Used vehicle information processing device, used vehicle information processing method, and program

Abstract

To provide a used vehicle information processing apparatus, a used vehicle information processing method, and a program for allowing a deteriorated state of a used vehicle to be appropriately recognized.SOLUTION: The used vehicle information processing apparatus comprises: an acquisition unit which acquires used vehicle information including specification information being information about specifications of a used vehicle and use information being information about the use of the used vehicle; a deterioration degree estimation unit which estimates a deterioration degree of the used vehicle using the used vehicle information as an explanatory variable; and a presentation unit which presents the estimated deterioration degree to a purchaser of the used vehicle.SELECTED DRAWING: Figure 23

Description

Technical Field

[0001] The present disclosure relates to a used vehicle information processing apparatus, a used vehicle information processing method, and a program.

Background Art

[0002] Patent Document 1 describes the following vehicle automatic appraisal system. In the vehicle automatic appraisal system described in Patent Document 1, first, a plurality of auction contract result information in which the vehicle basic information such as the vehicle type among the vehicle information of the vehicle to be appraised matches is selected. Next, an average value is calculated from the contract prices included in the auction contract result information. Next, each contract price is compared with the average value, and if the contract price that deviates the most from the average value is outside the range of the allowable value, that contract price is excluded. Next, new auction contract result information is supplemented, the average value is recalculated, and when the difference between each contract price and the average value is within the range of the allowable value, that average value is set as the appraisal value of the vehicle to be appraised.

[0003] In Patent Document 1, examples of vehicle information include vehicle type name, grade, engine type name, model year, shift (type), door (type), mileage, color, vehicle inspection (date), evaluation (points), air conditioner, sunroof, leather, multi, and other options. Here, the evaluation points are numerical values indicating a comprehensive evaluation of the degree of dirt on the exterior and interior of the vehicle. In Patent Document 1, the evaluation points are, for example, values from 1 to 10 in increments of 0.5. Also, in Patent Document 1, the evaluation points are usually relatively evaluated according to the degree of the state of the exterior and interior of the vehicle, but in a step evaluation of 1 to 10 in increments of 0.5, the most common evaluation point is said to be 4.5. Therefore, it is set to 4.5 by default, and when it is difficult for a layperson user to make a judgment, it remains the default.

Prior Art Documents

[0005] Generally, with used private passenger cars, it's often possible to roughly predict which parts need maintenance if you can confirm the make, model, year of manufacture, and total mileage. On the other hand, commercial vehicles, for example, are used in a wide variety of ways, and their state of deterioration differs from one another, making it difficult to identify which parts need maintenance.

[0006] However, the automated vehicle appraisal system described in Patent Document 1 has the problem that, since the information representing how the vehicle is used is solely based on mileage, it can be difficult for the buyer to properly understand the vehicle's deterioration status, for example.

[0007] This disclosure was made to solve the above-mentioned problems and aims to provide a used vehicle information processing device, a used vehicle information processing method, and a program that can appropriately grasp the deterioration state of used vehicles. [Means for solving the problem]

[0008] To solve the above problems, the used vehicle information processing device according to this disclosure includes an acquisition unit that acquires used vehicle information including specification information, which is information regarding the specifications of the used vehicle, and usage information, which is information regarding the use of the used vehicle; a deterioration degree estimation unit that estimates the degree of deterioration of the used vehicle using the used vehicle information as explanatory variables; and a presentation unit that presents the estimated degree of deterioration to the buyer of the used vehicle.

[0009] The used vehicle information processing method relating to this disclosure includes the steps of: acquiring used vehicle information which includes specification information which is information relating to the specifications of the used vehicle and usage information which is information relating to the use of the used vehicle; estimating the degree of deterioration of the used vehicle using the used vehicle information as explanatory variables; and presenting the estimated degree of deterioration to the buyer of the used vehicle.

[0010] The program relating to this disclosure causes a computer to perform the following steps: acquire used vehicle information, which includes specification information, which is information regarding the specifications of the used vehicle, and usage information, which is information regarding the use of the used vehicle; estimate the degree of deterioration of the used vehicle using the used vehicle information as explanatory variables; and present the estimated degree of deterioration to the buyer of the used vehicle. [Effects of the Invention]

[0011] According to the used vehicle information processing device, used vehicle information processing method, and program of this disclosure, the deterioration status of used vehicles can be appropriately determined. [Brief explanation of the drawing]

[0012] [Figure 1] This is a block diagram showing an example configuration of a used vehicle information processing device according to the first embodiment of this disclosure. [Figure 2] This is a schematic diagram showing an example of used vehicle label information according to the embodiment of this disclosure. [Figure 3] This is a schematic diagram showing an example of the configuration of a used vehicle information database according to the present disclosure. [Figure 4] This is a schematic diagram showing an example of the configuration of a purchase and sales performance database according to the embodiment of this disclosure. [Figure 5] This is a schematic diagram showing an example of the configuration of the maintenance record database according to the embodiment of this disclosure. [Figure 6] This is a schematic diagram showing an example of the configuration of a maintenance cost performance database according to the embodiment of this disclosure. [Figure 7] This is a schematic diagram showing an example of the degree of usedness according to the embodiment of this disclosure. [Figure 8] This is a schematic diagram showing an example of the degree of usedness according to the embodiment of this disclosure. [Figure 9] This is a schematic diagram showing an example of a maintenance menu display according to the embodiment of this disclosure. [Figure 10] This is a schematic diagram showing an example of a maintenance menu display according to the embodiment of this disclosure. [Figure 11]It is a flowchart showing an operation example of a used vehicle information processing apparatus according to a first embodiment of the present disclosure. [Figure 12] It is a diagram showing an operation example of a used vehicle information processing apparatus according to a first embodiment of the present disclosure. [Figure 13] It is a diagram showing an operation example of a used vehicle information processing apparatus according to a first embodiment of the present disclosure. [Figure 14] It is a diagram showing an operation example of a used vehicle information processing apparatus according to a first embodiment of the present disclosure. [Figure 15] It is a diagram showing an operation example of a used vehicle information processing apparatus according to a first embodiment of the present disclosure. [Figure 16] It is a diagram showing an operation example of a used vehicle information processing apparatus according to a first embodiment of the present disclosure. [Figure 17] It is a diagram showing an operation example of a used vehicle information processing apparatus according to a first embodiment of the present disclosure. [Figure 18] It is a diagram showing an operation example of a used vehicle information processing apparatus according to a first embodiment of the present disclosure. [Figure 19] It is a flowchart showing another operation example of a used vehicle information processing apparatus according to a first embodiment of the present disclosure. [Figure 20] It is a block diagram showing a configuration example of a used vehicle information processing apparatus according to a second embodiment of the present disclosure. [Figure 21] It is a flowchart showing an operation example of a used vehicle information processing apparatus according to a second embodiment of the present disclosure. [Figure 22] It is a schematic diagram for explaining an operation example of a used vehicle information processing apparatus according to a second embodiment of the present disclosure. [Figure 23] It is a block diagram showing a basic configuration example of a used vehicle information processing apparatus according to an embodiment of the present disclosure. [Figure 24] It is a schematic block diagram showing a configuration of a computer according to at least one embodiment.

MODE FOR CARRYING OUT THE INVENTION

[0013] Hereinafter, a used vehicle information processing device, a used vehicle information processing method, and a program according to the embodiments of this disclosure will be described with reference to the drawings. In each drawing, the same or corresponding components are given the same reference numerals, and their descriptions will be omitted as appropriate.

[0014] <First Embodiment> Hereinafter, a used vehicle information processing device, a used vehicle information processing method, and a program according to the first embodiment of this disclosure will be described with reference to Figures 1 to 19. Figure 1 is a block diagram showing an example configuration of the used vehicle information processing device according to the first embodiment of this disclosure. Figure 2 is a schematic diagram showing an example of used vehicle label information according to the embodiment of this disclosure. Figure 3 is a schematic diagram showing an example configuration of a used vehicle information DB (database) according to the embodiment of this disclosure. Figure 4 is a schematic diagram showing an example configuration of a purchase / sales performance DB according to the embodiment of this disclosure. Figure 5 is a schematic diagram showing an example configuration of a maintenance performance DB according to the embodiment of this disclosure. Figure 6 is a schematic diagram showing an example configuration of a maintenance cost performance DB according to the embodiment of this disclosure. Figures 7 and 8 are schematic diagrams showing examples of used vehicle status according to the embodiment of this disclosure. Figures 9 and 10 are schematic diagrams showing examples of maintenance menu display according to the embodiment of this disclosure. Figure 11 is a flowchart showing an example of operation of the used vehicle information processing device according to the first embodiment of this disclosure. Figures 12 to 18 are diagrams showing examples of operation of the used vehicle information processing device according to the first embodiment of this disclosure. Figure 19 is a flowchart showing another example of operation of the used vehicle information processing device according to the first embodiment of this disclosure.

[0015] In the description of the first embodiment and the second embodiment described later, examples are given of applying the used vehicle information processing device according to the embodiment to a system that mediates the buying and selling of used vehicles, for example, in which an intermediary such as an operator buys a used vehicle from a seller and sells it to a buyer. Forklifts are given as an example of used vehicles. However, the used vehicle information processing device according to the embodiment of this disclosure is not limited to this example, and can be applied to used vehicles and nearly new vehicles such as cargo transport vehicles such as trucks and trailers, mixer trucks, tank trucks, dump trucks, bulldozers, and power shovels. Alternatively, the used vehicle information processing device according to the embodiment of this disclosure can be applied to all kinds of used vehicles and nearly new vehicles for commercial or private use, such as construction vehicles such as pile drivers, agricultural vehicles such as tractors, and passenger transport vehicles such as buses. Furthermore, the form of buying and selling is not limited to the above examples. For example, it may be applied to a form of buying and selling in which the intermediary receives a commission from one or both parties by mediating the sale from a seller to a buyer.

[0016] (Configuration of used vehicle information processing system) The used vehicle information processing device 1 shown in Figure 1 can be configured using one or more computers, such as a server or personal computer. Furthermore, some or all of the one or more computers may be configured on a cloud, for example. The used vehicle information processing device 1 has a functional configuration consisting of a combination of hardware such as one or more computers and software such as programs executed by one or more computers, and includes the following parts. Specifically, the used vehicle information processing device 1 shown in Figure 1 includes a used vehicle information processing unit 10, a used vehicle purchase system 11, a used vehicle sales system 12, a used vehicle information DB 13, a purchase / sales performance DB 14, a maintenance work performance DB 15, and a maintenance cost performance DB 16. The used vehicle information processing unit 10 also includes a price estimation module 101, a usedness estimation module 102, and a maintenance module 103.

[0017] The used vehicle purchase system 11 is, for example, a web server, and sends and receives predetermined information via a communication line with the user terminal 2 operated by the seller of the used vehicle. The user terminal 2 is, for example, a computer such as a personal computer, smartphone, or tablet, and is the terminal operated by the seller. The used vehicle purchase system 11 acquires used vehicle label information, which includes specification information, which is information about the specifications of the used vehicle to be purchased from the seller, and usage information, which is information about the use of the used vehicle, entered on the user terminal 2. In the following, "seller" will also be referred to as "buyer," and "buyer" will also be referred to as "buyer."

[0018] Figure 2 shows an example of used vehicle label information 201. The used vehicle label information 201 shown in Figure 2 includes specification information 202 and usage information 203. Specification information 202 is information about the specifications of the used vehicle. Usage information 203 is information about the use of the used vehicle. In addition, the specification information 202 shown in Figure 2 includes information representing the manufacturer, fuel, engine type, displacement, transmission, maximum load, year of manufacture, warranty, attachments, and special specifications. Note that the manufacturer is the name of the manufacturer, the fuel is the type of fuel, the transmission is the type of transmission, the warranty is the remaining warranty period and warranty details, the attachment is the type of attachment (fork type, etc.), and the special specifications are the presence and details of special specifications. In addition, the usage information 203 shown in Figure 2 includes information representing the hour meter (usage hours), type of use, main cargo, breakdown / repair history, maintenance history, dirt and scratches, and country of use. Note that the configuration of used vehicle label information 201 is not limited to the configuration example shown in Figure 2.

[0019] The method of verifying each piece of information is based on the seller's declaration. However, information numbered 1-6 is model information and can be verified by entering the model number, etc. This information is relatively reliable. Also, information numbered 7 can be verified from the vehicle plate, etc. This information is also relatively reliable. On the other hand, information numbered 8-18 is relatively prone to errors. However, the reliability of information numbered 8-18 can be improved by requesting the input (submission) of additional information. For example, the reliability of information numbered 8 can be improved by checking the warranty certificate. The reliability of information numbered 9 and 10 can be improved by obtaining sales records from the used vehicle seller or by verifying with photographs. The reliability of information numbered 11 can be improved by verifying with photographs or, depending on the vehicle, obtaining telematics information. The reliability of information numbered 12 can be improved by checking sales information from the dealer or obtaining telematics information. The reliability of information numbered 13 can be improved by checking sales information from the dealer. The reliability of information numbered 14 can be improved by checking sales information from the dealer or obtaining telematics information. The reliability of information numbered 15 and 16 can be improved by obtaining records of inspections, repairs, and maintenance, if available, or by adding or correcting information based on post-purchase inspections. The reliability of information numbered 17 can be improved by verifying photographs or adding or correcting information based on post-purchase inspections. The reliability of information numbered 18 can be improved by verifying sales information from dealerships, etc.

[0020] For example, information numbered 12 and 14 affects the deterioration of the prime mover, cargo handling system, and drive mechanism. Also, for example, information numbered 13 and 18 affects the deterioration and rust of the prime mover, cargo handling system, and drive mechanism.

[0021] The used vehicle purchase system 11 retrieves used vehicle label information 201 from the user terminal 2, presents the usedness level (described later) to the user terminal 2, presents the purchase price of the used vehicle (hereinafter also referred to as the purchase price) to the user terminal 2, and handles communication regarding acceptance or rejection of the purchase.

[0022] Here, with reference to Figures 3 to 6, examples of the information managed by the used vehicle information DB13, purchase / sales record DB14, maintenance work record DB15, and maintenance cost record DB16 will be described. In this embodiment, a database (DB) is defined as a system including files or collections of files containing data and software that processes them. Figures 3 to 6 show examples of the configuration of each record that makes up the tables managed by each DB.

[0023] Each record in the used vehicle information DB13 stores the following information linked to the vehicle identification information, as shown in Figure 3, for example: seller identification information, buyer identification information, used vehicle label information (specifications), used vehicle label information (usage information), condition of use, date and time of purchase, and date and time of sale. If the same vehicle is bought and sold multiple times, for example, each item other than the vehicle identification information will be added to the same record.

[0024] Each record in the Purchase / Sales History DB14 stores the following information, linked to the vehicle identification information, as shown in Figure 4, for example: seller identification information, buyer identification information, used vehicle label information (specifications), used vehicle label information (usage information), condition of use, date and time of sale (purchase), purchase price, date and time of sale (sale), and sale price. If the same vehicle is bought and sold multiple times, for example, each item other than the vehicle identification information will be added to the same record.

[0025] Each record in the Maintenance Work Records DB15 stores information such as failure / repair history, maintenance history, and maintenance menu selection, linked to vehicle identification information, as shown in Figure 5, for example. Failure / repair history information represents the details and dates of zero or more failures and repairs. Maintenance history information represents the details and dates of zero or more maintenance. Maintenance menu selection information represents the maintenance selected by the buyer and the estimated cost, as will be described later. The information managed by the Maintenance Work Records DB15 is registered, for example, based on the seller's declaration, or based on information representing records of failures, repairs, and maintenance obtained from other systems, for example.

[0026] Each record in the Maintenance Cost History DB16 stores, linked to vehicle identification information, zero or more repair cost entries, zero or more maintenance cost entries, and maintenance menu selection information, as shown in Figure 6, for example. Repair cost information represents the cost of repairs. Maintenance cost information represents the cost of maintenance. Maintenance menu selection information is the same as that in the Maintenance Work History DB15. The information managed by the Maintenance Cost History DB1 is registered, for example, based on the seller's declaration, or based on information representing records of failures, repairs, and maintenance obtained from other systems, for example.

[0027] Next, the price estimation module 101 shown in Figure 1 estimates a fair purchase price from sellers and a fair selling price to buyers for the target vehicle. Here, a fair purchase price or fair selling price means, for example, that the used vehicle label information 201 is close to (within a predetermined range of) the actual purchase price or selling price of other used vehicles similar to the target used vehicle. The price estimation module 101 estimates the fair purchase price and selling price using a trained machine learning model that has been trained using a dataset containing past used vehicle purchase and sales records and the used vehicle label information 201 from those transactions as training data. In this case, the trained machine learning model is input with numerical values ​​representing each item of the used vehicle label information 201 of the target used vehicle as explanatory variables. The trained machine learning model then outputs the fair purchase price and selling price.

[0028] Alternatively, the price estimation module 101 estimates an appropriate selling price using a machine learning model trained on a dataset of past used vehicle sales data (including selling price and used vehicle label information 201) and the usedness level described later as training data. In this case, the trained machine learning model is input with the used vehicle label information 201 (the numerical values ​​of each item) and the usedness level of the used vehicle to be estimated as explanatory variables. The trained machine learning model then outputs an appropriate selling price. Furthermore, the price estimation module 101 calculates a purchase price based on the estimated selling price and empirical rules, etc.

[0029] The usedness estimation module 102 estimates the usedness of a target vehicle, which is a value representing the degree of deterioration, using the used car label information 201 as an explanatory variable. In this embodiment, the evaluation score for the usedness of the entire used vehicle is set to 0 to 100, with 100 representing the least deterioration and 0 representing the most deterioration. Figures 7 and 8 show examples of usedness estimation. Figure 7 shows the correspondence between major and minor categories and main check items for the vehicle's components (or component parts), etc. Figure 8 shows the range of weighted points and item evaluation points, an example of estimated item evaluation points, an example of estimated major category evaluation points, and an example of overall evaluation points, corresponding to the same major and minor categories as shown in Figure 7. In this embodiment, the usedness (degree of deterioration) includes item evaluation points, major category evaluation points, and overall evaluation points.

[0030] In the examples shown in Figures 7 and 8, the degree of usedness is evaluated by classifying it into major and minor categories. The major categories are prime movers, running gear, cargo handling equipment, and driver's cab, etc. The minor categories for prime movers are general (number: 1), engine body (number: 2), lubrication system (number: 3), and fan belt (number: 4). The minor categories for running gear are general (number: 5), wheels (number: 6), suspension (number: 7), steering system (number: 8), clutch (number: 9), power steering (number: 10), tires (number: 11), and brakes (number: 12). The minor categories for cargo handling equipment are general (number: 13), forks (number: 14), mast (number: 15), lift cylinder (number: 16), lift chain (number: 17), lift bracket (number: 18), backrest (number: 19), and tilt cylinder (number: 20). The subcategories for the driver's seat and other related items are: General (No. 21), Driving controls (No. 22), Head guard (No. 23), Seat (No. 24), Lighting (No. 25), Mirrors (No. 26), and Warning devices (No. 27).

[0031] In this embodiment, the usedness estimation module 102 estimates the usedness using a trained machine learning model that has been trained on a dataset that includes, for example, the results of an actual inspection of a used vehicle by a worker (inspector) and used vehicle label information 201 entered by the seller of the used vehicle, as training data. In this case, the usedness estimation module 102 inputs the used vehicle label information 201 of the target vehicle as an explanatory variable into the trained machine learning model and calculates the evaluation score for each item in each subcategory as the output of the machine learning model. The usedness estimation module 102 then calculates the evaluation score for each major category using the weighted points for each major category, and also calculates an overall evaluation score.

[0032] Furthermore, the main items that the worker checks for each subcategory can be as follows, for example, as shown in Figure 7: For subcategory 1, it is the hour meter. For subcategory 2, it is the abnormal noise and exhaust color. For subcategory 3, it is the oil level. The subcategory for number 4 is belt tension. The subcategory for number 5 is hour meter. The subcategory for number 6 is bearing play. The subcategory for number 7 is presence or absence of cracks. The subcategory for number 8 is chain tension. The subcategory for number 9 is play during switching. The subcategory for number 10 is oil leakage. The subcategory for number 11 is shoe wear and oil level. The subcategory for number 12 is wear and cracking. The subcategory for number 13 is hour meter. The subcategory for number 14 is bending, cracking, and wear. The subcategory for number 15 is tilting and cracking. The subcategory for number 16 is oil leakage. The subcategory for number 17 is chain tension. The subcategory for number 18 is bending and cracking. The subcategory for number 19 is bending and cracking. The subcategory for number 20 is oil leakage. The subcategory for number 21 is the hour meter. The subcategory for number 22 is malfunction. The subcategory for number 23 is bending and cracking. The subcategory for number 24 is scratches and tears. The subcategory for number 25 is illumination. The subcategory for number 26 is cracking. The subcategory for number 27 is sound.

[0033] Furthermore, the weighted points shown in Figure 8 represent the point allocation for each major category when the total is 100 points. In the example shown in Figure 8, the weighted points are set as follows: 20 points for the prime mover, 25 points for the running gear, 35 points for the cargo handling equipment, and 20 points for the driver's cab and other components. In this case, the maximum major category evaluation score for the prime mover is 20. The maximum major category evaluation score for the running gear is 25. The maximum major category evaluation score for the cargo handling equipment is 35. The maximum major category evaluation score for the driver's cab and other components is 20. The weighted points can be set based on, for example, the maintenance priority for each major category or the importance of each major category's performance in relation to the vehicle as a whole.

[0034] The item evaluation score is an evaluation score for each subcategory. The item evaluation score will be a value within the evaluation score range. For example, for the engine body of subcategory number 2, the degree of deterioration will be evaluated with an item evaluation score in the range of 0 to 5. When an operator inspects, for example, an item evaluation score of 0 is when the confirmed result is a clear abnormal noise and the exhaust color is gray to black. An item evaluation score of 1 is when the confirmed result is an abnormal noise and the exhaust color is slightly white. An item evaluation score of 2 is when the confirmed result is an abnormal noise and the exhaust color is clear. An item evaluation score of 3 is when the confirmed result is a slight abnormal noise and the exhaust color is slightly white. An item evaluation score of 4 is when the confirmed result is a slight abnormal noise and the exhaust color is clear. An item evaluation score of 5 is when the confirmed result is no abnormal noise and the exhaust color is clear. For example, an item evaluation score of 0 corresponds to a condition in which maintenance is recommended before use. For example, an item evaluation score of 5 corresponds to a condition in which maintenance is not required before use.

[0035] The major category evaluation score is calculated by dividing the weighted points for each major category by the sum of the maximum values ​​within each evaluation point range for each item evaluation point, and then multiplying the sum of the evaluation points for each major category by this coefficient. For example, for prime movers, the sum of the maximum values ​​within each evaluation point range is 20, and the weighted points are 20, so the coefficient is 1, and the sum of the evaluation points for each item becomes the major category evaluation score. Similarly, for running gear, the sum of the maximum values ​​within each evaluation point range is 36, and the weighted points are 25, so the coefficient is 25 / 36 (= approximately 0.69), and the major category evaluation score is approximately 13.9, which is the sum of the evaluation points for each item (20) multiplied by the coefficient 25 / 36.

[0036] Furthermore, the overall score is the sum of the scores for each major category. In the example shown in Figure 8, the score is 34.2 out of a possible 100 points.

[0037] The maintenance module 103 shown in Figure 1 estimates suitable maintenance options and their costs based on the vehicle's used condition. In this embodiment, the list of maintenance options estimated by the maintenance module 103 is called the maintenance menu. Figure 9 shows an example of the maintenance menu displayed on the user terminal 3. The user terminal 3 is a computer such as a personal computer, smartphone, or tablet, and is operated by the buyer. In the example shown in Figure 9, three maintenance courses (Course A, Course B, and Course C), which are maintenance options, are displayed. In this example, Course A is a maintenance option that increases the evaluation score for each item shown in Figure 8 to approximately 70% to 80% of the maximum value. In the example shown in Figure 9, the estimated result that the overall used condition evaluation score increases from 34.2 to 79.2 in the case of Course A, along with the expected cost, is displayed. Course B is a maintenance option that increases the evaluation score for each item in the major category of cargo handling equipment shown in Figure 8 to approximately 70% to 80% of its maximum value, and also increases the evaluation score for each item other than cargo handling equipment that is currently at 0 to 1. In the example shown in Figure 9, the estimated result that the overall usedness evaluation score will increase from 34.2 to 56.7 in the case of Course B, along with the expected cost, is displayed. Figure 10 shows an example of the display screen shown in Figure 9 when Course B is selected. In the example shown in Figure 10, the estimated results of each evaluation score before and after maintenance in the case of Course B are shown for comparison. Course C is a maintenance option that increases the evaluation score for each item that is currently at 0 to 1. In the example shown in Figure 9, the estimated result that the overall usedness evaluation score will increase from 34.2 to 41.7 in the case of Course C, along with the expected cost, is displayed.

[0038] The maintenance module 103 determines the combination of maintenance items and content using a pre-trained machine learning model that takes information (numerical values) representing the combination of maintenance items and content, and the evaluation score for each item of the usedness before maintenance as explanatory variables, and outputs the evaluation score for each item after maintenance. Using this pre-trained machine learning model, the maintenance module 103 takes the evaluation score for each item of the usedness before maintenance as input, tries multiple combinations of maintenance items and content as input, and determines the combination of maintenance items and content that yields an output as close as possible (within a predetermined threshold) to the target value of the evaluation score for each item after maintenance set for each maintenance candidate. In this case, the machine learning model is trained using a dataset that includes combinations of maintenance items and content with a proven track record in the past, and the evaluation scores for each item of the usedness before and after maintenance, as training data.

[0039] The maintenance module 103 estimates the expected cost when the determined combination of maintenance items and content is performed. For example, the maintenance module 103 refers to the maintenance work record DB 15 and the maintenance cost record DB 16 to extract costs similar to the combination of maintenance items and content, and based on the extracted costs, estimates the cost corresponding to the determined combination of maintenance items and content.

[0040] Furthermore, the used vehicle sales system 12 is, for example, a web server, and sends and receives predetermined information via a communication line with the user terminal 3 operated by the buyer of the used vehicle. For example, the used vehicle sales system 12 presents the user terminal 3 with the selling price estimated by the price estimation module 101 for the used vehicle identified by the seller. The used vehicle sales system 12 also presents the user terminal 3 with the selling price estimated by the usedness estimation module 102. The used vehicle sales system 12 also presents the user terminal 3 with the usedness level estimated by the usedness estimation module 102. The used vehicle sales system 12 also presents the user terminal 3 with the maintenance menu and estimated cost estimated by the maintenance module 103. The used vehicle sales system 12 also communicates with the user terminal 3 regarding acceptance or rejection of the sale.

[0041] (Example of operation of a used vehicle information processing device) Referring to Figure 11, an example of the operation of the used vehicle information processing device 1 shown in Figure 1 will be explained. In the process shown in Figure 11, steps S101 to S110 are processes between the seller's user terminal 2 and the used vehicle information processing device 1, and steps S111 to S118 are processes between the buyer's user terminal 3 and the used vehicle information processing device 1.

[0042] In the process shown in Figure 11, the used vehicle purchase system 11 performs a predetermined authentication process, and then receives vehicle-related information such as used vehicle label information 201 from the user terminal 3 (step S101). The vehicle-related information includes, for example, the used vehicle label information 201 and information to improve its reliability.

[0043] Next, the used vehicle purchase system 11 assigns a predetermined identification number to the vehicle (step S102). Next, the usedness estimation module 102 estimates the usedness of the vehicle (step S103). Next, the used vehicle purchase system 11 presents the estimated usedness of the vehicle to the user terminal 2 (step S104). Next, the price estimation module 101 estimates the purchase price (step S105). Next, the used vehicle purchase system 11 presents the estimated purchase price to the user terminal 2 (step S106). Next, the used vehicle purchase system 11 determines whether the user terminal 2 has agreed to purchase the vehicle at the presented purchase price (step S107).

[0044] If no agreement is reached (Step S107: No), the used vehicle purchase system 11 determines whether the number of disagreements has exceeded a predetermined number (Step S108). If the number of disagreements has not exceeded a predetermined number (Step S108: No), the price estimation module 101 re-estimates the purchase price after, for example, changing the estimation method (Step S105). Next, the used vehicle purchase system 11 presents the estimated purchase price to the user terminal 2 (Step S106). If the number of disagreements has exceeded a predetermined number (Step S108: Yes), the used vehicle purchase system 11 terminates the process shown in Figure 11.

[0045] On the other hand, if an agreement is reached (Step S107: Yes), the used vehicle information DB13 and the purchase / sales record DB14 record that a sales contract has been concluded and store the purchase information (Step S109). The purchase information includes, for example, vehicle identification information, buyer identification information, used vehicle label information (specification information), used vehicle label information (usage information), used condition information, date and time of sale (purchase), and purchase price information, as shown in Figures 3 and 4.

[0046] Subsequently, in the used vehicle sales system 12, after the buyer's user terminal 3 performs a predetermined authentication process, if an inquiry about the vehicle is received, the maintenance module 103 calculates the maintenance menu, cost, and the used condition after maintenance (step S111). Next, the used vehicle sales system 12 presents the maintenance menu, cost, and used condition after maintenance to the user terminal 3 (step S112). Next, the price estimation module 101 estimates the selling price (step S113). Next, the used vehicle sales system 12 presents the vehicle's used condition before maintenance and the selling price to the user terminal 3 (step S114).

[0047] Next, the used vehicle sales system 12 determines whether an agreement has been reached to sell the vehicle at the price presented on the user terminal 3 (step S115).

[0048] If no agreement is reached (Step S115: No), the used vehicle sales system 12 determines whether the number of disagreements has exceeded a predetermined number (Step S116). If the number of disagreements has not exceeded a predetermined number (Step S116: No), the price estimation module 101 re-estimates the selling price after, for example, changing the estimation method (Step S113). Next, the used vehicle sales system 12 presents the estimated selling price to the user terminal 3 (Step S114). If the number of disagreements has exceeded a predetermined number (Step S116: Yes), the used vehicle sales system 12 terminates the process shown in Figure 11.

[0049] On the other hand, if an agreement is reached (Step S115: Yes), the used vehicle information DB13 and the purchase / sales record DB14 record that a sales contract has been concluded and store the sales information (Step S117). The sales information includes, for example, seller identification information, the date and time of sale, and the sales price, as shown in Figures 3 and 4. At this point, the process shown in Figure 11 is completed.

[0050] Next, with reference to Figures 12 to 18, examples of the flow of information in the used vehicle information processing device 1, user terminal 2, and user terminal 3 will be explained. Figure 12 shows the flow of information corresponding to steps S101 to S105 shown in Figure 11. Figure 13 shows the flow of information corresponding to steps S106, S107:Yes to S110 shown in Figure 11. Figure 14 shows the flow of information corresponding to steps S106, S107: No to S108: Yes shown in Figure 11. Figure 15 shows the flow of information corresponding to steps S111 to S112 shown in Figure 11. Figure 16 shows the flow of information corresponding to steps S113 to S114 shown in Figure 11. Figure 17 shows the flow of information corresponding to steps S114, S115: Yes to S118 shown in Figure 11. Figure 18 shows the flow of information corresponding to steps S114, S115: No to S115: Yes shown in Figure 11.

[0051] In the example shown in Figure 12, when used vehicle label information 201 is entered at user terminal 2 (S201), the used vehicle label information 201 is sent from user terminal 2 to used vehicle purchase system 11 (S202). The used vehicle purchase system 11 assigns a number (S203), and the used vehicle label information 201 is sent to used vehicle information DB 13 for registration (S204). The usedness estimation module 102 retrieves the used vehicle label information 201 from the used vehicle information DB 13 in accordance with the numbering by the used vehicle purchase system 11 (S203), for example (S205). The price estimation module 101 also retrieves the used vehicle label information 201 from the used vehicle information DB 13 in accordance with the numbering by the used vehicle purchase system 11 (S203), for example (S206), and retrieves sales performance information from the purchase / sales performance information DB 14 (S207).

[0052] When the usedness estimation module 102 estimates the usedness (S208), the usedness information indicating the estimated usedness is sent to the used vehicle purchase system 11 (S209) and registered in the used vehicle information DB 13 (S210). The used vehicle purchase system 11 sends the usedness information to the user terminal 2 (S211), and the usedness is displayed on the user terminal 2 (S212).

[0053] Meanwhile, the price estimation module 101 estimates the purchase price (S213 (Figures 12 and 13)), and purchase price information showing the estimated purchase price is sent to the used vehicle purchase system 11 (S214). The used vehicle purchase system 11 sends the purchase price information to the user terminal 2 (S215), and the purchase price is displayed on the user terminal 2 (S216). At this point, if the user terminal 2 inputs an agreement (S217), the sale agreement information is sent from the user terminal 2 to the used vehicle purchase system 11 (S218).

[0054] The sales agreement information is sent from the used vehicle purchase system 11 to the used vehicle information DB 13 (S219), and then from the used vehicle information DB 13 to the purchase / sales record information DB 14 (S220). Next, the purchase / sales record information DB 14 sends the sales completion information to the used vehicle information DB 13 (S221), and the used vehicle information DB 13 sends the sales completion information to the used vehicle purchase system 11 (S222). The used vehicle purchase system 11 sends the sales completion information to the user terminal 2 (S223), and the sales completion information is displayed on the user terminal 2 (S224).

[0055] On the other hand, as shown in Figure 14, if the user terminal 2 inputs a rejection of the purchase price (S225) in response to the offer of the purchase price (S216), the sales refusal information is sent from the user terminal 2 to the used vehicle purchase system 11 (S226).

[0056] The information regarding the refusal to sell is sent from the used vehicle purchase system 11 to the used vehicle information DB 13 (S227), and then from the used vehicle information DB 13 to the purchase / sales record information DB 14 (S228). Next, the purchase / sales record information DB 14 deletes the information related to the assigned number and then sends the unsuccessful sale information to the used vehicle information DB 13 (S229). After the used vehicle information DB 13 deletes the information related to the assigned number, it sends the unsuccessful sale information to the used vehicle purchase system 11 (S230). The used vehicle purchase system 11 sends the unsuccessful sale information to the user terminal 2 (S231), and the unsuccessful sale information is displayed on the user terminal 2 (S232).

[0057] In the example shown in Figure 15, when information identifying a vehicle (vehicle identification information) is entered on the user terminal 3 (S301), the vehicle identification information is sent from the user terminal 3 to the used vehicle sales system 12 (S302). Here, the used vehicle sales system 12 identifies the target vehicle (S303). Next, in response to the vehicle identification (S303), the maintenance module 103 obtains usedness information from the used vehicle information DB 13 (S304), and the price estimation module 101 also obtains usedness information from the used vehicle information DB 13 (S305).

[0058] The maintenance module 103 further acquires maintenance work performance information from the maintenance work performance DB 16 (S306) and maintenance cost performance information from the maintenance cost performance DB 15 (S307). Next, the maintenance module 103 generates a maintenance menu (S308), and the maintenance menu information indicating the maintenance menu is sent to the used vehicle sales system 12 (S309). The used vehicle sales system 12 sends the maintenance menu information to the user terminal 3 (S310), and the maintenance menu is presented to the user terminal 3 (S311).

[0059] Furthermore, as shown in Figure 16, the price estimation module 101 obtains sales performance information from the purchase / sales performance DB 14 (S312) and estimates the selling price (S313). The estimated selling price information is sent to the used vehicle sales system 12 (S314). The used vehicle sales system 12 sends the selling price information to the user terminal 3 (S315), and the selling price is displayed on the user terminal 3 (S316).

[0060] Furthermore, as shown in Figure 17, when the user terminal 3 inputs an agreement to the proposed sale price (S316) (S317) and inputs an option to select from the proposed maintenance menu (S311) (S318), the purchase agreement information is sent to the used vehicle sales system 12 (S319), and the maintenance menu selection information is also sent to the used vehicle sales system 12 (S320). The used vehicle sales system 12 registers the maintenance menu selection information in the maintenance work performance DB 16 (S321) and in the maintenance cost performance DB 15 (S322). The used vehicle sales system 12 also sends the purchase agreement information to the purchase / sale performance DB 14 (S323).

[0061] Next, the purchase / sales record DB14 sends the transaction completion information to the used vehicle sales system 12 (S324), the used vehicle sales system 12 sends the transaction completion information to the user terminal 3 (S325), and the transaction completion information is displayed on the user terminal 3 (S326). The purchase / sales record DB14 sends the transaction completion information to the used vehicle information DB13 (S327).

[0062] Furthermore, as shown in Figure 18, when the user terminal 3 inputs a rejection of the offered selling price (S316) (S328), the purchase rejection information is sent to the used vehicle sales system 12 (S329). The used vehicle sales system 12 sends the purchase rejection information to the purchase / sales record DB 14 (S330). The purchase / sales record DB 14 sends the unsuccessful sale information to the used vehicle sales system 12 (S331), the used vehicle sales system 12 sends the unsuccessful sale information to the user terminal 3 (S332), and the unsuccessful sale information is displayed on the user terminal 3 (S333).

[0063] Next, referring to Figure 19, a modified example of the operation example of the used vehicle information processing device 1 described with reference to Figure 11 will be explained. In the process shown in Figure 11, the timing of the conclusion of the purchase contract and the conclusion of the sale contract are different. In contrast, in the process shown in Figure 19, the timing of the conclusion of the purchase contract and the conclusion of the sale contract are simultaneous. In the process shown in Figure 19, the process of step S109 shown in Figure 11 is deleted, and the processes of steps S117 and S118 are modified (shown as steps S117a and S118a). Other processes are the same in Figures 11 and 19. In step S117a, processing related to the conclusion of the purchase and sale contracts is performed, and in step S118a, the purchase and sale information is stored.

[0064] (Effects and modifications of the first embodiment) As described above, according to this embodiment, both the buyer and the seller can appropriately understand the deterioration state of a used vehicle.

[0065] According to this embodiment, when a sales contract is concluded between the seller, intermediary, and buyer, the information on the used vehicle, along with the purchase / sale information and the vehicle's used condition information, is linked and added to the database, so that it can be used as data for future price estimations and used condition estimations.

[0066] Furthermore, it is possible to set an arbitrary time period for the data held in each database. For example, data prior to an arbitrary time period may be excluded from use for price estimation, used-condition estimation, and maintenance menu selection and cost estimation.

[0067] Furthermore, a function may be added to flag unique data in each database, such as cases where an item was sold extremely cheaply or extremely expensively due to special circumstances, cases where maintenance not normally performed was carried out, cases where maintenance was performed extremely cheaply or extremely expensively due to special circumstances. For example, this data could be excluded when estimating prices, estimating the degree of use, selecting maintenance menus, and estimating their costs.

[0068] When setting up algorithms for price estimation, deterioration estimation, maintenance menu selection, and cost estimation, it may be possible to construct a price estimation algorithm using statistical methods (such as multiple regression analysis) or various AI (artificial intelligence) with, for example, 80% of all data related to both types of equipment (e.g., forklifts), and then perform trial calculations with the remaining 20% ​​to select the algorithm with the highest accuracy.

[0069] The process shown in Figure 11 involves presenting the seller with a purchase price calculated by a price estimation algorithm plus a certain profit margin, and a usedness rating calculated by a usedness estimation algorithm. If an agreement is reached, a purchase contract is concluded even if the buyer is not yet determined. Furthermore, prospective buyers can be presented with the vehicle's current usedness rating, various maintenance options and their prices, and how the usedness rating would change if maintenance were performed.

[0070] Furthermore, the process shown in Figure 19 involves first presenting the seller with a purchase price calculated by a price estimation algorithm plus a certain profit margin, and the usedness level calculated by a usedness estimation algorithm. If an agreement is reached, the purchase is tentatively reserved. Next, the buyer is presented with the sales price, usedness level, selected maintenance menu, and maintenance costs calculated by adding a certain profit margin to the estimate. Once an agreement is reached, a purchase contract is concluded with the seller and a sales contract with the buyer. For prospective buyers, it is possible to present not only the current usedness level of the vehicle, but also various maintenance menus and their prices, as well as the change in usedness level after maintenance is performed.

[0071] For commercial vehicles, accurate price estimation is difficult due to the limited amount of data available compared to private vehicles and the numerous factors influencing price estimation. Furthermore, sellers lack objective data on the degree of deterioration of their used vehicles, making it difficult to judge the reasonableness of their selling prices. Buyers, on the other hand, lack quantitative data on how much performance can be improved through various maintenance procedures and the associated costs. This embodiment improves the accuracy of price estimation and provides information on the degree of wear and tear, the degree of recovery of the vehicle's condition through various maintenance procedures, and the associated costs. As a result, it is expected that fair used car transactions will become possible, and the volume of such transactions will increase.

[0072] Furthermore, when a sales contract is concluded between a seller, intermediary, and buyer, the information on the used vehicle, purchase and sales information, vehicle condition information, and the maintenance menus performed and their costs are linked and added to the database. This makes it easy to use the data for future price estimation (price assessment), condition estimation, and maintenance menu selection and cost estimation. As a result, the accuracy of price estimation, condition estimation, and maintenance menu selection and cost estimation improves as the number of contracts increases.

[0073] Furthermore, by eliminating outdated data that no longer reflects current conditions, the accuracy of price estimation, used-condition estimation, and maintenance menu selection and cost estimation is improved.

[0074] Furthermore, eliminating outlier data improves the accuracy of price estimation, usedness estimation, maintenance menu selection, and cost estimation.

[0075] Furthermore, once a certain amount of new data has been accumulated, the estimation accuracy can be further improved by updating the algorithm in the same way, utilizing this data as well.

[0076] Furthermore, according to the process shown in Figure 11, it becomes possible to purchase vehicles from sellers in a relatively short time, and sellers feel more secure in making buying and selling decisions by referring to the objective usedness of the vehicles they are putting up for sale, thus stabilizing the availability of used vehicles for transaction intermediaries. On the other hand, if no buyer is found, the vehicles become inventory, posing a risk of reduced profits. However, since the degree of usedness is presented to buyers, as well as maintenance menus and their prices, buyers feel more secure and have more freedom of choice, reducing the risk of unsold inventory. As the accuracy of price estimation and usedness estimation improves, and the accuracy of maintenance menu selection and cost estimation decreases, it is desirable to use this method when the amount of information accumulated in each database is substantial and the accuracy of price estimation and usedness estimation has improved.

[0077] Furthermore, according to the process shown in Figure 19, since the vehicle is purchased from the seller only after a buyer has been secured, there is no risk of it becoming unsold inventory. However, there is no guarantee that the seller will continue the provisional contract until a buyer is found, which increases the risk of difficulty in securing used vehicles. By presenting the vehicle's used condition to both the seller and the buyer through the intermediary, both parties can use this common indicator to determine the purchase price. Additionally, the buyer is presented with a maintenance menu and its price, which increases their sense of security and freedom of choice, thus increasing the success rate of the contract. It is desirable to use this method when the amount of information stored in the database is small and the accuracy of price estimation or medium-to-high-level estimation is low.

[0078] <Second Embodiment> Next, a used vehicle information processing device, a used vehicle information processing method, and a program according to the second embodiment of this disclosure will be described with reference to Figures 20 to 22. Figure 20 is a block diagram showing an example configuration of the used vehicle information processing device according to the second embodiment of this disclosure. Figure 21 is a flowchart showing an example of operation of the used vehicle information processing device according to the second embodiment of this disclosure. Figure 22 is a schematic diagram illustrating an example of operation of the used vehicle information processing device according to the second embodiment of this disclosure.

[0079] The used vehicle information processing device 1a according to the second embodiment shown in Figure 20 differs from the used vehicle information processing device 1 according to the first embodiment shown in Figure 1 in that the used vehicle information processing device 10a, which corresponds to the used vehicle information processing device 10 shown in Figure 1, is newly equipped with a used vehicle information conversion module 104. Other configurations are the same as those in the first and second embodiments. The used vehicle information conversion module 104 has a functional configuration that estimates missing information when there is a omission in the usage information 203 included in the used vehicle label information 201, which was explained with reference to Figure 2.

[0080] In the used vehicle information processing device 1a of the second embodiment, for example, in the process of step S103 shown in Figures 11 and 19, if there is a gap in the usage information 203 (step S401: Yes), as shown in Figure 21, the used vehicle information conversion module 104 estimates the missing information to fill it in (step S402), and the usedness estimation module 102 estimates the usedness (step S403).

[0081] For example, in the case of used vehicles where the operator cannot obtain repair or maintenance records, the usage information 203 depends on information from the seller of the used vehicle, and therefore, information other than the hour meter (usage time) shown as numerical data may contain errors or omissions. Furthermore, even if data such as maintenance records are available, it is conceivable that the inspection items may differ from general maintenance items. Therefore, there is a risk that it may be difficult to input the data directly into the usedness estimation module 102. In addition, if usedness estimation is performed with missing (incomplete) data, there is a concern that the accuracy will decrease. Therefore, if there is a omission in the usage information 203, the used vehicle information conversion module 104 estimates the missing information from other usage information 203 using a trained machine learning model. An example of operation by the used vehicle information conversion module 104 will be explained.

[0082] (1) Process of forming missing data estimation logic (1-1) Classification of used vehicles for sale First, the usage information 203 is classified by the type of engine and the size and type of vehicle. For example, for forklifts are classified into a total of 10 categories: three types of engine-powered forklifts (small, medium, and large) and seven types of battery-powered forklifts (counterbalance, reach, picking, rack forklift, walkie, explosion-proof, and refrigerated / freezer type). The aim is to improve the accuracy of the estimation by using usage information 203 for similar vehicles.

[0083] (1-2) Collection of usage information for each category (203 items) The used vehicle label information (usage information) stored in the used vehicle information DB13 will be aggregated by category (1-1) to serve as the source data for machine learning. In addition, the usage information 203 associated with each individual vehicle will be used as a set of datasets. It is assumed that hour meter information is always present.

[0084] (1-3) Creating training data From the datasets aggregated in (1-2), select an arbitrary number of sets to be used as candidates for creating training data (the more sets selected, the higher the accuracy, but the more effort required to build the logic). Additionally, create a dataset by intentionally removing one usage information 203 (let's call it label information A) from the selected category datasets, and use this as training data. Furthermore, create a dataset by randomly removing one or more usage information 203 other than label information A, in addition to label information A, and use this as training data. The dataset before removing level information A is used as the ground truth data.

[0085] (1-4) Building machine learning and estimation logic using training data The machine learning model is trained using the dataset prepared in (1-3) above, and a logic is formed within the machine learning model to estimate the value of label A from other data if label information A is missing.

[0086] (1-5) Development of a lineup of logic for estimating missing data For all usage information 203 except for hour meter information, (1-3) and (1-4) will be performed to create a lineup that allows estimation even if any data is missing. If, for reasons such as a small dataset, the accuracy rate of estimation by the trained machine learning model for any item of usage information 203 falls below a predetermined value (e.g., 90%), the following simplified estimation will be used as a provisional measure until more data is accumulated. If the coefficient information for each item is unknown, it will be calculated as 1.0.

[0087] Simplified Estimated Evaluation Score = Maximum Value in Evaluation Score Range × (Hour Meter Evaluation Score / 10) × Industry Coefficient × Environmental Coefficient × Cargo Coefficient × Failure History Coefficient × Country of Use Coefficient

[0088] An example of the coefficient is shown in Figure 22.

[0089] (2) Estimation process for missing data (2-1) Classification of target used vehicles The seller of a used vehicle obtains used vehicle label information 201 through the used vehicle purchase system 11 and determines which category (1-1) the vehicle falls into.

[0090] (2-2) Display of usage information 202 for the applicable category On user terminal 2, the sheet containing usage information 203 for the relevant category is displayed.

[0091] (2-3) Importing usage information 203 Usage information 203 is imported into the sheet from the used car purchase system 11. Note that any fields without corresponding data will be left blank.

[0092] (2-4) Manual supplementation of missing data Regarding missing data (blank spaces), we will supplement the data obtained by conducting interviews with sellers of used vehicles or, if the used vehicles have already been acquired, performing simple inspections.

[0093] (2-5) Estimation of missing data using a trained machine learning model When the usage information sheet 203, with the data supplemented as described in (2-4) above, is input into the trained machine learning model of the used car information conversion module 104, estimation is performed using the missing data estimation logic prepared in (1-5). The estimation results are entered into the blank spaces on the usage information sheet 203 and output.

[0094] According to this embodiment, even if there are missing items in the usage information 203, the missing blind content can be estimated and supplemented.

[0095] <Basic Configuration Example of an Embodiment> Figure 23 is a block diagram showing basic configuration examples of the used vehicle information processing device 1 according to the first embodiment and the used vehicle information processing device 1a according to the second embodiment. The used vehicle information processing device 300 shown in Figure 23 has a configuration corresponding to the used vehicle information processing device 1 according to the first embodiment and the used vehicle information processing device 1a according to the second embodiment. The used vehicle information processing device 300 shown in Figure 23 includes an acquisition unit 301, a deterioration degree estimation unit 302, a presentation unit 303, a maintenance estimation unit 304, and a used vehicle information estimation unit 305.

[0096] The acquisition unit 301 acquires used vehicle information, which includes specification information, which is information regarding the specifications of the used vehicle, and usage information, which is information regarding the use of the used vehicle. The acquisition unit 301 corresponds to the used vehicle purchase system 11. The used vehicle information corresponds to the used vehicle label information 201.

[0097] The deterioration degree estimation unit 302 estimates the degree of deterioration of a used vehicle using used vehicle information as an explanatory variable. The deterioration degree estimation unit 302 corresponds to the used vehicle degree estimation module 102.

[0098] The display unit 303 presents the estimated degree of deterioration to the buyer of the used vehicle. The display unit 303 corresponds to the used vehicle sales system 12.

[0099] The maintenance estimation unit 304 estimates suitable maintenance options and their associated costs for the used vehicle based on its degree of deterioration. The presentation unit 303 then presents the estimated degree of deterioration, options, and costs to the buyer. The maintenance estimation unit 304 corresponds to the maintenance module 103.

[0100] Furthermore, the display unit 303 may also display the estimated degree of deterioration to the seller of the used vehicle. In this case, the display unit 303 also supports the used vehicle purchase system 11.

[0101] Furthermore, if there are omissions in the content of the usage information 203, the used vehicle information estimation unit 305 estimates the missing content using other available content from the usage information 203 as explanatory variables. The used vehicle information estimation unit 305 corresponds to the used vehicle information conversion module 104.

[0102] The used vehicle is equipped with a prime mover, a running gear, and a working gear, and the deterioration degree estimation unit 302 may estimate the degree of deterioration for each predetermined component of the prime mover, each predetermined component of the running gear, and each predetermined component of the working gear. Here, the working gear corresponds to a cargo handling device.

[0103] Furthermore, the deterioration degree estimation unit 302 can estimate the degree of deterioration by setting different weights for the prime mover, the running gear, and the working gear.

[0104] According to the used vehicle information processing device 300, the deterioration status of used vehicles can be appropriately determined.

[0105] <Effects and Effects> The used vehicle information processing device, used vehicle information processing method, and program configured as described above include an acquisition unit 301 that acquires used vehicle information (used vehicle label information 201) which includes specification information 202, which is information regarding the specifications of the used vehicle, and usage information 203, which is information regarding the use of the used vehicle, and a deterioration degree estimation unit 302 that estimates the degree of deterioration of the used vehicle using the used vehicle information as explanatory variables. Therefore, according to the used vehicle information processing device, used vehicle information processing method, and program of the embodiment, the deterioration state of the used vehicle can be appropriately grasped.

[0106] <Other Embodiments> Although embodiments of this disclosure have been described in detail above with reference to the drawings, the specific configuration is not limited to these embodiments and may include design changes and the like that do not depart from the gist of this disclosure.

[0107] <Computer Configuration> Figure 24 is a schematic block diagram showing the configuration of a computer according to at least one embodiment. The computer 90 includes a processor 91, main memory 92, storage 93, and an interface 94. The used vehicle information processing devices 1, 1a, and 300 described above are implemented in the computer 90. The operation of each processing unit described above is stored in storage 93 in the form of a program. The processor 91 reads the program from storage 93, loads it into main memory 92, and executes the above processing according to the program. The processor 91 also allocates storage areas in main memory 92 corresponding to each of the storage units described above, according to the program.

[0108] The program may be for implementing some of the functions that the computer 90 is to perform. For example, the program may perform functions in combination with other programs already stored in storage, or in combination with other programs implemented in other devices. In other embodiments, the computer may include a custom LSI (Large Scale Integrated Circuit) such as a PLD (Programmable Logic Device) in addition to, or instead of, the above configuration. Examples of PLDs include PAL (Programmable Array Logic), GAL (Generic Array Logic), CPLD (Complex Programmable Logic Device), FPGA (Field Programmable Gate Array), etc. In this case, some or all of the functions implemented by the processor may be implemented by the integrated circuit.

[0109] Examples of storage 93 include HDDs (Hard Disk Drives), SSDs (Solid State Drives), magnetic disks, magneto-optical disks, CD-ROMs (Compact Disc Read Only Memory), DVD-ROMs (Digital Versatile Disc Read Only Memory), and semiconductor memory. Storage 93 may be an internal medium directly connected to the bus of the computer 90, or an external medium connected to the computer 90 via an interface 94 or a communication line. Furthermore, if this program is distributed to the computer 90 via a communication line, the computer 90 that receives the program may expand it into main memory 92 and execute the above processing. In at least one embodiment, storage 93 is a tangible storage medium that is not temporary.

[0110] <Note> The used vehicle information processing device described in each embodiment can be understood, for example, as follows:

[0111] (1) The used vehicle information processing devices 1, 1a and 300 according to the first embodiment include an acquisition unit 301 that acquires used vehicle information including specification information, which is information regarding the specifications of the used vehicle, and usage information, which is information regarding the use of the used vehicle; a deterioration degree estimation unit 302 that estimates the degree of deterioration of the used vehicle using the used vehicle information as an explanatory variable; and a presentation unit 303 that presents the estimated degree of deterioration to the buyer of the used vehicle. According to this embodiment and the following embodiments, the deterioration state of a used vehicle can be appropriately grasped.

[0112] (2) The used vehicle information processing devices 1, 1a and 300 according to the second embodiment are the used vehicle information processing devices 1, 1a and 300 of (1), further comprising a maintenance estimation unit 304 that estimates suitable maintenance candidates and the costs required for such maintenance based on the degree of deterioration, and the presentation unit 303 presents the estimated degree of deterioration, the candidates and the costs to the buyer.

[0113] (3) The used vehicle information processing devices 1, 1a and 300 according to the third embodiment are the used vehicle information processing devices 1, 1a and 300 of (1) or (2), wherein the presentation unit 303 also presents the estimated degree of deterioration to the seller of the used vehicle.

[0114] (4) The used vehicle information processing devices 1, 1a and 300 according to the fourth embodiment are the used vehicle information processing devices 1, 1a and 300 of (1) to (3), further comprising a used vehicle information estimation unit 305 that, when there is a omission in the content of the usage information, estimates the omission using other content of the usage information that is not missing as an explanatory variable.

[0115] (5) The used vehicle information processing devices 1, 1a and 300 according to the fifth embodiment are the used vehicle information processing devices 1, 1a and 300 of (1) to (4), wherein the used vehicle is equipped with a prime mover, a running gear and a work gear, and the deterioration degree estimation unit estimates the degree of deterioration for each predetermined component of the prime mover, each predetermined component of the running gear and each predetermined component of the work gear.

[0116] (6) The used vehicle information processing devices 1, 1a and 300 according to the sixth embodiment are the used vehicle information processing devices 1, 1a and 300 of (1) to (4), wherein the deterioration degree estimation unit 302 estimates the deterioration degree by setting different weights for the prime mover, the running gear and the work gear. [Explanation of symbols]

[0117] 1, 1a, 300... Used Vehicle Information Processing Device 10, 10a... Used Vehicle Information Processing Unit 11…Used vehicle purchase system 12…Used vehicle sales system 101...Price Estimation Module 102... Used condition estimation module 103... Maintenance Module 104...Used Vehicle Information Conversion Module 301…Acquisition Department 302...Deterioration degree estimation section 303…Deterioration degree presentation section 304... Maintenance Estimation Department 305... Used Vehicle Information Estimation Department

Claims

1. An acquisition unit that acquires used vehicle information including specification information, which is information regarding the specifications of the used vehicle, and usage information, which is information regarding the use of the used vehicle. A deterioration estimation unit that estimates the degree of deterioration of a used vehicle using the used vehicle information as an explanatory variable, A display unit that presents the estimated degree of deterioration to the buyer of the used vehicle, Based on the degree of deterioration, a maintenance estimation unit estimates suitable maintenance options for the used vehicle and the costs required for those maintenance options. Equipped with, The presentation unit presents the estimated degree of deterioration, the candidate, and the cost to the buyer. Each of the multiple maintenance candidates has a target value set for the evaluation score indicating the degree of deterioration after maintenance. The aforementioned maintenance estimation unit is Using a trained machine learning model that takes information representing the combination of maintenance items and content, and the evaluation score of the degree of deterioration before maintenance as explanatory variables, and outputs the evaluation score of the degree of deterioration after maintenance, a combination of maintenance items and content that obtains an evaluation score within a predetermined threshold for the target value is determined for each of the multiple maintenance candidates. Furthermore, by referring to information showing actual maintenance work and maintenance costs, costs similar to the determined combination of maintenance items and content are extracted, and the cost corresponding to the determined combination of maintenance items and content is estimated based on the extracted costs. Used vehicle information processing device.

2. The display unit also displays the estimated degree of deterioration to the seller of the used vehicle. The used vehicle information processing device according to claim 1.

3. If there are omissions in the content of the aforementioned usage information, the system further comprises a used vehicle information estimation unit that estimates the missing content by using other available content of the usage information as explanatory variables. The used vehicle information processing device according to claim 1 or 2.

4. The aforementioned used vehicle is equipped with a prime mover, a running gear, and a work device. The deterioration degree estimation unit estimates the degree of deterioration for each predetermined component of the prime mover, each predetermined component of the running gear, and each predetermined component of the working gear. The used vehicle information processing device according to claim 3.

5. The deterioration degree estimation unit estimates the degree of deterioration by setting different weights for the prime mover, the running gear, and the working gear. The used vehicle information processing device according to claim 4.

6. A used vehicle information processing device acquires used vehicle information which includes specification information which is information relating to the specifications of a used vehicle and usage information which is information relating to the use of the used vehicle, The used vehicle information processing device performs the step of estimating the degree of deterioration of the used vehicle using the used vehicle information as an explanatory variable, The used vehicle information processing device provides the estimated degree of deterioration to the buyer of the used vehicle. The used vehicle information processing device performs the steps of estimating suitable maintenance candidates for the used vehicle and the cost required for those maintenance, based on the degree of deterioration. Includes, In the aforementioned step, the estimated degree of deterioration, the candidate, and the cost are further presented to the buyer. Each of the multiple maintenance candidates has a target value set for the evaluation score indicating the degree of deterioration after maintenance. In the estimation step described above, further, The process involves determining, for each of several maintenance candidates, a combination of maintenance items and content that yields an evaluation score within a predetermined threshold relative to the target value, using a trained machine learning model that takes information representing the combination of maintenance items and content, and the evaluation score of the degree of deterioration before maintenance as explanatory variables, and outputs the evaluation score of the degree of deterioration after maintenance. The steps include: referring to information showing actual maintenance work and maintenance costs, extracting costs similar to the determined combination of maintenance items and content, and estimating the cost corresponding to the determined combination of maintenance items and content based on the extracted costs; including, Method for processing information on used vehicles.

7. A step of obtaining used vehicle information which includes specification information which is information regarding the specifications of the used vehicle and usage information which is information regarding the use of the used vehicle, The steps include: estimating the degree of deterioration of the used vehicle using the aforementioned used vehicle information as an explanatory variable; The steps include presenting the estimated degree of deterioration to the buyer of the used vehicle, Based on the degree of deterioration, the steps include: estimating suitable maintenance options for the used vehicle and the costs required for those maintenance options; Have the computer run it, In the aforementioned step, the estimated degree of deterioration, the candidate, and the cost are further presented to the buyer. Each of the multiple maintenance candidates has a target value set for the evaluation score indicating the degree of deterioration after maintenance. In the estimation step described above, further, The process involves determining, for each of several maintenance candidates, a combination of maintenance items and content that yields an evaluation score within a predetermined threshold relative to the target value, using a trained machine learning model that takes information representing the combination of maintenance items and content, and the evaluation score of the degree of deterioration before maintenance as explanatory variables, and outputs the evaluation score of the degree of deterioration after maintenance. The steps include: referring to information showing actual maintenance work and maintenance costs, extracting costs similar to the determined combination of maintenance items and content, and estimating the cost corresponding to the determined combination of maintenance items and content based on the extracted costs; Make the computer execute it. program.

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