JP2022156868A - Buying/selling price estimation program - Google Patents

Abstract

To highly accurately and automatically estimate buying and selling prices of products without depending more on manpower.SOLUTION: A buying/selling estimation program for estimating buying and selling prices of products makes a computer perform: an information acquisition step for acquiring support information related to support contents of a manufacturer regarding estimation target products; and an estimation step for estimating buying and selling prices of buying and selling target products, with the use of three levels or more of relation degrees between reference support information related to the support contents of the manufacturer regarding the products and the buying and selling prices of the products and on the basis of the reference support information in accordance with the support information acquired in the information acquisition step.SELECTED DRAWING: Figure 1

Description

The present invention relates to a trading price estimation program and system suitable for estimating the trading price of an article with high accuracy.

When you want to sell the goods you used, you may want to know the appropriate price. In addition, there is a case where a purchaser of an article wants to know the proper selling price of the article to be bought and sold.

This sales price is not only the product name and brand, but also factors such as how long it has been used, how much damage it has, and general auction prices. There are a lot of Setting an appropriate trading price while referring to each of these factors is a difficult task that requires experience and knowledge, and has the problem of requiring a great deal of labor.

For this reason, there has been a demand for a system capable of highly accurately evaluating the sales price of an article without relying on human labor.

Therefore, the present invention has been devised in view of the above-mentioned problems, and its object is to enable highly accurate and automatic estimation of the sales price of an article without relying on human labor. It is to provide a trading price estimation program and system.

A trading price estimation program according to the present invention is a trading price estimation program for estimating a trading price of an article, comprising: an information acquisition step of acquiring support information regarding the support content of the manufacturer of the article to be estimated; The trading price of the goods to be traded based on the reference support information according to the support information acquired in the information acquisition step, using three or more levels of correlation between the reference support information and the trading price of the goods. and an estimating step of estimating .

Anyone can easily estimate the sales price of an article with high accuracy without special skill or experience.

1 is a block diagram showing the overall configuration of a system to which the present invention is applied; FIG. It is a figure which shows the specific structural example of a search apparatus. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention.

Hereinafter, a trading price estimation program to which the present invention is applied will be described in detail with reference to the drawings.

FIG. 1 is a block diagram showing the overall configuration of a trading price estimation system 1 in which a trading price estimation program to which the present invention is applied is installed. The trading price estimation system 1 includes an information acquisition unit 9 , an estimation device 2 connected to the information acquisition unit 9 , and a database 3 connected to the estimation device 2 .

The information acquisition unit 9 is a device for a person using this system to input various commands and information. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an imaging device capable of capturing an image, such as a camera. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper document. Further, the information acquisition unit 9 may be integrated with the estimation device 2 to be described later. The information acquisition unit 9 outputs the detected information to the estimation device 2 . Further, the information acquisition unit 9 may be configured by means for specifying position information by scanning map information. Further, the information acquisition unit 9 may be configured with an illuminance sensor for measuring a temperature sensor, a humidity sensor, and a wind direction sensor. The information acquisition unit 9 may also be configured with a communication interface that acquires weather data from the Meteorological Agency or a private weather forecast company. The information acquisition unit 9 may be composed of a body sensor worn on the body to detect body data, and the body sensor detects, for example, body temperature, heart rate, blood pressure, number of steps, walking speed, and acceleration. It may be configured with a sensor for The information acquisition unit 9 may be configured as a device that acquires information such as drawings by scanning or reading from a database. The information acquisition unit 9 may be configured by an odor sensor that detects odors and scents in addition to these.

The database 3 accumulates various information necessary for estimating the trading price. The information necessary for estimating the sales price includes reference image information obtained by photographing the appearance of an article in the past, and a reference consisting of either the size or amount of scratches on an article extracted by photographing the article in the past. Appearance information for use, reference brand information related to the brand of the product imaged when acquiring the reference image information, reference product name information related to the product name of the product imaged when acquiring the reference image information, the reference image information reference use years information relating to the years of use of the article imaged when obtaining the reference image information, reference successful bid price information relating to the successful bid price of the article imaged when obtaining the reference image information at another auction, reference image information reference external environment information related to the external environment at the time of imaging the article when acquiring the above reference market condition information related to market conditions at the time of imaging the article when acquiring the above reference image information, the above reference image information Reference text information written by the user to various information sites at the time of capturing the image of the item when acquiring the reference image information, reference support information related to the support content of the manufacturer of the item captured when acquiring the reference image information, Warranty information for reference obtained by reading the warranty period and warranty details written on the warranty card of the item imaged when acquiring the image information for reference, and the item imaged when acquiring the image information for reference A data set of reference container information about the containers to be contained and the sales prices of the articles for which judgments have actually been made is stored.

In other words, in addition to such reference image information, the database 3 stores reference appearance information, reference brand information, reference product name information, reference use years information, reference bid price information, reference external environment information, One or more of reference market information, reference text information, reference support information, reference warranty information, and reference container information, and the transaction price of the article are stored in association with each other.

The estimating device 2 is composed of an electronic device such as a personal computer (PC), for example, but in addition to the PC, it is embodied in any other electronic device such as a mobile phone, a smartphone, a tablet terminal, a wearable terminal, etc. It may be one that is made into. A user can obtain a search solution by this estimation device 2 .

FIG. 2 shows a specific configuration example of the estimation device 2 . The estimating device 2 performs wired or wireless communication with a control unit 24 for controlling the entire estimating device 2 and an operating unit 25 for inputting various control commands via operation buttons, a keyboard, or the like. A communication unit 26 for searching, an estimating unit 27 for making various judgments, and a storage unit 28, represented by a hard disk, for storing a program for performing a search to be executed are connected to the internal bus 21, respectively. . Further, the internal bus 21 is connected with a display unit 23 as a monitor for actually displaying information.

The control unit 24 is a so-called central control unit for controlling each component implemented in the estimating device 2 by transmitting control signals via the internal bus 21 . In addition, the control unit 24 transmits commands for various controls through the internal bus 21 according to operations through the operation unit 25 .

The operation unit 25 is embodied by a keyboard or a touch panel, and a user inputs an execution command for executing a program. The operation unit 25 notifies the control unit 24 when the execution command is input by the user. Upon receiving this notification, the control unit 24 cooperates with each component including the estimation unit 27 to execute desired processing operations. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

The estimation unit 27 estimates a search solution. The estimation unit 27 reads various information stored in the storage unit 28 and various information stored in the database 3 as necessary information in executing the estimation operation. This estimation unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any known artificial intelligence technology.

The display unit 23 is composed of a graphic controller that creates a display image based on control by the control unit 24 . The display unit 23 is implemented by, for example, a liquid crystal display (LCD).

When the storage unit 28 is configured with a hard disk, predetermined information is written to each address based on the control by the control unit 24, and this information is read as necessary. The storage unit 28 also stores a program for executing the present invention. This program is read and executed by the control unit 24 .

The operation of the trading price estimation system 1 configured as described above will be described. The goods referred to below include all goods distributed at auctions, mail-order sales, over-the-counter sales, and pawn shops, and include electrical products, leisure products, automobiles, interior furniture, food, kitchen goods, health care goods, jewelry, household goods, and tools. , books, games, accessories, watches, etc.

The trading price estimating system 1 estimates the trading price when an item owned by oneself is purchased by an auction, mail order, over-the-counter sales, pawn shop, etc., or when the purchaser purchases an item owned by the seller. applied when For example, as shown in FIG. 3, the trading price estimating system 1 is based on the premise that three or more degrees of association between the reference image information and the trading price of the article are set in advance. The image information for reference is obtained from image information obtained by imaging the article regarding the appearance of the article, and can be obtained by analyzing the image information. This image may be a moving image as well as a still image. Also, although this image is composed of visible light, it may be composed of a so-called spectrum image in which the display color is switched according to the spectrum instead of the image. This reference image information may specify the appearance of an article based on any of the size, shape, and color of the article by analyzing an image of the article. Appearances of these articles may be determined based on previously learned feature values. At this time, artificial intelligence is used to learn image data such as the size, shape, and color of the item, as well as the selling price of the item. The sales price of the item may be determined by comparing with the image data obtained.

In such a case, a predictive model machine-learned using image information and the appearance of any of the size, shape, and color of the article as teacher data is used, and the input is the image information and the output is the appearance. Appearance may be specified based on the results.

The selling price of the article is the actual selling price of the article imaged as the reference image information. The transaction price may be obtained by accessing a database stored in a store, e-commerce, pawn shop or other operating company, or may be manually input each time.
A data set consisting of such reference image information and product sales prices is obtained and learned.

In the example of FIG. 3, it is assumed that the input data are reference image information P01 to P03, for example. The reference image information P01 to P03 as such input data are linked to the sales price of the article as output. In this output, the selling price of the item is displayed as the output solution.

The reference image information is associated with each other through three or more levels of association with the product sales prices A to D as the output solutions. Assuming that the goods are of the same type, for example, the sales price A is 5,000 yen, the sales price B is 4,000 yen, the sales price C is 3,500 yen, and the sales price D is 3,000 yen. They are arranged like circles. The image information for reference is arranged on the left side through the degree of association, and the trading price of each article is arranged on the right side through the degree of association. The degree of relevance indicates the degree of relevance to the trading price of which item with respect to the reference image information arranged on the left side. In other words, the degree of association is an index indicating whether each piece of reference image information is highly likely to be associated with the sale price of an article, and the most probable sale price of the article is selected from the reference image information. It indicates the accuracy in doing so. In the example of FIG. 3, w13 to w19 are shown as association degrees. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of correlation between each combination as an intermediate node and the trading price of an article as an output. Conversely, the closer to one point, the lower the degree of correlation between each combination as an intermediate node and the price as an output.

The estimating device 2 acquires in advance the degrees of association w13 to w19 of three or more stages shown in FIG. In other words, the estimating device 2 accumulates past data sets that determine which of the reference image information and the transaction price of the article in that case was adopted and evaluated in order to determine the actual search solution. is analyzed to create the degree of association shown in FIG.

For example, it is assumed that the reference image information is α in which the size, shape, color, degree of damage, etc. of the appearance of the article are evaluated by a plurality of numerical values. It is assumed that the sale price A of the article is highly evaluated as the sale price of the article for such reference image information. By collecting and analyzing such data sets, the degree of association with the reference image information is strengthened.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference image information P01, analysis is performed from various data obtained as a result of evaluation of past sales prices of articles. In the case of the reference image information P01, if there are many examples of the trading price A of the product, the degree of association leading to the evaluation of the trading price of this product is set higher, and there are many examples of the trading price B of the product. In this case, the degree of association leading to the evaluation of the trading price of this item is set higher. For example, in the example of the reference image information P01, the product sales price A and the product sales price C are linked. is set to 2 points.

In addition, as shown in FIG. 4, this degree of association may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficients for the outputs of the nodes of this neural network correspond to the degrees of association described above. Moreover, it is not limited to a neural network, and may be composed of all decision-making factors that constitute artificial intelligence.

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data through a data set of the image of the appearance of the previous evaluation object and the actually estimated and evaluated sales price of the product, the new sales price of the product is actually determined from now on. , the above-described learned data is used to search for the selling price of the item. In such a case, new image information is actually acquired in the region to be determined. Image information to be newly acquired is input by the information acquisition unit 9 described above. The image information is obtained by photographing the article for which the selling price is to be determined. This determination method may be performed by the same method as for the reference image information described above.

Based on the image information newly acquired in this way, the sales price of the article is determined. In such a case, reference is made to the degree of association shown in FIG. 3 (Table 1), which has been acquired in advance. For example, when the newly acquired image information is the same as or similar to P02, the item trading price B is associated with w15 and the item trading price C is associated with the degree of association w16. there is In such a case, the trading price B of the item with the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of correlation as the optimum solution, and the trading price C of an article whose degree of correlation is low but whose correlation itself is recognized may be selected as the optimum solution. In addition, it is of course possible to select an output solution that is not connected by an arrow, and any other priority may be used as long as it is based on the degree of association.

In this way, it is possible to search for the most suitable trading price of an article from the newly obtained image information and display it to the seller and the trading side. By looking at this search result, the seller and the buyer can mutually determine the selling price of the article based on the searched selling price of the article. Moreover, since the selling prices of the articles can be predicted through the imaging of the images without contacting the articles, even if the articles are valuables or antiques, they will not be damaged.

In FIG. 3, as a substitute for reference image information, reference appearance information consisting of either the size or amount of flaws on an article extracted by imaging the article in the past and the sales price of the article. Three or more stages of association degrees may be learned in advance. When acquiring the reference appearance information, as shown in FIG. 5, image data of the appearance of the article to be bought and sold and appearance information consisting of either the size or amount of scratches on the article are used as teacher data. You may make it use the prediction model by which machine learning was carried out as. Appearance information may be newly acquired by using the image data as the input and the appearance information as the output. That is, the appearance information, which is either the size or amount of the flaw, may be determined based on the previously learned feature amount. It should be noted that the scratches here include not only scratches actually made on the article, but also attached stains.

A data set is obtained in the same manner as described above by constructing reference appearance information from the appearance information obtained in this way, and obtaining the actual sales price (sales price) of the photographed article, and FIG. 4 is formed in advance. Next, image data is acquired by actually capturing an image of the article for which the purchase price is to be estimated. For this image data, the degree of association shown in FIG. 5 is actually referred to obtain appearance information. It is possible to search for a sale price through the acquired appearance information by referring to three or more grades of degree of association between the appearance information for reference and the sale price of the article learned in advance.

In the example of FIG. 6, it is assumed that a combination of reference image information and reference brand information is formed. The reference brand information is information indicating the manufacturer or distributor of the article, or information regarding the brand of the trademark attached to the article.

The selling price of an article varies depending on the reference image information and the brand of the article. In particular, the price of accessories greatly depends on whether they are famous brands or not. Therefore, this reference brand information is also added as an explanatory variable.

In the example of FIG. 6, it is assumed that the input data are reference image information P01 to P03 and reference brand information P14 to P17. An intermediate node shown in FIG. 6 is a combination of reference image information as input data and reference brand information. Each intermediate node is also connected to an output. In this output, the selling price of the item is displayed as the output solution.

Each combination (intermediate node) of the reference image information and the reference brand information is associated with the selling price of the article as the output solution through three or more degrees of association. The image information for reference and the brand information for reference are arranged on the left side through the degree of association, and the trading price of the goods is arranged on the right side through the degree of association. The degree of relevance indicates the degree to which the reference image information and the reference brand information arranged on the left side are highly related to the trading price of the article. In other words, the degree of association is an index indicating whether each item of reference image information and reference brand information is highly likely to be associated with the sales price of an article. It indicates the accuracy in selecting the most probable selling price of goods from the Therefore, by combining the reference image information and the reference brand information, the optimum selling price of the article is searched for.

In the example of FIG. 6, w13 to w22 are shown as association degrees. These w13 to w22 are shown in 10 stages as shown in Table 1. The closer to 10 points, the higher the degree of correlation between each combination as an intermediate node and the output. The closer it is, the lower the degree of correlation between each combination as an intermediate node and the output.

The estimating device 2 acquires in advance the degrees of association w13 to w22 of three or more stages shown in FIG. In other words, the estimating device 2 accumulates past data as to which of the reference image information, the reference brand information, and the transaction price of the article in that case was more suitable for determining the actual search solution. Then, by analyzing these, the degree of association shown in FIG. 6 is created.

For example, it is assumed that reference image information in an actual case in the past is α. It is also assumed that the brand information for reference is a certain famous brand W. FIG. In such a case, the selling price of an item, which indicates the actual selling price of the item, is learned as a data set and defined in the form of the degree of association described above. Such reference image information and reference brand information may be extracted from a management database managed by a distributor, a distributor, or the like.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference image information P01 and reference brand information P16, the sales price of the article is analyzed from past data. If there are many cases where the trading price of goods is A, the degree of association leading to this trading price A is set higher. , the degree of association leading to the trading price B of the article is set high, and the degree of association leading to the trading price A of the article is set low. For example, in the example of the intermediate node 61a, it is linked to the output of the trading price A and the trading price B of the goods, but from the previous example, the degree of association of w13 connected to the trading price A of the goods is set to 7 points, and the trading price of the goods The association degree of w14 connected to B is set to 2 points.

Further, the degree of association shown in FIG. 6 may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficients for the outputs of the nodes of this neural network correspond to the degrees of association described above. Moreover, it is not limited to a neural network, and may be composed of all decision-making factors that constitute artificial intelligence.

In the example of the degree of association shown in FIG. 6, the node 61b is a node of the combination of the brand information for reference P14 with respect to the image information for reference P01. The association degree of E is w16. The node 61c is a node that is a combination of the reference position information P15 and P17 with respect to the reference image information P02. ing.

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data, the above-described learned data will be used when actually determining the selling price of an article. In such a case, image information and brand information are actually acquired. For the brand information, the trading price of the ring whose trading price is to be estimated is entered or selected.

Based on the image information and brand information newly acquired in this way, the optimum selling price of the article is searched for. In such a case, reference is made to the degrees of association shown in FIG. 6 (Table 1) that have been acquired in advance. For example, when the newly acquired image information is the same as or similar to P02 and the brand information is P17, the node 61d is associated via the degree of association, and this node 61d is associated with the item trading price C with w19 and with the degree of association w20 with the item trading price D. In such a case, the trading price C of the item with the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the purchase price D of an article whose degree of association is low but whose association itself is recognized may be selected as the optimum solution. In addition, it is of course possible to select an output solution that is not connected by an arrow, and any other priority may be used as long as it is based on the degree of association.

Table 2 below shows examples of degrees of association w1 to w12 extending from the input.

Intermediate nodes 61 may be selected based on degrees of association w1 to w12 extending from this input. In other words, the greater the degree of association w1 to w12, the heavier the weight in selecting the intermediate node 61 may be. However, the degrees of association w1 to w12 may all have the same value, and the weighting in selecting the intermediate nodes 61 may all be the same.

In FIG. 7, in addition to the reference image information described above, three or more levels of association are set between combinations of reference article name information instead of reference brand information described above, and the selling prices of goods for the combinations. example.

This reference product name information, which is added as an explanatory variable instead of the reference position information, relates to the product name of the product, and can be classified through, for example, the model number and product name. It is already known that the product name from the product is related to the sales price of the product. Therefore, by combining the reference product name information to form a degree of association, the purpose is to improve the judgment accuracy of the trading price of the product.

In the example of FIG. 7, it is assumed that the input data are, for example, reference image information P01 to P03 and reference product name information P18 to P21. An intermediate node shown in FIG. 7 is a combination of the reference image information as input data and the reference product name information. Each intermediate node is also connected to an output. In this output, the selling price of the item is displayed as the output solution.

Each combination (intermediate node) of the reference image information and the reference product name information is associated with the selling price of the article as the output solution through three or more degrees of association. The reference image information and the reference article name information are arranged on the left side through the degree of association, and the trading prices of the goods are arranged on the right side through the degree of association. The degree of relevance indicates the degree to which the reference image information and the reference item name information arranged on the left side are highly related to the trading price of the item. In other words, the degree of association is an index indicating whether each item of reference image information and reference item name information is likely to be associated with the sales price of an article. It indicates the accuracy in selecting the most probable selling price of goods from the

The estimating device 2 acquires in advance the degrees of association w13 to w22 of three or more stages shown in FIG. In other words, in determining the actual search solution, the estimating device 2 uses the reference image information, the reference product name information of the product imaged when the reference image information is acquired, and the selling price of the product in that case. By accumulating past data as to which one was preferable, and analyzing the data, the degree of association shown in FIG. 7 is created. The reference product name information may be acquired by, for example, capturing an image of a box or instruction sheet on which the product name is written, or an image of the product using a camera or the like, and converting the product name into text using OCR or the like.
This analysis may be performed by artificial intelligence. In such a case, for example, when the reference image information P01 is the reference product name information P20, the sales price of the product is analyzed from the past data. If there are many cases of goods trading price A, the degree of association that leads to this goods trading price A is set higher. If it is less, the degree of association in which the trading price of the article leads to B is set high, and the degree of association in which the trading price of the article leads to A is set low. For example, in the example of the intermediate node 61a, the outputs of the item trading price A and the item trading price B are linked. The association degree of w14 leading to the trading price B is set to 2 points.

Also, the degree of association shown in FIG. 7 may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficients for the outputs of the nodes of this neural network correspond to the degrees of association described above. Moreover, it is not limited to a neural network, and may be composed of all decision-making factors that constitute artificial intelligence.

In the example of the degree of association shown in FIG. 7, the node 61b is a node of the combination of the reference image information P01 and the reference article name information P18, the degree of association of the item trading price C is w15, and the item trading price E is w16. The node 61c is a node of a combination of the reference product name information P19 and P21 with respect to the reference image information P02. ing.

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data, the above-described learned data will be used when actually searching for the sale price of an article. In such a case, the image information for which the sale price of the article is to be determined and the article name information are actually acquired. Here, the product name information is newly acquired when actually estimating the selling price of the product, and the acquisition method is the same as that for the reference product name information described above.

Based on the image information newly acquired in this way and the product name information, the optimum selling price of the product is searched for. In such a case, reference is made to the degrees of association shown in FIG. 7 (Table 1) that have been acquired in advance. For example, when the newly acquired image information is the same as or similar to P02 and the product name information is the same or similar to P21, the node 61d is associated via the degree of association. This node 61d is associated with the item trading price C with w19 and with the item trading price D with the degree of association w20. In such a case, the trading price C of the item with the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the purchase price D of an article whose degree of association is low but whose association itself is recognized may be selected as the optimum solution. In addition, it is of course possible to select an output solution that is not connected by an arrow, and any other priority may be used as long as it is based on the degree of association.

In FIG. 8, in addition to the above-described reference image information, three or more levels of association are set between combinations of the above-described reference use years information instead of the above-described reference brand information, and the transaction prices of goods for the combinations. example.

This reference age information added as an explanatory variable is any information regarding the age of the article. The reference use age information may be acquired as the number of days calculated from the production date or sales date of the article, if available. The date of manufacture and date of sale may be obtained by analyzing an image captured by a camera in which the date of manufacture and date of sale are described by OCR, or may be obtained by manual input.

In the example of FIG. 8, it is assumed that the input data are image information for reference P01 to P03 and years of use information for reference P18 to 21, for example. The intermediate node shown in FIG. 8 is a combination of the reference image information as such input data and the reference use age information. Each intermediate node is also connected to an output. In this output, the selling price of the item is displayed as the output solution.

Each combination (intermediate node) of the reference image information and the reference age information is associated with the selling price of the article as the output solution through three or more degrees of association. The image information for reference and the years of use information for reference are arranged on the left side through the degree of association, and the sales price of the article is arranged on the right side through the degree of association. The degree of relevance indicates the degree to which the reference image information and the reference years of use information arranged on the left side are highly related to the sales price of the article. In other words, the degree of association is an index indicating whether each piece of reference image information and reference use years information is highly likely to be associated with the sales price of an article. This indicates the accuracy in selecting the most probable sales price of the article from the age information.

The estimating device 2 acquires in advance the degrees of association w13 to w22 of three or more stages shown in FIG. In other words, in determining the actual search solution, the estimating device 2 determines which of the reference image information, the reference age information obtained when the reference image information is acquired, and the selling price of the article in that case. was suitable, accumulate past data, analyze the past data, and build up the degree of association shown in FIG.

For example, it is assumed that the reference usage years information is 3 years for certain reference image information at the time of evaluating the actual selling price of an article in the past. In such a case, if there are many cases where the transaction price of the article is determined to be A, these are learned as a data set and defined in the form of the degree of association described above.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference image information P01 and reference use years information P20, the sales price of the item is analyzed from past data. If there are many cases of goods trading price A, the degree of association that leads to this goods trading price A is set higher. If it is less, the degree of association in which the trading price of the article leads to B is set high, and the degree of association in which the trading price of the article leads to A is set low. For example, in the example of the intermediate node 61a, the outputs of the item trading price A and the item trading price B are linked. The association degree of w14 leading to the trading price B is set to 2 points.

Further, the degree of association shown in FIG. 8 may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficients for the outputs of the nodes of this neural network correspond to the degrees of association described above. Moreover, it is not limited to a neural network, and may be composed of all decision-making factors that constitute artificial intelligence.

In the example of the degree of association shown in FIG. 8, the node 61b is a node of the combination of the reference image information P01 and the reference use years information P18, the degree of association of the item trading price C is w15, and the item trading price The association degree of E is w16. A node 61c is a node of a combination of the reference image information P19 and P21 for the reference image information P02, and the degree of relevance of the product sales price B is w17, and the relevance of the product sales price D is w18. It's becoming

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data, the above-described learned data will be used when actually searching for the sale price of an article. In such a case, image information for which the selling price of the article is to be determined and information on years of use are actually obtained. Here, the years of use information is newly acquired when actually estimating the sales price of the article, and the acquisition method is the same as that for the reference years of use information described above.

Based on the image information newly acquired in this way and the information on the number of years of use, the optimum selling price of the article is searched for. In such a case, reference is made to the degrees of association shown in FIG. 8 (Table 1) that have been acquired in advance. For example, if the newly acquired image information is the same as or similar to P02, and if the years of use information is the same or similar to P21, the node 61d is associated via the degree of association. This node 61d is associated with the item trading price C with w19 and the item trading price D with the association degree w20. In such a case, the trading price C of the item with the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the purchase price D of an article whose degree of association is low but whose association itself is recognized may be selected as the optimum solution. In addition, it is of course possible to select an output solution that is not connected by an arrow, and any other priority may be used as long as it is based on the degree of association.

In FIG. 9, in addition to the reference image information described above, three or more levels of association are set between combinations of reference successful bid price information in place of the reference brand information described above, and the transaction prices of goods for the combinations. example.

This reference successful bid price information added as an explanatory variable is information relating to the successful bid price in another auction of the article imaged when the reference image information is acquired. Other auctions are assumed to be auctions operated on various sites open to the public on the Internet. Through this auction, the highest bid price or a bid price equivalent thereto is acquired and learned together with the reference image information.

In the example of FIG. 9, it is assumed that the input data are reference image information P01 to P03 and reference successful bid price information P18 to 21, for example. An intermediate node shown in FIG. 9 is a combination of reference image information as input data and reference successful bid price information. Each intermediate node is also connected to an output. In this output, the selling price of the item is displayed as the output solution.

Each combination (intermediate node) of the reference image information and the reference successful bid price information is associated with the selling price of the article as the output solution through three or more degrees of association. The image information for reference and the successful bid price information for reference are arranged on the left side through the degree of association, and the trading price of the article is arranged on the right side through the degree of association. The degree of relevance indicates the degree to which the reference image information and the reference successful bid price information arranged on the left side are highly related to the sales price of the article. In other words, the degree of association is an index indicating whether each piece of reference image information and reference successful bid price information is highly likely to be associated with the sales price of an article. It indicates the accuracy in selecting the most probable trading price of an article from the value information.

The estimating device 2 acquires in advance the degrees of association w13 to w22 of three or more stages shown in FIG. In other words, the estimating device 2 determines which of the reference image information, the reference successful bid price information obtained when acquiring the reference image information, and the selling price of the article in that case, when determining the actual search solution. was suitable, the past data is accumulated, and the degree of association shown in FIG. 9 is created by analyzing the past data.

For example, it is assumed that the successful bid price information for reference is 500 yen with respect to certain reference image information at the time of evaluating the actual selling price of an article in the past. In such a case, if there are many cases where the transaction price of the article is determined to be A, these are learned as a data set and defined in the form of the degree of association described above.

This analysis may be performed by artificial intelligence. In such a case, for example, if the reference image information P01 is the reference successful bid price information P20, the sales price of the item is analyzed from the past data. If there are many cases of goods trading price A, the degree of association that leads to this goods trading price A is set higher. If it is less, the degree of association in which the trading price of the article leads to B is set high, and the degree of association in which the trading price of the article leads to A is set low. For example, in the example of the intermediate node 61a, the outputs of the item trading price A and the item trading price B are linked. The association degree of w14 leading to the trading price B is set to 2 points.

Further, the degree of association shown in FIG. 9 may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficients for the outputs of the nodes of this neural network correspond to the degrees of association described above. Moreover, it is not limited to a neural network, and may be composed of all decision-making factors that constitute artificial intelligence.

In the example of the degree of association shown in FIG. 9, the node 61b is a node of the combination of the reference image information P01 and the reference successful bid price information P18. The association degree of E is w16. A node 61c is a node of a combination of reference successful bid price information P19 and P21 with respect to reference image information P02. It's becoming

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data, the above-described learned data will be used when actually searching for the sale price of an article. In such a case, the image information for which the sale price of the item is actually determined and the successful bid price information are obtained. Here, the winning bid price information is newly acquired when actually estimating the selling price of an item, and the acquisition method is the same as that for the reference winning bid price information described above.

Based on the image information newly acquired in this way and the successful bid price information, the optimum selling price of the article is searched for. In such a case, reference is made to the degrees of association shown in FIG. 9 (Table 1) that have been acquired in advance. For example, if the newly acquired image information is the same as or similar to P02 and the successful bid price information is the same or similar to P21, the node 61d is associated via the degree of association. This node 61d is associated with the item trading price C with w19 and the item trading price D with the association degree w20. In such a case, the trading price C of the item with the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the purchase price D of an article whose degree of association is low but whose association itself is recognized may be selected as the optimum solution. In addition, it is of course possible to select an output solution that is not connected by an arrow, and any other priority may be used as long as it is based on the degree of association.

Further, in the present invention, in addition to the above-described reference image information, a combination of reference external environment information instead of the above-described reference brand information, and three or more levels of association with the transaction price of the product for the combination. may be used.

The reference external environment information and the external environment information here are, for example, economic data (GDP, employment statistics, industrial production index, capital investment, labor force survey, etc.), household data (household consumption survey, household data, one week average working hours, statistical data on savings, statistical data on annual income, etc.), real estate data (office vacancy rate, unit price per tsubo, rent market, land price, vacant house data, etc.), natural environment data (disaster data, temperature data, precipitation data, etc.) volume data, wind direction data, humidity data, etc.). The external environment information includes all kinds of external information in addition to those reflecting some or all of these data. The external environment information for reference may categorize the external environment itself. For example, it may be classified by separating data in employment statistics. In addition, they may be categorized according to patterns (for example, patterns such as whether the growth rate of GDP increases rapidly or gradually). This external environment information for reference indicates the external environment of the past sales date or several days before and after that date.

Three or more degrees of association between a combination having such reference image information and reference external environment information and the sales price of an article are obtained in advance. Then, image information about the external appearance of the article to be estimated and external environment information about the external environment at the time of image pickup (estimation) are newly acquired. Then, based on the reference image information corresponding to the acquired image information and the reference external environment information corresponding to the external environment information, the selling price of the article is estimated as described above in FIGS. Same as description.
Further, in the present invention, in addition to the above-described reference image information, a combination of reference market information instead of the above-described reference brand information, and three or more levels of association with the transaction price of the product for the combination. may be used.

The reference market condition information and market condition information referred to here are various information relating to market conditions. Examples of this reference market information include interest rates, exchange rates, stock prices of various brands, crude oil, futures, precious metals, bitcoins, and other price movements. This reference market condition information may be displayed as a time-series chart, a line graph, or the like for these targets. Further, information such as Bollinger band, trading volume, MACD, moving average line, etc. may be attached. Also, this market information may be accompanied by information such as charts, Bollinger bands, MACDs, moving averages, etc. of each issue. Information such as charts, Bollinger bands, MACD, moving averages, etc. showing price movements between currencies may also be attached to exchange rates.

Three or more degrees of association between a combination having such reference image information and reference market information and the sales price of the article are obtained in advance. Then, image information about the appearance of the article to be estimated and market condition information about the market condition at the time of image capturing (estimation time) are newly acquired. Then, based on the reference image information corresponding to the acquired image information and the reference market condition information corresponding to the market condition information, the point of estimating the selling price of the article is the same as the description of FIGS. 3 to 9 above. It is the same.

Further, in the present invention, in addition to the reference image information described above, a combination of the reference text information instead of the reference brand information described above and the selling price of the article for the combination are evaluated in three or more stages of association. may be used.

The reference text information and text information referred to here include all text data written on various information sites by past purchasers and sellers of goods, and users who have used goods in the past. This reference text information also includes writing about impressions after using the product, how long the product lasted, advantages and disadvantages of the product, and the like. That is, the reference text information includes all text information that may affect the selling price for each item. The reference text information may simply be a prediction as to whether the sale price of each item will go up, down, or stay the same. The reference text information may include comments posted on the Internet by ordinary people and forecasts of increases or decreases in trading prices.

Three or more degrees of association between a combination having such reference image information and reference text information and the sales price of an article are obtained in advance. Then, in addition to the image information, text information written on various information sites is acquired for the article to be newly estimated. Then, based on the reference image information corresponding to the acquired image information and the reference text information corresponding to the text information, the point of estimating the sales price of the article is the same as the description of FIGS. 3 to 9 above. It is the same.
In addition, in the present invention, in addition to the reference image information described above, a combination of reference support information in place of the reference brand information described above, and a degree of association of three or more stages with the transaction price of the product for the combination. may be used.

The reference support information and support information referred to here include all information related to the content of support provided by the manufacturer of the article. The reference support information and support information here are, for example, the period of support for the item, the content of support (for example, to what level the repair will be done, to what level the support for how to use it will be provided, etc.) (whether or not they will respond, etc.). As a method for obtaining this reference support information and support information, it is possible to use a camera to capture an image of an instruction manual, document, etc., in which the support content is described, and to read the support content using OCR technology. You may input by manual input.

Three or more degrees of association between a combination having such reference image information and reference support information and the sales price of an article are obtained in advance. Then, image information about the appearance of the article to be estimated and support information about the content of support for the article are newly acquired. Then, based on the reference image information corresponding to the acquired image information and the reference support information corresponding to the support information, the selling price of the article is estimated in the same manner as the description of FIGS. 3 to 9 above. It is the same.
In addition, in the present invention, in addition to the reference image information described above, a combination of the reference warranty information instead of the reference brand information described above, and the degree of association of three or more stages with the transaction price of the product for the combination. may be used.

The warranty information for reference and warranty information referred to here include all information related to the details of warranty when the article is damaged. The reference warranty information and warranty information here are, for example, the warranty period of the product, the warranty details (in the case of smartphones, if the touch panel is broken, is it supported to replace it? It is information about whether the repair is supported if the performance has deteriorated, etc.). As a method of acquiring this warranty information for reference and warranty information, a camera may be used to capture an image of a warranty card, document, etc., in which the warranty details are described, and the warranty details may be read using OCR technology. You may input by input.

Three or more degrees of association between a combination having such reference image information and reference warranty information and the sales price of an article are acquired in advance. Then, image information about the appearance of the item to be estimated and warranty information about the warranty details of the item are newly acquired. Then, based on the reference image information corresponding to the acquired image information and the reference warranty information corresponding to the warranty information, the point of estimating the sales price of the article is the same as the description of FIGS. 3 to 9 above. It is the same.

Further, in the present invention, in addition to the above-described reference image information, a combination of reference container information instead of the above-described reference brand information, and three or more levels of association with the transaction price of the article for the combination may be used.

The reference container information and container information referred to here include information such as whether or not a container or box for containing the article is attached, and if so, the feeling of use, the presence or absence of flaws, dirt, etc. be As a method for obtaining the warranty information for reference and the warranty information, an image of the container may be taken with a camera, and automatic determination may be made from the feature amount, or manual input may be performed.

Three or more degrees of association between a combination having such reference image information and reference container information and the sales price of an article are obtained in advance. Then, image information about the appearance of the article to be estimated and container information about the container of the article are newly acquired. Then, based on the reference image information corresponding to the acquired image information and the reference container information corresponding to the container information, the selling price of the article is estimated, as shown in FIGS. Same as description.

In the above-mentioned degree of relevance, the degree of relevance is expressed by a 10-level evaluation, but it is not limited to this, and it may be expressed by a degree of relevance of 3 or more stages. For example, 100 steps or 1000 steps may be used. On the other hand, this degree of association does not include those expressed in two stages, that is, whether or not they are associated with each other, either 1 or 0.

According to the present invention configured as described above, anyone can easily search for the sales price of an article without special skill or experience. Further, according to the present invention, it is possible to judge the search solution with a higher degree of accuracy than a human being does. Furthermore, by configuring the degree of association described above with artificial intelligence (neural network, etc.) and making it learn, it is possible to further improve the discrimination accuracy.

Since the above-described input data and output data may not be exactly the same in many cases during the learning process, information obtained by classifying these input data and output data according to type may be used. That is, the information P01, P02, . . . P15, 16, . A data set may be created between the data and the output data for learning.

In addition to the image information for reference, the degree of association described above includes appearance information for reference, brand information for reference, product name information for reference, usage years information for reference, successful bid price information for reference, external environment information for reference, external environment information for reference, The case where it is composed of any combination of market information, reference text information, reference support information, reference warranty information, and reference container information has been described as an example, but it is not limited to this. . In other words, in addition to reference image information, the degree of association includes reference appearance information, reference brand information, reference product name information, reference use years information, reference bid price information, reference external environment information, reference market information, It may be composed of a combination of two or more of reference text information, reference support information, reference warranty information, and reference container information. In addition to reference image information, the degree of association includes reference appearance information, reference brand information, reference product name information, reference use years information, reference bid price information, reference external environment information, reference market information, In addition to any one or more of the reference text information, the reference support information, the reference warranty information, and the reference container information, other factors may be added to this combination to form the degree of association.
In the above example, reference image information is used as a basis, and the degree of association is formed by combining this with other reference information. The degree of association may be formed by combining the appearance information for reference with other information for reference. That is, in addition to reference appearance information, reference brand information, reference article name information, reference use age information, reference bid price information, reference external environment information, reference market information, reference text information, and reference support. The degree of association may be configured by a combination of one or more of the information, the warranty information for reference, and the container information for reference.

In either case, data is input in accordance with the reference information of the degree of association, and the purchase price of the item is obtained using the degree of association.

Moreover, according to the present invention, it is characterized in that the optimum solution search is performed through the degree of association set to three or more stages. The degree of association can be described by a numerical value of, for example, 0 to 100% in addition to the 10 stages described above, but is not limited to this, and can be described by a numerical value of 3 or more stages. may be configured.

By determining the most probable sales price of the item based on the degree of association represented by numerical values of three or more levels, it is possible to determine the degree of association in a situation where there are multiple candidates for the possibility of a search solution. It is also possible to search and display in descending order. If it is possible to display to the user in descending order of degree of association in this way, it is also possible to preferentially display more probable search solutions.

In addition to this, according to the present invention, it is possible to make a judgment without overlooking even a very low output discrimination result such as a correlation degree of 1%. Remind the user that even discrimination results with an extremely low degree of association are connected as slight signs, and that once in dozens or hundreds of times, the discrimination results may be useful. be able to.

Furthermore, according to the present invention, there is an advantage that a search policy can be determined by setting thresholds by performing a search based on three or more degrees of association. If the threshold value is lowered, even if the degree of association is 1%, it can be picked up without omission. be. On the other hand, if the threshold is set high, there is a high possibility that the optimal search solution can be detected with a high probability. Sometimes the solution is overlooked. It is possible to decide which one to place emphasis on based on the way of thinking of the user side and the system side, and it is possible to increase the degree of freedom in selecting such points to place emphasis on.

Furthermore, in the present invention, the degree of association described above may be updated. This update may reflect information provided via a public communication network such as the Internet, for example. Reference image information, reference appearance information, reference brand information, reference article name information, reference use age information, reference bid price information, reference external environment information, reference market information, reference text information, reference When acquiring support information for use, warranty information for reference, and container information for reference, and acquiring knowledge, information, and data related to the trading price of goods and improvement measures for these, the degree of association is increased or decreased according to these Let

In other words, this update corresponds to learning in terms of artificial intelligence. Since new data is acquired and reflected in the learned data, it can be said that it is a learning act.

In addition to updating the degree of association based on information that can be obtained from the public communication network, the system side or the user side based on the contents of research data, papers, conference presentations, newspaper articles, books, etc. by experts It may be updated manually or automatically. Artificial intelligence may be used in these update processes.

Also, the process of initially building a trained model and the above-described updating may use not only supervised learning but also unsupervised learning, deep learning, reinforcement learning, and the like. In the case of unsupervised learning, instead of loading and learning datasets of input and output data, learning is performed by loading information corresponding to the input data, and from there self-forming the degree of association related to the output data. You can let it run.

When the degree of association shown in FIG. 3 is composed of nodes of a neural network in artificial intelligence, as shown in FIG. A trading price may be output, and at least one or more hidden layers may be provided between the input node and the output node for machine learning.

Further, according to the present invention, as shown in FIG. 10, the selling price of an article is determined based on the degree of relevance of a combination of two or more kinds of information, ie, information for reference U and information for reference V. FIG. This reference information U is reference image information, and reference information V is reference brand information, reference product name information, reference use years information, reference successful bid price information, reference external environment information, and reference market information. , reference text information, reference support information, reference guarantee information, reference containment information, and the like.

At this time, as shown in FIG. 10, the output obtained for reference information U is used as input data as it is, and is associated with the output (sales price of goods) through an intermediate node 61 in combination with reference information V. may For example, after providing an output solution for reference information U (reference image information) as shown in FIG. You may make it search for an output (trading price of goods).

It should be noted that the present invention is not limited to the embodiments described above. For example, as shown in FIG. You may do so. In such a case, the solution search is performed based on three or more levels of association with the trading price of the article corresponding to the newly acquired information. The basic reference information includes, for example, reference image information, reference appearance information, reference brand information, reference product name information, reference use years information, reference bid price information, reference external environment information, reference Any reference information described above, such as market information, reference text information, reference support information, reference warranty information, reference container information, etc., is applicable.

In these cases, similarly, when information corresponding to reference information used as learning data is input, solution search is performed based on the above-described method.

The search solution obtained through the degree of association may be further modified or weighted based on other reference information.

The other reference information referred to here corresponds to any reference information other than the basic reference information when any of the reference information described above is used as the basic reference information.

For example, it is assumed that in certain reference image information P14, which is one of other reference information, B has often been determined as the selling price of goods in the past. When the image information corresponding to such reference image information P14 is newly acquired, the search solution B as the trading price of the article is weighted. A setting is made in advance so that a process leading to solution B is performed.

For example, the other reference information G is an analysis result that suggests the search solution C as the trading price of the product, and the reference information F is the analysis result that suggests the search solution D as the trading price of the product. It shall be a valid analysis result. After the setting with the reference information in this way, if the actually acquired information is the same as or similar to the reference information G, a process of increasing the weight of the search solution C as the trading price of the article is performed. . On the other hand, when the actually obtained information is the same as or similar to the reference information F, processing is performed to increase the weight of the search solution D as the trading price of the article. In other words, it is also possible to control the degree of association itself leading to the selling price of an item based on the reference information FH. Alternatively, after determining the selling price of the item only by the degree of association described above, the search solution obtained may be corrected based on the reference information FH. In the latter case, how and with what weight the trading price of the article as the search solution is to be corrected based on the reference information FH is to reflect what is designed on the system side each time.

The reference information is not limited to any one type, and the solution search may be performed based on two or more types of reference information. Similarly, in such a case, the more the case leads to the sale price of the product suggested by the reference information, the higher the sale price of the product as the search solution obtained through the degree of association may be corrected.

Similarly, as shown in FIG. 12, in the case of forming the degree of association with the trading price of an article for a combination of reference information that is the keynote and other reference information, the reference information that is the keynote any of the above-mentioned reference information (reference image information, reference appearance information, reference brand information, reference product name information, reference use years information, reference bid price information, reference external environment information, reference market conditions, etc.) information, reference text information, reference support information, reference warranty information, reference containment information) are also applicable. Other reference information includes any reference information other than the underlying reference information.

At this time, if the basic reference information is the reference position information, the other reference information includes any other reference information.

In such a case as well, it is possible to estimate the selling price of the article by searching for a solution in the same way. At this time, as shown in FIG. 11 described above, for the search solution obtained through the degree of association, through other reference information (reference information F, G, H, etc.), the trading price of the item is corrected. You may make it

As shown in FIGS. 13 and 14, a solution search may be performed using three or more degrees of association between the basic reference information and the transaction price of the article.

Determining the selling price of an article only from the information for reference. For example, as shown in FIGS. 13 and 14, reference information acquired in the past (reference image information, reference appearance information, reference brand information, reference article name information, reference use years information, reference bid price information, external environment information for reference, market information for reference, text information for reference, support information for reference, warranty information for reference, containment information for reference, etc.) and the selling price of the goods actually determined in the past Use the degree of association above.

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data, the above-described learned data will be used when actually determining a new transaction price of an article. In such a case, reference information (reference image information, reference appearance information, reference brand information, reference product name information, reference use years information, reference bid price information, reference external environment information, reference market conditions information, text information for reference, support information for reference, warranty information for reference, container information for reference, etc.) are newly acquired.

Based on the information newly acquired in this way, the sales price of the article is determined. In such a case, reference is made to the association degrees obtained in advance and shown in FIGS. Since the specific method of estimating the sales price of an article is the same as described above, the description thereof will be omitted.

1 trading price estimation system 2 estimation device 21 internal bus 23 display unit 24 control unit 25 operation unit 26 communication unit 27 estimation unit 28 storage unit 61 node

Claims (8)

In a trading price estimation program for estimating the trading price of an item,
an information acquisition step of acquiring support information regarding the content of support provided by the manufacturer of the article to be estimated;
Based on the reference support information corresponding to the support information acquired in the information acquisition step, using the degree of association of three or more levels between the support information for reference regarding the support content of the manufacturer of the product and the sales price of the product, and an estimating step of estimating a trading price of an article to be traded. In a trading price estimation program for estimating the trading price of an item,
an information acquisition step of acquiring warranty information by reading the warranty period and warranty content described in the warranty of the item to be presumed;
The warranty obtained in the above information acquisition step by using the reference warranty information obtained by reading the warranty period and warranty content described in the warranty card of the product and the degree of association with the sales price of the product in three or more levels. and an estimating step of estimating the trading price of the article to be traded based on the information-based reference guarantee information. In a trading price estimation program for estimating the trading price of an item,
an information acquisition step of acquiring container information about a container containing an article to be estimated;
Using three or more levels of degree of association between the reference container information about the container containing the goods and the transaction price of the goods, the reference container information corresponding to the container information acquired in the information acquisition step is obtained. and an estimating step of estimating the trading price of the article to be traded based on the above. In the information acquisition step, text information written on various information sites regarding the goods to be bought and sold is acquired,
The trading price according to any one of claims 1 to 3, wherein the estimating step further estimates the trading price of the item to be traded based on the text information acquired in the information acquiring step. estimation program. In the information acquisition step, image information obtained by imaging the appearance of the article to be traded is acquired,
The trading price according to any one of claims 1 to 3, wherein the estimating step further estimates the trading price of the item to be traded based on the image information acquired in the information acquiring step. estimation program. In the information acquisition step, brand information relating to the brand of the product to be estimated is acquired,
The trading price according to any one of claims 1 to 3, wherein the estimating step further estimates the trading price of the item to be traded based on the brand information acquired in the information acquiring step. estimation program. The information acquisition step acquires product name information related to the product name of the product to be estimated,
The trading price according to any one of claims 1 to 3, wherein the estimating step further estimates the trading price of the item to be traded based on the product name information acquired in the information acquiring step. estimation program. The trading price estimation program according to any one of claims 1 to 7, wherein the estimation step uses the degree of association corresponding to a weighting factor for each output of a neural network node in artificial intelligence.

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