WO2022050342A1

WO2022050342A1 - Used car sales price estimation system

Info

Publication number: WO2022050342A1
Application number: PCT/JP2021/032254
Authority: WO
Inventors: 綾子澤田
Original assignee: Ａｓｓｅｓｔ株式会社
Priority date: 2020-09-03
Filing date: 2021-09-02
Publication date: 2022-03-10
Also published as: JP2022043000A; JP2022042957A; JP2022043001A; JP2022042959A; JP2022042956A; JP2022042958A; JP2022042955A

Abstract

[Problem] To highly accurately and automatically determine a sales price of a used car with less manpower. [Solution] A used car sales price estimation program for estimating a sales price of a used car causes a computer to execute: an information acquisition step for acquiring appearance image information obtained by taking an image of appearance of a used car to be sold; and an estimation step for using a learned model, in which three or more degrees of relevance between reference appearance image information obtained by taking an image of appearance of a used car that was sold in the past and a sales price are defined and of which input is the reference appearance image information and output is a sales price, to estimate, on the basis of reference appearance image information that is the same as or similar to the appearance image information acquired in the information acquisition step, a sales price by prioritizing a sales price having a higher degree of relevance.

Description

Used car sales price estimation program

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 the used car is set by the used car dealer to be the optimum price that is likely to be negotiated 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 sales price estimation program according to the present invention is an information acquisition step for acquiring appearance image information obtained by capturing an image of the appearance of a used car to be sold in the used car sales price estimation program for estimating the selling price of a used car. , The reference appearance image information that captured the appearance image of the used car sold in the past and the degree of association with the selling price are specified in three or more stages, the input is the reference appearance image information, and the output is the selling price. Using the trained model, based on the appearance image information for reference that is the same as or similar to the appearance 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.

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 of the used car sales price estimation system 1 having the above-mentioned 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 the ranking evaluated by the system side or the user side in 5 or 10 stages.

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 related to 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 highly 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 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 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 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 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. Incidentally, the appearance image information and the appearance image information for reference may use a normal RGB camera or a so-called spectrum camera.

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 related to 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 discrimination 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 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 external 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 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.

Search for the optimum selling price based on the market information and appearance image information newly acquired in this way. 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.

Table 2 below shows an example of the degree of association w1 to w12 extending from the input.

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 degrees of association w1 to w12 may all have the same value, and the weights 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 reference external image information, the reference in-house information obtained by imaging the inside of the 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.

In addition, the reference vehicle interior image and vehicle interior image include the 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 addition to the above-mentioned reference market condition information, instead of the above-mentioned reference appearance image information, a combination with the reference odor information and the selling price for the combination may be set to three or more levels of association.

This reference odor information, which is added as an explanatory variable instead of the reference appearance image information, is the 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.

In such a case, the combination of the reference odor information acquired in the past 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 odor information regarding the odor 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 odor information according to the acquired odor information and the reference market condition information according to the market condition information.

In addition to the above-mentioned reference market condition information, even if the combination with the reference driving information and the selling price for the combination are set in three or more stages instead of the above-mentioned reference appearance image information. good.

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 used cars, 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 such a case, the combination of the reference driving information acquired in the past, the reference market information, and the selling price should be acquired 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. 5 based on the reference driving information according to the acquired driving information and the reference market information according to the market condition information.

In addition to the above-mentioned reference market condition information, instead of the above-mentioned reference appearance image information, a combination with the reference record book information and the selling price for the combination are set to have three or more levels of association. May be good.

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 or vehicle inspection, 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, 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 such a case, obtain in advance the degree of association between the reference record book information acquired in the past, the combination having the reference market condition information, and the selling price. 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. 5 based on the reference record book information according to the acquired record book information and the reference market condition information according to the market condition information.

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 levels or more 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. 5 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 exterior image information, the reference vehicle model information regarding the vehicle model 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 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 a substitute for 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, year information, is the 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 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 year information and the reference market information according to the market information.

Further, as a substitute for 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, obtain in advance the degree of association with the selling price and 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. 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.

The example in FIG. 6 is an example of using three or more levels of association between the reference market information and the selling price.

It is premised that the degree of association between the reference market information and the selling price is formed. In the example of FIG. 6, 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 this output solution through three or more levels of linking. 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 6 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. 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.

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

Such 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, search for the selling price based on the newly acquired market information. In such a case, the degree of association shown in FIG. 6 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 without any 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-mentioned 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.

Note that the above-mentioned input data and output data may not be completely the same in the process of learning, so that the input data and the output data may be classified by type. 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 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. 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, the degree of association is any two or more of the reference market condition information, 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. 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 calculated using the degree of association.

Further, as shown in FIG. 7, the present invention determines the selling price based on the degree of association of 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. 7, 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 in three or more stages. The degree of association can be described by, for example, a numerical value from 0 to 100% in addition to the above-mentioned 10 steps, but is not limited to this, and any step can be described as long as it can be described by a numerical value of 3 or more steps. 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 extremely low output such as 1% of the degree of association. It warns 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 a merit 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 is equivalent to learning in terms 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 the public communication network. It may be updated artificially or automatically. Artificial intelligence may be utilized in these update processes.

In addition, 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 training 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 8, it is premised that the degree of association between the reference appearance image information and the selling price is set in advance. The reference appearance image information is the same as that described in the first embodiment.

In the example of FIG. 8, 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 related to 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. 8, 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 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. 8 is created.

For example, it is assumed that the selling price A is highly 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 the w14 connected to the selling price C The degree of association 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.

In such a case, as shown in FIG. 9, 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 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. 8 (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 w15 and the selling price C is associated with the association degree w16 via the degree of association. 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 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 this way, it is possible to search for the most suitable selling price from the newly acquired external 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.

In addition, in FIGS. Reference information such as year type information, reference color information, reference equipment information, etc. may be applied and learned from the selling price. Then, the search solution can be obtained in the same manner as described above by inputting the information corresponding to the reference information learned between the selling price and the selling price.

FIG. 10 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. 10, it is assumed that the input data is, for example, reference external image information P01 to P03 and reference vehicle interior image information P18 to 21. The intermediate node shown in FIG. 10 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. Also in FIG. 10, as an alternative to the reference appearance image information, the reference vehicle interior image information, the reference odor information, the reference driving information, the reference record book information, the reference sales location information, the reference vehicle model information, and the reference vehicle are used. Any reference information such as year information, reference color information, reference equipment information, etc. may be applied and learned from the selling price. Then, the search solution can be obtained in the same manner as described above by inputting the information corresponding to the reference information learned between the selling price and the selling price.

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. 10 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 a substitute for the image information in the reference vehicle, the reference 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. This reference year information, year information, is the 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 reference year information 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 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. 10 based on the reference year information according to the acquired year 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 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 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. 10 based on the reference color information according to the acquired color information and the reference appearance image information according to the appearance image information.

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. 10 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 and the 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 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. 10 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, obtain in advance the degree of association between the reference record book information of the used car sold in the past, the combination having the reference appearance image information, and the selling price. 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. 10 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. 10 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, obtain in advance the degree of association between the reference information of the used car sold in the past, the combination having the reference appearance image information, and the selling price. 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. 11, it is an example of using three or more levels of association between the reference external image and the selling price.

It is premised that the degree of association between the appearance image information for reference and the selling price is linked to each other. 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 as such input data is linked to the output. In this output, it is assumed that the selling price is the output solution.

The external appearance image information for reference is related to each other through the degree of association of 3 or more levels with respect to the selling price as this 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. 11 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. 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.

Such 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, search for the selling price based on the newly acquired appearance image information. In such a case, the degree of association shown in FIG. 11 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 interior 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 type 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. 12 and 13. May be good. The reference information as input in FIGS. 12 and 13 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, obtain in advance the degree of association between the quality of the used car and each reference information of the used car sold in the past, or the combination of this and other reference information. 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. 12 and 13 based on the reference information according to the acquired information.

In addition, in FIGS. Any reference information such as model year information, reference color information, reference equipment information, etc. may be applied so as to learn from the quality. Then, the search solution can be obtained in the same manner as described above by inputting the information corresponding to the reference information trained with this quality.

The present invention is not limited to the above-described embodiment, and uses, for example, as shown in FIG. 14, three or more levels of association between the reference information as the keynote and the selling price and quality. You may do so. In such a case, the solution search will be performed based on the degree of association with the selling price and the quality according to the newly acquired information in three or more stages. As the reference information as the keynote, for example, all the reference information after the first embodiment can be applied.

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

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

The other reference information referred to here corresponds to any reference information other than the reference information which is the keynote when any of the above-mentioned reference information is used as the keynote reference information.

For example, as one of the other reference information, it is assumed that there was a lot of circumstances in which B was previously determined as the selling price in a certain reference vehicle model information P02. When the selling price corresponding to the reference vehicle type information P02 is newly acquired, the search solution B as the selling price is subjected to a process of increasing the weighting, which leads to the search solution B as the selling price. It is set in advance to perform the processing to be performed.

For example, another reference information G is an analysis result that suggests a search solution C as a selling price, and reference information F is an analysis result that suggests a search solution D as a selling price. Suppose there is. After the setting with the reference information in this way, if the actually acquired information is the same as or similar to the reference information G, the process of increasing the weighting of the search solution C as the selling price is performed. On the other hand, when the actually acquired information is the same as or similar to the reference information F, a process of increasing the weighting of the search solution D as the selling price is performed. That is, the degree of association itself that leads to the selling price may be controlled based on the reference information F to H. Alternatively, after the selling price is determined only by the above-mentioned degree of association, the search solution obtained may be modified based on the reference information F to H. In the latter case, how to modify the selling price as a search solution based on the reference information F to H with what weight will reflect the one designed on the system side each time.

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

Similarly, as shown in FIG. 15, even in the case of forming a degree of association with the selling price for a combination having the keynote reference information and other reference information, the keynote reference information is still available. Any reference information from the first embodiment described above can be applied. Other reference information includes any reference information other than the underlying reference information.

At this time, if the reference information that is the keynote is the reference distance information, any other reference information is included as the other reference information.

In such a case, the selling price can be estimated by searching for a solution in the same way. At this time, as shown in FIG. 14 described above, the selling price is corrected for the search solution obtained through the degree of association through further reference information (reference information F, G, H, etc.). You may do it.

As shown in FIGS. 16 and 17, the solution search may be performed by using the reference information as the keynote and the degree of association between the selling price and the selling price in three or more stages.

The selling price is determined only from the reference information. For example, as shown in FIGS. 16 and 17, the degree of association between the reference information acquired in the past (including any reference information after the first embodiment) and the selling price actually determined in the past is three or more stages. To use.

Such degree of association is what is called learned data in artificial intelligence. After creating such trained data, the above-mentioned trained data will be used when actually determining a new selling price from now on. In such a case, new information corresponding to the reference information is acquired.

The selling price is determined based on the newly acquired information in this way. In such a case, the degree of association acquired in advance is referred to. Since the specific method for estimating the selling price is the same as described above, the description below will be omitted.

In the above examples, examples of searching for the selling price, quality, etc. of used cars have been described, but the present invention is not limited to this, and can be applied to searching for selling prices, quality, etc. of new cars. Of course it is good.

Third Embodiment In the third embodiment, instead of searching for the selling price and quality, the repair cost, the repair period, and the repair method are searched. In this embodiment, the target is not limited to used vehicles, but is expanded to the entire vehicle including new vehicles.

The repair cost refers to the repair cost when the appearance or interior of the vehicle, as well as any system or device mounted on the vehicle, is damaged. The repairs mentioned here are, for example, those that have actually caused scratches or dents on the appearance of the vehicle, and those that have not actually been damaged at this time, but that will lead to damage if left as they are. Including cases of repair and maintenance in advance. In addition, the repair includes the case where the dirt is attached although it is not damaged and it is removed. In addition, although it is not damaged, repairs that remove odors and defects that have occurred at the time of purchase are also included in this repair.

The repair period indicates the period until the repair is completed based on the date when the repair is sent or the date when the repair is applied in the case of the above-mentioned damage. The repair method is a specific content of repair for the above-mentioned damage, and includes total replacement, partial replacement, replacement with a compatible product, and one bolt re-tightening.

For a detailed explanation of the solution search when any one of the repair cost, the repair period, and the repair method is used as the search solution, the selling price and the quality in the above-mentioned first embodiment to the second embodiment are the repair cost, the repair period, and the repair. The explanation will be replaced with the method, and the explanation below will be omitted.

Specifically, it is possible to search for a solution in the same manner by learning the repair cost, the repair period, and the data set for learning the repair method in the same manner for each of the above-mentioned reference information.

The reference contract information may be applied as the reference information applicable in the third embodiment. Reference contract information is any information regarding the content of the vehicle repair support contract. When purchasing or renting a vehicle, various warranty and repair support contracts are often concluded, but in such repair support, it is clearly stated what kind of damage and how much repair is supported. It is often done. In the reference contract information, the specific repair support content for the damage condition, the cost borne by the contractor and the customer, and the ratio thereof are expressed. Specifically, repair support conditions (period, cost, ratio) and the like may be extracted and learned. In the case of learning such reference contract information, the contract information is acquired when a new solution search is performed. The type of data of this contract information is the same as that of the reference contract information.

In the solution search in the third embodiment, when learning the reference market condition information, it is possible to learn the data consisting of the time-series transition of the repair cost for each vehicle type. The market price of repair costs changes according to the working environment at that time, the unit price of labor costs, interest rates, etc. It is also possible to learn the time-series transition of the unit price of such repair costs as reference market information by accumulating data for each repair genre. In such a case, data consisting of the time-series transition of the repair cost according to the genre of the repair is newly acquired as the market condition information, and the solution search is performed in the same manner.

In the third embodiment, as shown in the first to second embodiments, the quality of the vehicle may be searched, and the repair cost, repair period, and repair method may be searched from the searched quality. .. As mentioned above, the quality here is an evaluation of the condition of a used car from various viewpoints such as beauty, worn-outness, usability, odor, and defects. In the embodiment, if the damage has occurred, the degree of the damage, the content of the damage, and if there is a feeling of strangeness or a defect, the degree of the damage may be indicated.

As for the vehicle quality solution search method, the description below is omitted by replacing the used vehicle quality solution search in the above-mentioned first embodiment to the second embodiment with the vehicle quality.

Based on the quality of the vehicle obtained in this way, the repair cost, repair period, and repair method are calculated. In such a case, prepare a template in which the repair cost, repair period, and repair method are linked to each quality of the vehicle in advance. That is, if the quality of the vehicle is obtained, the repair cost, the repair period, and the repair method can be easily obtained by referring to the template. This makes it possible to obtain the quality of the vehicle from various information and estimate the repair cost, repair period, and repair method from it.

Similarly, the insurance premium paid to the trader or the customer may be calculated from the quality of this vehicle. Similarly, in such a case, prepare a template in which insurance premiums are associated with each quality of the vehicle in advance. If the quality of the vehicle is desired, the template can be referred to to facilitate the corresponding insurance premiums. This makes it possible to obtain the quality of the vehicle from various information and estimate the insurance premium from it.

When seeking insurance premiums, you may search for repair costs, repair periods, and repair methods, and then ask for insurance premiums from there. Similarly, in such a case, prepare a template in which insurance premiums are associated with each vehicle repair cost, repair period, and repair method in advance. If the repair cost, repair period, and repair method are obtained, the template can be referred to to make it easy to obtain the corresponding insurance premium. This makes it possible to obtain the repair cost, repair period, and repair method from various information, and estimate the insurance premium from them.

Of course, also in the third embodiment, the solution search may be performed as shown in FIGS. 14 to 17.

Fourth Embodiment In the fourth embodiment, the discrimination device 2, or the discrimination device 2 and the information acquisition unit 9 use a spectacle-type terminal or a head-mounted display (HMD) among wearable terminals. This HMD is integrally or partially attached to the user's head or eyeglasses, and uses technologies such as Augmented Reality (AR) or Mixed Reality (MR) based on various acquired video information. It is provided with a display unit that displays the information generated in the transparent state. The user can visually recognize and understand the information to be displayed through the display unit that is transparently displayed on the HMD. This enables the user to check the information and various contents generated based on the acquired various video information while observing the situation in front of the user.

Therefore, in the present invention, for example, external image information and vehicle interior image information are acquired via the information acquisition unit 9 mounted on the HMD. Then, a solution search is performed by the discrimination device 2 mounted in the HMD, and the obtained search solution (sales price, quality, repair cost, repair period, repair method, etc.) is displayed in a transparent state via the display unit. You may do so.

In such a fourth embodiment, the reference external appearance image information to be learned as learning data and the reference vehicle interior image information may be actually captured by a spectacle-type terminal such as an HMD or the like. The image is not limited to this, and may be an image taken by a normal digital camera, a smartphone, or the like.

Further, in order to actually obtain the reference appearance image information and the reference vehicle interior image information, it is possible to link what part of the used car is being imaged.

For example, in obtaining external appearance image information for reference, a skilled veteran engineer who detects scratches and stains on the surface of a used car detects what part of the used car is being viewed. It is detected whether the veteran technician is visually recognizing the part of the car door as the center, the part of the bumper as the center, or the part of the bonnet as the center.

For this detection, for example, a veteran technician is made to wear a spectacle-type terminal to actually check for scratches and stains, and during that time, the veteran technician is at any time via the information acquisition unit 9 mounted on the spectacle-type terminal. Continues to capture images in the direction that is being viewed by. Then, after the fact, by analyzing the recorded image or playing back the image, what part of the used car is visually recognized by a veteran technician when actually checking for scratches and stains. It becomes possible to detect.

In such a case, as shown in FIG. 18, when the moving images captured by the spectacle-type terminal are arranged in chronological order, the image of the door (P1), the image of the bumper (P2), and the enlarged image of the bumper (P3) are arranged. ), It is assumed that it was an image of the bonnet (P4). The image pickup target site information may be detected from the images obtained in time series in this way. The image pickup target portion information referred to here is information regarding what portion of the used car is captured by the image captured by the spectacle-type terminal. As shown in FIG. 18, the image pickup target site information may be composed of the name of the site actually being imaged, such as a door, bumper, bonnet, or the like, or a symbol for specifying the site. It may be represented by a numerical value, a number, or the like. Further, the imaging target portion information may include, for example, information such as whether or not the imaging is an enlarged image or a reduced image, and information such as the imaging direction and the angle of view at the time of imaging.

The acquisition of the image target part information may be manually input by a human being discriminating the image target part each time, but the acquired image may be obtained by using a well-known image analysis technique. The acquisition of the image target portion information may be determined based on the feature amount learned in the past. For example, images of various parts of an automobile such as a door, a bumper, and a bonnet may be discriminated by extracting them through artificial intelligence using deep learning technology. In such a case, a machine learning model in which the used car part included in the reference appearance image information and the image pickup target part information are used as teacher data, the input is the reference appearance image information, and the output is the image pickup target part information. To use. Then, the image target portion information is newly acquired based on the reference appearance image information imaged via the user terminal. In addition to this, the acquisition of the image target part information is the direction of the line of sight detected by using the eye tracking function installed in the HMD or the spectacle-type terminal, and the head detected by using the acceleration sensor or the gyro sensor. Information on the part to be imaged may be acquired via the orientation of the unit, the movement of the user's hand using the operation device or the hand tracking function, and the like.

By recording the information of the part to be photographed thus obtained in association with the above-mentioned images P1 to P4 and the like, it is possible to determine what part of the used car is imaged by each of the images P1 to P4. It can be obtained as a set. By acquiring the images P1 to P4 constituting such reference appearance image information from a veteran technician and the information on the part to be photographed, any part of the veteran technician can actually inspect the used car. It is possible to acquire the shooting environment such as the order in which the images are confirmed, whether or not the image is an enlarged image, and the shooting angle. Similarly, for the image information in the vehicle for reference, it is possible to acquire the information on the part to be photographed and record it in association with the information.

In the fourth embodiment, the convenience at the time of actual solution search can be enhanced by utilizing the information on the part to be imaged. When the user wearing the HMD implements the present invention after constructing the above-mentioned learning data in advance, the search for the search solution (quality, selling price, etc.) will be described in the above-mentioned first to third embodiments. Execute based on the method you did. At this time, in the process of acquiring the external image information and the vehicle interior image information captured in the direction that the user wearing the HMD is visually recognizing, the image shooting target portion information may be similarly obtained. As a method of acquiring the image target part information from the external image information and the vehicle interior image information, the determination may be made based on the feature amount learned as described above, for example, the door, the bumper, and the like in the external image information. Images of various parts of an automobile such as a bonnet may be discriminated by extracting them through artificial intelligence using deep learning technology. In such a case, a machine learning model is used in which the used car part included in the appearance image information and the image pickup target part information are used as teacher data, the input is the appearance image information, and the output is the image pickup target part information. Then, the image pickup target site information is newly acquired based on the appearance image information captured via the user terminal. Similarly, information on the part to be imaged can be obtained from the image information in the vehicle.

Hereinafter, the image target part information acquired from the information such as the exterior image information and the vehicle interior image information is also referred to as the first image capture target part information, and is used for reference such as the reference exterior image information and the reference vehicle interior image information. The image target part information acquired from the information is also referred to as a second image capture target part information.

In the process of acquiring exterior image information and vehicle interior image information, there is an excellent effect of explaining how to acquire such first image target part information each time. It is possible to confirm each time whether the first imaged target part information of the captured appearance image information and the second imaged target part information of the reference appearance image information match. If the first imaged target part information of the captured external image information and the second imaged target part information of the reference external image information do not match, a warning can be displayed to the user via the HMD display unit. can.

For example, when the first imaged target part information of the captured external image information is a “door” and the second imaged target part information of the reference external image information corresponding to this is a “back mirror”, It means that the part of the used car in which the user wearing the HMD captures the appearance image information is photographing the wrong part. In such a case, it is possible to urge the user to adjust the imaging target to the correct portion by calling attention as described above. Further, while the second imaged target part information associated with the reference external image information is for visually recognizing the bonnet in an enlarged manner, the first imaged part information associated with the external image information is. If the image of the same bonnet is not magnified and visually recognized, it is possible to similarly encourage the user to magnify and visually recognize the image.

In this way, the degree of coincidence between the first imaged target part information of the captured external image information and the second imaged target part information of the reference external image information, or the imaged target part information of the captured vehicle interior image information is referred to. Through the degree of matching of the image information in the vehicle with the image target part information, various suggestions are made to the user wearing the HMD about the actual appearance image information and the method of photographing the image information in the vehicle, or various corrections are made. It will be possible to encourage. At this time, the AR or MR described above may be realized in which the promotion of such suggestions and corrections is displayed in a transparent state via the display unit of the HMD or the spectacle-type terminal. It should be noted that the suggestion about this imaging method may display any suggestion as long as it is based on the first imaging target site information and the second imaging target site information.

In the example of FIG. 18 described above, the case where the shooting target of a used car is sequentially switched at a plurality of places has been described as an example, but the present invention is not limited to this, and the case where the reference external image information or the like is one place. Similarly, by acquiring and associating information on the part to be imaged, it is possible to perform the above-mentioned guidance to the user who actually acquires the appearance image information and the like.

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

In the used car sales price estimation program that estimates the selling price of used cars
An information acquisition step to acquire appearance image information obtained by capturing an image of the appearance of a used car to be sold,
Learning that the degree of association between the reference appearance image information, which is an image of the appearance of a used car sold in the past, and the selling price is defined, and the input is the reference appearance image information and the output is the selling price. Using the completed model, based on the appearance image information for reference that is the same as or similar to the appearance 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 in-vehicle image information obtained by capturing the in-vehicle image of the used car to be sold is acquired.
In the above estimation step, a combination having the reference in-vehicle image information obtained by capturing an image of the inside of a used car sold in the past and the above-mentioned reference appearance image information, and the degree of association with the selling price in three or more stages are defined. , Using the trained model with the input as the reference external image information and the reference vehicle interior image information and the output as the selling price, and the reference vehicle interior image information that is the same as or similar to the vehicle interior image 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 based on the above.
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 appearance 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 external image information and the 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 year year information regarding the year year of the used car sold in the past, the above reference appearance image information, 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 appearance image information and reference year information and output as the selling price, and based on the same or similar reference year information as the 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, 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 appearance 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 appearance image information and the 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, travel information regarding the total travel time or 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 and the external appearance image information for the reference, and the degree of association with the selling price in three or more stages are specified. The input is the reference appearance image information and the reference driving information, the output is the selling price, and the trained model is used. Further, based on the same or similar reference driving information as 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 used car sales price estimation program that estimates the selling price of used cars
An information acquisition step to acquire external image information obtained by capturing an image of the external appearance 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 appearance image information, which is an image of the appearance of a used car sold in the past, and the selling price is defined, and the input is the reference appearance image information and the output is the selling price. Using the completed model, based on the appearance image information for reference that is the same as or similar to the appearance 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 estimation auxiliary information, the in-vehicle image information obtained by capturing the image of the inside of the used car to be sold, the vehicle type information regarding the model of the used car to be sold, and the year of the used car to be sold are used. Year information about, color information about the color of the used car for sale, equipment information about the standard equipment and options of the used car for sale, running information about the total running time or mileage of the used car for sale, The used car selling price estimation program according to claim 7, wherein any one or more of the record book information about the used car record book to be sold and the selling place information about the selling place to be sold is acquired.
The used car sales price estimation program according to any one of claims 1 to 8, 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 appearance image information obtained by capturing an image of the appearance of a used car to be sold,
The degree of association between the reference appearance image information, which is an image of the appearance of a used car sold in the past, and the quality of the used car is defined, and the input is the reference appearance image information and the output is the used car. Estimating the quality by using the trained model as the quality and giving priority to the one with the higher degree of association based on the appearance image information for reference that is the same as or similar to the appearance image information acquired in the above information acquisition step. 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 10, wherein the selling price is estimated based on the reference market condition information of the above and the reference quality information corresponding to the estimated quality.
In the used car quality estimation system that estimates the quality of used cars,
An information acquisition method for acquiring appearance image information obtained by capturing an image of the appearance of a used car to be sold,
In the past, the degree of association between the reference appearance image information obtained by capturing the image of the appearance of the used car and the quality of the used car is defined, and the input is the appearance image information for reference and the output is the quality of the used car. Based on the same or similar reference appearance image information as the appearance image information acquired by the above information acquisition means, the quality of the used car is estimated by giving priority to the one with the higher degree of association. Equipped with an estimation means to
At least the above information acquisition means is included in the user terminal worn on the head or eyeglasses, and is included in the user terminal.
The user terminal is a used car quality estimation system characterized by having a display unit that displays information on the quality of the used car estimated by the estimation means in a transparent state.
In the used car quality estimation system that estimates the quality of used cars,
It is included in the user terminal worn on the user's head or eyeglasses, and the appearance image information consisting of the appearance of the used car to be sold is captured through the user terminal, and the first imaging target relating to the imaged part of the used car is captured. Information acquisition means for acquiring site information and
In the past, the reference appearance image information captured by the appearance of a used car and the degree of association with quality are specified in three or more stages, and a trained model with input as reference appearance image information and output as quality is used. Based on the appearance image information for reference that is the same as or similar to the appearance image information acquired by the information acquisition means, the estimation means that estimates the quality by giving priority to the one with a higher degree of association is used.
It is provided with a recording means for recording the second image target part information regarding the part of the used car included in the reference appearance image information in association with the reference appearance image information.
The user terminal is associated with the first imaging target site information acquired by the information acquisition means and the reference appearance image information that is the same as or similar to the appearance image information acquired by the information acquisition means, and the recording means. A used car quality estimation system characterized by having a display unit that displays a suggestion about an imaging method to the user in a transparent state based on the second image pickup target portion information recorded in the above.