KR20220068502A - Method and server for purchasing products through time-series product price prediction - Google Patents

Abstract

The present invention relates to a method for purchasing a product through time-series product price prediction. The product purchase method is executed by a processor of a product purchase server. The method includes the following steps of: acquiring a purchase option of a product from a customer; predicting a sale price of the product satisfying the purchase option by date and time using a price prediction model trained to predict the sale price of the product during the period from the purchase option acquisition to product use initiation; acquiring a preferred purchase date and a reverse auction start date from the customer based on the prediction result; and generating product purchase information on the preferred purchase date or conducting a reverse auction for the product on the reverse auction start date.

Description

Product purchase method and server through time series-based product price prediction

The present invention relates to a product purchase method and server through time series-based product price prediction.

In general, in the case of a product group with large price fluctuations due to various vendors and various purchasing options, it is difficult for customers to determine whether the product they want to purchase is at an appropriate price or the current purchase time is appropriate.

Accordingly, price comparison services that collect information of vendors selling products, collect and compare sales prices in one place, and provide the results to customers are in the spotlight. This price comparison service improves the customer's purchasing convenience by finding the lowest price for a product through one purchase option input.

However, since the current price comparison service provides the lowest price information of the product on sale based on the current price search, customers who purchase the product in advance with a spare date before using the product will lose the transaction price. There is a possibility. For example, if a manufacturer that provides a product or various vendors that sells a service suddenly conducts a discount event, or if the price of a product falls due to circumstances, customers who have purchased the product in advance will inevitably incur losses.

The description underlying the invention has been prepared to facilitate understanding of the invention. It should not be construed as an admission that the matters described in the background technology of the invention exist as prior art.

Therefore, in order to minimize the damage caused by the purchase of the product by customers and to increase the satisfaction of using the service, a technology capable of predicting the future price information of the product is required.

Accordingly, the inventors of the present invention collect past price information on a product, and use various price fluctuation factors to predict sales price information of a product that meets the purchase option of a product presented by a customer within a specified time limit, and a method for predicting the same I wanted to develop a server that runs.

As a result, the inventors of the present invention receive the purchase price of the product desired by the customer, and provide the expected date and accuracy of the product to be sold according to the purchase price, so that the customer can purchase the product at the optimal price. It came to develop a method and a server.

The problems of the present invention are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the following description.

In order to solve the above problems, there is provided a product purchase method according to an embodiment of the present invention. The method is a product purchase method performed by a processor of a product purchase server based on a time series, comprising: obtaining a purchase option of a product from a customer; Predicting the sales price of a product satisfying the purchase option by date and time by using a price prediction model trained to predict the sales price of the product during the period up to the time before the start of use, the sales price by date and time Acquiring a purchase preference date and a reverse auction start date from the customer based on a prediction result for is configured to

According to a feature of the present invention, the predicting includes: using the sales trend prediction model trained to predict the inventory quantity and prediction accuracy of the product by inputting the purchase option as an input, the inventory quantity of the product satisfying the purchase option and It may be a step of predicting the prediction accuracy for each date and time.

According to another feature of the present invention, the predicting may further include confirming the customer's intention to purchase the product when the sales price of the product predicted for each date is higher than or equal to the sales price searched for at the current time. can

According to another feature of the present invention, the step of obtaining the date includes: obtaining the desired purchase price range and the purchase option adjustment range of the product from the customer; and temporarily holding the purchase of the product until the purchase preference date; The method may further include periodically monitoring the selling price and inventory quantity of the product.

According to still another feature of the present invention, the monitoring may include periodically monitoring the selling price and the stock quantity of a product that satisfies the purchase option adjustment range.

According to another feature of the present invention, the monitoring may further include generating a purchase notification of the product when a product having a low selling price that is out of the desired purchase price range is searched for.

According to another feature of the present invention, the step of confirming the purchase intention includes generating purchase information of the product when the customer confirms the purchase intention for the product and purchasing the product before the start of using the product. The method may include periodically monitoring the sale price, and generating a purchase notification of the product when a product having a sale price lower than the sales price searched for at the current time is found.

According to another feature of the present invention, the purchase preference date is a date earlier than the reverse auction start date, and in the step of performing the reverse auction, when the purchase information of the product is not generated on the purchase preference date, the purchase option is Accordingly, the reverse auction may be immediately proceeded.

According to another feature of the present invention, in the step of conducting the reverse auction, if the sales price successful in the reverse auction is higher than the sales price of the product predicted on the purchase preference date, the difference between the two prices is calculated, and the customer The method may further include compensating for the calculated difference with electronic money.

According to another feature of the present invention, the purchase option may include product identification information, product use start date, use end date, use time, product payment method, and additional service options for each product type. .

In order to solve the above problems, there is provided a product purchase server according to another embodiment of the present invention. The server includes a communication unit, a storage unit, and a processor operatively connected to the communication unit and the storage unit, wherein the processor obtains a purchase option of a product from a customer, and uses the product from the time of obtaining the purchase option Using the price prediction model trained to predict the sale price of the product during the period before the start time, predicts the sale price of the product satisfying the purchase option by date and time, and purchases from the customer based on the prediction result and acquire a preference date and a reverse auction start date, generate purchase information of the product on the purchase preference date, or conduct a reverse auction on the product on the reverse auction start date.

According to a feature of the present invention, the processor uses the sales trend prediction model trained to predict the inventory quantity and prediction accuracy of the product by inputting the purchase option, the inventory quantity and prediction accuracy of the product satisfying the purchase option may be configured to predict by date and time.

According to another feature of the present invention, the processor may be configured to confirm the intention of the customer to purchase the product when the sales price of the product predicted for each date is higher than or equal to the sales price searched for at the current time.

According to another feature of the present invention, the processor obtains the desired purchase price range and the purchase option adjustment range of the product from the customer, temporarily suspends the purchase of the product until the purchase preference date, and determines the selling price of the product and It may be configured to periodically monitor inventory quantities.

According to another feature of the present invention, the processor may be configured to periodically monitor the selling price and the stock quantity of the product satisfying the purchase option adjustment range.

According to another feature of the present invention, the processor may be configured to generate a purchase notification of a product when a product having a low selling price out of the desired purchase price range is searched for.

According to another feature of the present invention, when the processor confirms the intention to purchase the product from the customer, the processor generates purchase information of the product, and periodically monitors the selling price of the product before the start of using the product. Thus, when a product having a sale price lower than the search price searched for at the current time is searched for, a purchase notification of the product may be generated.

According to another feature of the present invention, the purchase preference date is a date earlier than the reverse auction start date, and when the purchase information of the product is not generated on the purchase preference date, the processor immediately performs a reverse auction according to the purchase option. can be configured to proceed.

According to another feature of the present invention, the processor calculates a difference between the two prices, and calculates the difference between the two prices, when the sales price successfully bid through the reverse auction is higher than the sales price of the product predicted on the purchase preference date It may be configured to compensate as much as electronic money.

According to another feature of the present invention, the purchase option may include product identification information, product use start date, use end date, use time, product payment method, and additional service options for each product type. .

Specific details of other embodiments are included in the detailed description and drawings.

According to the present invention, by predicting the price of a product by date based on past price information of the product and various price fluctuation factors before the date of use of the product, the customer can purchase the product at a reasonable price.

In addition, according to the present invention, a customer can conveniently purchase a product without having to search various vendors for a product with a large price change, compare prices, or search for a discount event.

In addition, the present invention predicts the price of a product and provides the predicted result and prediction accuracy to the customer, so that the customer can purchase the product at an appropriate purchase price at a desired time.

In addition, in the present invention, if the price of the product does not fall by the specified deadline, a reverse auction can be conducted, so that the transaction can be concluded before the date desired by the customer, and thus customer satisfaction can be improved.

The effect according to the present invention is not limited by the contents exemplified above, and more various effects are included in the present invention.

1 is a schematic diagram for explaining an outline of a product purchase method according to an embodiment of the present invention.
2 is a schematic diagram of a product purchase system according to an embodiment of the present invention.
3 is a block diagram illustrating the configuration of a product purchase server according to an embodiment of the present invention.
4A to 4D are schematic diagrams for explaining a product purchase process through product price prediction according to an embodiment of the present invention.
5 is a schematic flowchart of a product purchase method according to an embodiment of the present invention.
6 is a schematic diagram illustrating a product price prediction result using a price prediction model according to an embodiment of the present invention.

Advantages and features of the present invention, and a method for achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but will be implemented in a variety of different forms, only these embodiments allow the disclosure of the present invention to be complete, and common knowledge in the technical field to which the present invention pertains It is provided to fully inform the possessor of the scope of the invention, and the present invention is only defined by the scope of the claims. In connection with the description of the drawings, like reference numerals may be used for like components.

In this document, expressions such as "have," "may have," "includes," or "may include" refer to the presence of a corresponding characteristic (eg, a numerical value, function, operation, or component such as a part). and does not exclude the presence of additional features.

In this document, expressions such as “A or B,” “at least one of A or/and B,” or “one or more of A or/and B” may include all possible combinations of the items listed together. . For example, "A or B," "at least one of A and B," or "at least one of A or B" means (1) includes at least one A, (2) includes at least one B; Or (3) it may refer to all cases including both at least one A and at least one B.

As used herein, expressions such as "first," "second," "first," or "second," may modify various elements, regardless of order and/or importance, and refer to one element. It is used only to distinguish it from other components, and does not limit the components. For example, the first user equipment and the second user equipment may represent different user equipment regardless of order or importance. For example, without departing from the scope of rights described in this document, the first component may be named as the second component, and similarly, the second component may also be renamed as the first component.

A component (eg, a first component) is "coupled with/to (operatively or communicatively)" to another component (eg, a second component); When referring to "connected to", it will be understood that the certain element may be directly connected to the other element or may be connected through another element (eg, a third element). On the other hand, when it is said that a component (eg, a first component) is "directly connected" or "directly connected" to another component (eg, a second component), the component and the It may be understood that other components (eg, a third component) do not exist between other components.

The expression "configured to (or configured to)" as used in this document, depending on the context, for example, "suitable for," "having the capacity to ," "designed to," "adapted to," "made to," or "capable of." The term “configured (or configured to)” may not necessarily mean only “specifically designed to” in hardware. Instead, in some circumstances, the expression “a device configured to” may mean that the device is “capable of” with other devices or parts. For example, the phrase "a processor configured (or configured to perform) A, B, and C" refers to a dedicated processor (eg, an embedded processor) for performing the corresponding operations, or by executing one or more software programs stored in a memory device. , may mean a generic-purpose processor (eg, a CPU or an application processor) capable of performing corresponding operations.

Terms used in this document are only used to describe specific embodiments, and may not be intended to limit the scope of other embodiments. The singular expression may include the plural expression unless the context clearly dictates otherwise. Terms used herein, including technical or scientific terms, may have the same meanings as commonly understood by one of ordinary skill in the art described in this document. Among terms used in this document, terms defined in a general dictionary may be interpreted with the same or similar meaning as the meaning in the context of the related art, and unless explicitly defined in this document, ideal or excessively formal meanings is not interpreted as In some cases, even terms defined in this document cannot be construed to exclude embodiments of this document.

Each feature of the various embodiments of the present invention may be partially or wholly combined or combined with each other, and as those skilled in the art will fully understand, technically various interlocking and driving are possible, and each embodiment may be implemented independently of each other, It may be possible to implement together in a related relationship.

For clarity of interpretation of the present specification, terms used herein will be defined below.

1 is a schematic diagram for explaining the outline of a product purchase method according to an embodiment of the present invention.

Referring to FIG. 1 , in the product purchase method according to an embodiment of the present invention, the product purchase server 100 predicts the selling price of a product for product groups with large price fluctuations according to a customer's request for a sales price search, and predicts can provide results. Here, the product groups with large price fluctuations refer to product groups with a large change in price compared to the list price depending on various price fluctuation factors such as season, region, expiration date, etc. can As a representative example, a rental car, air ticket, accommodation voucher, etc. may be included in a product group with large price fluctuations. However, the product purchase method according to an embodiment of the present invention is not limited to the above-described product group, and various goods and services may be applied to the present invention.

Depending on the type of product that a customer wants to purchase, a customer can input various purchase option information and request a sales price search accordingly. You can enter purchase option information such as the end date (use date, use end date) and other options (rear camera installation).

As such, the product purchase method according to an embodiment of the present invention can provide a product with an optimal price to a customer using a price prediction model learned to predict the sale price of a product based on purchase option information of the product, If there is no dealer matching the sales price desired by the customer within the specified period, a reverse auction may be conducted through sellers and a successful bid price may be provided.

2 is a schematic diagram of a product purchase system according to an embodiment of the present invention.

Referring to FIG. 2 , the product purchase system 1000 may include a customer device 10 , a seller device 20 , and a product purchase server 100 .

The customer device 10 is a device owned by a customer who wants to purchase a product, and may include various electronic devices capable of communication, such as a PC, a tablet PC, and a smart phone. The customer device 10 may make a request for a sale price search of a product to be purchased to the product purchase server 100 using the application provided by the product purchase server 100 , and sell the product from the product purchase server 100 . A price prediction result or a reverse auction result of the product may be received.

The seller device 20 is a device owned by a seller who wants to sell a product, and may include various electronic devices capable of communication, such as a PC, a tablet PC, and a smart phone. The seller device 20 may participate in the reverse auction by presenting a sale price of a product that matches the product purchase option presented by the customer to the product purchase server 100 using the application provided by the product purchase server 100 .

The product purchase server 100 may predict and provide product purchase information to the customer by means of a processor operating the same, and may include various electronic devices such as a PC, a tablet PC, and a data server. The product purchase server 100 may predict the sale price of the product and provide an application for making a product transaction according to the prediction result. Here, the application is implemented as a web or mobile application, and may be installed and executed on the customer device 10 and the seller device 20, or may be executed without separate installation through a URL, an image code, or the like.

The product purchase server 100 may predict the sales price of the product by date and time by using the price prediction model learned to predict the sales price of the product. In detail, the product purchase server 100 may acquire one output data of a selling price of a product based on one input data of a purchase option through a price prediction model. Here, the purchase option defined as input data may include information on essential matters in purchasing a product. For example, in the case of a ticket, purchase options may include departure location, arrival location, departure date, arrival date, number of people, seat type, direct flight, number of stops, etc. may be included in this purchase option.

In addition, the sale price of the product defined by the output data may mean the sale price of the product predicted for each date and time. According to an embodiment, the sales price of the product is the sales price predicted during the period from the current time when the purchase option of the product is acquired to the time when the customer starts using the product, based on the date, but in units of time according to the predicted result can be divided into

According to an embodiment, the price prediction model may be implemented through various well-known machine learning models. In addition, the product purchase server 100 may implement a price prediction model by selecting a model with excellent performance from among different machine learning-based price prediction models through k-fold cross validation. have. In addition, the price prediction model may be learned using at least one artificial intelligence algorithm among a machine learning algorithm, a neural network algorithm (DNN, CNN, DCNN, RNN, RBM, DBN, SSD), and a deep learning algorithm.

As described above, as the sale price of the product is predicted by date and time through the price prediction model, the product purchase server 100 according to an embodiment of the present invention allows the user to reserve the purchase of the product at the current time, and You can provide information so that you can purchase products at low prices.

On the other hand, in the case of a product group in which the product purchase server 100 provides a service for estimating the sale price, the customer purchases the product in advance with a margin of several days or months before the start date of use of the product. , a situation may arise where a product with a price range desired by the customer does not exist as a result of prediction/search for that period. Accordingly, the product purchase server 100 may conduct a reverse auction of the product through the sellers, and the customer may make a transaction with the seller who has provided the most satisfactory product purchase condition through the customer device 10 .

According to an embodiment, the product purchase server 100 obtains not only the use start date of the product, but also the purchase preference date and the reverse auction start date of the product through the customer device 10, and proceeds with the product transaction according to the date. have. Here, the purchase preference date means a date on which the product is sold according to the price desired by the customer from among the price information for each date predicted by the product purchase server 100, and the reverse auction start date is to conduct an auction of the product according to the customer's request. means the date

That is, the product purchase server 100 of the present invention can provide a product purchase service to the customer so that he or she can purchase the product according to the optimal purchase conditions using any method, and the product purchase server 100 provides such a service. This can increase the success rate of transactions between customers and sellers.

So far, the product purchase system 1000 according to an embodiment of the present invention has been described. According to the present invention, the product purchase server 100 can increase the satisfaction of purchasing a product so that the customer can purchase the product at a desired price range through the prediction of the product price, and the seller is willing to purchase the product through the reverse auction. It can provide an opportunity to meet customers.

Hereinafter, the product purchase server 100 that predicts the sale price of a product and performs a reverse auction will be described in detail.

3 is a block diagram illustrating the configuration of a product purchase server according to an embodiment of the present invention.

Referring to FIG. 3 , the product purchase server 100 may include a communication unit 110 , a storage unit 120 , and a processor 130 .

The communication unit 110 may exchange data with the customer device 10 and the seller device 20 through a wired/wireless network. For example, the communication unit 110 may receive a purchase option of a product from the customer device 10 , and transmit a price prediction result of the product by date and time to the customer device 10 , and to the customer device 10 . According to the reverse auction, the selling price presentation information and product information of each seller may be transmitted, and seller selection information of the customer may be received from the customer device 10 . For another example, the communication unit 110 may transmit a purchase option and a desired purchase price range for each customer product to the seller device 20 , and receive selling price presentation information and product information from the seller device 20 .

In addition, the communication unit 110 may exchange data related to sales information of a product with an open market server (not shown). For example, the communication unit 110 may receive new product information, production information of an existing product, and affiliate information from the open market server.

The storage unit 120 may store various data for predicting the lowest price for each product and for making a transaction between the customer and the seller. Specifically, the storage unit 120 may store data to be used in the price prediction model. For example, the storage unit 120 includes past price information according to purchase options of the product, price fluctuation factors (day of the week, holiday, It is possible to store data such as season, region, vehicle model, date of use and purchase date, company, period of use, etc.) and information on previous demand for products. Here, the company data may include information on events that are conducted periodically for each company, information on affiliates of the companies, the founding date of the companies, etc., and such data may be used in the process of predicting whether a discount event for each company will proceed.

On the other hand, the processor 130, which will be described later, stores the company information together with the sales information of the product together with the sales information of the product when there is a new company that is not stored in the storage unit 120 according to the product sale price search result. can be stored in

In addition, the storage unit 120 may store customer identification information, product purchase option, purchase preference date, and reverse auction start date information for each customer, which may be utilized in the process of periodically searching for the sale price of the product. For example, the purchase option of the product may include identification information of the product, the use start date of the product, the use end date, the use time, the payment method of the product, an additional service option for each product type, and the like.

In addition, the processor 130 to be described later may predict the sale price of the product based on the information stored in the storage unit 120, but the prediction result is based on the date and time provided as a basis, but the customer's residential area However, it may be provided based on an adjustable period of use according to the customer's request. For example, forecast results are provided based on date and time, such as “January 1st 100,000 won, January 2nd 110,000 won, January 3rd 105,000 won, January 3 10:00 PM to midnight 90,000 won” In addition, “January 1st Seoul ㅇㅇ store 100,000 won, January 1 Guri/Namyangju ㅁㅁ ㅁ branch 95,000 won”, “January 1~Jan 8 500,000 won, January 2~January 9 It can be predicted based on region or request, such as “400,000 won”.

In various embodiments, the storage unit 120 may include a volatile or nonvolatile recording medium capable of storing various data, commands, and information. For example, the storage unit 120 is a flash memory type, hard disk type, multimedia card micro type, card type memory (SD, XD memory, etc.), RAM, SRAM, ROM, EEPROM, PROM, network storage storage, cloud , may include at least one type of storage medium among the block chain database.

Also, the storage unit 120 may record commands for the operation of the product purchase server 100 . In various embodiments, an application (not shown) for predicting the sale price of a product and performing a reverse auction may be recorded in the storage unit 120 .

The processor 130 is operatively connected to the communication unit 110 and the storage unit 120 , and can control the overall operation of the product purchase server 100 , and by driving an application or program stored in the storage unit 120 , , it can execute various commands so that a transaction between the customer and the seller can be completed.

The processor 130 may correspond to a computing device such as a central processing unit (CPU) or an application processor (AP). In addition, the processor 130 may be implemented in the form of an integrated chip (IC), such as a system on chip (SoC) in which various computing devices are integrated.

According to an embodiment, the processor 130 obtains the purchase option of the product from the customer, and uses the price prediction model learned to predict the sales price for a predetermined period of time to determine the sales price of the product satisfying the purchase option on the date and time not very predictable. Here, the predetermined period means a period from the time when the purchase option is acquired to the time before the start of use of the product, and the processor 130 receives one output data of the selling price of the product based on one input data of the purchase option. can be obtained Here, the purchase option defined as input data includes product identification information, product usage start date, usage end date, usage time, product payment method, and additional service options for each product type. information may be included. For example, in the case of a ticket, purchase options may include departure location, arrival location, departure date, arrival date, number of people, seat type, direct flight, number of stops, etc. may be included in this purchase option.

In addition, the sale price of the product defined by the output data may mean the sale price of the product predicted for each date and time. According to an embodiment, the sales price of the product is the sales price predicted during the period from the current time when the purchase option of the product is acquired to the time when the customer starts using the product, based on the date, but in units of time according to the predicted result can be divided into

According to an embodiment, the price prediction model may be implemented through various well-known machine learning models. In addition, the processor 130 may implement the price prediction model by selecting a model with excellent performance from among different machine learning-based price prediction models through k-fold cross validation. In addition, the price prediction model may be learned using at least one artificial intelligence algorithm among a machine learning algorithm, a neural network algorithm (DNN, CNN, DCNN, RNN, RBM, DBN, SSD), and a deep learning algorithm.

4A to 4D are schematic diagrams for explaining a product purchase process through product price prediction according to an embodiment of the present invention.

Referring to FIG. 4A , the product purchase server 100 may be divided into an API server 100a and an analysis server 100b according to functions performed by the processor 130 , and the functions shown in the dotted line are the processor 130 . ) can be understood as being performed by

When the product purchase server 100 provides a car rental service, the customer has a purchase option of the product through the customer device 10 (A. Avante, 2019 model year, rear camera, car insurance KRW 10,000 B. Avante Bluetooth, rear camera , car insurance premium 11,000 won), the reservation start date and reservation end date are entered, and the API server 100a that provides the product purchase service interface to the customer device 100 obtains it and can then transmit it to the analysis server 100b have. When the analysis server 100b, that is, the processor 130 receives the first input of the purchase option and the reservation start date/end date from the customer device 10, first, the processor 130 may search for the price of the product. After inquiring about available vehicles, it may be transmitted to the customer device 10 through the communication unit 110 .

Referring to FIG. 4B , when the customer does not purchase a product according to the current selling price, the customer device 10 may request a price prediction for a specific product with the API server 100a providing a product purchase service. have. Here, the price prediction request may include a purchase option of the product, and may further include a desired purchase price range and a purchase option adjustment range of the product.

For example, in the case of the rental price, the purchase options include a reservation start date, reservation end date, vehicle type, insurance premium, and rental fee, and further, a range for adjusting the reservation start date and reservation end date, a range available for purchase of rental fees, and the like may be included.

Referring back to FIG. 3 , the processor 130 may store a purchase option (user condition) of a product for each customer in the storage unit 120 , and after predicting the sale price of the product by date and time through a price prediction model, , the predicted result may be transmitted to the customer device 10 through the communication unit 110 . Specifically, the processor 130 may predict the inventory quantity and prediction accuracy of the product as well as the daily sales price by using the sales trend prediction model. Here, the sales trend prediction model is a model that uses purchase options as input data and product inventory quantity and prediction accuracy as output data, and may be implemented through various known machine learning models in the same manner as the price prediction model. That is, the processor 130 may provide additional information so that the customer can make a selection in consideration of the inventory quantity and prediction accuracy through the sales trend prediction model even if the sale price of the product is low on a specific date.

Meanwhile, the processor 130 may confirm the customer's intention to purchase the product through the communication unit 110 when the sales price of the product predicted for each date is higher than or equal to the sales price found at the current time. This is to prepare for the case where variable data that has not been learned in the price prediction model exists, and the processor 130 may check whether the customer will purchase the product according to the current selling price through the customer device 10 .

If the customer confirming the prediction result does not purchase the product according to the current selling price, the processor 130 may obtain the customer's preferred purchase date and the reverse auction start date from the customer device 10 through the communication unit 110 . have.

For example, if the period in which the sale price of the product is predicted by the processor 130 is from the current time (1 day) to the beginning of use (10 days), the customer may select the preferred date of purchase of the product from 2 to 9 days. You can choose the start date of the reverse auction. However, although the purchase preference date and the reverse auction start date have been described in units of days, selection in units of hours/minutes may be possible depending on the specific start time of use.

That is, the processor 130 may receive a purchase date designated from the customer device 10 based on the predicted result so that the customer can complete the product purchase before the start of use, and purchase the product before the purchase preference date. You can make a temporary reservation, and periodically monitor the selling price and stock quantity of the product.

Referring to FIG. 4C , the processor 130 may search for the price of a product based on the purchase option of each customer (user) stored in the storage unit 120 , the desired purchase price range, and the purchase option adjustment range, and perform the search. The ERP (Enterprise Resource Planning) server 200 that collects sales information (eg, vehicle rental information) of the seller device 20 may be utilized as the data for this purpose.

Referring back to FIG. 3 , the processor 130 may generate a purchase notification of a product when a product having a low selling price that is out of the customer's desired purchase price range is searched for. The product purchase notification may be transmitted to the customer device 10 through the communication unit 110 , and the customer device 10 provides a product at a lower price through the product purchase server 100 that continuously monitors the sale price of the product. can be purchased

In addition, the processor 130 may confirm the purchase intention for the product from the customer device 10 through the communication unit 110 on the purchase preference date specified through the customer, and generate purchase information of the product, and accordingly, the customer The device 10 may purchase a product based on the generated purchase information. However, the processor 130 periodically monitors the price of the product even after the point of purchase and before the start of using the product, and when a product with a sale price lower than the sales price searched for at the current time is found, a new product purchase notification can create Here, the sales price searched for at the current time means the sales price of the product paid by the customer, and through this, the customer device 10 can purchase the product at an appropriate price that the customer is satisfied with.

The processor 130 provides virtual currency available in the service to the seller device 20 to the seller who may be damaged due to purchase cancellation, or when other sellers with the same conditions exist in the process of providing search/prediction results , it is possible to provide compensation services such as exposing the seller's sales information at the top. In addition, the processor 130 has a product with a sale price lower than the price purchased by the customer, and in the process of providing the corresponding information to the customer device 10 through the communication unit 110 , a previous transaction from the customer device 10 . When the intention not to cancel is received, virtual currency that can be used in the product purchase service may be provided to the customer device 10 .

Meanwhile, the processor 130 may present the product selling price to the customer in a reverse auction method. The reverse auction may be conducted for various reasons. For example, the reverse auction may be conducted at the request of a customer who needs to purchase the product in a hurry, or if the product cannot be purchased on the preferred purchase date specified by the person, the reverse auction may be conducted. Here, the second example is related to the reverse auction start date obtained by the processor 130 through the communication unit 110, and the customer waits for the product to be sold at the desired price according to the prediction result, but meets the customer's desired selling price When a vendor is not searched for, a reverse auction may be performed by the processor 130 . In order to prepare for a situation in which the reverse auction is in progress, the processor 130 may provide date selection information to the customer device 10 so that the customer selects a purchase preference date on a date earlier than the start date of the reverse auction. In other words, if the period in which the product price was predicted is from the current point (1 day) to the start of use (10 days), and the customer selects the preferred purchase date as 5 days, the customer can choose between 6 and 9 days. You can choose the start date of the reverse auction.

Accordingly, when the purchase information of the product is not generated on the purchase preference date (when the product cannot be purchased on the specified purchase preference date), the processor 130 may immediately proceed with the reverse auction according to the purchase option.

Referring to FIG. 4D , the API server 100a providing the product purchase service interface receives the reservation start date, reservation end date, vehicle model, insurance, rental fee, etc. from the customer device 10 , and transmits them to the seller device 20 . have. In addition, the API server 100a may receive a sale price according to the purchase option of the product from the seller device 20 in response thereto, and transmit it to the customer device 10 . The customer can check the sales price suggested by the sellers and select any one of them, and the processor 130 checks the sales information of the seller selected by the customer through the customer device 10 , and makes a purchase together with the successful bid price. information can be created.

On the other hand, here, the processor 130 calculates the difference between the two prices when the sales price successful bid through the reverse auction is higher than the sales price of the product predicted on the purchase preference date, and compensates the customer by the calculated difference with electronic money. can Through this, it is possible to minimize customer dissatisfaction with the product or service.

So far, the product purchase server 100 according to an embodiment of the present invention has been described. According to the present invention, the product purchase server 100 predicts the price of the product and provides the predicted result and the accuracy of the prediction, so that the customer device 10 can purchase the product at an appropriate purchase price at a desired time by the customer. In addition, when the price of the product does not fall by the specified deadline, the product purchase server 100 performs a reverse auction so that the transaction can be finally completed before the date desired by the customer, thereby improving customer satisfaction.

Hereinafter, a product purchase method using the product purchase server 100 will be described.

5 is a schematic flowchart of a product purchase method according to an embodiment of the present invention.

Referring to FIG. 5 , the product purchase server 100 obtains a purchase option from the customer device 10 ( S110 ). Specifically, the purchase option includes information on essential matters in purchasing a product, such as product identification information, product use start date, use end date, use time, product payment method, and additional service options for each product type can do.

After step S110, the product purchase server 100 predicts the sale price of the product satisfying the purchase option by date and time using the price prediction model (S120). The price prediction model is a model trained to predict the sales price for a predetermined period, and may acquire one output data called the sales price of a product based on one input data called a purchase option. In this case, the predetermined period may mean a period from the time the purchase option is acquired to the time before the start of using the product.

According to an embodiment, the price prediction model may be implemented through various well-known machine learning models. In addition, the product purchase server 100 may implement a price prediction model by selecting a model with excellent performance from among different machine learning-based price prediction models through k-fold cross validation. have. In addition, the price prediction model may be learned using at least one artificial intelligence algorithm among a machine learning algorithm, a neural network algorithm (DNN, CNN, DCNN, RNN, RBM, DBN, SSD), and a deep learning algorithm.

For example, the product purchase server 100 uses the departure place, arrival place, departure date, arrival date, number of people, seat type, direct flight, number of stops, etc. as input data of the price prediction model to predict the price of the ticket. And, it is possible to obtain output data such as the sales price of the product predicted for each date and time. According to an embodiment, the sales price of the product is the sales price predicted during the period from the current time when the purchase option of the product is acquired to the time when the customer starts using the product, based on the date, but more specifically, in units of time can be distinguished.

In addition, the product purchase server 100 may predict the inventory quantity and prediction accuracy of the product as well as the daily sales price by using the sales trend prediction model. Here, the sales trend prediction model is a model that uses purchase options as input data and product inventory quantity and prediction accuracy as output data, and may be implemented through various known machine learning models in the same manner as the price prediction model. That is, the product purchase server 100 may provide additional information so that the customer can make a selection in consideration of the inventory quantity and prediction accuracy through the sales trend prediction model even if the sale price of the product is low on a specific date.

Through step S120, the product purchase server 100 provides the sales price prediction results for each date and time, and the customer can check this and determine whether to purchase the product at the present time.

According to an embodiment, the product purchase server 100 may receive an intention to purchase a product from the customer device 10 ( S130 - 1 ), generate product purchase information accordingly, and transmit it to the customer device 10 . (S140-1).

According to another embodiment, the product purchase server 100 may receive a purchase preference date and a reverse auction preference date from the customer device 10 ( S130 - 10 ). For example, if the period in which the product purchase server 100 predicts the sale price of the product is from the current time (1 day) to the start of use (10 days), the customer prefers to purchase the product from 2 to 9 days and a reverse auction start date. However, in this case, although the purchase preference date and the reverse auction start date have been described in units of days, it may be possible to select in units of hours/minutes depending on the specific start time of use.

That is, the product purchase server 100 may receive a purchase date designated from the customer device 10 based on the predicted result so that the customer can complete the product purchase before the start of use, and purchase the product before the purchase preference date. may be temporarily suspended, and the sale price and inventory quantity of the product may be periodically monitored. In this case, the product purchase server 100 may additionally receive a desired purchase price range and a purchase option adjustment range of the product from the customer device 10 , and monitor the product according to the reference range.

Meanwhile, the product purchase server 100 may generate a product purchase notification when a product having a low selling price that is out of the customer's desired purchase price range is searched for. The product purchase notification may be transmitted to the customer device 10 , and the customer device 10 may purchase the product at a lower price through the product purchase server 100 that continuously monitors the sale price of the product.

In addition, the product purchase server 100 may confirm the purchase intention for the product from the customer device 10 on the purchase preference date specified through the customer, and generate purchase information of the product (S140-10), and accordingly The customer device 10 may purchase a product based on the generated purchase information of the product. However, the product purchase server 100 periodically monitors the price of the product even after the point of purchase and before the start of using the product, and when a product with a sale price lower than the sales price found at the current time is found, purchase notification of the product can be newly created. Here, the sales price searched for at the current time means the sales price of the product paid by the customer, and through this, the customer device 10 can purchase the product at an appropriate price that the customer is satisfied with.

The product purchase server 100 provides virtual currency available in the service to the seller device 20 to the seller who may be damaged due to purchase cancellation, or other sellers with the same conditions in the process of providing search/prediction results If present, a compensation service such as exposing the sales information of the seller to the top can be provided. In addition, the product purchase server 100 does not cancel the previous transaction from the customer device 10 in the process of providing the information to the customer device 10 because there is a product with a sale price lower than the price purchased by the customer. When the intention is received, virtual currency usable in the product purchase service may be provided to the customer device 10 .

In addition, the product purchase server 100 may perform a reverse auction through sellers ( S140 - 10 ) and provide the seller's product selling price through the reverse auction to the customer device 10 . The reverse auction may be conducted for various reasons. For example, the reverse auction may be conducted at the request of a customer who needs to purchase the product in a hurry, or if the product cannot be purchased on the preferred purchase date specified by the person, the reverse auction may be conducted. That is, when the customer waits for the product to be sold at the desired price according to the prediction result of the product purchase server 100 , but a vendor matching the sales price desired by the customer is not found, the reverse auction is performed by the product purchase server 100 . can proceed.

6 is a schematic diagram illustrating a product price prediction result using a price prediction model according to an embodiment of the present invention.

Referring to FIG. 6 , in the product purchase server 100 , in order to prepare for the situation exemplified above, the customer purchases the product between the date of acquiring the purchase option (day 2) and the start of using the product (day 20). The date selection information may be provided to the customer device 10 to select the preferred date on a date earlier than the reverse auction start date.

That is, when the customer selects the product purchase preference date as the 11th, the reverse auction start date may be selected between the 12th and the 19th, and the product purchase server 100 sets the seller on the 16th according to the customer's selection of the reverse auction start date. A reverse auction can be conducted through them.

On the other hand, the product purchase server 100 calculates the difference between the two prices when the sales price successful bid through the reverse auction is higher than the sales price of the product predicted on the purchase preference date, and compensates the customer by the calculated difference in electronic money. can do. Through this compensation, it is possible to minimize customer dissatisfaction with the product purchase service provided by the product purchase server 100 .

So far, a product purchase method according to an embodiment of the present invention has been described. According to the present invention, a customer can conveniently purchase a product without having to search various vendors for a product with a large price change and compare the price or search for a discount event.

Although one embodiment of the present invention has been described in more detail with reference to the accompanying drawings, the present invention is not necessarily limited to these embodiments, and various modifications may be made within the scope without departing from the technical spirit of the present invention. . Therefore, the embodiments disclosed in the present invention are not intended to limit the technical spirit of the present invention, but to explain, and the scope of the technical spirit of the present invention is not limited by these embodiments. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. The protection scope of the present invention should be construed by the following claims, and all technical ideas within the scope equivalent thereto should be construed as being included in the scope of the present invention.

1000: product purchase system
10: Customer device
20: Merchant device
100: product purchase server
110: communication department
120: storage
130: processor
200: ERP server

Claims (20)

As a product purchase method performed by a processor of a product purchase server,
obtaining a purchase option of the product from the customer;
By inputting the purchase option as an input, the price prediction model learned to predict the sale price of the product during the period from the time the purchase option is acquired to the time before the start of use of the product is used to determine the value of the product satisfying the purchase option. predicting the sales price by date and time;
obtaining a purchase preference date and a reverse auction start date from the customer based on a result of predicting the sales price for each date and time; and
generating purchase information of the product on the purchase preference date or performing a reverse auction on the product on the reverse auction start date; A method of purchasing products through time series-based product price prediction, including According to claim 1,
The predicting step is
Using the sales trend prediction model trained to predict the inventory quantity and prediction accuracy of products by inputting the purchase option,
Predicting the inventory quantity and prediction accuracy of the product satisfying the purchase option by date and time, further comprising: a product purchase method through time series-based product price prediction. 3. The method of claim 2,
The predicting step is
When the sales price of the product predicted for each date is higher than or equal to the sales price searched for at the current time, confirming the customer's intention to purchase the product. 3. The method of claim 2,
The step of obtaining the date is
obtaining a desired purchase price range of the product and an adjustment range of the purchase option from the customer; and
Temporarily suspending the purchase of the product until the purchase preference date, and periodically monitoring the sale price and inventory quantity of the product; 5. The method of claim 4,
The monitoring step is
A method of purchasing a product through time-series-based product price prediction, which is a step of periodically monitoring the selling price and inventory quantity of a product that satisfies the purchase option adjustment range. 5. The method of claim 4,
The monitoring step is
When a product with a low selling price out of the desired purchase price range is found, generating a purchase notification of the product; 4. The method of claim 3,
The step of confirming the purchase intention is,
generating purchase information of the product when confirming the intention to purchase the product from the customer; and
Periodically monitoring the sale price of the product before the start of use of the product, and generating a purchase notification of the product when a product with a sale price lower than the sales price searched for at the current time is found. How to purchase products through product price prediction of According to claim 1,
The purchase preference date is a date prior to the reverse auction start date,
The steps of conducting the reverse auction include:
When the purchase information of the product is not generated on the purchase preference date, a reverse auction is immediately performed according to the purchase option. 9. The method of claim 8,
The steps of conducting the reverse auction include:
If the sales price successful bid through the reverse auction is higher than the sales price of the product predicted on the purchase preference date, calculating the difference between the two prices, and compensating the customer for the calculated difference with electronic money, further comprising: A method of purchasing products through time series-based product price prediction. According to claim 1,
The purchase options are:
Product purchase method through time series-based product price prediction including product identification information, product use start date, use end date, use time, product payment method, and additional service options for each product type. communication department;
storage; and
a processor operatively connected to the communication unit and the storage unit; including,
The processor is
to obtain the purchase option of the product from the customer,
Predict the sales price of the product satisfying the purchase option by date and time by using the price prediction model trained to predict the sale price of the product during the period from the time the purchase option is acquired to the time before the start of using the product and
obtaining a purchase preference date and a reverse auction start date from the customer based on the prediction result;
and to generate purchase information of the product on the purchase preference date or to conduct a reverse auction for the product on the reverse auction start date. 12. The method of claim 11,
The processor is
Using the sales trend prediction model trained to predict the inventory quantity and prediction accuracy of products by inputting the purchase option,
The product purchase server, configured to predict the inventory quantity and prediction accuracy of the product satisfying the purchase option by date and time. 13. The method of claim 12,
The processor is
The product purchase server, configured to confirm the intention of the customer to purchase the product when the sales price of the product predicted for each date is higher than or equal to the sales price searched for at the current time. 13. The method of claim 12,
The processor is
obtaining a desired purchase price range of the product and an adjustment range of the purchase option from the customer;
The product purchase server, configured to temporarily suspend the purchase of the product until the purchase preference date, and periodically monitor the selling price and the stock quantity of the product. 15. The method of claim 14,
The processor is
and a product purchase server, configured to periodically monitor a sale price and a stock quantity of a product that satisfies the purchase option adjustment range. 15. The method of claim 14,
The processor is
and generate a purchase notification of the product when a product with a low selling price that is out of the desired purchase price range is found. 14. The method of claim 13,
The processor is
When the customer confirms the purchase intention for the product, the product purchase information is generated,
The product purchase server is configured to periodically monitor the sale price of the product before the start of using the product, and generate a purchase notification of the product when a product having a sale price lower than the sales price searched for at the current time is found. 12. The method of claim 11,
The purchase preference date is a date prior to the reverse auction start date,
The processor is
and, if the purchase information of the product is not generated on the purchase preference date, immediately proceed with a reverse auction according to the purchase option. 19. The method of claim 18,
The processor is
When the sales price successful through the reverse auction is higher than the sales price of the product predicted on the purchase preference date, the product purchase server is configured to calculate the difference between the two prices and compensate the customer for the calculated difference in electronic money. . 12. The method of claim 11,
The purchase options are:
Product purchase server, including product identification information, product use start date, use end date, use time, product payment method, and additional service options for each product type.

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