JP2022042679A - Secondhand car selling price estimation program - Google Patents

Abstract

To automatically and precisely determine a selling price of a secondhand car without depending on manual effort.SOLUTION: A secondhand car selling price estimation program for estimating a selling price of a secondhand car causes a computer to execute: an information acquisition step of acquiring indoor image information obtained by capturing an image of the inside of a secondhand car for sale; and an estimation step of estimating a selling price, on the basis of reference indoor image information which is the same as or similar to the indoor image information acquired in the information acquisition step by using a learned model for which three or more stages of associations between the reference indoor image information obtained by capturing images of the inside of secondhand cars sold in the past and selling prices are defined and which uses the reference indoor image information for input and outputs a selling price, in accordance with priority of the association.SELECTED DRAWING: Figure 17

Description

The present invention relates to a used car selling price estimation program that estimates the selling price of a used car.

The selling price of a used car is set by the used car dealer to be the optimum price that is likely to come to terms with the buyer. The used car dealer sets the selling price by referring to the data such as the vehicle type and the market condition of the used car market.

However, the purchaser who actually purchased the used car may later complain that the inside of the car is dirty or that there is a flaw on the outside. If the inside of the car is dirty or there is a flaw on the outside, the dealer will set a cheap price including the situation, but since there are many used cars to sell, these It takes a lot of effort to set the selling price including the condition of the used car.

For this reason, when narrowing down the selling price of used cars, a system that can automatically estimate the selling price with high accuracy, including the condition of such fine used cars such as dirt and scratches, has been desired from the past. Was there.

Therefore, the present invention has been devised in view of the above-mentioned problems, and the purpose thereof is to be able to determine the selling price of a used car with high accuracy and automatically without relying on human hands. It is to provide a used car sales price estimation program.

The used car selling price estimation program according to the present invention is an information acquisition step for acquiring in-vehicle image information obtained by capturing an image of the inside of a used car to be sold in the used car selling price estimation program for estimating the selling price of a used car. , The degree of association between the reference car image information obtained by capturing the image of the inside of the used car sold in the past and the selling price is specified, and the input is the reference car image information and the output is the selling price. Using the trained model, based on the in-vehicle image information for reference that is the same as or similar to the in-vehicle image information acquired in the above information acquisition step, the one with the higher degree of association is prioritized and the selling price is estimated. Is characterized by having a computer execute the above.

Anyone can easily estimate the selling price of a used car with high accuracy and automatically without any special skill or experience.

It is a block diagram which shows the whole structure of the system to which this invention is applied. It is a figure which shows the specific configuration example of a search device. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention.

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

The first embodiment FIG. 1 is a block diagram showing an overall configuration of a used car sales price estimation system 1 to which a used car sales price estimation program to which the present invention is applied is implemented. The used car sales price estimation system 1 includes an information acquisition unit 9, a discrimination device 2 connected to the information acquisition unit 9, and a database 3 connected to the discrimination device 2.

The information acquisition unit 9 is a device for a person using this system to input various commands and information, and specifically, is composed of a keyboard, buttons, a touch panel, a mouse, a switch, and the like. 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 image pickup device capable of taking an image of a camera or the like. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper-based document. Further, the information acquisition unit 9 may be integrated with the discrimination device 2 described later. The information acquisition unit 9 outputs the detected information to the discrimination device 2. Further, the information acquisition unit 9 may be configured by means for specifying the position information by scanning the map information. Further, the information acquisition unit 9 may be composed of an illuminance sensor for measuring a temperature sensor, a humidity sensor, and a wind direction sensor. Further, the information acquisition unit 9 may be configured by a communication interface for acquiring data about the weather from the Japan Meteorological Agency or a private weather forecast company. Further, the information acquisition unit 9 may be composed of a body sensor that is attached to 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 composed of a sensor for the purpose. Further, the body sensor may acquire biological data of not only humans but also animals. Further, the information acquisition unit 9 may be configured as a device for acquiring information such as drawings by scanning or reading from a database. In addition to these, the information acquisition unit 9 may be configured by an odor sensor that detects odors and scents.

Database 3 stores various information necessary for estimating the selling price of used cars. Information necessary for estimating the used car selling price includes reference market information regarding the market conditions of each used car model in the past, reference appearance image information obtained by capturing images of the appearance of used cars sold in the past, and past. Reference appearance image information that captured the image of the inside of the used car sold in the past, reference odor information that measured the odor degree in the used car sold in the past, total running time or total running of the used car sold in the past Actual judgment was made on the reference driving information on the distance, the reference record book information on the used car record book sold in the past, and the reference sales place information on the sales place of the used car sold in the past. A dataset with the selling price is stored.

That is, in addition to such reference market condition information, the database 3 contains reference appearance image information, reference appearance image information, reference odor information, reference travel information, reference record book information, and reference sales location information. Any one or more of them and the selling price are stored in association with each other.

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

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

The control unit 24 is a so-called central control unit for controlling each component mounted in the discrimination device 2 by transmitting a control signal via the internal bus 21. Further, the control unit 24 transmits various control commands via the internal bus 21 according to the operation via the operation unit 25.

The operation unit 25 is embodied by a keyboard or a touch panel, and an execution command for executing a program is input from the user. When the execution command is input by the user, the operation unit 25 notifies the control unit 24 of the execution command. Upon receiving this notification, the control unit 24, including the estimation unit 27, executes a desired processing operation in cooperation with each component. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

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

The display unit 23 is configured by a graphic controller that creates a display image based on the control by the control unit 24. The display unit 23 is realized by, for example, a liquid crystal display (LCD) or the like.

When the storage unit 28 is composed of a hard disk, predetermined information is written to each address based on the control by the control unit 24, and is read out as needed. Further, the storage unit 28 stores a program for executing the present invention. This program will be read and executed by the control unit 24.

The operation in the used car selling price estimation system 1 having the above-described configuration will be described.

In the used car sales price estimation system 1, for example, as shown in FIG. 3, it is premised that three or more levels of association between the reference market information and the sales price are set in advance. The reference market information indicates the market conditions in the used car sales market, for example, the time-series transition of the selling price, the time-series transition of the number of inquiries, the number of searches and page views on the Internet, and the actual number of page views. Includes all data that represent market conditions, such as time-series changes in the number of sales. This reference market information may be grouped and accumulated for each vehicle type.

The selling price here is the actual selling price of the used car, but when creating a data set with the reference market information, the lowest price so far, the average price leading to the transaction, the highest price, etc. , Either may be adopted.

This selling price may be expressed not by the actual price but by a ranking evaluated on a 5-point or 10-point set by the system side or the user side.

For the selling price, the data accumulated from the past sales time actually held by the seller may be used. In addition, the reference market information is published on the Internet and books, etc., in addition to the past sales price changes for each model, the time-series changes in the number of inquiries held by each dealer, and the number of searches on the Internet. Or the data of the number of page views may be used.

In the example of FIG. 3, it is assumed that the input data is, for example, reference market condition information P01 to P03. The reference market conditions P01 to P03 as such input data are linked to the selling price as an output. In this output, the selling prices A (2.5 million yen), B (1.26 million yen), C (910,000 yen), and D (1.84 million yen) as output solutions are displayed.

The reference market information is associated with each other through three or more levels of association with the selling prices A to D as the output solution. Reference market information is arranged on the left side through this degree of association, and each selling price is arranged on the right side via this degree of association. The degree of association indicates the degree of which selling price is highly relevant to the reference market information arranged on the left side. In other words, this degree of association is an indicator of what selling price each reference market information is likely to be associated with, and is used to select the most probable selling price from the reference market information. It shows the accuracy. In the example of FIG. 3, w13 to w19 are shown as the degree of association. 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 relevance of each combination as an intermediate node to the selling price as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the price as an output.

The discrimination device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the discriminating device 2 accumulates past data sets and analyzes which of the reference market condition information and the selling price in that case is adopted and evaluated in discriminating the actual search solution. , The degree of association shown in FIG. 3 is created by analysis.

For example, it is assumed that the selling price A is often evaluated as the selling price for the reference market information acquired in the past. By collecting and analyzing such data sets, the degree of association with the reference market information becomes stronger.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference market condition information P01, analysis is performed from various data as a result of evaluating the past selling price. In the case of reference market information P01, if there are many cases of selling price A, the degree of association that leads to the evaluation of this selling price is set higher, and if there are many cases of selling price B, this selling price is set. Set a higher degree of association that leads to the evaluation of. For example, in the example of the reference market information P01, the selling price A and the selling price C are linked. The degree of association is set to 2 points.

Further, the degree of association shown in FIG. 3 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In such a case, as shown in FIG. 4, reference market condition information is input as input data, the selling price is output as output data, and at least one hidden layer is provided between the input node and the output node. You may let it learn by machine. The above-mentioned degree of association is set in either one or both of the input node and the hidden layer node, and this is the weighting of each node, and the output is selected based on this. Then, when this degree of association exceeds a certain threshold value, the output may be selected.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, the selling price will be searched for by using the above-mentioned learned data in actually determining the selling price from now on. In such a case, the current market condition information of the used car model to be sold is newly acquired. The market condition information to be newly acquired is input by the above-mentioned information acquisition unit 9. The market information is similar to the reference market information, for example, the current selling price and the time-series transition up to the present, the current number of inquiries, and the time-series transition up to the present, on the current Internet. Acquires the actual number of sales of the vehicle model at present and the time-series transition up to the present, such as the number of searches, the number of page views, and the data up to the present. The current data may be acquired on average for 3 days, 1 week, 1 month, etc. from the present.

The selling price is determined based on the market information newly acquired in this way. In such a case, the degree of association shown in FIG. 3 (Table 1) acquired in advance is referred to. For example, when the newly acquired market condition information is the same as or similar to P02, the selling price B is associated with w15 and the selling price C is associated with the association degree w16 through the association degree. In such a case, the selling price B having 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 selling price C, which has the lowest degree of association but the association itself is recognized, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In this way, the most suitable selling price can be searched for and displayed to the user from the newly acquired market condition information. By seeing this search result, the user and the seller can propose the optimum selling price based on this search result, and by setting the optimum price with the buyer, the transaction is completed. The possibility can be increased.

In the example of FIG. 5, it is premised that the degree of association is formed by the combination of the reference market condition information and the reference appearance image information. Here, the reference appearance image information is information obtained by capturing an image of the appearance of a used car sold in the past. Among them, there are some colors that are particularly popular and some that are not popular in terms of appearance. In addition to the color, if there are scratches or dents on the appearance, after rubbing or if there are damaged parts, it will cause the selling price to drop, and conversely, if the appearance is very clean, it will also be a factor to raise the selling price. Become. By combining and determining the reference appearance image information in addition to the reference market condition information, the selling price can be determined with higher accuracy. Therefore, in addition to the reference market condition information, the reference appearance image information is combined to form the above-mentioned degree of association.

In the example of FIG. 5, it is assumed that the input data is, for example, reference market condition information P01 to P03 and reference appearance image information P14 to 17. The intermediate node shown in FIG. 5 is a combination of reference market condition information and reference appearance image information as such input data. Each intermediate node is further linked to the output. In this output, the selling price as the output solution is displayed.

Each combination (intermediate node) of the reference market condition information and the reference appearance image information is associated with each other through three or more levels of association with the selling price as this output solution. The reference market condition information and the reference appearance image information are arranged on the left side through this degree of association, and the selling price is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the selling price with respect to the reference market condition information and the reference appearance image information arranged on the left side. In other words, this degree of association is an index showing what kind of selling price each reference market condition information and reference appearance image information is likely to be associated with, and is a reference market condition information and reference appearance image information. It shows the accuracy in selecting the most probable selling price from. Therefore, the optimum selling price will be searched for by combining the reference market condition information and the reference appearance image information.

In the example of FIG. 5, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The discriminating device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the discriminating device 2 accumulates past data as to which of the reference market condition information, the reference appearance image information, and the selling price in that case is suitable for discriminating the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 5 is created.

For example, it is assumed that the reference market condition information in the actual case in the past is P01. Further, it is assumed that the external appearance image information for reference is the image data β. In such a case, the selling price indicating how much the selling price was actually was learned as a data set and defined in the form of the above-mentioned degree of association. In addition, such reference market condition information and reference appearance image information may be extracted from the management database managed by the seller.

These reference appearance image information may be discriminated based on the feature amount learned in the past. For example, if there are flaws, stains, dents or damage, deep learning techniques may be combined to extract only these parts through artificial intelligence.

The analysis and analysis for forming the degree of association shown in FIG. 5 may be performed by artificial intelligence. In such a case, for example, in the case of the reference market condition information P01 and the reference appearance image information P16, the selling price thereof is analyzed from the past data. If there are many cases where the selling price is A, the degree of association that leads to this selling price A is set higher, and if there are many cases of selling price B and there are few cases of selling price A, it leads to selling price B. The degree of association is set high, and the degree of association that leads to the selling price A is set low. For example, in the example of the intermediate node 61a, it is linked to the output of the selling price A and the quality B, but from the previous case, the degree of association of w13 connected to the selling price A is 7 points, and the degree of association of w14 connected to the selling price B is Is set to 2 points.

Further, the degree of association shown in FIG. 5 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 5, the node 61b is a node of the combination of the reference appearance image information P14 with respect to the reference market condition information P01, the degree of association of the selling price C is w15, and the association of the selling price E. The degree is w16. The node 61c is a node that is a combination of the reference appearance image information P15 and P17 with respect to the reference market condition information P02, and the degree of association of the selling price B is w17 and the degree of association of the selling price D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually determining the selling price from now on, the above-mentioned learned data will be used. In such a case, the market information of the used car model for which the selling price is to be actually determined and the external image information captured about the market information are input or selected.

Based on the newly acquired market condition information and appearance image information in this way, the optimum selling price is searched for. In such a case, the degree of association shown in FIG. 5 (Table 1) acquired in advance is referred to. For example, when the newly acquired market condition information is the same as or similar to P02 and the external image information is P17, the node 61d is associated with the node 61d via the degree of association. The node 61d is associated with the selling price C by w19 and the selling price D by the degree of association w20. In such a case, the selling price C having 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 selling price D, which has the lowest degree of association but is recognized for the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

Further, an example of the degree of association w1 to w12 extending from the input is shown in Table 2 below.

The intermediate node 61 may be selected based on the degree of association w1 to w12 extending from this input. That is, the larger the degree of association w1 to w12, the heavier the weighting in the selection of the intermediate node 61 may be. However, the association degrees w1 to w12 may all have the same value, and the weightings in the selection of the intermediate node 61 may all be the same.

In addition to the above-mentioned reference market condition information, a spectrum image may be used as the above-mentioned reference appearance image information.

Further, as an alternative to the external appearance image information for reference, in-house information for reference, which is an image of the inside of a used car, may be used. In such a case, the combination of the reference in-vehicle image information obtained by capturing the in-vehicle image of the used car sold in the past and the reference market condition information and the degree of association with the selling price in three or more stages are acquired in advance. deep. Then, the in-vehicle image information is acquired by taking an image of the inside of the used car to be sold. Next, the selling price is estimated based on the degree of association shown in FIG. 5 based on the reference vehicle interior image information according to the acquired vehicle interior image information and the reference market condition information according to the market condition information. Similarly, scratches and stains may be extracted from the reference vehicle interior image and the vehicle interior image through deep learning.

Further, the reference vehicle interior image and the vehicle interior image include an image of the engine room. Traces of oil leaks can be extracted through the image of the engine room and reflected in the selling price.

In FIG. 6, in addition to the above-mentioned reference market condition information, a combination with the reference odor information instead of the above-mentioned reference appearance image information and a degree of association with the selling price for the combination are set in three or more stages. An example is shown.

This reference odor information, which is added as an explanatory variable instead of the reference appearance image information, is information obtained as a result of sensing the odor and odor in the company. This reference odor information can be detected by, for example, an odor sensor, and the oxidation-reduction reaction of odor molecules is used to detect reducing odors such as hydrogen sulfide, acetaldehyde, and ammonia on the surface of the conductor. A semiconductor-type odor sensor that utilizes changes in the resistance value of semiconductors due to adsorption of odor molecules and surface reactions, and an odor-sensitive film made of a lipid film made of natural or synthetic lipids that selectively adsorbs molecules is attached to the surface of the crystal oscillator. It may be composed of a attached crystal oscillator type odor sensor, a molecular selection FET biosensor in the air, or the like.

Since the odor contained in such reference odor information also affects the selling price of the used car, it is possible to improve the discrimination accuracy by determining the selling price through the degree of association in combination with the reference market condition information. ..

In the example of FIG. 6, it is assumed that the input data is, for example, reference market condition information P01 to P03 and reference odor information P18 to 21. The intermediate node shown in FIG. 6 is a combination of the reference market condition information and the reference odor information as such input data. Each intermediate node is further linked to the output. In this output, the selling price as the output solution is displayed.

Each combination (intermediate node) of the reference market condition information and the reference odor information is associated with each other through three or more levels of association with the selling price as this output solution. The reference market information and the reference odor information are arranged on the left side through this degree of association, and the selling price is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the selling price with respect to the reference market condition information and the reference odor information arranged on the left side. In other words, this degree of association is an index showing what kind of selling price each reference market condition information and reference odor information is likely to be associated with, and is the most from the reference market information and the reference odor information. It shows the accuracy in selecting a reliable selling price.

The discrimination device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the discrimination device 2 determines the reference market condition information and the reference odor information based on the odor degree actually measured from the used car when acquiring the reference market condition information in determining the actual search solution. In addition, past data on which selling price was suitable in that case is accumulated, and by analyzing and analyzing these, the degree of association shown in FIG. 6 is created.

This reference odor information may be determined by a majority vote by a panelist for determining the odor, in addition to the measurement via, for example, an odor meter or an odor sensor.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference market condition information P01 and the reference odor information P20, the selling price thereof is analyzed from the past data. When there are many cases of selling price A, the degree of association that this selling price leads to A is set higher, and when there are many cases where the selling price is B and there are few cases where the selling price is A, the selling price is Set the degree of association that leads to B high and the degree of association that the selling price leads to A low. For example, in the example of the intermediate node 61a, it is linked to the output of the selling price A and the selling price B, but from the previous case, the degree of association of w13 connected to the selling price A is 7 points, and the association of w14 connected to the selling price B The degree is set to 2 points.

Further, the degree of association shown in FIG. 6 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 6, the node 61b is a node of the combination of the reference market condition information P01 and the reference odor information P18, and the degree of association of the selling price C is w15 and the degree of association of the selling price E is. It is w16. The node 61c is a node that is a combination of the reference odor information P19 and P21 with respect to the reference market condition information P02, and the degree of association of the selling price B is w17 and the degree of association of the selling price D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for a selling price from now on, the above-mentioned learned data will be used. In such a case, the image information to be determined for the selling price and the odor information are actually acquired. Here, the odor information is based on the degree of odor actually measured from the inside of the used car to be sold, but the acquisition method thereof is the same as the above-mentioned reference odor information.

Based on the newly acquired market condition information and odor information in this way, the optimum selling price is searched for. In such a case, the degree of association shown in FIG. 6 (Table 1) acquired in advance is referred to. For example, when the newly acquired market condition information is the same as or similar to P02 and the odor information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the selling price C by w19 and the selling price D by the degree of association w20. In such a case, the selling price C having 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 selling price D, which has the lowest degree of association but is recognized for the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In FIG. 7, in addition to the above-mentioned reference market condition information, the combination with the reference driving information instead of the above-mentioned reference appearance image information and the selling price for the combination are set to three or more levels of association. An example is shown.

This reference travel information, which is added as an explanatory variable instead of the reference appearance image information, is information on the total travel time or total mileage of the used car sold in the past. That is, the reference traveling information is all information indicating how much the used car sold in the past is used. If a used car is used considerably and the total mileage is longer than usual, the selling price will be cheaper because the parts etc. are consumed accordingly.

Since such reference driving information also affects the selling price of the used car, it is possible to improve the discrimination accuracy by discriminating the selling price through the degree of association in combination with the reference market condition information.

In the example of FIG. 7, it is assumed that the input data is, for example, reference market condition information P01 to P03 and reference travel information P18 to 21. The intermediate node shown in FIG. 7 is a combination of reference travel information and reference market information as such input data. Each intermediate node is further linked to the output. In this output, the selling price as the output solution is displayed.

Each combination (intermediate node) of the reference market condition information and the reference travel information is associated with each other through three or more levels of association with the selling price as this output solution. The reference market information and the reference driving information are arranged on the left side through this degree of association, and the selling price is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the selling price with respect to the reference market condition information and the reference driving information arranged on the left side. In other words, this degree of association is an index showing what kind of selling price each reference market condition information and reference driving information is likely to be associated with, and is the most from the reference market information and the reference driving information. It shows the accuracy in selecting a probable selling price.

The discrimination device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 7. That is, the discrimination device 2 discriminates the actual search solution, and when acquiring the reference market condition information and the reference market condition information, the reference travel information based on the total mileage actually measured from the used car. , And which was the most suitable selling price in that case, the past data is accumulated, and these are analyzed and analyzed to create the degree of association shown in FIG. 7. By the way, the total mileage and total mileage that compose the reference mileage information are not limited to those analyzed accurately to 1 minute unit and 1 km unit, but are summarized by roughly ranked ones. You may.

This analysis may be performed by artificial intelligence. In such a case, for example, when the reference market condition information P01 is the reference travel information P20 (for example, the total travel time is 720 hours), the selling price is analyzed from the past data. When there are many cases of selling price A, the degree of association that this selling price leads to A is set higher, and when there are many cases where the selling price is B and there are few cases where the selling price is A, the selling price is Set the degree of association that leads to B high and the degree of association that the selling price leads to A low. For example, in the example of the intermediate node 61a, it is linked to the output of the selling price A and the selling price B, but from the previous case, the degree of association of w13 connected to the selling price A is 7 points, and the association of w14 connected to the selling price B The degree is set to 2 points.

Further, the degree of association shown in FIG. 7 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 7, the node 61b is a node in which the reference travel information P18 is combined with the reference market condition information P01, the degree of association of the selling price C is w15, and the degree of association of the selling price E is. It is w16. The node 61c is a node that is a combination of the reference travel information P19 and P21 with respect to the reference market condition information P02, and the degree of association of the selling price B is w17 and the degree of association of the selling price D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for a selling price from now on, the above-mentioned learned data will be used. In such a case, the image information to be determined for the selling price and the running information are actually acquired. Here, the method of acquiring the traveling information is the same as the above-mentioned reference traveling information.

Based on the newly acquired market condition information and driving information in this way, the optimum selling price is searched for. In such a case, the degree of association shown in FIG. 7 (Table 1) acquired in advance is referred to. For example, when the newly acquired market condition information is the same as or similar to P02 and the traveling information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the selling price C by w19 and the selling price D by the degree of association w20. In such a case, the selling price C having 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 selling price D, which has the lowest degree of association but is recognized for the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In FIG. 8, in addition to the above-mentioned reference market condition information, the combination with the reference record book information instead of the above-mentioned reference appearance image information and the selling price for the combination are set to three or more levels of association. Here is an example.

This reference record book information, which is added as an explanatory variable instead of the reference appearance image information, includes all information related to items checked at the time of legal inspection, vehicle inspection, etc., replaced parts, and the like. It is generally believed that used cars with a description of "repair history" have had an accident in the past, but such information is also included in this reference record book information. As an example of repair history, it can be considered that eight skeletal parts of the frame, cross member, inside panel, pillar, dash panel, roof panel, floor, and trunk floor have been damaged and have been repaired. Not limited to this, easily replaceable bolted parts such as fenders, doors and trunks may be included, or replacement history of any part may be included in this reference record book information. .. Since the repair history included in the reference record book information also affects the selling price, the discrimination accuracy can be improved by determining the selling price through the degree of association in combination with the reference market condition information.

In the example of FIG. 8, it is assumed that the input data is, for example, reference market condition information P01 to P03 and reference record book information P18 to 21. The intermediate node shown in FIG. 8 is a combination of reference market condition information and reference record book information as such input data. Each intermediate node is further linked to the output. In this output, the selling price as the output solution is displayed.

Each combination (intermediate node) of the reference market condition information and the reference record book information is associated with each other through three or more levels of association with the selling price as this output solution. The reference market information and the reference record book information are arranged on the left side through this degree of association, and the selling price is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the selling price with respect to the reference market condition information and the reference record book information arranged on the left side. In other words, this degree of association is an index showing what selling price each reference market condition information and reference record book information is likely to be associated with, and is a reference market condition information and reference record book information. It shows the accuracy in selecting the most probable selling price from.

The discrimination device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, in determining the actual search solution, the discrimination device 2 is preferably suitable for the reference market condition information, the reference record book information obtained when acquiring the reference market condition information, and the selling price in that case. Or, by accumulating past data and analyzing and analyzing these, the degree of association shown in FIG. 8 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference market condition information P01 and the reference record book information P20, the selling price thereof is analyzed from the past data. When there are many cases of selling price A, the degree of association that this selling price leads to A is set higher, and when there are many cases where the selling price is B and there are few cases where the selling price is A, the selling price is Set the degree of association that leads to B high and the degree of association that the selling price leads to A low. For example, in the example of the intermediate node 61a, it is linked to the output of the selling price A and the selling price B, but from the previous case, the degree of association of w13 connected to the selling price A is 7 points, and the association of w14 connected to the selling price B The degree is set to 2 points.

Further, the degree of association shown in FIG. 8 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 8, the node 61b is a node in which the reference record book information P18 is combined with the reference market condition information P01, the degree of association of the selling price C is w15, and the degree of association of the selling price E is the degree of association. Is w16. The node 61c is a node that is a combination of the reference record book information P19 and P21 with respect to the reference market condition information P02, and the degree of association of the selling price B is w17 and the degree of association of the selling price D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for a selling price from now on, the above-mentioned learned data will be used. In such a case, the image information to be determined for the selling price and the record book information are actually acquired. Here, the record book information is newly acquired when the selling price is actually estimated, and the acquisition method is the same as the reference record book information described above. The method of acquiring record book information and reference record book information can be obtained by keyboard input to a device such as a PC or smartphone, reading the record book with a scanner and converting it into information, or accessing the database of the record book. You may try to do it.

Based on the market information newly acquired in this way and the record book information, the optimum selling price is searched for. In such a case, the degree of association shown in FIG. 8 (Table 1) acquired in advance is referred to. For example, if the newly acquired market condition information is the same as or similar to P02, and the record book information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the selling price C by w19 and the selling price D by the degree of association w20. In such a case, the selling price C having 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 selling price D, which has the lowest degree of association but is recognized for the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

Further, as an alternative to the reference record book information, the reference sales location information regarding the sales location of the used car may be used. Since the place of sale also affects the selling price of used cars, it is possible to search for a more accurate selling price by making a judgment including this.

In such a case, by acquiring the sales location of the used car sold in the past, the combination of the reference sales location information and the reference market condition information and the degree of association with the selling price at three or more levels are acquired in advance. Keep it. Then, the sales location information regarding the sales location of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 8 based on the reference sales place information according to the acquired sales place information and the reference market condition information according to the market condition information.

Further, as a substitute for the reference vehicle interior image information and the reference external appearance image information, the reference vehicle model information regarding the vehicle model of the used vehicle may be used. Since the vehicle type also affects the selling price of used cars, it is possible to search for a more accurate selling price by making a judgment including this. The reference vehicle type information and vehicle type information are composed of data such as a used car manufacturer, brand, product name, and model number.

In such a case, by acquiring the model of the used car sold in the past, the combination having the reference vehicle type information and the reference market condition information and the degree of association with the selling price at three levels or more are acquired in advance. .. Then, the vehicle type information regarding the vehicle type of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 5 based on the reference vehicle type information according to the acquired vehicle type information and the reference market condition information according to the market condition information.

Further, as an alternative to the reference appearance image information, the reference year information regarding the year of the used car sold in the past may be used. Since the model year of a used car also affects the selling price of a used car, it is possible to search for a more accurate selling price by making a judgment including this. This reference year information and model year information is the model year of the used car, that is, the year when the used car was manufactured or the year when it was first registered in Japan. The data of this model year is the model year information for reference and the model year information.

In such a case, the combination of the reference year information acquired through the model year of the used car sold in the past and the reference market information, and the degree of association with the selling price in three or more stages are acquired in advance. Keep it. Then, the model year information regarding the model year of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 5 based on the reference year information according to the acquired model year information and the reference market information according to the market information.

Further, as an alternative to the reference appearance image information, the reference color information regarding the color of the used car sold in the past may be used. Since the color of the exterior of a used car also affects the selling price of a used car, it is possible to search for a more accurate selling price by making a judgment including this. The reference color information and color information are the colors of the exterior of the used car. The data of this color is the reference color information and the color information.

In such a case, the combination of the reference color information regarding the color of the used car sold in the past and the reference market condition information, and the degree of association with the selling price in three or more stages are acquired in advance. Then, the color information regarding the color of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 5 based on the reference color information according to the acquired color information and the reference market condition information according to the market condition information.

Further, as a substitute for the image information in the reference vehicle, the reference equipment information regarding the standard equipment and options of the used car sold in the past may be used. Since the standard equipment and options of used cars also affect the selling price of used cars, it is possible to search for a more accurate selling price by making a judgment including these. This reference equipment information and equipment information may be composed of categorized types of standard equipment possessed by used cars and converted into data, or categorized options implemented in used cars. It may be converted into data and configured as data. Standard equipment and options for used cars include car navigation systems, sunroofs, security systems, various electronic equipment, floor mats, side visors, aero parts, audio, etc., and these are implemented as standard equipment or are usually implemented. It indicates whether it is not added and is added as an option. The reference equipment information and equipment information may be configured depending on whether or not these equipments are installed as standard and whether or not they are optional.

In such a case, the combination of the reference equipment information regarding the standard equipment and options of the used car sold in the past and the reference market condition information, and the degree of association with the selling price at three or more levels are acquired in advance. Then, the equipment information about the standard equipment and options of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 5 based on the reference equipment information according to the acquired equipment information and the reference market condition information according to the market condition information.

Further, as an alternative to the reference appearance image information, the reference timing information regarding the sales timing of the used car sold in the past may be used. The sales time is expressed by season, month, week, etc., and indicates what time of the year the sales time was. For example, there are seasons when the demand for used cars increases, such as July and the end of the year, and the selling price rises accordingly. Therefore, it is possible to search for a highly accurate selling price by making a judgment including this.

In such a case, the combination of the reference time information regarding the sales time of the used car sold in the past and the reference market condition information and the degree of association with the selling price in three or more stages are acquired in advance. Then, the time information regarding the desired purchase time of the used car to be sold is acquired. This desired purchase time may be the time when the person who wants to purchase actually wants to purchase, or if he / she wants to purchase at the time after 3 months, for example, it corresponds to that time. Next, the selling price is estimated based on the degree of association shown in FIG. 5 based on the reference time information according to the acquired time information and the reference market information according to the market condition information.

In the example of FIG. 9, it is an example of using the degree of association between the reference market condition information and the selling price in three or more stages.

It is premised that the degree of association between the reference market information and the selling price is formed. In the example of FIG. 9, it is assumed that the input data is, for example, reference market condition information P01 to P03. The reference market information as such input data is linked to the output. In this output, it is assumed that the selling price is the output solution.

The reference market information is linked to the selling price as the output solution through three or more levels of linkage. The reference market information is arranged on the left side through this degree of association, and the selling price is arranged on the right side through this degree of association.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, in determining the actual search solution, the search device 2 accumulates past data on what the selling price was at the time of the reference market condition information acquired in the past, and analyzes and analyzes these. The degree of association shown in FIGS. 3 and 9 is created in.

For example, it is assumed that the reference market information is P01. It is assumed that A (2.5 million yen) was a large selling price for such P01. By collecting and analyzing such a data set, the degree of association between the reference market condition information P01 and the selling price becomes stronger.

This analysis may be performed by artificial intelligence. Further, the degree of association shown in FIG. 9 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the process of constructing the trained model based on the degree of association, the appearance image information for reference is also acquired. This reference appearance image information is not included in the above-mentioned degree of association.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, the solution will be searched. In such a case, the market condition information is acquired and the appearance image information is also acquired in the same manner.

First, the selling price is searched based on the newly acquired market information. In such a case, the degree of association shown in FIG. 9 acquired in advance is used. For example, when the newly acquired market condition information is the same as or similar to the reference market condition information P02, B (1.26 million yen) is linked to w15 and C (910,000 yen) through the degree of association. ) Is associated with the degree of association w16. In such a case, B (1.26 million yen), which has the highest degree of association, is selected as the optimum solution. However, it is not essential to select the solution having the highest degree of association as the optimum solution, and the solution may be selected as the solution that has a low degree of association but the association itself is recognized. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association. Further, the output solution to be selected is not limited to one, and two or more may be selected. In such a case, two or more may be selected in order from the highest degree of association, but the present invention is not limited to this, and may be based on the priority of any other degree of association.

The selling price obtained through the degree of association may be further modified based on the appearance image information, or the weighting may be changed.

For example, it is assumed that the image content of the reference appearance image information P13 is α, the image content of the reference appearance image information P14 is β, and the image content of the reference appearance image information P15 is γ. At this time, if the image α of the reference appearance image information P13 is an image having many scratches on the appearance, a process of lowering the selling price is performed. On the other hand, if the surface is clean with no scratches as in the image γ of the reference timetable information P15, a process of raising the selling price is performed.

In this way, after the setting with the reference appearance image information, if the actually acquired appearance image is the same as or similar to the reference appearance image information P13, a process of lowering the weighting of the selling price is performed, in other words. If so, the processing to lower the selling price itself is performed. On the other hand, when the actually acquired appearance image information is the same as or similar to the reference appearance image information P15, a process of increasing the weighting of the selling price is performed, in other words, a process of increasing the selling price itself is performed.

By making rules for adjusting the reference appearance image information and the selling price in advance in this way, the selling price is set according to the rule based on the reference image information that is the same as or similar to the actually input appearance image information. adjust. At this time, in addition to the case of actually adjusting the selling price, the degree of association itself may be adjusted.

As an alternative to the external appearance image information for reference, the in-vehicle image information obtained by capturing the image of the inside of the used car to be sold, the odor information measuring the odor degree in the used car to be sold, and the used car to be sold. Travel information on the total mileage or mileage, record book information on the record book of the used car to be sold, sales location information on the sales location to be sold, vehicle type information on the used car model to be sold, the above. Time information about the desired purchase time of the used car for sale, year information about the year of the used car for sale, color information about the color of the used car for sale, standard equipment and options of the used car for sale It is also possible to acquire one or more of the equipment information and the estimation auxiliary information for estimating the selling price, and adjust the selling price based on this.

Appearance image information for reference, in-vehicle image information obtained by capturing an image of the inside of the used car to be sold, odor information measured by measuring the odor level of the used car to be sold, and total running of the used car to be sold. Travel information regarding time or total mileage, record book information regarding the record book of used cars to be sold, sales location information regarding sales locations to be sold, vehicle model information regarding used car models to be sold, and sales targets. Time information about when you want to buy a used car, year information about the year of the used car to be sold above, color information about the color of the used car to be sold above, equipment information about standard equipment and options of the used car to be sold above , Any one or more of the estimation auxiliary information for estimating the selling price is called the estimation auxiliary information. This estimated auxiliary information is used to adjust the output solution of the selling price searched through the selling price to the market information, although the degree of association with the selling price is not formed. At this time, how to adjust the selling price is ruled in advance between the reference estimation auxiliary information and the adjustment to the selling price, and corresponds to the actually acquired estimation auxiliary information (same or Make adjustments according to the rules for adjusting the selling price defined with the (similar) reference estimation supplementary information. In other words, it may be in any form as long as the selling price is adjusted based on the estimation auxiliary information when it is input. The rule between the estimation auxiliary information for reference and the adjustment to the selling price is to make an adjustment to lower the selling price in inverse proportion to the total mileage, for example, taking the driving information (driving information for reference) as an example. However, it is not essential to use the degree of association and artificial intelligence.

In the above-mentioned degree of association, the degree of association is expressed by a 10-step evaluation, but it is not limited to this, and it may be expressed by a degree of association of 3 or more levels, and conversely, it may be expressed by 3 or more levels. 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 related to each other, either 1 or 0.

According to the present invention having the above-described configuration, anyone can easily determine and search the selling price without any special skill or experience. Further, according to the present invention, it is possible to make a judgment of this search solution with higher accuracy than that made by a human being. Further, by configuring the above-mentioned degree of association with artificial intelligence (neural network or the like), it is possible to further improve the discrimination accuracy by learning this.

Since there are many cases where the input data and the output data described above do not exist completely the same in the process of learning, the information may be classified by type between the input data and the output data. That is, the information P01, P02, ... P15, 16, ... That constitute the input data are classified according to the criteria classified in advance on the system side or the user side according to the content of the information, and the classified inputs. A data set may be created between the data and the output data and trained.

In the above-mentioned degree of association, in addition to the reference market condition information, any of the reference appearance image information, the reference vehicle interior image information, the reference odor information, the reference driving information, the reference record book information, and the reference sales location information. The explanation has been given by taking the case of being composed of a combination of heels as an example, but the explanation is not limited to this. In other words, in addition to the reference market condition information, the degree of association is any two or more of the reference external image information, the reference vehicle interior image information, the reference odor information, the reference driving information, the reference record book information, and the reference sales location information. It may be configured in combination with. In addition to the reference market information, the degree of association is one or more of the reference external image information, the reference vehicle interior image information, the reference odor information, the reference driving information, the reference record book information, and the reference sales location information. In addition, other factors may be added to this combination to form a degree of association.

In either case, data is input according to the reference information of the degree of association, and the selling price is obtained using the degree of association.

Further, as shown in FIG. 10, the present invention determines the selling price based on the degree of association between two or more types of information, the reference information U and the reference information V. The reference information Y is the reference market condition information, and the reference information V is the reference appearance image information, the reference appearance image information, the reference odor information, the reference driving information, the reference record book information, and the reference sales place. It shall be one of the information.

At this time, as shown in FIG. 10, even if the output obtained for the reference information U is used as input data as it is and is associated with the output (selling price) via the intermediate node 61 in combination with the reference information V. good. For example, for reference information U (reference market information), after outputting an output solution as shown in FIG. 3, this is used as an input as it is, and the degree of association with other reference information V is used. The output (selling price) may be searched.

Further, according to the present invention, there is a feature 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 from 0 to 100%, for example, in addition to the above-mentioned 10 levels, but is not limited to this, and any stage can be described as long as it can be described by a numerical value of 3 or more levels. It may be configured.

By determining the most probable selling price based on the degree of association expressed by the numerical values of three or more stages, in a situation where there are multiple possible candidates for the search solution, the order of the degree of association is high. It is also possible to search and display. If the user can be displayed in descending order of the degree of association in this way, it is possible to preferentially display more probable search solutions.

In addition to this, according to the present invention, it is possible to judge without overlooking the discrimination result of the output having an extremely low degree of association such as 1%. Remind the user that even a judgment result with an extremely low degree of association is connected as a slight sign, and may be useful as the judgment result once every tens or hundreds of times. be able to.

Further, according to the present invention, there is an advantage that the search policy can be determined by the method of setting the threshold value by performing the search based on the degree of association of three or more stages. If the threshold value is lowered, even if the above-mentioned degree of association is 1%, it can be picked up without omission, but it is unlikely that a more appropriate discrimination result can be detected favorably, and a lot of noise may be picked up. be. On the other hand, if the threshold value is raised, there is a high possibility that the optimum search solution can be detected with high probability, but the degree of association is usually low and it is passed through, but it is suitable to appear once in tens or hundreds of times. Sometimes the solution is overlooked. It is possible to decide which one should be emphasized based on the ideas of the user side and the system side, but it is possible to increase the degree of freedom in selecting the points to be emphasized.

Further, in the present invention, the above-mentioned degree of association may be updated. This update may reflect information provided, for example, via a public communication network such as the Internet. In addition, when each reference information such as reference market information is acquired and knowledge, information, and data regarding the selling price and improvement measures for these are acquired, the degree of association is increased or decreased according to these.

In other words, this update corresponds to learning in the sense of artificial intelligence. It can be said that it is a learning act because it acquires new data and reflects it in the learned data.

In addition, this update of the degree of association is done by the system side or the user side based on the contents of research data, papers, conference presentations, newspaper articles, books, etc. by experts, except when it is based on information that can be obtained from public communication networks. It may be updated artificially or automatically. Artificial intelligence may be utilized in these update processes.

Further, the process of first creating a trained model and the above-mentioned update 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 reading and learning the data set of input data and output data, information corresponding to the input data is read and trained, and the degree of association related to the output data is self-formed from there. You may let it.

Second Embodiment Next, the second embodiment will be described.

In the second embodiment, the selling price as an output solution is searched based on the appearance image information.

For example, as shown in FIG. 11, it is premised that three or more levels of association between the reference appearance image information and the selling price are set in advance. The reference appearance image information is the same as that described in the first embodiment.

In the example of FIG. 11, it is assumed that the input data is, for example, reference appearance image information P01 to P03. The reference appearance image information P01 to P03 as such input data is linked to the selling price as an output. In this output, the selling prices A (2.5 million yen), B (1.26 million yen), C (910,000 yen), and D (1.84 million yen) as output solutions are displayed.

The reference appearance image information is associated with each other through three or more levels of association with the selling prices A to D as the output solution. The reference appearance image information is arranged on the left side through this degree of association, and each selling price is arranged on the right side through this degree of association. The degree of association indicates the degree of which selling price is highly relevant to the reference appearance image information arranged on the left side. In other words, this degree of association is an indicator of what selling price each reference appearance image information is likely to be associated with, and is used to select the most probable selling price from the reference appearance image information. It shows the accuracy in. In the example of FIG. 11, w13 to w19 are shown as the degree of association. 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 relevance of each combination as an intermediate node to the selling price as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the price as an output.

The discriminating device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the discriminating device 2 accumulates a past data set as to which of the reference appearance image information and the selling price in that case is adopted and evaluated in discriminating the actual search solution, and these are stored. By analyzing and analyzing, the degree of association shown in FIG. 11 is created.

For example, it is assumed that the selling price A is often evaluated as the selling price for the reference appearance image information acquired in the past. By collecting and analyzing such a data set, the degree of association with the reference appearance image information is strengthened.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference appearance image information P01, analysis is performed from various data as a result of evaluating the past selling price. In the case of the reference appearance image information P01, if there are many cases of selling price A, the degree of association leading to the evaluation of this selling price is set higher, and if there are many cases of selling price B, this sale is made. Set a higher degree of association that leads to price evaluation. For example, in the example of the external appearance image information P01 for reference, the selling price A and the selling price C are linked, but from the previous case, the degree of association of w13 connected to the selling price A is 7 points, and w14 connected to the selling price C. The degree of association is set to 2 points.

Further, the degree of association shown in FIG. 11 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In such a case, as shown in FIG. 12, reference appearance image information is input as input data, the selling price is output as output data, and at least one hidden layer is provided between the input node and the output node. , Machine learning may be done. The above-mentioned degree of association is set in either one or both of the input node and the hidden layer node, and this is the weighting of each node, and the output is selected based on this. Then, when this degree of association exceeds a certain threshold value, the output may be selected.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, the selling price will be searched for by using the above-mentioned learned data in actually determining the selling price from now on. In such a case, the current appearance image information of the used car model to be actually sold is newly acquired. The newly acquired external image information is input by the above-mentioned information acquisition unit 9.

The selling price is determined based on the appearance image information newly acquired in this way. In such a case, the degree of association shown in FIG. 11 (Table 1) acquired in advance is referred to. For example, when the newly acquired appearance image information is the same as or similar to P02, the selling price B is associated with the association degree w15 and the selling price C is associated with the association degree w16 through the association degree. In such a case, the selling price B having 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 selling price C, which has the lowest degree of association but the association itself is recognized, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In this way, the most suitable selling price can be searched for and displayed to the user from the newly acquired external appearance image information. By seeing this search result, the user and the seller can propose the optimum selling price based on this search result, and by setting the optimum price with the buyer, the transaction is completed. The possibility can be increased.

FIG. 13 shows an example in which, in addition to the above-mentioned reference external image information, a combination with the reference vehicle interior image information and a selling price for the combination are set to three or more levels of association.

In the example of FIG. 13, it is assumed that the input data is, for example, reference appearance image information P01 to P03 and reference vehicle interior image information P18 to 21. The intermediate node shown in FIG. 13 is a combination of the reference vehicle interior image information and the reference external image information as such input data. Each intermediate node is further linked to the output. In this output, the selling price as the output solution is displayed.

Each combination (intermediate node) of the reference appearance image information and the reference vehicle interior image information is associated with each other through three or more levels of association with the selling price as this output solution. The reference appearance image information and the reference vehicle interior image information are arranged on the left side through this degree of association, and the selling price is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the selling price with respect to the reference external image information and the reference in-vehicle image information arranged on the left side. In other words, this degree of association is an index showing what kind of selling price each reference external image information and reference vehicle interior image information is likely to be associated with, and is a reference external image information and reference vehicle interior. It shows the accuracy in selecting the most probable selling price from the image information.

The discriminating device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the discrimination device 2 accumulates past data as to which of the reference external image information, the reference vehicle interior image information, and the selling price in that case was suitable for discriminating the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 13 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference appearance image information P01 and the reference vehicle interior image information P20, the selling price thereof is analyzed from the past data. When there are many cases of selling price A, the degree of association that this selling price leads to A is set higher, and when there are many cases where the selling price is B and there are few cases where the selling price is A, the selling price is Set the degree of association that leads to B high and the degree of association that the selling price leads to A low. For example, in the example of the intermediate node 61a, it is linked to the output of the selling price A and the selling price B, but from the previous case, the degree of association of w13 connected to the selling price A is 7 points, and the association of w14 connected to the selling price B The degree is set to 2 points.

Further, the degree of association shown in FIG. 13 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 13, the node 61b is a node in which the reference vehicle interior image information P18 is combined with the reference appearance image information P01, and the association degree of the selling price C is w15 and the association of the selling price E. The degree is w16. The node 61c is a node that is a combination of the reference vehicle interior image information P19 and P21 with respect to the reference external image information P02, and the degree of association of the selling price B is w17 and the degree of association of the selling price D is w18. ..

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for a selling price from now on, the above-mentioned learned data will be used. In such a case, the image information to be determined for the selling price and the in-vehicle image information are actually acquired. Here, the in-vehicle image information is newly acquired when the selling price is actually estimated, and the acquisition method is the same as the above-mentioned reference in-vehicle image information. The method of acquiring the in-vehicle image information and the in-vehicle image information for reference can be obtained by inputting the keyboard to the device such as a PC or smartphone, reading the record book with a scanner and converting it into information, or accessing the database of the record book. You may try to do it.

Based on the newly acquired exterior image information and the vehicle interior image information in this way, the optimum selling price is searched for. In such a case, the degree of association shown in FIG. 13 (Table 1) acquired in advance is referred to. For example, when the newly acquired external appearance image information is the same as or similar to P02 and the in-vehicle image information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the selling price C by w19 and the selling price D by the degree of association w20. In such a case, the selling price C having 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 selling price D, which has the lowest degree of association but is recognized for the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

Further, as a substitute for the image information in the reference vehicle, the reference vehicle type information regarding the vehicle type of the used car may be used. Since the vehicle type also affects the selling price of used cars, it is possible to search for a more accurate selling price by making a judgment including this. The reference vehicle type information and vehicle type information are composed of data such as a used car manufacturer, brand, product name, and model number.

In such a case, by acquiring the model of the used car sold in the past, the combination having the reference vehicle model information and the reference appearance image information and the degree of association with the selling price at three or more levels are acquired in advance. deep. Then, the vehicle type information regarding the vehicle type of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 13 based on the reference vehicle type information according to the acquired vehicle type information and the reference appearance image information according to the appearance image information.

Further, as an alternative to the in-vehicle image information for reference, the model year information for reference regarding the model year of the used car sold in the past may be used. Since the model year of a used car also affects the selling price of a used car, it is possible to search for a more accurate selling price by making a judgment including this. This reference year information and model year information is the model year of the used car, that is, the year when the used car was manufactured or the year when it was first registered in Japan. The data of this model year is the model year information for reference and the model year information.

In such a case, the combination of the reference year model information acquired through the model year of the used car sold in the past and the reference appearance image information, and the degree of association with the selling price in three or more stages are acquired in advance. I will do it. Then, the model year information regarding the model year of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 13 based on the reference year information according to the acquired model year information and the reference appearance image information according to the appearance image information.

Further, as an alternative to the image information in the reference vehicle, the reference color information regarding the color of the used car sold in the past may be used. Since the color of the exterior of a used car also affects the selling price of a used car, it is possible to search for a more accurate selling price by making a judgment including this. The reference color information and color information are the colors of the exterior of the used car. The data of this color is the reference color information and the color information.

In such a case, the combination having the reference color information regarding the color of the used car sold in the past and the reference appearance image information, and the degree of association with the selling price in three or more stages are acquired in advance. Then, the color information regarding the color of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 13 based on the reference color information according to the acquired color information and the reference appearance image information according to the appearance image information.

Further, as a substitute for the image information in the reference vehicle, the reference equipment information regarding the standard equipment and options of the used car sold in the past may be used. Since the standard equipment and options of used cars also affect the selling price of used cars, it is possible to search for a more accurate selling price by making a judgment including these. This reference equipment information and equipment information may be composed of categorized types of standard equipment possessed by used cars and converted into data, or categorized options implemented in used cars. It may be converted into data and configured as data. Standard equipment and options for used cars include car navigation systems, sunroofs, security systems, various electronic equipment, floor mats, side visors, aero parts, audio, etc., and these are implemented as standard equipment or are usually implemented. It indicates whether it is not added and is added as an option. The reference equipment information and equipment information may be configured depending on whether or not these equipments are installed as standard and whether or not they are optional.

In such a case, the combination of the reference equipment information regarding the standard equipment and options of the used car sold in the past, the appearance image information for reference, and the degree of association with the selling price at three levels or more are acquired in advance. .. Then, the equipment information about the standard equipment and options of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 13 based on the reference equipment information according to the acquired equipment information and the reference appearance image information according to the appearance image information.

Further, as a substitute for the image information in the reference vehicle, the reference travel information of the used car sold in the past may be used. Since driving information also affects the selling price of used cars, it is possible to search for a more accurate selling price by making a judgment including this. The description of the reference travel information and the details of the travel information will be quoted in the first embodiment, and the description below will be omitted.

In such a case, the combination of the reference driving information of the used car sold in the past, the external appearance image information for reference, and the selling price are obtained in advance at three or more levels of association. Then, the driving information of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 13 based on the reference travel information according to the acquired travel information and the reference appearance image information according to the appearance image information.

Further, as a substitute for the image information in the reference vehicle, the reference record book information of the used car sold in the past may be used. Since record book information also affects the selling price of used cars, it is possible to search for a more accurate selling price by making a judgment including this. The details of the reference record book information and the record book information will be described in the first embodiment, and the description thereof will be omitted below.

In such a case, the combination of the reference record book information of the used car sold in the past and the external appearance image information for reference and the degree of association with the selling price in three or more stages are acquired in advance. Then, the record book information of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 13 based on the reference record book information according to the acquired record book information and the reference appearance image information according to the appearance image information.

Further, as a substitute for the image information in the reference vehicle, the reference sales location information of the used car sold in the past may be used. Since sales location information also affects the selling price of used cars, it is possible to search for a more accurate selling price by making a judgment including this. The details of the reference sales place information and the sales place information will be described in the first embodiment, and the description below will be omitted.

In such a case, the combination of the reference sales location information of the used car sold in the past and the reference appearance image information, and the degree of association with the selling price in three or more stages are acquired in advance. Then, the sales location information of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 13 based on the reference sales place information according to the acquired sales place information and the reference appearance image information according to the appearance image information.

Further, as an alternative to the image information in the vehicle for reference, any one or more other reference information described in the first embodiment (in addition to the market condition information for reference, the external image information for reference, the external image information for reference, and the odor for reference). Information, reference travel information, reference record book information, reference sales location information) may be used.

In such a case, the combination of the reference information of the used car sold in the past and the external appearance image information for reference and the degree of association with the selling price at three or more levels are acquired in advance. Then, information according to the reference information (market condition information, in-vehicle image information, odor information, driving information, record book information, sales location information) is acquired, and the selling price is similarly estimated based on the degree of association shown in FIG. do.

In the example of FIG. 14, it is an example of using three or more levels of association between the reference appearance image and the selling price.

It is premised that the appearance image information for reference and the selling price are linked to each other to form a degree of association. In the example of FIG. 14, it is assumed that the input data is, for example, reference appearance image information P01 to P03. The reference appearance image information as such input data is linked to the output. In this output, it is assumed that the selling price is the output solution.

The reference appearance image information is associated with each other through three or more levels of association with the selling price as the output solution. The reference appearance image information is arranged on the left side through this degree of association, and the selling price is arranged on the right side through this degree of association.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, in determining the actual search solution, the search device 2 accumulates past data on what the selling price was at the time of the reference appearance image information acquired in the past, and analyzes and analyzes these. By doing so, the degree of association shown in FIG. 14 is created.

For example, it is assumed that the reference appearance image information is P01. It is assumed that A (2.5 million yen) was a large selling price for such P01. By collecting and analyzing such a data set, the degree of association between the reference appearance image information P01 and the selling price becomes stronger.

This analysis may be performed by artificial intelligence. Further, the degree of association shown in FIG. 14 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the process of constructing the trained model based on the degree of association, the appearance image information for reference is also acquired. This reference appearance image information is not included in the above-mentioned degree of association.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, the solution will be searched. In such a case, the appearance image information is acquired and the appearance image information is also acquired in the same manner.

First, the selling price is searched based on the newly acquired appearance image information. In such a case, the degree of association shown in FIG. 14 acquired in advance is used. For example, when the newly acquired appearance image information is the same as or similar to the reference appearance image information P02, B (1.26 million yen) is related to w15 and C (91) through the degree of association. 10,000 yen) is associated with the degree of association w16. In such a case, B (1.26 million yen), which has the highest degree of association, is selected as the optimum solution. However, it is not essential to select the solution having the highest degree of association as the optimum solution, and the solution may be selected as the solution that has a low degree of association but the association itself is recognized. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association. Further, the output solution to be selected is not limited to one, and two or more may be selected. In such a case, two or more may be selected in order from the highest degree of association, but the present invention is not limited to this, and may be based on the priority of any other degree of association.

The selling price obtained through the degree of association may be further modified based on the in-vehicle image information, or the weighting may be changed.

For example, it is assumed that the image content of the reference vehicle interior image information P13 is α, the image content of the reference vehicle interior image information P14 is β, and the image content of the reference vehicle interior image information P15 is γ. At this time, if the image α of the reference vehicle in-vehicle image information P13 is an image with many scratches in the vehicle, a process of lowering the selling price is performed. On the other hand, if there are no scratches in the vehicle and the vehicle is clean as in the image γ of the reference vehicle interior image information P15, a process of raising the selling price is performed.

In this way, after setting with the reference appearance image information, if the actually acquired in-vehicle image information is the same as or similar to the reference in-vehicle image information P13, a process of lowering the weighting of the selling price is performed. In other words, the process of lowering the selling price itself is performed. On the other hand, when the actually acquired in-vehicle image information is the same as or similar to the reference in-vehicle image information P15, a process of increasing the weighting of the selling price is performed, in other words, a process of increasing the selling price itself is performed.

By making rules for adjusting the reference in-vehicle image information and the selling price in advance in this way, the selling price is based on the same or similar reference in-vehicle image information as the actually input in-vehicle image information. To adjust. At this time, in addition to the case of actually adjusting the selling price, the degree of association itself may be adjusted.

As an alternative to the in-vehicle image information for reference, one or more of the odor information, the traveling information, the record book information, the sales location information, etc. described in the first embodiment is acquired, and the selling price is adjusted based on the acquisition. You may do so. Further, the vehicle model information, the model year information, the color information, and the equipment information described in the second embodiment may be applied in the same manner.

These reference vehicle in-vehicle image information, reference odor information, reference driving information, reference record book information, reference sales location information, reference vehicle model information, reference year information, reference color information, reference equipment information Any one or more is called estimation auxiliary information. Although this estimation auxiliary information does not form a degree of association with the selling price, it is used to adjust the output solution of the selling price searched through the selling price with the appearance image information. At this time, how to adjust the selling price is ruled in advance between the reference estimation auxiliary information and the adjustment to the selling price, and corresponds to the actually acquired estimation auxiliary information (same or Make adjustments according to the rules for adjusting the selling price defined with the (similar) reference estimation supplementary information. In other words, it may be in any form as long as the selling price is adjusted based on the estimation auxiliary information when it is input. The rule between the estimation auxiliary information for reference and the adjustment to the selling price is to make an adjustment to lower the selling price in inverse proportion to the total mileage, for example, taking the driving information (driving information for reference) as an example. However, it is not essential to use the degree of association and artificial intelligence.

The present invention is not limited to the case where the selling price is linked and learned as a search solution, and the quality of the used car is learned as an alternative to the selling price as shown in FIGS. 15 and 16. May be good. The reference information as input in FIGS. 15 and 16 includes all the reference information described in the first embodiment and the second embodiment. The quality of this used car is an evaluation of the condition of the used car by experts and vendors in the industry from various viewpoints such as its beauty, usedness, usability, odor, and defects. For example, 5 The quality is evaluated in 10 stages, and the degree of association is formed by learning this with a data set with reference information.

In such a case, the degree of association between each reference information of the used car sold in the past, or a combination having this and other reference information, and the quality of the used car is acquired in advance. Then, the information corresponding to the reference information of the used car to be sold is acquired. Next, the quality is estimated based on the degree of association shown in FIGS. 15 and 16 based on the reference information according to the acquired information.

Third Embodiment Next, the third embodiment will be described.

In the third embodiment, the selling price as an output solution is searched based on the in-vehicle image information.

For example, as shown in FIG. 17, it is premised that three or more levels of association between the reference vehicle in-vehicle image information and the selling price are set in advance. The image information in the vehicle for reference is the same as that described in the first embodiment.

In the example of FIG. 17, it is assumed that the input data is, for example, reference vehicle in-vehicle image information P01 to P03. The reference vehicle in-vehicle image information P01 to P03 as such input data is linked to the selling price as an output. In this output, the selling prices A (2.5 million yen), B (1.26 million yen), C (910,000 yen), and D (1.84 million yen) as output solutions are displayed.

The reference vehicle in-vehicle image information is associated with each other through three or more levels of association with the selling prices A to D as the output solution. The image information in the reference vehicle is arranged on the left side through this degree of association, and each selling price is arranged on the right side through this degree of association. The degree of association indicates the degree of which selling price is highly relevant to the reference vehicle in-vehicle image information arranged on the left side. In other words, this degree of association is an index showing what kind of selling price each reference in-vehicle image information is likely to be associated with, and is used to select the most probable selling price from the reference in-vehicle image information. It shows the accuracy in. In the example of FIG. 17, w13 to w19 are shown as the degree of association. 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 relevance of each combination as an intermediate node to the selling price as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the price as an output.

The discriminating device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the discriminating device 2 accumulates a past data set as to which of the reference vehicle in-vehicle image information and the selling price in that case is adopted and evaluated in discriminating the actual search solution, and these are stored. By analyzing and analyzing, the degree of association shown in FIG. 17 is created.

For example, it is assumed that the selling price A is often evaluated as the selling price for the reference vehicle in-vehicle image information acquired in the past. By collecting and analyzing such a data set, the degree of association with the image information in the vehicle for reference is strengthened.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference vehicle in-vehicle image information P01, analysis is performed from various data as a result of evaluating the past selling price. In the case of reference vehicle in-vehicle image information P01, if there are many cases of selling price A, the degree of association that leads to the evaluation of this selling price is set higher, and if there are many cases of selling price B, this sale is made. Set a higher degree of association that leads to price evaluation. For example, in the example of the reference vehicle in-vehicle image information P01, the selling price A and the selling price C are linked. The degree of association is set to 2 points.

Further, the degree of association shown in FIG. 17 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In such a case, as shown in FIG. 18, reference vehicle in-vehicle image information is input as input data, the selling price is output as output data, and at least one hidden layer is provided between the input node and the output node. , Machine learning may be done. The above-mentioned degree of association is set in either one or both of the input node and the hidden layer node, and this is the weighting of each node, and the output is selected based on this. Then, when this degree of association exceeds a certain threshold value, the output may be selected.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, the selling price will be searched for by using the above-mentioned learned data in actually determining the selling price from now on. In such a case, the current in-vehicle image information of the used car model to be sold is newly acquired. The newly acquired vehicle interior image information is input by the above-mentioned information acquisition unit 9.

The selling price is determined based on the in-vehicle image information newly acquired in this way. In such a case, the degree of association shown in FIG. 17 (Table 1) acquired in advance is referred to. For example, when the newly acquired in-vehicle image information is the same as or similar to P02, the selling price B is associated with the association degree w15 and the selling price C is associated with the association degree w16 through the association degree. In such a case, the selling price B having 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 selling price C, which has the lowest degree of association but the association itself is recognized, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In this way, it is possible to search for the most suitable selling price from the newly acquired in-vehicle image information and display it to the user. By seeing this search result, the user and the seller can propose the optimum selling price based on this search result, and by setting the optimum price with the buyer, the transaction is completed. The possibility can be increased.

FIG. 19 shows an example in which, in addition to the above-mentioned in-vehicle image information for reference, a combination with the reference vehicle type information and a selling price for the combination are set to three or more levels of association.

In the example of FIG. 19, it is assumed that the input data is, for example, reference vehicle in-vehicle image information P01 to P03 and reference vehicle type information P18 to 21. The intermediate node shown in FIG. 19 is a combination of the reference vehicle type information and the reference vehicle interior image information as such input data. Each intermediate node is further linked to the output. In this output, the selling price as the output solution is displayed.

Each combination (intermediate node) of the image information in the reference vehicle and the vehicle type information for reference is associated with each other through three or more levels of association with the selling price as this output solution. The reference vehicle in-vehicle image information and the reference vehicle type information are arranged on the left side through this degree of association, and the selling price is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the selling price with respect to the reference vehicle in-vehicle image information and the reference vehicle model information arranged on the left side. In other words, this degree of association is an index showing what kind of selling price each reference vehicle image information and reference vehicle model information is likely to be associated with, and is a reference vehicle interior image information and reference vehicle model information. It shows the accuracy in selecting the most probable selling price from.

The discriminating device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the discriminating device 2 accumulates past data as to which of the reference vehicle in-vehicle image information, the reference vehicle model information, and the selling price in that case was suitable for discriminating the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 19 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference vehicle in-vehicle image information P01 and the reference vehicle type information P20, the selling price thereof is analyzed from the past data. When there are many cases of selling price A, the degree of association that this selling price leads to A is set higher, and when there are many cases where the selling price is B and there are few cases where the selling price is A, the selling price is Set the degree of association that leads to B high and the degree of association that the selling price leads to A low. For example, in the example of the intermediate node 61a, it is linked to the output of the selling price A and the selling price B, but from the previous case, the degree of association of w13 connected to the selling price A is 7 points, and the association of w14 connected to the selling price B The degree is set to 2 points.

Further, the degree of association shown in FIG. 19 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 19, the node 61b is a node in which the reference vehicle type information P18 is combined with the reference vehicle in-vehicle image information P01, the association degree of the selling price C is w15, and the association degree of the selling price E is. Is w16. The node 61c is a node that is a combination of the reference vehicle type information P19 and P21 with respect to the reference vehicle in-vehicle image information P02, and the association degree of the selling price B is w17 and the association degree of the selling price D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for a selling price from now on, the above-mentioned learned data will be used. In such a case, the in-vehicle image information of the vehicle whose selling price is to be determined and the vehicle model information are actually acquired. Here, the vehicle type information is newly acquired when the selling price is actually estimated, and the acquisition method is the same as the reference vehicle type information described above. The method of acquiring in-vehicle image information and reference vehicle type information is acquired by keyboard input to a device such as a PC or smartphone, reading the record book with a scanner and converting it into information, or accessing the database of the record book. You may do so.

Based on the vehicle interior image information newly acquired in this way and the vehicle model information, the optimum selling price is searched for. In such a case, the degree of association shown in FIG. 19 (Table 1) acquired in advance is referred to. For example, when the newly acquired in-vehicle image information is the same as or similar to P02 and the vehicle type information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the selling price C by w19 and the selling price D by the degree of association w20. In such a case, the selling price C having 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 selling price D, which has the lowest degree of association but is recognized for the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

Further, as an alternative to the reference vehicle type information, the reference year information regarding the year of the used car sold in the past may be used. Since the model year of a used car also affects the selling price of a used car, it is possible to search for a more accurate selling price by making a judgment including this.

In such a case, the combination of the reference model year information acquired through the model year of the used car sold in the past and the image information in the reference vehicle, and the degree of association with the selling price in three or more stages are acquired in advance. I will do it. Then, the model year information regarding the model year of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 19 based on the reference model year information according to the acquired model year information and the reference vehicle interior image information according to the vehicle interior image information.

Further, as an alternative to the reference vehicle type information, the reference color information regarding the color of the used car sold in the past may be used. Since the color of the exterior of a used car also affects the selling price of a used car, it is possible to search for a more accurate selling price by making a judgment including this.

In such a case, the combination of the reference color information regarding the color of the used car sold in the past and the image information in the reference car and the degree of association with the selling price in three or more stages are acquired in advance. Then, the color information regarding the color of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 19 based on the reference color information according to the acquired color information and the reference vehicle interior image information according to the vehicle interior image information.

Further, as a substitute for the reference vehicle type information, the reference equipment information regarding the standard equipment and options of the used car sold in the past may be used. Since the standard equipment and options of used cars also affect the selling price of used cars, it is possible to search for a more accurate selling price by making a judgment including these.

In such a case, the combination of the reference equipment information regarding the standard equipment and options of the used car sold in the past, the image information in the reference vehicle, and the degree of association with the selling price at three levels or more are acquired in advance. .. Then, the equipment information about the standard equipment and options of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 19 based on the reference equipment information according to the acquired equipment information and the reference vehicle interior image information according to the vehicle interior image information.

Further, as a substitute for the reference vehicle type information, the reference travel information of the used vehicle sold in the past may be used. Since driving information also affects the selling price of used cars, it is possible to search for a more accurate selling price by making a judgment including this. The description of the reference travel information and the details of the travel information will be quoted in the first embodiment, and the description below will be omitted.

In such a case, the combination of the reference driving information of the used car sold in the past, the image information in the reference vehicle, and the selling price are obtained in advance at three or more levels of association. Then, the driving information of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 19 based on the reference travel information according to the acquired travel information and the reference vehicle image information according to the vehicle interior image information.

Further, as a substitute for the reference vehicle type information, the reference record book information of the used car sold in the past may be used. Since record book information also affects the selling price of used cars, it is possible to search for a more accurate selling price by making a judgment including this. The details of the reference record book information and the record book information will be described in the first embodiment, and the description thereof will be omitted below.

In such a case, the combination of the reference record book information of the used car sold in the past and the image information in the reference car and the degree of association with the selling price in three or more stages are acquired in advance. Then, the record book information of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 19 based on the reference record book information according to the acquired record book information and the reference vehicle in-vehicle image information according to the in-vehicle image information.

Further, as a substitute for the reference vehicle type information, the reference sales location information of the used car sold in the past may be used. Since sales location information also affects the selling price of used cars, it is possible to search for a more accurate selling price by making a judgment including this. The details of the reference sales place information and the sales place information will be described in the first embodiment, and the description below will be omitted.

In such a case, the combination of the reference sales location information of the used car sold in the past, the image information in the reference vehicle, and the selling price are obtained in advance at three or more levels of association. Then, the sales location information of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 19 based on the reference sales place information according to the acquired sales place information and the reference in-vehicle image information according to the in-vehicle image information.

Further, as an alternative to the reference vehicle type information, any one or more reference information described in the first embodiment (in addition to the reference market condition information, the reference vehicle in-vehicle image information, the reference in-vehicle image information, and the reference odor information). , Reference driving information, reference record book information, reference sales location information) may be used.

In such a case, the combination of the reference information of the used car sold in the past, the image information in the reference car, and the selling price are obtained in advance at three or more levels of association. Then, information according to the reference information (market condition information, in-vehicle image information, odor information, driving information, record book information, sales location information) is acquired, and the selling price is similarly estimated based on the degree of association shown in FIG. do.

In the example of FIG. 20, it is an example of using three or more levels of association between the reference vehicle in-vehicle image information and the selling price.

It is premised that the degree of association in which the image information in the reference vehicle and the selling price are linked to each other is formed. In the example of FIG. 20, it is assumed that the input data is, for example, reference vehicle in-vehicle image information P01 to P03. The reference vehicle interior image information as such input data is linked to the output. In this output, it is assumed that the selling price is the output solution.

The reference vehicle in-vehicle image information is associated with each other through three or more levels of association with the selling price as the output solution. The image information in the reference vehicle is arranged on the left side through this degree of association, and the selling price is arranged on the right side through this degree of association.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. 20. That is, in determining the actual search solution, the search device 2 accumulates past data on what the selling price was at the time of the reference vehicle in-vehicle image information acquired in the past, and analyzes and analyzes these. By doing so, the degree of association shown in FIG. 20 is created.

For example, it is assumed that the image information in the reference vehicle is P01. It is assumed that A (2.5 million yen) was a large selling price for such P01. By collecting and analyzing such a data set, the degree of association between the reference vehicle in-vehicle image information P01 and the selling price becomes stronger.

This analysis may be performed by artificial intelligence. Further, the degree of association shown in FIG. 20 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the process of constructing the trained model based on the degree of association, the image information in the vehicle for reference is also acquired. This reference vehicle in-vehicle image information is not included in the above-mentioned degree of association.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, the solution will be searched. In such a case, the in-vehicle image information is acquired and the in-vehicle image information is also acquired in the same manner.

First, the selling price is searched based on the newly acquired in-vehicle image information. In such a case, the degree of association shown in FIG. 20 acquired in advance is used. For example, when the newly acquired in-vehicle image information is the same as or similar to the reference in-vehicle image information P02, B (1.26 million yen) is associated with the degree of association w15 and C (91). 10,000 yen) is associated with the degree of association w16. In such a case, B (1.26 million yen), which has the highest degree of association, is selected as the optimum solution. However, it is not essential to select the solution having the highest degree of association as the optimum solution, and the solution may be selected as the solution that has a low degree of association but the association itself is recognized. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association. Further, the output solution to be selected is not limited to one, and two or more may be selected. In such a case, two or more may be selected in order from the highest degree of association, but the present invention is not limited to this, and may be based on the priority of any other degree of association.

The selling price obtained through the degree of association may be further modified based on the vehicle type information, or the weighting may be changed.

For example, it is assumed that the vehicle type of the reference vehicle type information P13 is α, the vehicle type of the reference vehicle type information P14 is β, and the vehicle type of the reference vehicle type information P15 is γ. At this time, in the case of an image having a large number of vehicle models α in the reference vehicle model information P13, a process of lowering the selling price is performed. On the other hand, when there are many vehicle models γ in the reference vehicle model information P15, a process of raising the selling price is performed.

In this way, after setting with the reference vehicle in-vehicle image information, if the actually acquired vehicle model information is the same as or similar to the reference vehicle model information P13, a process of lowering the weighting of the selling price is performed, in other words. If so, the processing to lower the selling price itself is performed. On the other hand, when the actually acquired vehicle type information is the same as or similar to the reference vehicle type information P15, a process of increasing the weighting of the selling price is performed, in other words, a process of increasing the selling price itself is performed.

By creating rules for reference vehicle type information and sales price adjustment in advance in this way, the sales price is adjusted according to the rules based on the reference vehicle type information that is the same as or similar to the actually input vehicle type information. .. At this time, in addition to the case of actually adjusting the selling price, the degree of association itself may be adjusted.

As an alternative to the in-vehicle image information for reference, one or more of the odor information, the traveling information, the record book information, the sales location information, etc. described in the first embodiment is acquired, and the selling price is adjusted based on the acquisition. You may do so. Further, the model year information, the color information, and the equipment information described in the second embodiment may be applied in the same manner.

Estimate one or more of these reference odor information, reference driving information, reference record book information, reference sales location information, reference vehicle model information, reference model year information, reference color information, and reference equipment information. It is called auxiliary information. This estimated auxiliary information is used to adjust the output solution of the selling price searched through the selling price to and from the in-vehicle image information, although the degree of association with the selling price is not formed. At this time, how to adjust the selling price is ruled in advance between the reference estimation auxiliary information and the adjustment to the selling price, and corresponds to the actually acquired estimation auxiliary information (same or Make adjustments according to the rules for adjusting the selling price defined with the (similar) reference estimation supplementary information. In other words, it may be in any form as long as the selling price is adjusted based on the estimation auxiliary information when it is input. The rule between the estimation auxiliary information for reference and the adjustment to the selling price is to make an adjustment to lower the selling price in inverse proportion to the total mileage, for example, taking the driving information (driving information for reference) as an example. However, it is not essential to use the degree of association and artificial intelligence.

The present invention is not limited to the case where the selling price is linked and learned as a search solution, and the quality of the used car is learned as an alternative to the selling price as shown in FIGS. 21 and 22. May be good. The reference information as input in FIGS. 21 and 21 includes all the reference information described in the first embodiment and the second embodiment. The quality of this used car is the quality evaluated by experts and vendors in the industry on a scale of, for example, 5 or 10 from various viewpoints such as its beauty, usedness, usability, odor, and defects. Is trained in a data set with reference information to form a degree of association.

In such a case, the degree of association between each reference information of the used car sold in the past, or a combination having this and other reference information, and the quality of the used car is acquired in advance. Then, the information corresponding to the reference information of the used car to be sold is acquired. Next, the quality is estimated based on the degree of association shown in FIGS. 21 and 22 based on the reference information according to the acquired information.

Fourth Embodiment Next, the fourth embodiment will be described.

In the fourth embodiment, the selling price as an output solution is searched based on the vehicle type information.

FIG. 23 shows an example in which, in addition to the reference vehicle type information, the combination with the reference model year information and the selling price for the combination are set to three or more levels of association.

In the example of FIG. 23, it is assumed that the input data is, for example, reference vehicle type information P01 to P03 and reference year information P18 to 21. The intermediate node shown in FIG. 23 is a combination of the reference vehicle type information and the reference model year information as such input data. Each intermediate node is further linked to the output. In this output, the selling price as the output solution is displayed.

Each combination (intermediate node) of the reference vehicle type information and the reference vehicle year information is associated with each other through three or more levels of association with the selling price as this output solution. The reference vehicle type information and the reference model year information are arranged on the left side through this degree of association, and the selling price is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the selling price with respect to the reference vehicle type information and the reference model year information arranged on the left side. In other words, this degree of association is an index showing what kind of selling price each reference vehicle model information and reference model year information is likely to be associated with, and is a reference vehicle model information and reference model year information. It shows the accuracy in selecting the most probable selling price from.

The discrimination device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 23. That is, the discrimination device 2 accumulates past data as to which of the reference vehicle type information, the reference model year information, and the selling price in that case was suitable for discriminating the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 23 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference vehicle type information P01 and the reference year information P20, the selling price thereof is analyzed from the past data. When there are many cases of selling price A, the degree of association that this selling price leads to A is set higher, and when there are many cases where the selling price is B and there are few cases where the selling price is A, the selling price is Set the degree of association that leads to B high and the degree of association that the selling price leads to A low. For example, in the example of the intermediate node 61a, it is linked to the output of the selling price A and the selling price B, but from the previous case, the degree of association of w13 connected to the selling price A is 7 points, and the association of w14 connected to the selling price B The degree is set to 2 points.

Further, the degree of association shown in FIG. 23 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 23, the node 61b is a node of the combination of the reference vehicle type information P01 and the reference year information P18, the degree of association of the selling price C is w15, and the degree of association of the selling price E. Is w16. The node 61c is a node that is a combination of the reference model year information P19 and P21 with respect to the reference vehicle type information P02, and the degree of association of the selling price B is w17 and the degree of association of the selling price D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for a selling price from now on, the above-mentioned learned data will be used. In such a case, the vehicle type information for which the selling price is to be determined and the model year information are actually acquired. Here, the model year information is newly acquired when the selling price is actually estimated, but the acquisition method is the same as the above-mentioned reference model year information. The method of acquiring vehicle type information and reference year information can be obtained by keyboard input to a device such as a PC or smartphone, reading the record book with a scanner and converting it into information, or accessing the database of the record book. You may do so.

Based on the newly acquired vehicle model information and model year information in this way, the optimum selling price is searched for. In such a case, the degree of association shown in FIG. 23 (Table 1) acquired in advance is referred to. For example, if the newly acquired vehicle type information is the same as or similar to P02, and the model year information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the selling price C by w19 and the selling price D by the degree of association w20. In such a case, the selling price C having 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 selling price D, which has the lowest degree of association but is recognized for the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

Further, as an alternative to the reference model year information, the reference model year information regarding the model year of the used car sold in the past may be used. Since the model year of a used car also affects the selling price of a used car, it is possible to search for a more accurate selling price by making a judgment including this.

In such a case, the combination of the reference model year information acquired through the model year of the used car sold in the past and the reference vehicle model information, and the degree of association with the selling price in three or more stages are acquired in advance. Keep it. Then, the model year information regarding the model year of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 19 based on the reference model year information according to the acquired model year information and the reference vehicle model information according to the vehicle model information.

Further, as an alternative to the reference year information, the reference color information regarding the color of the used car sold in the past may be used. Since the color of the exterior of a used car also affects the selling price of a used car, it is possible to search for a more accurate selling price by making a judgment including this.

In such a case, the combination of the reference color information regarding the color of the used car sold in the past and the reference vehicle type information and the degree of association with the selling price in three or more stages are acquired in advance. Then, the color information regarding the color of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 23 based on the reference color information according to the acquired color information and the reference vehicle type information according to the vehicle type information.

Further, as an alternative to the reference year information, reference equipment information regarding standard equipment and options of used cars sold in the past may be used. Since the standard equipment and options of used cars also affect the selling price of used cars, it is possible to search for a more accurate selling price by making a judgment including these.

In such a case, the combination of the reference equipment information regarding the standard equipment and options of the used car sold in the past and the reference vehicle type information, and the degree of association with the selling price at three or more levels are acquired in advance. Then, the equipment information about the standard equipment and options of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 23 based on the reference equipment information according to the acquired equipment information and the reference vehicle type information according to the vehicle type information.

Further, as a substitute for the reference year information, the reference driving information of the used car sold in the past may be used. Since driving information also affects the selling price of used cars, it is possible to search for a more accurate selling price by making a judgment including this. The description of the reference travel information and the details of the travel information will be quoted in the first embodiment, and the description below will be omitted.

In such a case, the combination of the reference driving information of the used car sold in the past and the reference vehicle type information and the degree of association with the selling price in three or more stages are acquired in advance. Then, the driving information of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 23 based on the reference driving information according to the acquired driving information and the reference vehicle type information according to the vehicle type information.

Further, as a substitute for the reference year information, the reference record book information of the used car sold in the past may be used. Since record book information also affects the selling price of used cars, it is possible to search for a more accurate selling price by making a judgment including this. The details of the reference record book information and the record book information will be described in the first embodiment, and the description thereof will be omitted below.

In such a case, the combination of the reference record book information of the used car sold in the past and the reference vehicle type information and the degree of association with the selling price in three or more stages are acquired in advance. Then, the record book information of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 23 based on the reference record book information according to the acquired record book information and the reference vehicle type information according to the vehicle type information.

Further, as a substitute for the reference year information, the reference sales location information of the used car sold in the past may be used. Since sales location information also affects the selling price of used cars, it is possible to search for a more accurate selling price by making a judgment including this. The details of the reference sales place information and the sales place information will be described in the first embodiment, and the description below will be omitted.

In such a case, the combination of the reference sales location information of the used car sold in the past and the reference vehicle type information and the degree of association with the selling price in three or more stages are acquired in advance. Then, the sales location information of the used car to be sold is acquired. Next, the selling price is estimated based on the degree of association shown in FIG. 23 based on the reference sales place information according to the acquired sales place information and the reference vehicle type information according to the vehicle type information.

In addition, as an alternative to the reference year information, any other reference information described in the first embodiment (in addition to the reference market condition information, the reference vehicle type information, the reference vehicle type information, the reference odor information, etc. Reference driving information, reference record book information, reference sales location information) may be used.

In such a case, the degree of association between the reference information of the used car sold in the past, the combination having the reference vehicle type information, and the selling price is acquired in advance. Then, information according to the reference information (market condition information, vehicle type information, odor information, driving information, record book information, sales location information) is acquired, and the selling price is similarly estimated based on the degree of association shown in FIG. ..

In the example of FIG. 24, it is an example of using the degree of association between the reference vehicle type information and the selling price in three or more stages.

It is premised that the degree of association between the reference vehicle type information and the selling price is formed. In the example of FIG. 24, it is assumed that the input data is, for example, reference vehicle model information P01 to P03. The reference vehicle type information as such input data is linked to the output. In this output, it is assumed that the selling price is the output solution.

The reference vehicle type information is associated with each other through three or more levels of association with the selling price as the output solution. The reference vehicle type information is arranged on the left side through this degree of association, and the selling price is arranged on the right side through this degree of association.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. 24. That is, in determining the actual search solution, the search device 2 accumulates past data on what the selling price was at the time of the reference vehicle type information acquired in the past, and analyzes and analyzes these. The degree of association shown in FIG. 24 is created in.

For example, it is assumed that the reference vehicle type information is P01. It is assumed that A (2.5 million yen) was a large selling price for such P01. By collecting and analyzing such a data set, the degree of association between the reference vehicle type information P01 and the selling price becomes stronger.

This analysis may be performed by artificial intelligence. Further, the degree of association shown in FIG. 24 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the process of constructing a trained model based on such a degree of association, reference year information is also acquired. This reference vehicle type information is not included in the above-mentioned degree of association.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, the solution will be searched. In such a case, the vehicle type information is acquired and the model year information is also acquired in the same manner.

First, the selling price is searched based on the newly acquired vehicle model information. In such a case, the degree of association shown in FIG. 24 acquired in advance is used. For example, when the newly acquired vehicle type information is the same as or similar to the reference vehicle type information P02, B (1.26 million yen) is linked to w15 and C (910,000 yen) through the degree of association. ) Is associated with the degree of association w16. In such a case, B (1.26 million yen), which has the highest degree of association, is selected as the optimum solution. However, it is not essential to select the solution having the highest degree of association as the optimum solution, and the solution may be selected as the solution that has a low degree of association but the association itself is recognized. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association. Further, the output solution to be selected is not limited to one, and two or more may be selected. In such a case, two or more may be selected in order from the highest degree of association, but the present invention is not limited to this, and may be based on the priority of any other degree of association.

It goes without saying that the output solution may be obtained from the selling price obtained through the degree of association, but in the present invention, this selling price may be further modified based on the model year information, or the weighting may be changed. good.

For example, it is assumed that the vehicle type of the reference year information P13 is α, the vehicle type of the reference year information P14 is β, and the vehicle type of the reference year information P15 is γ. At this time, in the case of an image having a large number of vehicle models α in the reference year information P13, a process of lowering the selling price is performed. On the other hand, when there are many vehicle models γ in the reference year information P15, a process of raising the selling price is performed.

In this way, after setting with the reference vehicle type information, if the actually acquired model year information is the same as or similar to the reference model year information P13, a process of lowering the weighting of the selling price is performed, in other words. If so, the selling price itself will be lowered. On the other hand, when the actually acquired model year information is the same as or similar to the reference model year information P15, a process of increasing the weighting of the selling price is performed, in other words, a process of increasing the selling price itself is performed.

By making rules for adjusting the reference year information and the selling price in advance in this way, the selling price is based on the same or similar reference year information as the actually input year information, and the selling price is followed by the rule. To adjust. At this time, in addition to the case of actually adjusting the selling price, the degree of association itself may be adjusted.

As an alternative to this reference vehicle type information, one or more of the odor information, the traveling information, the record book information, the sales location information, etc. described in the first embodiment should be acquired, and the selling price should be adjusted based on the acquisition. You may do it. Further, the color information, the equipment information, the appearance image information, and the vehicle interior image information described in the second embodiment may be applied in the same manner.

Any one of these reference odor information, reference travel information, reference record book information, reference sales location information, reference year information, reference color information, reference equipment information, exterior image information, and vehicle interior image information. The above is called estimation auxiliary information. This estimated auxiliary information is used to adjust the output solution of the selling price searched through the selling price with the vehicle type information, although the degree of association with the selling price is not formed. At this time, how to adjust the selling price is ruled in advance between the reference estimation auxiliary information and the adjustment to the selling price, and corresponds to the actually acquired estimation auxiliary information (same or Make adjustments according to the rules for adjusting the selling price defined with the (similar) reference estimation supplementary information. In other words, it may be in any form as long as the selling price is adjusted based on the estimation auxiliary information when it is input. The rule between the estimation auxiliary information for reference and the adjustment to the selling price is to make an adjustment to lower the selling price in inverse proportion to the total mileage, for example, taking the driving information (driving information for reference) as an example. However, it is not essential to use the degree of association and artificial intelligence.

Fifth Embodiment Next, the fifth embodiment will be described.

In the fifth embodiment, the quality of the used car as an output solution is searched based on the traveling information and the record book information.

For example, as shown in FIG. 25, it is premised that three or more levels of association between the reference travel information and the quality of the used car are preset. The reference travel information is the same as that described in the first embodiment.

In the example of FIG. 25, it is assumed that the input data is, for example, reference travel information P01 to P03. The reference travel information P01 to P03 as such input data is linked to the quality of the used car as an output.

The reference travel information is associated with each other through three or more levels of association with the qualities A to D of the used car as the output solution. The reference driving information is arranged on the left side through this degree of association, and the quality of each used car is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the quality of which used car with respect to the reference driving information arranged on the left side. In other words, this degree of association is an indicator of what kind of used car quality each reference driving information is likely to be associated with, and the most probable used car quality is selected from the reference driving information. It shows the accuracy in doing so. In the example of FIG. 25, w13 to w19 are shown as the degree of association. 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 relevance of each combination as an intermediate node to the quality of the used car as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the price as an output.

The discrimination device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. 25. That is, the discriminating device 2 accumulates past data sets as to which of the reference driving information and the quality of the used car in that case is adopted and evaluated in discriminating the actual search solution. By analyzing and analyzing the above, the degree of association shown in FIG. 25 is created.

For example, it is assumed that the quality A of the used car is highly evaluated as the quality of the used car with respect to the reference driving information acquired in the past. By collecting and analyzing such a data set, the degree of association with the reference driving information becomes stronger.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference driving information P01, analysis is performed from various data as a result of evaluating the quality of the used car in the past. In the case of reference driving information P01, if there are many cases of used car quality A, the degree of association that leads to the evaluation of the quality of this used car is set higher, and there are many cases of used car quality B. To set a higher degree of association that leads to the evaluation of the quality of this used car. For example, in the example of the reference driving information P01, the quality A of the used car and the quality C of the used car are linked. The degree of association of w14, which leads to the quality C of the car, is set to 2 points.

Further, the degree of association shown in FIG. 25 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, the quality of the used car will be searched for by using the above-mentioned learned data in actually determining the quality of the used car from now on. In such a case, the current driving information of the used car model to be sold is newly acquired. The newly acquired travel information is input by the above-mentioned information acquisition unit 9.

Based on the newly acquired driving information in this way, the quality of the used car is determined. In such a case, the degree of association shown in FIG. 25 (Table 1) acquired in advance is referred to. For example, when the newly acquired driving information is the same as or similar to P02, the quality B of the used car is associated with w15 and the quality C of the used car is associated with the association degree w16 via the degree of association. There is. In such a case, the quality B of the used car having 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 quality C of the used car, which has the lowest degree of association but the association itself is recognized, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In this way, it is possible to search for the most suitable quality of the used car from the newly acquired driving information and display it to the user. By looking at this search result, users and dealers can propose the optimum quality of used cars based on this search result, and by setting the optimum price with the buyer, transactions can be made. It is possible to increase the possibility that it will be established.

FIG. 26 shows an example in which, in addition to the above-mentioned reference driving information, a combination with the reference model year information and a degree of association with the quality of the used car with respect to the combination are set to three or more levels.

In the example of FIG. 26, it is assumed that the input data is, for example, reference travel information P01 to P03 and reference model year information P18 to 21. The intermediate node shown in FIG. 26 is a combination of reference travel information and reference year information as such input data. Each intermediate node is further linked to the output. In this output, the quality of the used car as an output solution is displayed.

Each combination (intermediate node) of the reference driving information and the reference vehicle model year information is associated with each other through three or more levels of association with the quality of the used vehicle as this output solution. The reference driving information and the reference model year information are arranged on the left side through this degree of association, and the quality of the used car is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the quality of the used car with respect to the reference driving information and the reference model year information arranged on the left side. In other words, this degree of association is an index showing what kind of used car quality each reference driving information and reference model year information is likely to be associated with, and is a reference driving information and a reference year. It shows the accuracy in selecting the most probable used car quality from the model year information.

The discrimination device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 26. That is, the discriminating device 2 accumulates past data as to which of the reference driving information, the reference year information, and the quality of the used car in that case was suitable for discriminating the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 26 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference driving information P01 and the reference model year information P20, the quality of the used car is analyzed from the past data. When there are many cases of used car quality A, the degree of association that the quality of this used car leads to A is set higher, there are many cases where the quality of the used car is B, and the case where the quality of the used car is A. If the number is low, the degree of association in which the quality of the used car leads to B is high, and the degree of association in which the quality of the used car leads to A is set low. For example, in the example of the intermediate node 61a, it is linked to the output of the quality A of the used car and the quality B of the used car. The degree of association of w14, which leads to the quality B of, is set to 2 points.

Further, the degree of association shown in FIG. 26 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 26, the node 61b is a node of the combination of the reference driving information P01 and the reference year information P18, the degree of association of the quality C of the used car is w15, and the quality of the used car. The degree of association of E is w16. The node 61c is a node that is a combination of the reference year information P19 and P21 with respect to the reference travel information P02, and the degree of association of the quality B of the used car is w17 and the degree of association of the quality D of the used car is w18. It has become.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for the quality of a used car from now on, the above-mentioned learned data will be used. In such a case, the driving information to be determined for the quality of the used car and the model year information are actually acquired. Here, the model year information is newly acquired when actually estimating the quality of the used car, but the acquisition method is the same as the above-mentioned reference model year information. The method of acquiring the driving information and the model year information for reference is acquired by keyboard input to the device such as a PC or smartphone, reading the record book with a scanner and converting it into information, or accessing the database of the record book. You may do so.

Based on the newly acquired driving information and model year information in this way, the optimum quality of the used car is searched for. In such a case, the degree of association shown in FIG. 26 (Table 1) acquired in advance is referred to. For example, if the newly acquired travel information is the same as or similar to P02 and the model year information is the same as or similar to P21, the node 61d is associated via the degree of association. In this node 61d, the quality C of the used car is associated with w19, and the quality D of the used car is associated with the degree of association w20. In such a case, the quality C of the used car having 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 quality D of the used car, which has the lowest degree of association but the association itself is recognized, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

Further, as an alternative to the reference year information, the reference color information regarding the color of the used car sold in the past may be used. Since the color of the exterior of a used car also affects the quality of the used car of the used car, it is possible to search for the quality of the used car with higher accuracy by making a judgment including this.

In such a case, the degree of association between the reference color information regarding the color of the used car sold in the past, the combination having the reference driving information, and the quality of the used car is acquired in advance. Then, the color information regarding the color of the used car to be sold is acquired. Next, the quality of the used car is estimated based on the degree of association shown in FIG. 26 based on the reference color information according to the acquired color information and the reference travel information according to the travel information.

Further, as an alternative to the reference year information, reference equipment information regarding standard equipment and options of used cars sold in the past may be used. Since the standard equipment and options of a used car also affect the quality of the used car, it is possible to search for the quality of the used car with higher accuracy by making a judgment including this.

In such a case, obtain in advance three or more levels of association between the combination of reference equipment information regarding standard equipment and options of used cars sold in the past, reference driving information, and the quality of used cars. deep. Then, the equipment information about the standard equipment and options of the used car to be sold is acquired. Next, the quality of the used car is estimated based on the degree of association shown in FIG. 26 based on the reference equipment information according to the acquired equipment information and the reference travel information according to the travel information.

Further, as a substitute for the reference year information, the reference record book information of the used car sold in the past may be used. Since the record book information also affects the quality of used cars, it is possible to search for the quality of used cars with higher accuracy by making a judgment including this. The details of the reference record book information and the record book information will be described in the first embodiment, and the description thereof will be omitted below.

In such a case, the degree of association between the reference record book information of the used car sold in the past, the combination having the reference driving information, and the quality of the used car is acquired in advance. Then, the record book information of the used car to be sold is acquired. Next, the quality of the used car is estimated based on the degree of association shown in FIG. 26 based on the reference record book information according to the acquired record book information and the reference travel information according to the travel information.

Further, as a substitute for the reference year information, the reference sales location information of the used car sold in the past may be used. Since the sales location information also affects the quality of used cars, it is possible to search for the quality of used cars with higher accuracy by making a judgment including this. The details of the reference sales place information and the sales place information will be described in the first embodiment, and the description below will be omitted.

In such a case, the degree of association between the reference sales location information of the used car sold in the past, the combination having the reference driving information, and the quality of the used car is acquired in advance. Then, the sales location information of the used car to be sold is acquired. Next, the quality of the used car is estimated based on the degree of association shown in FIG. 26 based on the reference sales location information according to the acquired sales location information and the reference travel information according to the travel information.

Further, as an alternative to the reference model year information, any other reference information described in the first embodiment (in addition to the reference market condition information, the reference travel information, the reference travel information, the reference odor information, etc. Reference driving information, reference record book information, reference sales location information) may be used.

In such a case, the degree of association between the reference information of the used car sold in the past, the combination having the reference driving information, and the quality of the used car is acquired in advance. Then, information (market condition information, driving information, odor information, driving information, record book information, sales location information) corresponding to the reference information is acquired, and the quality of the used car is similarly determined based on the degree of association shown in FIG. presume.

FIG. 27 shows an example in which three or more levels of association between the reference record book information and the quality of the used car are preset. The reference record book information is the same as that described in the first embodiment.

In the example of FIG. 27, it is assumed that the input data is, for example, reference record book information P01 to P03. The reference record book information P01 to P03 as such input data is linked to the quality of the used car as output.

The reference record book information is associated with each other through three or more levels of association with the qualities A to D of the used car as the output solution. The reference record book information is arranged on the left side through this degree of association, and the quality of each used car is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the quality of which used car with respect to the reference record book information arranged on the left side. In other words, this degree of association is an indicator of what kind of used car quality each reference record book information is likely to be associated with, and is the most probable used car quality from the reference record book information. It shows the accuracy in selecting. In the example of FIG. 27, w13 to w19 are shown as the degree of association. 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 relevance of each combination as an intermediate node to the quality of the used car as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the price as an output.

The discrimination device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. 27. That is, the discriminating device 2 stores the past data set as to which of the reference record book information and the quality of the used car in that case is adopted and evaluated in discriminating the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 27 is created.

For example, it is assumed that the quality A of the used car is highly evaluated as the quality of the used car with respect to the reference record book information acquired in the past. By collecting and analyzing such a data set, the degree of association with the reference record book information becomes stronger.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference record book information P01, analysis is performed from various data as a result of evaluating the quality of used cars in the past. In the case of reference record book information P01, if there are many cases of used car quality A, the degree of association that leads to the evaluation of the used car quality is set higher, and there are many cases of used car quality B. In some cases, the degree of association that leads to the evaluation of the quality of this used car is set higher. For example, in the example of the reference record book information P01, the quality A of the used car and the quality C of the used car are linked. The degree of association of w14, which leads to the quality C of used cars, is set to 2 points.

Further, the degree of association shown in FIG. 27 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, the quality of the used car will be searched for by using the above-mentioned learned data in actually determining the quality of the used car from now on. In such a case, the current record book information of the used car model to be actually sold is newly acquired. The newly acquired record book information is input by the above-mentioned information acquisition unit 9.

Based on the newly acquired record book information in this way, the quality of the used car is determined. In such a case, the degree of association shown in FIG. 27 (Table 1) acquired in advance is referred to. For example, when the newly acquired record book information is the same as or similar to P02, the quality B of the used car is associated with w15 and the quality C of the used car is associated with the association degree w16 via the degree of association. ing. In such a case, the quality B of the used car having 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 quality C of the used car, which has the lowest degree of association but the association itself is recognized, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In this way, it is possible to search for the most suitable quality of the used car from the newly acquired record book information and display it to the user. By looking at this search result, users and dealers can propose the optimum quality of used cars based on this search result, and by setting the optimum price with the buyer, transactions can be made. It is possible to increase the possibility that it will be established.

FIG. 28 shows an example in which, in addition to the above-mentioned reference record book information, a combination with the reference year information and a degree of association with the quality of the used car with respect to the combination are set to three or more levels.

In the example of FIG. 28, it is assumed that the input data is, for example, reference record book information P01 to P03 and reference year information P18 to 21. The intermediate node shown in FIG. 28 is a combination of the reference record book information and the reference year information as such input data. Each intermediate node is further linked to the output. In this output, the quality of the used car as an output solution is displayed.

Each combination (intermediate node) of the reference record book information and the reference vehicle year information is associated with each other through three or more levels of association with the quality of the used vehicle as this output solution. The reference record book information and the reference model year information are arranged on the left side through this degree of association, and the quality of the used car is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the quality of the used car with respect to the reference record book information and the reference model year information arranged on the left side. In other words, this degree of association is an indicator of what kind of used car quality each reference record book information and reference model year information is likely to be associated with, and is a reference record book information and reference. It shows the accuracy in selecting the most probable used car quality from the model year information.

The discrimination device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 28. That is, the discriminating device 2 accumulates past data as to which of the reference record book information, the reference year information, and the quality of the used car in that case was suitable for discriminating the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 26 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference record book information P01 and the reference year information P20, the quality of the used car is analyzed from the past data. When there are many cases of used car quality A, the degree of association that the quality of this used car leads to A is set higher, there are many cases where the quality of the used car is B, and the case where the quality of the used car is A. If the number is low, the degree of association in which the quality of the used car leads to B is high, and the degree of association in which the quality of the used car leads to A is set low. For example, in the example of the intermediate node 61a, it is linked to the output of the quality A of the used car and the quality B of the used car. The degree of association of w14, which leads to the quality B of, is set to 2 points.

Further, the degree of association shown in FIG. 28 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 28, the node 61b is a node of the combination of the reference record book information P01 and the reference year information P18, and the degree of association of the quality C of the used car is w15, and the used car. The degree of association of quality E is w16. The node 61c is a node that is a combination of the reference year information P19 and P21 with respect to the reference record book information P02, and the degree of association of the quality B of the used car is w17 and the degree of association of the quality D of the used car is w18. It has become.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for the quality of a used car from now on, the above-mentioned learned data will be used. In such a case, the record book information to be determined for the quality of the used car and the model year information are actually acquired. Here, the model year information is newly acquired when actually estimating the quality of the used car, but the acquisition method is the same as the above-mentioned reference model year information. The method of acquiring record book information and reference year information can be obtained by keyboard input to a device such as a PC or smartphone, reading the record book with a scanner and converting it into information, or accessing the database of the record book. You may try to do it.

Based on the newly acquired record book information and model year information in this way, the optimum quality of used cars is searched for. In such a case, the degree of association shown in FIG. 28 (Table 1) acquired in advance is referred to. For example, if the newly acquired record book information is the same as or similar to P02, and the model year information is the same as or similar to P21, the node 61d is associated via the degree of association. In this node 61d, the quality C of the used car is w19, and the quality D of the used car is associated with the degree of association w20. In such a case, the quality C of the used car having 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 quality D of the used car, which has the lowest degree of association but the association itself is recognized, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

Further, as an alternative to the reference year information, the reference color information regarding the color of the used car sold in the past may be used. Since the color of the exterior of a used car also affects the quality of the used car of the used car, it is possible to search for the quality of the used car with higher accuracy by making a judgment including this.

In such a case, the combination of the reference color information regarding the color of the used car sold in the past and the reference record book information and the quality of the used car are associated with each other in three or more stages in advance. Then, the color information regarding the color of the used car to be sold is acquired. Next, the quality of the used car is estimated based on the degree of association shown in FIG. 28 based on the reference color information according to the acquired color information and the reference record book information according to the record book information.

Further, as an alternative to the reference year information, reference equipment information regarding standard equipment and options of used cars sold in the past may be used. Since the standard equipment and options of used cars also affect the quality of used cars, it is possible to search for the quality of used cars with higher accuracy by making a judgment including these.

In such a case, the combination of the reference equipment information regarding the standard equipment and options of the used car sold in the past, the reference record book information, and the quality of the used car are obtained in advance at three levels or more. Keep it. Then, the equipment information about the standard equipment and options of the used car to be sold is acquired. Next, the quality of the used car is estimated based on the degree of association shown in FIG. 28 based on the reference equipment information according to the acquired equipment information and the reference record book information according to the record book information.

Further, as a substitute for the reference year information, the reference sales location information of the used car sold in the past may be used. Since the sales location information also affects the quality of used cars, it is possible to search for the quality of used cars with higher accuracy by making a judgment including this. The details of the reference sales place information and the sales place information will be described in the first embodiment, and the description below will be omitted.

In such a case, the combination of the reference sales location information of the used car sold in the past and the reference record book information and the quality of the used car are associated with each other in three or more stages in advance. Then, the sales location information of the used car to be sold is acquired. Next, the quality of the used car is estimated based on the degree of association shown in FIG. 28 based on the reference sales place information according to the acquired sales place information and the reference record book information according to the record book information.

Also, as an alternative to the reference year information, any other reference information described in the first embodiment (in addition to the reference market information, the reference record book information, the reference record book information, the reference odor). Information, reference record book information, reference record book information, reference sales location information) may be used.

In such a case, the degree of association between the reference information of the used car sold in the past, the combination having the reference record book information, and the quality of the used car is acquired in advance. Then, information (market condition information, record book information, odor information, record book information, record book information, sales location information) corresponding to the reference information is acquired, and the used car is similarly used based on the degree of association shown in FIG. 28. Estimate quality.

Further, in the present embodiment as well, as in the first to fourth embodiments, it is of course possible to obtain an output solution from the quality required through the degree of association, but this quality is shown in FIG. 29. In addition, it may be further modified based on the model year information, or the weighting may be changed.

For example, assume that the model year of the reference model year information P13 is α, the model year of the reference model year information P14 is β, and the model year of the reference model year information P15 is γ. At this time, in the case of an image having a large number of year αs in the reference year information P13, a process of lowering the quality is performed. On the other hand, when the model year γ of the reference model year information P15 is large, a process for improving the quality is performed.

When the model year information actually acquired is the same as or similar to the reference model year information P13 in this way, a process of lowering the weighting of the quality is performed, in other words, a process of lowering the quality itself is performed. On the other hand, when the actually acquired model year information is the same as or similar to the reference model year information P15, a process of increasing the weighting of the quality is performed, in other words, a process of increasing the quality itself is performed.

By creating rules for reference year information and quality adjustment in advance in this way, based on the reference year information that is the same as or similar to the actually input year information, as an output solution according to the rules. Adjust the quality of. At this time, in addition to the case of actually adjusting the quality, the degree of association itself may be adjusted.

As an alternative to this reference vehicle type information, one or more of the odor information, driving information, record book information, sales location information, etc. described in the first embodiment is acquired, and the quality is adjusted based on the acquisition. You may. Further, the color information, the equipment information, the appearance image information, and the vehicle interior image information described in the second embodiment may be applied in the same manner.

Any one of these reference odor information, reference travel information, reference record book information, reference sales location information, reference year information, reference color information, reference equipment information, exterior image information, and vehicle interior image information. The above is called estimation auxiliary information. This estimation auxiliary information is used to adjust the output solution of the quality searched through the quality with the vehicle type information, although the degree of association with the quality is not formed. At this time, how to adjust the quality is ruled in advance between the reference estimation auxiliary information and the adjustment to the quality, and corresponds to the actually acquired estimation auxiliary information (same or similar). ) Make adjustments according to the quality adjustment rules established with the reference estimation auxiliary information. In other words, it may be in any form as long as the quality is adjusted based on the estimation auxiliary information when it is input. The rule-making between the estimation auxiliary information for reference and the adjustment to the quality is to make an adjustment to lower the quality in inverse proportion to the total mileage, for example, taking the driving information (driving information for reference) as an example. Often, the degree of association and the use of artificial intelligence are not essential.

The above-mentioned FIG. 29 describes the case where the solution search is performed from the reference travel information via the association degree as an example, but the present invention is not limited to this, and the association degree is obtained from the reference record book information. The same applies when searching for a solution via.

FIG. 30 shows an example of obtaining a selling price from reference quality information and reference market condition information. In such a case, the combination of the reference market condition information and the reference quality information regarding the quality of the used car sold in the past and the degree of association with the selling price at three or more levels are acquired in advance. The reference market information is composed of, for example, past sales price changes for each vehicle type. The reference quality information relates to the quality of used cars sold in the past. By learning the data set of the selling price for the reference market condition information and the reference quality information, the above-mentioned degree of association is formed. Then, the market information on the market conditions of the used cars to be sold up to the present is acquired. Next, the selling price is estimated based on the reference quality information corresponding to the quality estimated through the above-mentioned degree of association. Incidentally, the quality estimated through the degree of association and the reference quality information may be associated with each other based on whether they are the same quality or similar qualities. It is also possible to estimate the selling price by such a method.

It should be noted that the selling price may be estimated not only from the fifth embodiment but also from the quality solved and searched through the first to fifth embodiments via the corresponding reference quality information. Of course.

1 Used car sales price estimation system 2 Discrimination device 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Estimate unit 28 Storage unit 61 Node

Claims (13)

In the used car sales price estimation program that estimates the selling price of used cars
An information acquisition step to acquire in-vehicle image information obtained by capturing an image of the inside of a used car to be sold, and
Learning that the degree of association between the reference car image information that captured the image of the inside of the used car sold in the past and the selling price is defined, the input is the reference car image information, and the output is the selling price. Using the completed model, based on the same or similar reference in-vehicle image information as the in-vehicle image information acquired in the above information acquisition step, the one with the higher degree of association is prioritized and the estimation step for estimating the selling price is performed. A used car sales price estimation program characterized by running on a computer. In the above information acquisition step, the vehicle type information regarding the vehicle type of the used car to be sold is acquired, and the vehicle type information is acquired.
In the above estimation step, the combination having the reference vehicle type information regarding the vehicle type of the used vehicle sold in the past, the above-mentioned reference vehicle in-vehicle image information, and the degree of association with the selling price in three or more stages are defined, and the input is used for reference. The selling price is estimated based on the same or similar reference vehicle type information as the vehicle type information acquired in the above information acquisition step, using the trained model with the in-vehicle image information and reference vehicle type information and the output as the selling price. The used car selling price estimation program according to claim 1, characterized in that. In the above information acquisition step, the model year information regarding the model year of the used car to be sold is acquired.
In the above estimation step, a combination having the reference model year information regarding the model year of the used car sold in the past, the above-mentioned in-vehicle image information for reference, and the degree of association with the selling price in three or more stages are defined, and the input is input. Using a trained model with reference vehicle in-vehicle image information and reference model year information and output as the selling price, and based on the same or similar model year information as the model year information acquired in the above information acquisition step. The used car selling price estimation program according to claim 1, which comprises estimating the selling price. In the above information acquisition step, color information regarding the color of the used car to be sold is acquired, and the color information is acquired.
In the above estimation step, the combination of the reference color information regarding the color of the used car sold in the past and the in-vehicle image information for reference and the degree of association with the selling price in three or more stages are defined, and the input is used for reference. The selling price is estimated based on the same or similar reference color information as the color information acquired in the above information acquisition step, using the trained model with the in-vehicle image information and reference color information and the output as the selling price. The used car sales price estimation program according to claim 1, characterized in that. In the above information acquisition step, equipment information related to the standard equipment and options of the used car to be sold is acquired.
In the above estimation step, the combination of the reference equipment information regarding the standard equipment and options of the used car sold in the past, the in-vehicle image information for reference, and the selling price are defined and input in three or more stages. The selling price is based on the same or similar reference equipment information as the equipment information acquired in the above information acquisition step, using the trained model with the reference vehicle in-vehicle image information and reference equipment information and the output as the selling price. The used car sales price estimation program according to claim 1, which comprises estimating. In the above information acquisition step, the travel information regarding the total travel time or the total mileage of the used car to be sold is acquired.
In the above estimation step, the combination of the reference driving information regarding the total traveling time or the total mileage of the used car sold in the past, the image information in the reference vehicle, and the degree of association with the selling price are defined in three or more stages. The input is the in-vehicle image information for reference and the driving information for reference, the trained model with the output as the selling price is used, and the driving information is the same as or similar to the driving information acquired in the above information acquisition step. , The used car sales price estimation program according to claim 1, wherein the sales price is estimated. In the above information acquisition step, the record book information regarding the record book of the used car to be sold is acquired.
In the above estimation step, the combination of the reference record book information regarding the record book of the used car sold in the past and the in-vehicle image information for reference and the degree of association with the selling price in three or more stages are defined, and the input is input. Using a trained model with reference vehicle in-vehicle image information and reference record book information and output as the selling price, and based on the same or similar reference record book information as the record book information acquired in the above information acquisition step. The used car sales price estimation program according to claim 1, wherein the sales price is estimated. In the above information acquisition step, the sales location information regarding the sales location of the used car to be sold is acquired, and the sales location information is acquired.
In the above estimation step, the combination of the reference sales place information regarding the sales place of the used car sold in the past and the in-vehicle image information for reference, and the degree of association with the selling price in three or more stages are defined, and the input is input. Using a trained model with reference vehicle in-vehicle image information and reference sales location information, and using the output as the selling price, and based on the same or similar reference sales location information as the sales location information acquired in the above information acquisition step. The used car sales price estimation program according to claim 1, wherein the sales price is estimated. In the used car sales price estimation program that estimates the selling price of used cars
An information acquisition step to acquire in-vehicle image information obtained by capturing an image of the inside of a used car to be sold, and to acquire estimation auxiliary information for estimating a selling price.
Learning that the degree of association between the reference car image information that captured the image of the inside of the used car sold in the past and the selling price is defined, the input is the reference car image information, and the output is the selling price. Using the completed model, based on the same or similar reference in-vehicle image information as the in-vehicle image information acquired in the above information acquisition step, the one with the higher degree of association is prioritized, the selling price is estimated, and the acquisition is performed. A used car sales price estimation program characterized by having a computer perform an estimation step that adjusts the sales price based on the above estimation auxiliary information. In the above information acquisition step, as the above estimation auxiliary information, vehicle type information regarding the vehicle type of the used car to be sold, year information regarding the year of the used car to be sold, color information regarding the color of the used car to be sold, Equipment information on the standard equipment and options of the used car to be sold, driving information on the total running time or mileage of the used car to be sold, record book information on the record book of the used car to be sold, the sale target The used car selling price estimation program according to claim 9, which comprises acquiring any one or more of the selling place information regarding the selling place of. The used car sales price estimation program according to any one of claims 1 to 10, wherein the degree of association is composed of nodes of a neural network in artificial intelligence. In the used car quality estimation program that estimates the quality of used cars
An information acquisition step to acquire in-vehicle image information obtained by capturing an image of the inside of a used car to be sold, and
The degree of association between the reference car image information obtained by capturing the image of the inside of the used car sold in the past and the quality of the used car is defined, and the input is the reference car image information and the output is the used car. Estimating the quality by prioritizing the one with the higher degree of association based on the reference in-vehicle image information that is the same as or similar to the in-vehicle image information acquired in the above information acquisition step using the trained model as the quality. A used car quality estimation program characterized by having a computer perform steps and. In the above information acquisition step, market information consisting of time-series changes in selling prices of used cars to be sold up to the present is acquired.
In the above estimation step, there are three or more stages: a combination of reference market conditions information consisting of past sales price changes for each vehicle type, reference quality information regarding the quality of used cars sold in the past, and the selling price of used cars. The degree of association is specified, the input is the reference market information and the reference quality information, and the output is the same or similar to the market information acquired in the above information acquisition step using the trained model with the selling price of the used car. The used car quality estimation program according to claim 12, wherein the selling price is estimated based on the reference market condition information and the reference quality information corresponding to the estimated quality.

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