic commerce, said method being performed in a computer system including a processor unit, said method.
When the processor unit receives information about a store in electronic commerce,
The processor unit predicts the future claim amount of the store based on the information about the store in the electronic commerce.
The processor unit executes funding to the dealer in accordance with the funding conditions for providing funds according to the predicted future credit amount, and the conditions are the provision amount and the recovery rate. Including, the offer amount is the amount provided in the funding, and the recovery rate indicates the rate at which the offer amount is to be recovered.
When the processor unit receives a payment request in the electronic commerce of the store from the terminal device of the consumer,
The processor unit makes a payment in the electronic commerce of the store, and
A method including a method in which the processor unit collects a collection rate portion of the sales proceeds acquired by the settlement and pays the rest to the store. The processor unit further includes creating a store in the electronic commerce and managing the created store.
The reception includes receiving information about the store from the management means, and the information about the store includes at least one information acquired in the creation of the store and the management of the store. see contains at least one information is acquired,
The prediction is to predict the future claim amount of the store based on at least one information acquired in the creation of the store and at least one information acquired in the management of the store. including method of claim 21. A program for providing funds to a dealer in electronic commerce, the program is executed in a computer system including a processor unit, and when the program is executed, the program is executed.
Receiving information about retailers in e-commerce and
Forecasting the future credit amount of the store based on the information about the store in the electronic commerce,
The provision of funds to the dealer is to be carried out in accordance with the conditions of funding to provide funds according to the predicted future claim amount, and the conditions include the provision amount and the recovery rate, and the provision amount is included. Is the amount of money provided in the funding, and the recovery rate indicates the rate at which the provided amount will be recovered.
Receiving a payment request in the electronic commerce of the store from the consumer's terminal device,
Making payments in the electronic commerce of the store and
A program that causes the processor unit to perform processing including collecting the collection rate portion of the sales proceeds acquired by the settlement and paying the rest to the store. The process further comprises creating a store in the e-commerce and managing the created store.
The reception includes receiving information about the store from the management means, and the information about the store includes at least one information acquired in the creation of the store and the management of the store. see contains at least one information is acquired,
The prediction is to predict the future claim amount of the store based on at least one information acquired in the creation of the store and at least one information acquired in the management of the store. the containing, program of claim 23.