Information processing systems, information processing methods, and programs

Abstract

We provide technologies that improve usability. [Solution] According to one aspect of the present invention, an information processing system is provided, comprising at least one processor, the processor being configured to execute a program such that the following steps are performed: in the acquisition step, vehicle information is acquired, the vehicle information includes image data of a target vehicle that has been damaged in an accident; in the presentation step, output information relating to the value of the target vehicle is presented based on the vehicle information and a trained model, the trained model is a model that has been pre-trained using data that includes at least first data as training data, the first data consisting of at least image data of a vehicle in its damaged state due to an accident and information relating to the transaction of the vehicle in its damaged state.

Description

Technical Field

[0001] The present invention relates to an information processing system, an information processing method, and a program.

Background Art

[0002] Patent Document 1 discloses a damaged vehicle appraisal system that appraises not only general used vehicles but also damaged vehicles and provides information on the appraisal results to users.

[0003] This damaged vehicle appraisal system includes a user terminal, which is an information processing device operated by a user who requests a used vehicle dealer to purchase a damaged vehicle, and a damaged vehicle appraisal server that appraises the purchase of the user's damaged vehicle. The damaged vehicle appraisal server receives damage information including information on the degree of damage to the damaged vehicle from the user terminal and calculates a purchase appraisal amount based on the received damage information.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, there is still room for improvement in the functions according to the above-known technology.

[0006] In view of the above circumstances, the present invention aims to provide a technology for improving user usability.

Means for Solving the Problems

[0007] According to one aspect of the present invention, an information processing system is provided, comprising at least one processor, the processor being configured to execute a program such that the following steps are performed: in the acquisition step, vehicle information is acquired, the vehicle information includes image data of a target vehicle that has been damaged in an accident; in the presentation step, output information relating to the value of the target vehicle is presented based on the vehicle information and a trained model, the trained model being a model that has been pre-trained using data including at least first data as training data, the first data comprising at least image data of a vehicle in its damaged state due to an accident and information relating to the transaction of the vehicle in its damaged state.

[0008] This disclosure provides technologies that improve usability. [Brief explanation of the drawing]

[0009] [Figure 1] This is a diagram showing the configuration of the information processing system 1 according to this embodiment. [Figure 2] This is a block diagram showing the hardware configuration of the information processing device 2. [Figure 3] This is a block diagram showing the hardware configuration of user terminal 3. [Figure 4] This is a functional block diagram showing the functions of the information processing system 1 according to this embodiment. [Figure 5] This flowchart shows an overview of the processes performed by Information Processing System 1. [Figure 6] This is an activity diagram showing specific examples of processes performed by Information Processing System 1. [Figure 7] This is a schematic diagram showing an example of information input screen 4a, which is one of the information input screens 4, and displays visual information for inputting information about the target vehicle Vt. [Figure 8] This is a schematic diagram showing an example of information input screen 4b, which is one of the information input screens 4, and shows the state in which information regarding the target vehicle Vt has been entered. [Figure 9]This is a schematic diagram showing an example of information input screen 4c, which is one of the information input screens 4, indicating that information regarding the target vehicle Vtx has been entered. [Figure 10] This is a schematic diagram showing an example of image registration screen 5a, which displays visual information for registering image data DImt of the target vehicle Vt, among the image registration screens 5. [Figure 11] This is a schematic diagram showing an example of image registration screen 5a, which is one of the image registration screens 5, and shows the state in which image data DImtx, in which the target vehicle Vtx has been captured, has been input. [Figure 12] This is a schematic diagram showing an example of the result display screen 6, which displays output information IFo regarding the value of the target vehicle Vtx. [Figure 13] This is a schematic diagram showing an example of image registration screen 5c, which displays visual information for registering image data DImt of the target vehicle Vt, among the image registration screens 5. [Modes for carrying out the invention]

[0010] Embodiments of this disclosure will be described below with reference to the drawings. The various features shown in the embodiments below are interchangeable.

[0011] Incidentally, the program for implementing the software appearing in one embodiment may be provided as a non-transitory computer-readable medium, or it may be provided as a downloadable medium from an external server, or it may be provided so that the program is launched on an external computer and its functions are realized on a client terminal (so-called cloud computing).

[0012] Also, in various information processing according to an embodiment, an input and an output corresponding to the input can be realized. Here, if an output is obtained as a result of the input, the form of the information (hereinafter referred to as reference information) referred to in such information processing is not limited. The reference information may be, for example, rule-based information such as a database, a lookup table, a predetermined function (including a judgment formula such as a regression formula constructed by a statistical method), a learned model in which the correlation between the input and the output is learned in advance, or a large language model capable of outputting a desired result by inputting a prompt.

[0013] Also, in one embodiment, the "unit" may include, for example, hardware resources implemented by a circuit in a broad sense and information processing of software that can be specifically realized by these hardware resources. Also, in one embodiment, various information is handled, and these information are represented, for example, by physical values of signal values representing voltage and current, the level of signal values as a set of binary bits composed of 0 or 1, or quantum superposition (so-called quantum bits), and communication and calculation can be executed on a circuit in a broad sense.

[0014] Furthermore, a circuit in a broad sense is a circuit realized by appropriately combining at least a circuit (Circuit), circuitry (Circuitry), a processor (Processor), a memory (Memory), etc. Also, the processor may be a general-purpose processor or a dedicated circuit. That is, it includes an application specific integrated circuit (ASIC), a programmable logic device (for example, a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA)), etc.

[0015] 1. Hardware Configuration In this section, the hardware configuration will be described.

[0016] 1.1 Information Processing System 1 FIG. 1 is a configuration diagram showing an information processing system 1 according to the present embodiment. The information processing system 1 includes an information processing apparatus 2 and a user terminal 3, which are connected through a general-purpose or dedicated communication network 11. Here, the system exemplified by the information processing system 1 consists of one or more devices or components. Therefore, even the information processing apparatus 2 alone or the user terminal 3 alone is included in the system exemplified by the information processing system 1. Hereinafter, each component included in the information processing system 1 will be further described.

[0017] 1.2 Information Processing Apparatus 2 FIG. 2 is a block diagram showing the hardware configuration of the information processing apparatus 2. The information processing apparatus 2 is preferably configured by a server. The information processing apparatus 2 has a communication unit 21, a storage unit 22, and a control unit 23, and these components are electrically connected via a communication bus 20 inside the information processing apparatus 2. Each component will be further described.

[0018] The communication unit 21 is preferably a wired communication means such as USB, IEEE1394, Thunderbolt (registered trademark), wired LAN network communication, etc., but may include wireless LAN network communication, mobile communication such as 3G / LTE / 5G, Bluetooth (registered trademark) communication, etc. as needed. That is, it is more preferable to implement it as a set of these plural communication means. That is, the information processing apparatus 2 may communicate various information from the outside via the communication unit 21 and the communication network 11.

[0019] The storage unit 22 stores various types of information as defined above. This can be done, for example, as a storage device such as a solid-state drive (SSD) that stores various programs related to the information processing device 2 executed by the control unit 23, or as a memory such as random access memory (RAM) that stores temporarily necessary information (arguments, arrays, etc.) related to program calculations. The storage unit 22 stores various programs and variables related to the information processing device 2 executed by the control unit 23.

[0020] The control unit 23 performs processing and control of the overall operation related to the information processing device 2. The control unit 23 is a general-purpose processor such as a Central Processing Unit (CPU) (not shown), or a dedicated processor specialized for a specific process. A dedicated processor is, for example, a GPU (graphics processing unit), FPGA (field-programmable gate array), or ASIC (application specific integrated circuit). The control unit 23 realizes various functions related to the information processing device 2 by reading a predetermined program stored in the memory unit 22. That is, information processing by software stored in the memory unit 22 is concretely realized by the control unit 23, which is an example of hardware, and can be executed as each functional unit included in the control unit 23. These will be described in more detail in the next section. Note that the control unit 23 is not limited to being a single unit, and may be implemented with multiple control units 23 for each function. It may also be a combination of such units. That is, the information processing system 1 includes a control unit 23 as at least one processor. The control unit 23, which is a processor, is configured to execute a program so that each of the steps described later is performed.

[0021] 1.3 User Terminal 3 Figure 3 is a block diagram showing the hardware configuration of the user terminal 3. The user terminal 3 can be operated by a user and can access the information processing device 2 via a smartphone, tablet, computer, or other telecommunication line, regardless of its form. Specifically, the user terminal 3 comprises a communication unit 31, a storage unit 32, a control unit 33, a display unit 34, an input unit 35, and an imaging unit 36, and these components are electrically connected within the user terminal 3 via a communication bus 30. The explanation of the communication unit 31, storage unit 32, and control unit 33 is the same as the explanation of each part of the information processing device 2 and is therefore omitted.

[0022] The display unit 34 may be included in the casing of the user terminal 3, for example, or it may be an external component. The display unit 34 displays a graphical user interface (GUI) screen that can be operated by the user. This is preferably done by using different display devices such as a CRT display, liquid crystal display, organic EL display, and plasma display, depending on the type of user terminal 3.

[0023] The input unit 35 may be included in the casing of the user terminal 3 or it may be an external component. For example, the input unit 35 may be integrated with the display unit 34 and implemented as a touch panel. If it is a touch panel, the user can input tap operations, swipe operations, etc. Of course, a switch button, mouse, QWERTY keyboard, etc. may be used instead of a touch panel. In other words, the input unit 35 receives operation input made by the user. This input is transmitted as a command signal to the control unit 33 via the communication bus 30, and the control unit 33 can perform predetermined controls and calculations as needed.

[0024] The imaging unit 36 ​​may be included in the housing of the user terminal 3 or it may be an external component. The imaging unit 36 ​​is a so-called vision sensor (e.g., a camera) configured to capture information from the outside world as still images or moving images. The image data DIm generated by the imaging unit 36 ​​is stored in the storage unit 32. In the following description, the imaging unit 36 ​​will be described as being included in the housing of the user terminal 3.

[0025] 2. Functional Configuration This section describes the functional configuration of this embodiment. As mentioned above, information processing by software stored in the memory unit 22 is specifically realized by the control unit 23, which is an example of hardware, and each functional unit included in the control unit 23 can be executed.

[0026] Figure 4 is a functional block diagram showing the functions of the information processing system 1 according to this embodiment. Specifically, an information processing device 2, which is an example of the information processing system 1, includes an acquisition unit 231, a reception unit 232, a calculation unit 233, a presentation unit 234, a display control unit 235, and a learning unit 236.

[0027] The acquisition unit 231 is configured to acquire various types of information as an acquisition step. For example, the acquisition unit 231 acquires information input by the user, image data DIm, training data DT, etc., from the storage unit 22 or from the user terminal 3 or other external devices via the communication network 11. In one embodiment, the various types of information received by the acquisition unit 231 are described as being stored in the storage unit 22.

[0028] The reception unit 232 is configured to receive various types of information. For example, the reception unit 232 receives data, user input information, etc., from the storage unit 22, or from the input unit 35 of the user terminal 3 or other external devices via the communication network 11. In one embodiment, the various types of information received by the reception unit 232 are described as being stored in the storage unit 22.

[0029] The arithmetic unit 233 is configured to perform various information processing calculations related to the information processing device 2.

[0030] The presentation unit 234 is configured to present various information as a presentation step. For example, it is configured to present output information IFo.

[0031] The display control unit 235 is configured to perform various display processes. For example, the display control unit 235 controls the display unit 34 of the user terminal 3 to display visually recognizable information such as images including screens, still images, or moving images, icons, and messages. The display control unit 235 may also generate only rendering information for displaying visually recognizable information on the display unit 34 of the user terminal 3.

[0032] The learning unit 236 is configured to generate a trained model M based on the acquired training data DT as a learning step. The trained model M may be stored in the storage unit 22, or, for example, in GPU memory such as the VRAM of the GPU included in the control unit 23. Examples of GPU memory include random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), and other forms of memory known in the art. In examples where the GPU is configured as part of another processor, such as a host processor, the GPU memory may be accessible by components other than the GPU.

[0033] 3. Operation of Information Processing System 1 This section describes the information processing method of the aforementioned information processing system 1 with reference to the figures. The order of processing can be changed as appropriate, multiple processes may be executed simultaneously, and some processes may be omitted. Furthermore, the results of the processes described below may be output in a manner that can be recognized by the user using the display unit 34 of the user terminal 3, etc. Note that this information processing may include arbitrary exception handling not shown. Exception handling includes interruption of the information processing and omission of each process. The selection or input performed in this information processing may be based on user operation or may be performed automatically without user operation.

[0034] 3.1 Overview Figure 5 is a flowchart outlining the process performed by the information processing system 1. In this process, first, the acquisition unit 231 acquires vehicle information IF (step S001). In this case, vehicle information IF includes image data DImt of a target vehicle Vt that has been damaged in an accident. Then, the presentation unit 234 presents output information IFo regarding the value of the target vehicle Vt based on the vehicle information IF and the trained model M (step S002). In this case, the trained model M is a model that has been pre-trained using training data DT which includes at least the first data D1. The first data D1 consists of at least image data DIm1 of a vehicle in its damaged state after an accident and information regarding the transaction of the vehicle in its damaged state.

[0035] In summary, the information processing system 1 according to one embodiment comprises at least one processor (for example, a control unit 23). The processor is configured to execute the following steps by reading a program. The acquisition unit 231 acquires vehicle information IF as an acquisition step. The vehicle information IF includes image data DImt of a target vehicle Vt that has been damaged in an accident. The presentation unit 234 presents output information IFo regarding the value of the target vehicle Vt as a presentation step, based on the vehicle information IF and a trained model M. The trained model M is a model that has been pre-trained using training data DT, which includes at least first data D1. The first data D1 consists of at least image data DIm1 of a vehicle in its damaged state due to an accident, and information regarding the transaction of the vehicle in its damaged state. With this embodiment, it is possible to output information regarding the value of the target vehicle Vt by imaging a target vehicle Vt that has been damaged in an accident and inputting the captured image data DImt into the trained model M. In other words, it is possible to reduce the number of operations such as input and selection by the user, and as a result, usability can be improved. Furthermore, this configuration allows for the stable output of information regarding the vehicle's value, without relying on the user's experience or knowledge. It should be noted that "accident" is not limited to collisions, but includes any event that causes damage, such as disasters. Specifically, in addition to traffic accidents, this includes fires, explosions, typhoons, tornadoes, floods, or earthquakes. Damage resulting from these accidents or disasters is referred to as "damage caused by an accident."

[0036] 3.2 Specific Examples Next, a specific example of the process according to one embodiment will be described with reference to the activity diagram. The specific example may be included within the scope defined in the overview described above.

[0037] Figure 6 is an activity diagram showing a specific example of processing performed by the information processing system 1. The following outlines the information processing flow for calculating the value of a vehicle damaged in an accident, following this activity diagram. Here, we will explain using an example where a user calculates the value of a target vehicle Vtx (an example of a target vehicle Vt) using user terminal 3.

[0038] Here, the vehicle in question, a VTX, is a ZZZ model manufactured by Company Y, first registered in 20XX, with a mileage of 86,300 km. Furthermore, the damage to the VTX in the accident is as follows: First, the front bumper, hood, front windshield, and engine were damaged as a result of the collision, preventing the engine from starting, and the airbags deployed as a result of the collision.

[0039] First, the user accesses a value calculation application, which is executed by the information processing system 1, using a browser on the user terminal 3. In this case, the value calculation application is an application that can calculate the value of a vehicle damaged in an accident based on the user's operations. The value calculation application may also be provided as a dedicated desktop or mobile application, in which case the user launches the application. Alternatively, the value calculation application may be provided as a web application or in SaaS (Software as a Service) format, which does not require downloading to the terminal.

[0040] When the value calculation application is accessed, the display control unit 235 controls the display unit 34 of the user terminal 3 to display the top screen (not shown) (Activity A001).

[0041] The reception unit 232 accepts the user's login information input (e.g., click, tap, swipe, or select operation) via the user's actions on the top screen, completing the login to the value calculation application. Subsequently, the display control unit 235 controls the display unit 34 of the user terminal 3 to display the information input screen 4 (see Figure 7) (Activity A002).

[0042] The reception unit 232 receives information about the target vehicle Vtx based on input from the user via the information input screen 4. Subsequently, the acquisition unit 231 acquires the information about the received target vehicle Vtx as vehicle information IF1 (activity A003).

[0043] The information regarding the target vehicle Vtx consists of various pieces of information about the vehicle Vtx. For example, the information regarding the target vehicle Vtx includes that it is a ZZZ model manufactured by Company Y, first registered in 20XX, has a mileage of 86,300 km, has damage from a collision, the engine cannot be started due to the damage, and the airbags deployed as a result of the collision. The acquisition unit 231 acquires this information as vehicle information IF1. The acquired vehicle information IF1 is also recorded as part of the vehicle information IF.

[0044] Then, in response to user input via the information input screen 4, the screen transitions from the information input screen 4 to the image registration screen 5 (see Figure 10). In other words, the display control unit 235 controls the display unit 34 of the user terminal 3 to display the image registration screen 5 (Activity A004).

[0045] The reception unit 232 receives image data DImtx (an example of image data DImt) of the target vehicle Vtx based on input from the user via the image registration screen 5. Subsequently, the acquisition unit 231 acquires the received image data DImtx as vehicle information IF2 (activity A005). The acquired vehicle information IF2 is also recorded as part of the vehicle information IF. In other words, the vehicle information IF includes image data DImtx of the target vehicle Vtx that has been damaged in an accident.

[0046] Furthermore, the image data DImtx may include, for example, still image data from four directions (front, rear, left, and right) of the target vehicle Vtx, still image data from eight directions (front, rear, left, front right, front left, rear right, and rear left) of the target vehicle Vtx, and video data capturing the exterior of the target vehicle Vtx. Additionally, the image data DImtx may be image data that has been captured in advance and stored in the storage unit 32 of the user terminal 3, or it may be image data captured by the imaging unit 36 ​​of the user terminal 3 in response to input from the user via the image registration screen 5.

[0047] Specifically, for example, the image data DImtx is data of still images of the target vehicle Vtx from the front, rear, left, and right four directions, captured by the imaging unit 36 ​​of the user terminal 3 in response to input from the user via the image registration screen 5. The reception unit 232 receives the data of these still images as image data DImtx. Subsequently, the acquisition unit 231 acquires the received image data DImtx as vehicle information IF2.

[0048] Next, the acquisition unit 231 acquires market price information IFm for a predetermined period T (activity A006). In this case, the predetermined period T is any past period, for example, the past 5 years, 3 years, 1 year, 6 months, 3 months, 1 month, or 1 week, starting from the day on which the value of the target vehicle Vtx is to be calculated. Specifically, for example, the acquisition unit 231 acquires market price information IFm for the past 6 months, starting from the day on which the value of the target vehicle Vtx is to be calculated.

[0049] Furthermore, the market information IFm consists of transaction information for vehicles related to the target vehicle Vtx, which are vehicles that have not suffered damage from an accident or vehicles that have had damage from an accident repaired. Vehicles related to the target vehicle Vtx are vehicles that have the same or nearly the same information, or related information, as the information for the target vehicle Vtx, such as initial registration, vehicle manufacturer, vehicle name, year, model, grade, mileage, etc. Related information includes, for example, vehicles of the same vehicle name manufactured by the same manufacturer, but of different models or different grades. Vehicles that have not suffered damage from an accident are vehicles that have not been in an accident or vehicles that have minor scratches, etc., that are not judged to have been caused by an accident. Vehicles that have had damage from an accident repaired are vehicles in which some or all of the damage from a past accident has been repaired and which currently have no damage or only minor damage, and include, for example, vehicles with a repair history or vehicles that have been hail-damaged. Information regarding the transaction of vehicles that have not been damaged in an accident or vehicles that have been repaired after being damaged in an accident includes the price at which they were bought and sold in a specific market, liquidity (frequency of transactions, time until a transaction is completed, etc.), and scarcity in the market. The market information IFm may be information regarding the transaction of one vehicle, for example, and preferably information regarding the transaction of multiple vehicles. In the following explanation, we will use the information regarding the transaction of vehicle V2x, obtained as market information IFm, as an example. The information regarding the transaction of vehicle V2x is as follows: Vehicle V2x is an accident-free vehicle, a Y-made ZZZ model that was first registered in 20XX, that is, it is the same first registration and the same model as the target vehicle Vtx. The mileage of vehicle V2x is 89,000 km, which is approximately the same as the mileage of the target vehicle Vtx, which is 86,300 km. In addition, vehicle V2x was bought and sold for 600,000 yen two months ago in a specific market.

[0050] Next, the calculation unit 233 calculates output information IFo regarding the value of the target vehicle Vt based on the vehicle information IF, the market price information IFm, and the trained model M (Activity A007).

[0051] In this case, the vehicle information IF consists of at least vehicle information IF1, which is information about the target vehicle Vtx, and vehicle information IF2, which is image data DImtx of the target vehicle Vtx. Furthermore, the market information IFm consists of at least information about the transaction of vehicle V2x.

[0052] Furthermore, the trained model M is a model that has been pre-trained using training data DT, which includes at least the first data D1. In this case, the first data D1 consists of at least image data DIm1 of a vehicle in its damaged state due to an accident, and information regarding the transaction of the vehicle in its damaged state.

[0053] Specifically, for example, a vehicle in an accident condition is a vehicle that has sustained damage in an accident at the time of transaction. An accident that qualifies a vehicle as being in an accident condition can be any event that causes damage, such as a collision, contact, flood, earthquake, fire, explosion, typhoon, tornado, theft, or breakdown.

[0054] Furthermore, image data DIm1 is image data of the exterior of a vehicle damaged in an accident. For example, image data DIm1 includes still images from four directions (front, rear, left, and right) of the vehicle, still images from eight directions (front, rear, left, front right, front left, rear right, and rear left) of the vehicle, and video data of the exterior of the vehicle.

[0055] Information regarding the trading of accident-damaged vehicles includes the price at which they were bought and sold in a specific market, liquidity (frequency of transactions, time until a transaction is completed, etc.), and scarcity in the market.

[0056] The first data set D1 consists of image data DIm1 of multiple accident-damaged vehicles and information regarding the transactions of these accident-damaged vehicles. Specifically, for example, the first data set D1 consists of information for 1000 accident-damaged vehicles, with image data DIm1 of each accident-damaged vehicle and information regarding the transactions of each accident-damaged vehicle.

[0057] Furthermore, the trained model M may be a model that has been pre-trained using training data DT, which includes at least the first data D1 and the second data D2. In other words, the training data DT includes the first data D1 and the second data D2. In this case, the second data D2 consists of at least information regarding transactions of vehicles that have not been damaged in an accident or vehicles that have been repaired after being damaged in an accident. Vehicles that have not been damaged in an accident are vehicles that have not been in an accident or vehicles that have minor scratches etc. that are not judged to have been damaged in an accident, and vehicles that have been repaired after being damaged in an accident are vehicles that have had some or all of the damage from past accidents repaired and are currently undamaged or have only minor damage. The second data D2 may consist of at least information regarding transactions of vehicles that have not been damaged in an accident.

[0058] The training data DT may be configured such that the ratio of the first data D1 to the second data D2 is arbitrarily determined. For example, the training data DT may be configured such that the amount of the first data D1 accounts for 50% or more of the total amount of training data DT. Specifically, for example, the training data DT may consist of information for 600 units as the first data D1 and information for 400 units as the second data D2. More preferably, the training data DT may be configured such that the amount of the first data D1 accounts for 95% or more of the total amount of training data DT. Specifically, for example, the training data DT may consist of information for 960 units as the first data D1 and information for 40 units as the second data D2. Specifically, for example, the ratio of the amount of the first data D1 to the total amount of training data DT may be 50, 55, 60, 65, 70, 75, 80, 85, 90, or 95%, and may be within the range of any two of the values ​​exemplified here. According to this embodiment, a more preferable trained model M can be created. Furthermore, it is preferable that the training data DT consists only of the first data D1. Specifically, for example, the training data DT may consist of information for 1000 units as the first data D1. According to this embodiment, a more preferable trained model M can be created.

[0059] Furthermore, the output information IFo is information about the value of the target vehicle Vt, and includes information about the predicted price that is expected if the target vehicle Vt is traded in a specific market. Specifically, for example, the calculation unit 233 performs the following processing. First, the vehicle information IF and the market price information IFm are input to the trained model M. The vehicle information IF includes at least information about the target vehicle Vtx (an example of vehicle information IF1) and image data DImtx of the target vehicle Vtx (an example of vehicle information IF2). The market price information IFm includes information about the transaction of vehicle V2x, which is a vehicle related to the target vehicle Vtx. Next, the process of extracting information about the transaction of vehicle V2x, which is a vehicle related to the target vehicle Vtx, from the market price information IFm is performed. Then, the process of calculating "388,500 yen," which is information about the predicted price that is expected if the target vehicle Vt is traded in a specific market, is performed so as not to exceed "600,000 yen," which is the buying and selling price of vehicle V2x. In this configuration, when trading the target vehicle Vt in a market other than the specific market, the predicted price at which the target vehicle Vt would be traded in the specific market can be used as a reference price. Furthermore, in this configuration, by using market price information IFm over a predetermined period T, it becomes possible to prevent hallucination in which the predicted price of the target vehicle Vt would be higher than the selling price of a vehicle V2x that has not been damaged in an accident, and as a result, the value of the target vehicle Vt can be output with higher accuracy.

[0060] Furthermore, the predicted price information may include at least one of the upper limit, median price, and lower limit price that is predicted when the subject vehicle Vt is traded in a specific market. The upper limit, median price, and lower limit price are set based on the variability in past transaction examples of the subject vehicle Vt. In this configuration, at least one of the upper limit, median price, and lower limit price can be used as a reference price for the predicted price. Furthermore, the output information IFo may be information on the value of the subject vehicle Vt, obtained based on the predicted price that is predicted when the subject vehicle Vt is traded in a specific market and information on predetermined costs that have been set in advance. In this case, predetermined costs are the costs required to trade the subject vehicle Vt in a specific market, and include labor costs such as car washing and coating, and sales and administrative expenses. In this configuration, the value of the subject vehicle Vt, taking into account predetermined costs, can be grasped as a reference price when trading the subject vehicle Vt in a market other than the specific market.

[0061] Next, in response to user input via the image registration screen 5, the screen transitions from the image registration screen 5 to the result presentation screen 6 (see Figure 12). In other words, the display control unit 235 controls the display unit 34 of the user terminal 3 to display the result presentation screen 6 (Activity A008).

[0062] The display unit 234 then displays output information IFo regarding the value of the target vehicle Vtx by drawing it on the result display screen 6 (Activity A009). In other words, the display unit 234 displays output information IFo regarding the value of the target vehicle Vtx based on the vehicle information IF, the market price information IFm, and the trained model M. Specifically, for example, the display unit 234 displays "388,500 yen" as output information IFo, which is the predicted price that the target vehicle Vtx is expected to be traded in a particular market. With this configuration, it is possible to capture images of the target vehicle Vtx that has been damaged in an accident, and input the captured image data DImtx into the trained model M to output information regarding the value of the target vehicle Vtx. In other words, it is possible to reduce the number of operations such as input and selection by the user, and as a result, usability can be improved.

[0063] 3.3 Details Next, the aforementioned information processing will be further explained using a separate diagram.

[0064] Figure 7 is a schematic diagram showing an example of information input screen 4a, which is one of the information input screens 4 and displays visual information for inputting information about the target vehicle Vt.

[0065] As shown in Figure 7, the information input screen 4a has areas 401 to 407 and a button 408. Areas 401 to 407 are configured to allow input of information about the target vehicle Vt.

[0066] Specifically, areas 401 to 403 are configured to allow input of the vehicle status of the target vehicle Vt. Area 401 is configured to allow input of the damage status by selecting at least one of "collision," "flood," "earthquake," "fire," "theft," and "malfunction." Area 402 is configured to allow input of the airbag deployment status by selecting either "not deployed" or "deployed." Area 403 is configured to allow input of whether the engine can be started by selecting one of "starts," "does not start," and "unknown." Furthermore, an area for inputting whether or not the vehicle has rolled over may be configured on the information input screen 4a. This is because information regarding whether or not the vehicle has rolled over also affects the value of the vehicle.

[0067] Furthermore, areas 404 to 407 are configured to allow input of vehicle information for the target vehicle Vt. Area 404 is configured to allow input of the initial registration year of the target vehicle Vt, area 405 is configured to allow input of the manufacturer of the target vehicle Vt, area 406 is configured to allow input of the model name of the target vehicle Vt, and area 407 is configured to allow input of the mileage of the target vehicle Vt.

[0068] Button 408 is labeled "Register vehicle image" and is configured to transition from the information input screen 4 to the image registration screen 5 when pressed after completing the required input.

[0069] Figure 8 is a schematic diagram showing an example of information input screen 4b, which is part of information input screen 4 and shows the state in which information about the target vehicle Vt has been entered. In information input screen 4b shown in Figure 8, information about the target vehicle Vtx has been entered in areas 401 to 407 of information input screen 4a. Note that the same configuration as information input screen 4a will not be explained.

[0070] Specifically, in area 401, "collision" is selected as the damage type; in area 402, "deployed" is selected as the airbag deployment status; and in area 403, "does not start" is selected as the engine start capability. Additionally, in area 404, "20XX" is selected as the initial registration year; in area 405, "Y Company" is selected as the manufacturer; in area 406, "ZZZ" is selected as the vehicle model name; and in area 407, "86,300" is entered as the mileage.

[0071] In this manner, information regarding the target vehicle Vtx is input, and the receiving unit 232 accepts the information. Subsequently, the acquisition unit 231 acquires the information regarding the received target vehicle Vtx as vehicle information IF1. The acquired vehicle information IF1 is then recorded as part of the vehicle information IF. In other words, the vehicle information IF includes information about the target vehicle Vtx (an example of the target vehicle Vt), such as the damage status, the year of initial registration, the manufacturer, the vehicle model name, and the mileage. Furthermore, the vehicle information IF further includes at least one piece of information from among the following regarding the target vehicle Vtx: information on whether the engine can be started, information on the operation status of the airbags, and information on whether or not the vehicle has rolled over. With this configuration, it is possible to acquire a vehicle information IF that can output the value of the target vehicle Vtx with higher accuracy.

[0072] Figure 9 is a schematic diagram showing an example of information input screen 4c, which is part of the information input screen 4 and shows the state in which information about the target vehicle Vtx has been entered. In the information input screen 4c shown in Figure 9, an example screen is shown in which it is possible to further input the airbag deployment position and number in response to "Deployed" being selected as the airbag activation status in information input screen 4b. Note that the same configuration as information input screens 4a and 4b will not be explained.

[0073] Specifically, in the information input screen 4c, area 402a is added in response to the selection of "deployed" as the airbag deployment status in area 402. Area 402a is configured to allow input of the airbag deployment location and number by selecting at least one of "driver's seat," "passenger seat," and "side." In the information input screen 4c, "driver's seat" and "passenger seat" are selected as the airbag deployment location and number. In this case, the information regarding the airbag deployment status includes at least one piece of information from the following: information regarding whether the airbag deployed or not, information regarding the number of airbags that deployed, and information regarding the location where the airbags deployed. With this configuration, it is possible to obtain vehicle information IF that can output the value of the target vehicle Vtx with even higher accuracy.

[0074] Figure 10 is a schematic diagram showing an example of the image registration screen 5a, which displays visual information for registering image data DImt of the target vehicle Vt, among the image registration screens 5. The image registration screen 5a is a screen that is accessed from the information input screen 4c when button 408 is pressed.

[0075] As shown in Figure 10, the image registration screen 5a has an area 501, a display 502, and a button 503. Area 501 is configured to allow registration of image data DImt.

[0076] Specifically, area 501 comprises area 501f, area 501b, area 501l, and area 501r. Furthermore, a camera icon is drawn as display 502 on the lower right side of each of areas 501f, 501b, 501l, and 501r. Area 501f is labeled "Front," area 501b is labeled "Rear," area 501l is labeled "Left Side," and area 501r is labeled "Right Side." Pressing areas 501f, 501b, 501l, and 501r transitions to a screen (not shown) for inputting a still image of the target vehicle Vt, captured from the indicated direction, as image data DImt. For example, pressing area 501f, labeled "Front," transitions to a screen for inputting a still image of the target vehicle Vt, captured from the front, as image data DImt. On the screen for inputting a still image of the target vehicle Vt as image data DImt, the user may input the image data DImt by selecting a still image that has been captured in advance and stored in the storage unit 32 of the user terminal 3, or the user may input the image data DImt by capturing a still image with the imaging unit 36 ​​of the user terminal 3 via the said screen.

[0077] Button 503 is labeled "Calculate Value" and is configured to transition from the image registration screen 5 to the results presentation screen 6 when pressed after completing the required input.

[0078] Figure 11 is a schematic diagram showing an example of image registration screen 5a, which is part of the image registration screen 5, and shows the state in which image data DImtx, in which the target vehicle Vtx was captured, has been input. In image registration screen 5b shown in Figure 11, the image data DImtx, in which the target vehicle Vtx was captured, is input and displayed in a visible state. Note that the same configuration as image registration screen 5a will not be explained.

[0079] Specifically, region 501f displays a still image of the target vehicle Vtx captured from the front, region 501b displays a still image of the target vehicle Vtx captured from the rear, region 501l displays a still image of the target vehicle Vtx captured from the left, and region 501r displays a still image of the target vehicle Vtx captured from the right.

[0080] In this way, when the image data DImtx of the target vehicle Vtx is input, the receiving unit 232 accepts the image data DImtx. Subsequently, the acquisition unit 231 acquires the accepted image data DImtx as vehicle information IF2. The acquired vehicle information IF2 is then recorded as part of the vehicle information IF.

[0081] Figure 12 is a schematic diagram showing an example of the results presentation screen 6, where output information IFo regarding the value of the target vehicle Vtx is displayed. The results presentation screen 6 is the screen transitioned to from the image registration screen 5b when button 503 is pressed on the image registration screen 5b.

[0082] As shown in Figure 12, the results presentation screen 6 has a region 601, a display 602, and a display 603. Region 601 displays still images of the target vehicle Vtx taken from various directions, which were drawn in region 501 of the image registration screen 5. Display 602 displays "388,500 yen" as output information IFo regarding the value of the target vehicle Vtx, which is information on the predicted price that the target vehicle Vtx is expected to be traded in a specific market. Display 603 shows status information regarding the condition of each part of the target vehicle Vtx. Specifically, display 603 shows status information regarding the condition of each part of the target vehicle Vtx, such as "Front bumper: Poor", "Left headlight: Good", "Right front fender: Dent". In other words, the presentation unit 234 presents status information regarding the condition of each part of the target vehicle Vtx by drawing it on the results presentation screen 6. In this configuration, the value of the target vehicle Vtx can be determined by understanding the condition of each component in the target vehicle Vtx.

[0083] 3.4 Details of the pre-trained model M Next, we will explain the pre-trained model M further.

[0084] The trained model M may be a machine learning model that is trained using a machine learning algorithm, for example, and may be a machine learning model built on a decision tree. Examples of machine learning models built on a decision tree include, but are not limited to, Light GBM and XGBoost. Alternatively, the prediction model may be a model generated based on a machine learning algorithm such as a Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), or other deep learning methods. The trained model M causes the information processing system 1 to function so that it outputs a target variable related to the value of the target vehicle Vt based on explanatory variables related to vehicle information IF.

[0085] Furthermore, the trained model M is a model that has been pre-trained using training data DT, which includes at least the first data D1. The first data D1 consists of at least image data DIm1 of a vehicle in its damaged state after an accident, and information regarding the transaction of the vehicle in its damaged state. A correlation has been observed between the vehicle's damage status and its value, and by performing machine learning using these datasets as training data, the trained model M can be obtained.

[0086] Image data DIm1 may include images of the accident vehicle taken from multiple directions. For example, image data DIm1 may include images of the accident vehicle from four directions: front, rear, left, and right. Furthermore, it may also include images of the accident vehicle from the front left, front right, rear left, and rear right. By increasing the amount of information in the data included in image data DIm1, it is possible to construct a more accurate predictive model.

[0087] Furthermore, the first data D1 may include status information regarding the condition of parts of the accident-damaged vehicle. Specifically, the first data D1 may include status information regarding the condition of each part of the accident-damaged vehicle, such as "Front bumper: Unacceptable," "Left headlight: Good," and "Right front fender: Dent." This status information regarding the condition of parts may be annotated on the corresponding images of the parts. Each annotated image is included in the image data DIm1.

[0088] Furthermore, the first data D1 may further include at least one piece of information from among the following: information regarding whether the engine of the accident vehicle can be started, information regarding the deployment status of the airbags, and information regarding whether or not the vehicle has rolled over. In addition, the first data D1 may further include at least one piece of information from among the following: information regarding whether or not the airbags have deployed, information regarding the number of airbags that have deployed, and information regarding the location where the airbags have deployed.

[0089] Furthermore, the trained model M is trained again at a predetermined frequency or when certain conditions are met. In other words, the learning unit 236 causes the trained model M to be trained again at a predetermined frequency or when certain conditions are met.

[0090] In this case, the predetermined frequency is a regular frequency such as weekly, bi-weekly, monthly, or every two months. Specifically, for example, the trained model M should be trained again on the 1st of each month. In other words, the learning unit 236 should train the trained model M again on the 1st of each month using the newly added first data D1 as training data DT.

[0091] Furthermore, the trained model M may be retrained in response to notifications from other external devices that new data has been added as the first data D1, which includes image data DIm1 of a vehicle damaged in an accident and information regarding the transaction of the vehicle. In other words, the learning unit 236 may retrain the trained model M using the newly added first data D1 as training data DT in response to notifications from other external devices.

[0092] Furthermore, the trained model M may be retrained in response to the completion of a transaction involving the target vehicle Vtx. For example, when a transaction involving the target vehicle Vtx is completed, the trained model M may be retrained using the image data DImtx of the target vehicle Vtx and the information regarding the transaction of the target vehicle Vtx as training data DT. In other words, when a transaction involving the target vehicle Vtx is completed, the learning unit 236 may retrain the trained model M using the image data DImtx of the target vehicle Vtx and the information regarding the transaction of the target vehicle Vtx as training data DT. This configuration makes it possible to create an even more favorable trained model M. As a result, the value of the target vehicle Vtx can be output with even higher accuracy.

[0093] [others] With respect to the information processing system 1 according to the above embodiment, the following configurations may be adopted.

[0094] In the above embodiment, the process of acquiring market price information IFm for a predetermined period T is performed in activity A006, that is, the market price information IFm for a predetermined period T is acquired as a result of the user accessing the value calculation application. However, the embodiment is not limited to this. For example, the acquisition unit 231 may acquire market price information IFm for a predetermined period T at a predetermined frequency. The predetermined frequency may be a regular frequency such as weekly, bi-weekly, monthly, or every two months. Alternatively, the acquisition unit 231 may acquire market price information IFm for a predetermined period T irregularly in response to user operations or notifications from other external devices.

[0095] Figure 13 is a schematic diagram showing an example of the image registration screen 5c, which displays visual information for registering image data DImt of the target vehicle Vt, among the image registration screens 5.

[0096] In the embodiment shown in Figure 10, the region 501 has regions 501f, 501b, 501l, and 501r, that is, the region 501 is capable of inputting still images of the target vehicle Vt captured from the "front," "rear," "left side," and "right side" as image data DImt. However, the embodiment is not limited to this. For example, as shown in Figure 13, the region 501 may have regions 501f, 501fl, 501fr, 501b, 501bl, 501br, 501l, and 501r. In other words, the region 501 is capable of inputting still images of the target vehicle Vt captured from the "front," "front left side," "front right side," "rear," "rear left side," "rear right side," "left side," and "right side" as image data DImt. Alternatively, the region 501 may be configured to be capable of inputting videos of the target vehicle Vt captured from each direction as image data DImt.

[0097] At least one of the devices included in the information processing system 1 may be located outside of Japan. For example, the information processing device 2 may be located outside of Japan, and a user in Japan may access the information processing device 2 using their user terminal 3, or the information processing device 2 may be located in Japan, and a user outside of Japan may access the information processing device 2 using their user terminal 3.

[0098] The information processing device 2 may be on-premise or in a cloud-based configuration. In the case of a cloud-based information processing device 2, for example, the above-mentioned functions and processing may be provided in the form of SaaS or cloud computing.

[0099] In one embodiment, the acquisition unit 231, reception unit 232, calculation unit 233, presentation unit 234, display control unit 235, and learning unit 236 are described as functional units realized by the control unit 23 of the information processing device 2. However, at least a part of these may be implemented as functional units realized by the control unit 33 of the user terminal 3. Furthermore, the various types of information described in the above example may be stored not only in the storage unit 22 of the information processing device 2, but also distributedly in other external devices. In such cases, distributed ledger management based on blockchain or the like may be implemented.

[0100] The product may be provided in any of the following embodiments.

[0101] (1) An information processing system comprising at least one processor, wherein the processor is configured to execute a program such that the following steps are performed: an acquisition step, in which vehicle information is acquired, wherein the vehicle information includes image data of a target vehicle having been damaged in an accident; and a presentation step, in which output information relating to the value of the target vehicle is presented based on the vehicle information and a trained model, wherein the trained model is a model that has been pre-trained using data including at least first data as training data, wherein the first data consists of at least image data of a vehicle in the state of an accident having been damaged in an accident and information relating to the transaction of the vehicle in the state of an accident.

[0102] In this configuration, it becomes possible to image a vehicle damaged in an accident and input the captured image data into a trained model to output information about the value of the vehicle. In other words, it becomes possible to reduce the number of operations such as input and selection performed by the user, and as a result, usability can be improved.

[0103] (2) The information processing system described in (1) above, wherein the training data further includes second data, wherein the second data consists of at least information relating to transactions of vehicles that have not been damaged in an accident or vehicles that have been repaired after being damaged in an accident, and the amount of the first data accounts for 50% or more of the total amount of the training data.

[0104] This configuration makes it possible to create a more favorable pre-trained model. As a result, the value of the target vehicle can be output with higher accuracy.

[0105] (3) In the information processing system described in (2) above, the training data is configured such that the amount of the first data accounts for 95% or more of the total amount of the training data.

[0106] This configuration makes it possible to create a more favorable pre-trained model. As a result, the value of the target vehicle can be output with higher accuracy.

[0107] (4) In the information processing system described in (1) above, the training data consists only of the first data.

[0108] This configuration makes it possible to create a more favorable pre-trained model. As a result, the value of the target vehicle can be output with even higher accuracy.

[0109] (5) An information processing system according to any one of (1) to (4) above, wherein the acquisition step further acquires market price information for a predetermined period, wherein the market price information consists of information relating to transactions of vehicles related to the target vehicle that are not damaged by an accident or have been repaired after being damaged by an accident, and the presentation step presents the output information relating to the value of the target vehicle based on the vehicle information, the market price information, and the trained model.

[0110] According to this configuration, by further using market information over a predetermined period, the value of the target vehicle can be output with higher accuracy.

[0111] (6) In the information processing system described in any one of (1) to (5) above, the vehicle information further includes at least one of the following pieces of information regarding the target vehicle: information regarding whether the engine can be started, information regarding the operation status of the airbags, and information regarding whether or not the vehicle has rolled over.

[0112] This configuration allows for the acquisition of vehicle information that enables the output of the value of the target vehicle with higher accuracy.

[0113] (7) In the information processing system described in (6) above, the information relating to the operating status of the airbags includes at least one of the following: information relating to whether or not the airbags have been deployed, information relating to the number of airbags that have been deployed, and information relating to the location where the airbags have been deployed.

[0114] This configuration allows for the acquisition of vehicle information that enables the output of the value of the target vehicle with even greater accuracy.

[0115] (8) An information processing system described in any one of (1) to (7) above, wherein the output information includes information on the predicted price that is expected when the subject vehicle is traded in a specific market.

[0116] In this configuration, when trading the vehicle in a market other than the specific market, the predicted price at which the vehicle would be traded in the specific market can be used as a reference price.

[0117] (9) The information processing system described in (8) above, wherein the information of the predicted price includes at least one of the upper limit price, the median price, and the lower limit price that are predicted when the subject vehicle is traded in a specific market.

[0118] In this configuration, at least one of the upper limit price, the median price, and the lower limit price can be used as a reference for the predicted price.

[0119] (10) An information processing system according to any one of (1) to (9) above, wherein the output information is information about the value of the subject vehicle, obtained based on a predicted price that is expected when the subject vehicle is traded in a specific market and information about predetermined costs set in advance.

[0120] In this configuration, the value of the vehicle, taking predetermined costs into account, can be determined as a reference price when trading the vehicle in a market other than a specific market.

[0121] (11) An information processing system described in any one of (1) to (10) above, wherein in the presentation step, status information relating to the state of each part of the target vehicle is presented.

[0122] In this configuration, the condition of each component of the vehicle can be determined as part of the value of the vehicle.

[0123] (12) In an information processing system described in any one of (1) to (11) above, the learning step further involves, when a transaction for the target vehicle is completed, using image data of the target vehicle and information regarding the transaction for the target vehicle as training data to retrain the previously trained model.

[0124] This configuration makes it possible to create a more favorable pre-trained model. As a result, the value of the target vehicle can be output with even higher accuracy.

[0125] (13) An information processing method comprising each step of the information processing system described in any one of (1) to (12) above.

[0126] In this configuration, it becomes possible to capture images of a vehicle damaged in an accident and input the image data into a trained model to output information about the value of the vehicle. In other words, it becomes possible to reduce the number of operations such as input and selection performed by the user, and as a result, usability can be improved.

[0127] (14) A program that causes at least one computer to perform each step of the information processing system described in any one of (1) to (12) above.

[0128] In this configuration, it becomes possible to capture images of a vehicle damaged in an accident and input the image data into a trained model to output information about the value of the vehicle. In other words, it becomes possible to reduce the number of operations such as input and selection performed by the user, and as a result, usability can be improved. Of course, this is not always the case.

[0129] Finally, while various embodiments relating to this disclosure have been described, these are presented as examples only and are not intended to limit the scope of the invention. These novel embodiments can be implemented in a variety of other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents. [Explanation of Symbols]

[0130] 1: Information Processing System 11: Communication Network 2: Information Processing Device 20: Communications bus 21: Communications Department 22: Storage section 23: Control Unit 231: Acquisition Department 232: Reception Department 233: Arithmetic section 234:Presentation part 235: Display Control Unit 236: Learning Department 3: User terminal 30: Communications bus 31: Communications Department 32: Storage section 33: Control Unit 34:Display section 35: Input section 36: Imaging Unit 4: Information input screen 4a: Information input screen 4b: Information input screen 4c: Information input screen 401 :Region 402 :Region 402a :Area 403 :Region 404 :Region 405 :Region 406 :Area 407 :Area 408: Button 5: Image registration screen 5a: Image registration screen 5b: Image registration screen 5c: Image registration screen 501 :Area 501f: area 501fl: area 501fr: area 501b :Region 501bl :Area 501br :Region 501l: area 501r :Region 502 :Display 503: Button 6:Result presentation screen 601 :Area 602 :Display 603 :Display D1: First data D2: Second data DIm: Image data DIm1: Image data DImt: Image data DImtx: Image data DT: Training data IF: Vehicle Information IF1: Vehicle Information IF2: Vehicle Information IFm: Market Information IFo: Output information M: Model T: Predetermined period Vt: Target vehicle Vtx: Applicable vehicles V2x: Vehicle

Claims

1. An information processing system, The system comprises at least one processor, the processor configured to execute a program such that the following steps are performed: In the acquisition step, vehicle information is acquired, and here the vehicle information includes image data of the target vehicle that has been damaged in an accident. In the presentation step, output information regarding the value of the target vehicle is presented based on the vehicle information and the trained model, and here, The aforementioned trained model is a model that has been pre-trained using data that includes at least the first data as training data. The first data comprises, at a minimum, image data of a vehicle in its damaged state due to an accident, and information relating to the transaction of the vehicle in its damaged state.

2. In the information processing system described in claim 1, The aforementioned training data is Further including a second set of data, wherein the second set of data consists of at least information relating to transactions of vehicles that are not damaged in an accident or vehicles that have been repaired after being damaged in an accident. A system configured such that the amount of the first data accounts for 50% or more of the total amount of the aforementioned training data.

3. In the information processing system described in claim 2, The system is configured such that the amount of the first data accounts for 95% or more of the total amount of the training data.

4. In the information processing system described in claim 1, The aforementioned training data consists only of the first data.

5. In the information processing system described in claim 1, In the acquisition step described above, market information for a predetermined period is further acquired, and here, The aforementioned market information consists of information relating to transactions of vehicles related to the aforementioned subject vehicle, which are vehicles that have not been damaged in an accident or vehicles that have been repaired after being damaged in an accident. In the presentation step, the system presents the output information regarding the value of the target vehicle based on the vehicle information, the market price information, and the trained model.

6. In the information processing system described in claim 1, The system further includes at least one piece of information from among the following regarding the target vehicle: information regarding whether the engine can be started, information regarding the operation status of the airbags, and information regarding whether the vehicle has rolled over.

7. In the information processing system described in claim 6, The system includes at least one of the following pieces of information regarding the operation status of the airbags: information regarding whether or not the airbags have been deployed, information regarding the number of airbags that have been deployed, and information regarding the location where the airbags have been deployed.

8. In the information processing system described in claim 1, The output information includes information on the predicted price that the target vehicle is expected to have if it were traded in a specific market.

9. In the information processing system described in claim 8, The system includes, for the aforementioned predicted price information, at least one of the following prices: an upper limit price, a median price, and a lower limit price, which are predicted to be the price at which the subject vehicle is traded in a particular market.

10. In the information processing system described in claim 1, The output information is information about the value of the subject vehicle, obtained based on a predicted price expected when the subject vehicle is traded in a specific market and information about predetermined costs set in advance, in a system.

11. In the information processing system described in claim 1, The system, in the aforementioned presentation step, presents status information regarding the condition of each part in the target vehicle.

12. In the information processing system described in claim 1, Furthermore, in the learning step, when a transaction involving the target vehicle is completed, the system uses image data captured of the target vehicle and information related to the transaction involving the target vehicle as training data to retrain the previously trained model.

13. Information processing method, A method comprising each step of the information processing system described in any one of claims 1 to 12.

14. It is a program, A program that causes at least one computer to perform each step of the information processing system described in any one of claims 1 to 12.

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