US-20260127627-A1 - METHOD OF PROVIDING PRODUCT PRICE PREDICTION SERVICE AND SERVER FOR PERFORMING SAME

US20260127627A1US 20260127627 A1US20260127627 A1US 20260127627A1US-20260127627-A1

Abstract

The present invention relates to a product price prediction method performed by a processor of a price prediction server, and the method comprises the steps of: receiving price inquiry data of a product from a customer device via a communication interface operably connected to the processor; generating multiple pieces of product condition data on the basis of multiple features predetermined for the product by using the price inquiry data; predicting a sales price per purchase date by inputting the multiple pieces of product condition data to a price prediction model that is trained to predict the sales price of a product per purchase date by using product condition data as an input; and providing, to the customer device, a price prediction service interface screen obtained by visualizing the sales price of the product into a graph over purchase dates.

Inventors

Ju Sang Lee
Ouk Seh LEE

Assignees

Nature Mobility Co., Ltd.

Dates

Publication Date: 20260507
Application Date: 20231108
Priority Date: 20221114

Claims (16)

1 . A product price prediction method performed by a processor of a price prediction server, comprising: receiving price query data for a product from a customer device through a communication interface operatively connected to the processor; generating multiple product condition data from the price query data based on predetermined multiple features for the product; predicting sales prices by purchase date by inputting the multiple product condition data into a price prediction model trained to predict product sales prices by purchase date using product condition data as input; and providing the customer device with a price prediction service interface screen that visualizes the product's sales prices graphically by purchase date.
2 . The method of claim 1 , wherein the price query data includes at least one of: product identification information, product purchasable dates, usage start date, usage end date, usage time, and product type-specific options.
3 . The method of claim 2 , wherein the generating step further includes: converting the query date and usage start date included in the price query data into data including usage year, usage week number of the year, usage day of week, day before holiday, holiday duration, day after holiday, and advance purchase days, according to the product type.
4 . The method of claim 2 , wherein the price prediction service interface screen is configured to display changes in sales prices by purchase date as a line graph for the period from the current time when the price query data was received until before the purchasable time.
5 . The method of claim 4 , wherein the price prediction service interface screen is configured to: display the case of purchasing the product at the current time as a reference point for the sales price, and display the range of predicted future sales prices by date until the usage start time as a bar graph on the reference point.
6 . The method of claim 4 , wherein the price prediction service interface screen is configured to display price changes with multiple product condition data selectively applied according to the product type.
7 . The method of claim 1 , wherein the multiple features include at least one data among: customer inquiry date, usage date/time, usage location, product type, seller type, and customer rating data.
8 . The method of claim 1 , wherein when one type of product condition data includes at least two options, the price prediction model is separately learned into first, second, and third price prediction models using: a first product condition dataset containing only one option, a second product condition dataset containing only the other option, and a third product condition dataset containing none of the options.
9 . A price prediction server comprising: a communication interface; memory; and a processor operatively connected to the communication interface and the memory, wherein the processor is configured to: receive price query data for a product from a customer device through the communication interface, generate multiple product condition data from the price query data based on predetermined multiple features for the product, predict sales prices by purchase date by inputting the multiple product condition data into a price prediction model trained to predict product sales prices by purchase date using product condition data as input, and provide the customer device with a price prediction service interface screen that visualizes the product's sales prices graphically by purchase date.
10 . The server of claim 9 , wherein the price query data includes at least one of: product identification information, product purchasable dates, usage start date, usage end date, usage time, and product type-specific options.
11 . The server of claim 10 , wherein the processor is further configured to: convert the query date and usage start date included in the price query data into data including usage year, usage week number of the year, usage day of week, day before holiday, holiday duration, day after holiday, and advance purchase days, according to the product type.
12 . The server of claim 10 , wherein the price prediction service interface screen is configured to display changes in sales prices by purchase date as a line graph for the period from the current time when the price query data was received until before the purchasable time.
13 . The server of claim 12 , wherein the price prediction service interface screen is configured to: display the case of purchasing the product at the current time as a reference point for the sales price, and display the range of predicted future sales prices by date until the usage start time as a bar graph on the reference point.
14 . (canceled)
15 . The server of claim 9 , wherein the multiple features include at least one data among: customer inquiry date, usage date/time, usage location, product type, seller type, and customer rating data.
16 . The server of claim 9 , wherein when one type of product condition data includes at least two options, the price prediction model is separately learned into first, second, and third price prediction models using: a first product condition dataset containing only one option, a second product condition dataset containing only the other option, and a third product condition dataset containing none of the options.

Description

TECHNICAL FIELD This invention relates to a method of providing service for estimating commodity prices and a server for performing the same. BACKGROUND ART For intangible commodities with significant temporal demand variations, the sales prices are not fixed and show large price fluctuations. For example, travel products experience dramatic demand changes between peak and off-peak seasons, weekends and holidays, and show large demand variations by region, which inevitably leads to significant price fluctuations. Typically, commodities with large price fluctuations based on demand are cheaper when purchased in advance. However, if future reservation rates (purchase rates) are low, sellers may lower the sales price of products. Consequently, unless consumers observe market prices for an extended period, it is difficult for them to determine whether the product they want to purchase is reasonably priced or if the current purchase timing is appropriate. Meanwhile, sellers need to continuously monitor competitors' sales prices to set competitive pricing. However, as product offerings diversify and the number of competitors increases, setting competitive sales prices requires significant time and cost. The background technology described here is intended to facilitate understanding of this invention. It should not be interpreted that matters stated in the background technology are acknowledged as prior art. DETAILED DESCRIPTION OF THE INVENTION Technical Problem Conventional price comparison services that compare sellers' sales prices have been provided. Since conventional price comparison services only provide the lowest current price, both consumers who purchase products in advance and sellers may risk financial losses. Therefore, a new price prediction service that forecasts future prices for product groups with high price volatility is required. As a result, the inventors of this invention sought to develop a method and server that can predict product sales prices by identifying factors affecting product sales prices, primarily peak season, off-peak season, and holiday information, and by refining customer (hereinafter referred to as “client”) price query data accordingly. In particular, the inventors of this invention sought to develop a method and server that can more accurately predict sales prices matching various conditions set by customers and sellers by learning product sales prices based on factors that influence them, while learning them as independent variables so they do not affect each other. Additionally, the inventors structured the method to provide an intuitive at-a-glance view of current and future prices by combining two types of graphs into a single price fluctuation graph in the product price prediction service interface screen. The objectives of this invention are not limited to those mentioned above, and other unmentioned objectives will be clearly understood by those skilled in the art from the following description. Technical Solution To solve the aforementioned problems, according to one embodiment of this invention, a method of providing product price prediction service is provided. The method, performed by a processor of a price prediction server, comprises: receiving price query data for a product from a customer device through a communication interface operatively connected to the processor; generating multiple product condition data based on predetermined multiple features for the product from the price query data; predicting sales prices by date of purchase by inputting the multiple product condition data into a price prediction model trained to predict product sales prices by purchase date using product condition data as input; and providing the customer device with a price prediction service interface screen that visualizes the product's sales prices graphically by purchase date. According to one feature of this invention, the price query data may include at least one of: product identification information, product purchasable dates, usage start date, usage end date, usage time, and product type-specific options. According to another feature of this invention, the generating step may further include converting the query date and usage start date included in the price query data into data including usage year, usage week number of the year, usage day of week, day before holiday, holiday duration, day after holiday, and advance purchase days, according to the product type. According to another feature of this invention, the price prediction service interface screen may be configured to display changes in sales prices by purchase date as a line graph for the period from the current time when the price query data was received until before the purchasable time. According to another feature of this invention, the price prediction service interface screen may be configured to display the case of purchasing the product at the current time as a reference point for the sales price, and