system

Abstract

The system according to this embodiment aims to easily determine defects in used cars and provide peace of mind to buyers. [Solution] The system according to this embodiment comprises a shooting unit, an analysis unit, and a provision unit. The shooting unit allows the user to take pictures of a used car with their smartphone. The analysis unit analyzes the images taken by the shooting unit and determines whether or not there are defects such as accident damage or flood damage. The provision unit provides the user with the results determined by the analysis unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to easily determine the defects of used cars, and there is a risk that purchasers may feel uneasy.

[0005] The system according to the embodiment aims to easily determine the defects of used cars and provide a sense of security to purchasers.

Means for Solving the Problems

[0006] The system according to the embodiment includes a photographing unit, an analysis unit, and a providing unit. The photographing unit photographs an image of a used car with a smartphone by the user. The analysis unit analyzes the image photographed by the photographing unit and determines whether there are defects such as a wrecked car or a flooded car. The providing unit provides the result determined by the analysis unit to the user.

Effects of the Invention

[0007] The system according to this embodiment can easily determine defects in used cars and provide peace of mind to buyers. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The used car diagnostic system according to an embodiment of the present invention is a system that uses a smartphone to diagnose whether a used car is defective for used car buyers. In this system, the user takes an image of the used car with their smartphone, a generating AI analyzes the image, and determines whether or not there are defects such as accident-damaged or flood-damaged cars, and provides the user with the determination result. This alleviates anxiety when purchasing a used car and allows consumers to purchase a used car with peace of mind. For example, the user takes an image of the used car with their smartphone. In this case, it is recommended to take pictures of the exterior and detailed parts of the interior of the car body. For example, pictures of scratches and dents on the car body, and the condition of the engine compartment. This image data is input to the generating AI. Next, the generating AI analyzes the input image data. The generating AI uses image recognition technology to analyze the exterior and interior condition of the car body in detail. For example, it identifies the presence or absence of scratches and dents on the car body, abnormal areas in the engine compartment, etc. This makes it possible to determine whether or not there are defects such as accident-damaged or flood-damaged cars. The determination result is provided to the user. For example, the determination result is displayed through a smartphone app. The determination result includes detailed information if there is a possibility that the car is an accident-damaged or flood-damaged car. This allows users to accurately understand the condition of used cars. The service is offered at an inexpensive price of a few hundred yen per use. This makes it easy for consumers to have used cars inspected. Furthermore, by using generative AI, highly accurate diagnoses are possible, alleviating consumer concerns. For example, when a user purchases a used car, they take a picture of the car with their smartphone and request an inspection through the app. The generative AI analyzes the image and determines whether or not the car may have been in an accident or flooded. The inspection result is displayed, and the user can consider purchasing the car based on that information. This allows consumers to purchase used cars with peace of mind. In short, the used car inspection system enables consumers to purchase used cars with confidence.

[0029] The used car diagnostic system according to this embodiment comprises a shooting unit, an analysis unit, and a provision unit. The shooting unit allows the user to take images of the used car with their smartphone. The shooting unit can, for example, photograph the exterior and detailed interior of the car body. For example, the shooting unit can photograph scratches and dents on the car body, the condition of the engine compartment, etc. The shooting unit can, for example, take high-resolution images using the smartphone camera. The shooting unit can also, for example, take images from multiple angles to capture an overall view of the car body. The analysis unit analyzes the images taken by the shooting unit and determines whether or not there are defects such as accident vehicles or flood-damaged vehicles. The analysis unit analyzes the images using, for example, a generative AI. The generative AI can analyze the exterior and interior condition of the car body in detail using image recognition technology. For example, the analysis unit can identify the presence or absence of scratches and dents on the car body, abnormal areas in the engine compartment, etc. The analysis unit can, for example, learn from past accident vehicle and flood-damaged vehicle data to improve the accuracy of the analysis. The provision unit provides the user with the results determined by the analysis unit. The providing unit can, for example, display the judgment results through a smartphone app. The providing unit can, for example, include detailed information if the judgment results indicate the possibility of the vehicle being involved in an accident or being submerged in water. The providing unit can, for example, provide the judgment results in text or graphical display. As a result, the used car diagnostic system according to this embodiment can determine defects in a used car by having the user take an image of the used car with their smartphone and provide the analysis results. This allows the user to accurately understand the condition of the used car.

[0030] The photography unit takes pictures of used cars using the user's smartphone. The photography unit can, for example, photograph the exterior and detailed interior of the car. Specifically, it can capture high-resolution images using the smartphone's camera. Users are encouraged to take pictures from multiple angles to capture the overall appearance of the car. For example, taking pictures from the front, sides, rear, and top of the car allows for a detailed record of the car's condition. It is also required to photograph areas requiring special attention, such as scratches, dents, and the condition of the engine compartment. When photographing the engine compartment, it is important to take pictures from multiple angles to check the condition of each engine component and wiring, and whether there are any oil leaks. Furthermore, when photographing the interior, it is recommended to photograph the condition of the seats, dashboard, and the wear on the pedals and steering wheel in detail. This allows the photography unit to accurately record the exterior and interior condition of used cars by having users take detailed pictures of them using their smartphones. The captured images are uploaded to a cloud server in real time, making them accessible to the analysis unit. This allows the photography unit to efficiently and effectively record the condition of used cars, improving the overall system performance.

[0031] The analysis unit analyzes images captured by the camera unit to determine whether or not there are defects such as those found in accident-damaged or flood-damaged vehicles. The analysis unit uses, for example, a generative AI to analyze the images. The generative AI uses image recognition technology to analyze the external and internal condition of the vehicle body in detail. Specifically, the generative AI can identify the presence or absence of scratches and dents on the vehicle body, abnormalities in the engine compartment, etc. For example, from images of the vehicle body's exterior, it can detect paint peeling, rust, and panel distortion. From images of the engine compartment, it can identify oil leaks, wiring abnormalities, and component deterioration. Furthermore, the generative AI can learn from past data on accident-damaged and flood-damaged vehicles to improve its analysis accuracy. For example, using past data, it can determine whether a particular pattern of scratches or dents is unique to accident-damaged vehicles. In the case of flood-damaged vehicles, it can analyze and determine the pattern of rust and corrosion caused by water intrusion. Based on these analysis results, the analysis unit comprehensively evaluates the condition of the vehicle and generates information to provide to the user. This allows the analysis unit to quickly and accurately analyze captured images and determine defects in used cars. Furthermore, the analysis unit can continuously revise its analysis results based on real-time updated data, enabling it to adapt to the latest situation. This allows the analysis unit to always provide highly accurate analysis based on the latest information, helping users accurately understand the condition of used cars.

[0032] The service provider provides users with the results determined by the analysis unit. The service provider can display the results, for example, through a smartphone app. Specifically, if the results indicate a potential accident or flood damage, detailed information can be included. For example, the location and extent of scratches and dents on the body, and any abnormalities in the engine compartment can be displayed in detail. The service provider can provide the results in text or graphical format. Text display provides a concise report summarizing the analysis results for easy user understanding. Graphical display overlays the analysis results onto an image of the vehicle, making it visually easier for users to understand. For example, the location of scratches and dents can be marked on the vehicle image, and detailed information can be displayed as a pop-up. Additionally, abnormalities in the engine compartment can be highlighted, and their causes and countermeasures can be explained. Furthermore, the service provider can collect user feedback to continuously improve the accuracy and effectiveness of its offerings. For example, feedback on user actions based on the provided information can be collected and used to improve the analysis algorithm and display methods. This allows the service provider to provide users with quick and accurate information, helping them accurately understand the condition of used cars.

[0033] The analysis unit can identify scratches and dents on the vehicle body, abnormalities in the engine compartment, etc., using image recognition technology. For example, the analysis unit can identify scratches and dents on the vehicle body using deep learning. For example, the analysis unit can identify abnormalities in the engine compartment using computer vision technology. For example, the analysis unit can identify abnormalities on the vehicle body using image processing algorithms. In this way, abnormalities on the vehicle body can be identified by using image recognition technology. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input an image of the vehicle body into a generative AI, and the generative AI can analyze the image to identify abnormalities.

[0034] The service provider can display the judgment results through a smartphone app. For example, the service provider can provide the judgment results in text format. For example, the service provider can provide the judgment results in a graphical format. For example, the service provider can provide the judgment results in voice format. This makes it easier for users to check the results by displaying them through a smartphone app. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the judgment results into the AI, and the AI ​​can determine how to display the results.

[0035] The analysis unit can learn based on data from past accident vehicles and flooded vehicles. The analysis unit can learn from past data using, for example, machine learning algorithms. For example, the analysis unit can learn using training datasets. For example, the analysis unit can learn using image data from past accident vehicles and flooded vehicles. This improves the accuracy of the analysis by learning from past data. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input past data into a generative AI, and the generative AI can learn from the data to improve the accuracy of the analysis.

[0036] The camera unit can photograph the exterior and detailed interior of the vehicle. For example, it can photograph the condition of the engine compartment. For example, it can photograph the underside of the vehicle. For example, it can photograph scratches and dents on the vehicle in detail. By photographing these detailed parts, more accurate analysis becomes possible. Some or all of the above processing in the camera unit may be performed using AI, or not. For example, the camera unit can input the area to be photographed into the AI, which can then suggest the optimal shooting method.

[0037] The service provider may include detailed information if the assessment result indicates a possibility of the vehicle being involved in an accident or being flooded. For example, if the vehicle is likely to be involved in an accident, the service provider may provide detailed information in text format. For example, if the vehicle is likely to be flooded, the service provider may provide detailed information in a report with images. For example, if the vehicle is likely to be involved in an accident or is flooded, the service provider may provide detailed information in audio format. This allows users to accurately understand the condition of the used car by providing detailed information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider may input the assessment result into AI, which may generate and provide detailed information.

[0038] The camera unit can automatically detect specific parts of the vehicle body and suggest the optimal shooting angle. For example, when photographing the engine compartment, the camera unit can guide the user to the optimal angle and distance. For example, when photographing tires, the camera unit can suggest an angle that clearly shows the tire wear. For example, when photographing door gaps, the camera unit can indicate an angle that clearly shows the condition of the gaps. By suggesting the optimal shooting angle, the camera unit can accurately photograph the condition of important parts. Some or all of the above processing in the camera unit may be performed using AI or not. For example, the camera unit can input specific parts of the vehicle body into the AI, which can then suggest the optimal shooting angle.

[0039] The imaging unit can apply filtering techniques to minimize light reflection and shadows. For example, the imaging unit can automatically apply a polarizing filter to reduce light reflection. For example, the imaging unit can suggest shooting from multiple angles to minimize shadows. For example, the imaging unit can automatically adjust the exposure according to the intensity of light. This allows for the capture of sharper images by suppressing light reflection and shadows. Some or all of the above processing in the imaging unit may be performed using AI or not. For example, the imaging unit can input light reflection and shadows into the AI, which can then apply filtering techniques.

[0040] The camera unit can automatically select a shooting mode suitable for specific weather conditions, taking into account the user's geographical location information. For example, the camera unit can automatically select a waterproof mode in rainy weather. For example, the camera unit can select a mode to adjust brightness in sunny weather. For example, the camera unit can select a mode to prevent overexposure on snowy days. By selecting a shooting mode suitable for the weather conditions, optimal shooting results can be obtained. Some or all of the above processing in the camera unit may be performed using AI, or it may be performed without AI. For example, the camera unit can input the user's geographical location information into the AI, which can then select a shooting mode suitable for the weather conditions.

[0041] The camera unit can automatically apply the optimal shooting settings by referring to the user's past shooting history. For example, the camera unit can suggest the optimal shooting settings based on the settings the user has used in the past. For example, the camera unit can automatically apply the most successful settings from the user's past shooting history. For example, the camera unit can analyze the user's past shooting history and suggest the optimal settings. This ensures that the optimal shooting settings are applied by referring to the past shooting history. Some or all of the above processes in the camera unit may be performed using AI or not. For example, the camera unit can input the user's past shooting history into the AI, which can then apply the optimal shooting settings.

[0042] The analysis unit can not only identify abnormal locations on the vehicle body but also add the function of estimating their causes. For example, if there is damage to the vehicle body, the analysis unit can estimate whether the cause is an accident or natural deterioration. For example, if there is an abnormality in the engine compartment, the analysis unit can estimate whether the cause is deterioration of parts or improper maintenance. For example, if there is tire wear, the analysis unit can estimate whether the cause is mileage or improper tire pressure. By estimating the cause of the abnormal location, more detailed information is provided. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input data on the abnormal location on the vehicle body into a generation AI, and the generation AI can estimate the cause.

[0043] The analysis unit can estimate the repair costs for abnormal parts of the vehicle body and provide this information to the user. For example, if there are scratches on the vehicle body, the analysis unit can estimate the repair costs. For example, if there are abnormalities in the engine compartment, the analysis unit can estimate the repair costs. For example, if there is tire wear, the analysis unit can estimate the replacement costs. By estimating repair costs, the user can understand the cost of repairs. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input data on abnormal parts of the vehicle body into a generation AI, and the generation AI can estimate the repair costs.

[0044] The analysis unit can evaluate the likelihood of recurrence of an abnormality by referring to the repair history of the abnormal part of the vehicle body. For example, the analysis unit can evaluate the likelihood of recurrence of an area that has been repaired in the past. For example, the analysis unit can estimate the likelihood of recurrence of a specific part from the repair history. For example, the analysis unit can evaluate the likelihood of recurrence of an abnormality based on the repair history. In this way, the likelihood of recurrence of an abnormality can be evaluated by referring to the repair history. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the analysis unit can input the vehicle body's repair history into a generating AI, and the generating AI can evaluate the likelihood of recurrence.

[0045] The analysis unit can display location information of abnormal areas on the vehicle body on a map and provide it to the user visually. For example, the analysis unit can map and display the abnormal areas on the vehicle body on a map. For example, the analysis unit can display location information of abnormal areas on a map and provide it to the user. For example, the analysis unit can display location information of abnormal areas on a map and provide the repair location visually. This allows the abnormal areas to be visually identified by displaying location information of abnormal areas on a map. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input location information of abnormal areas on the vehicle body into a generation AI, and the generation AI can display it on a map.

[0046] The service provider can suggest recommended repair companies and repair methods based on the analysis results. For example, the service provider can suggest the nearest repair company based on the analysis results. For example, the service provider can suggest the optimal repair method based on the analysis results. For example, the service provider can suggest recommended repair companies based on the analysis results. This allows users to have appropriate repairs done by suggesting repair companies and repair methods based on the analysis results. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the analysis results into AI, and the AI ​​can suggest repair companies and repair methods.

[0047] The service provider can evaluate the market value of a used car based on the analysis results and provide it to the user. For example, the service provider can evaluate the market value of a used car based on the analysis results. For example, the service provider can provide the user with the market value of a used car based on the analysis results. For example, the service provider can evaluate the market value of a used car based on the analysis results and provide it to the user. This allows the user to purchase a used car at an appropriate price by evaluating its market value. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the analysis results into AI, and the AI ​​can evaluate the market value.

[0048] The service provider can suggest the nearest repair shop or retailer, taking into account the user's geographical location. For example, the service provider can suggest the nearest repair shop based on the user's current location. For example, the service provider can suggest the nearest retailer based on the user's current location. For example, the service provider can suggest the nearest repair shop or retailer based on the user's current location. This allows the service provider to suggest the most suitable repair shop or retailer by taking into account the user's geographical location. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location information into the AI, which can then suggest the nearest repair shop or retailer.

[0049] The service provider can suggest the optimal purchase timing by referring to the user's past purchase history. For example, the service provider can suggest the optimal purchase timing based on the user's past purchase history. For example, the service provider can analyze the user's past purchase history and suggest the optimal purchase timing. For example, the service provider can suggest the optimal purchase timing by referring to the user's past purchase history. In this way, the service provider can suggest the optimal purchase timing by referring to past purchase history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past purchase history into AI, and the AI ​​can suggest the optimal purchase timing.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The used car diagnostic system can evaluate the reliability of diagnostic results by referring to the user's past diagnostic history. For example, it can compare the results of previously diagnosed vehicles with the actual condition to evaluate the accuracy of the diagnostic results. For example, if past diagnostic results were accurate, their reliability can be highly evaluated. For example, if past diagnostic results were inaccurate, the cause can be analyzed and feedback can be provided to improve the accuracy of future diagnostics. In this way, by referring to past diagnostic history, the reliability of diagnostic results can be evaluated and more accurate information can be provided to the user. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit can input past diagnostic history into a generating AI, and the generating AI can evaluate the reliability.

[0052] The used car diagnostic system can suggest the optimal timing for purchase by referring to the user's past purchase history. For example, it can suggest the optimal timing based on the user's past purchase history. For example, it can suggest the optimal timing by analyzing the user's past purchase history. For example, it can suggest the optimal timing by referring to the user's past purchase history. In this way, the optimal timing for purchase can be suggested by referring to past purchase history. Some or all of the above processing in the service provision unit may be performed using AI or not. For example, the service provision unit can input the user's past purchase history into the AI, and the AI ​​can suggest the optimal timing for purchase.

[0053] The used car diagnostic system can automatically select a shooting mode suitable for specific weather conditions, taking into account the user's geographical location. For example, it can automatically select a waterproof mode in rainy weather. For example, it can select a mode that adjusts brightness in sunny weather. For example, it can select a mode that prevents overexposure on snowy days. By selecting a shooting mode suitable for the weather conditions, optimal shooting results can be obtained. Some or all of the above processing in the shooting unit may be performed using AI or not. For example, the shooting unit can input the user's geographical location information into the AI, which can then select a shooting mode suitable for the weather conditions.

[0054] The used car diagnostic system can estimate the repair costs for any defects in the vehicle body and provide this information to the user. For example, if there are scratches on the vehicle body, the repair costs can be estimated. For example, if there are defects in the engine compartment, the repair costs can be estimated. For example, if there is tire wear, the replacement costs can be estimated. This allows the user to understand the repair costs by estimating them. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input data on the defects in the vehicle body into a generation AI, which can then estimate the repair costs.

[0055] The used car diagnostic system can evaluate the likelihood of recurrence of an abnormality by referring to the repair history of the abnormal part of the vehicle body. For example, it can evaluate the likelihood of recurrence of an area that has been repaired in the past. For example, it can estimate the likelihood of recurrence of a specific part from the repair history. For example, it can evaluate the likelihood of recurrence of an abnormality based on the repair history. In this way, the likelihood of recurrence of an abnormality can be evaluated by referring to the repair history. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the analysis unit can input the vehicle body's repair history into a generating AI, and the generating AI can evaluate the likelihood of recurrence.

[0056] The used car diagnostic system can display the location information of vehicle defects on a map and provide it to the user visually. For example, it can map and display the vehicle defects on a map. For example, it can display the location information of defects on a map and provide it to the user. For example, it can display the location information of defects on a map and visually provide the repair location. This allows the user to visually understand the defects by displaying the location information of defects on a map. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the location information of vehicle defects into a generation AI, and the generation AI can display it on a map.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The user takes pictures of the used car with their smartphone. The camera can capture detailed shots of the car's exterior and interior. For example, it can capture scratches and dents on the car body, and the condition of the engine compartment. The camera can capture high-resolution images using the smartphone's camera. It can also capture images from multiple angles to capture the overall appearance of the car. Step 2: The analysis unit analyzes the images captured by the camera unit to determine whether or not there are defects such as those found in accident vehicles or flooded vehicles. The analysis unit uses generating AI to analyze the images and can perform a detailed analysis of the vehicle's exterior and interior condition. For example, it can identify the presence or absence of scratches and dents on the vehicle body, and abnormal areas in the engine compartment. The analysis unit can improve its analysis accuracy by learning from past data on accident and flooded vehicles. Step 3: The service provider provides the user with the results determined by the analysis unit. The service provider can display the determination results via a smartphone app. If the determination results indicate a possible accident vehicle or a flood-damaged vehicle, detailed information may be included. The service provider can provide the determination results in text or graphical format.

[0059] (Example of form 2) The used car diagnostic system according to an embodiment of the present invention is a system that uses a smartphone to diagnose whether a used car is defective for used car buyers. In this system, the user takes an image of the used car with their smartphone, a generating AI analyzes the image, and determines whether or not there are defects such as accident-damaged or flood-damaged cars, and provides the user with the determination result. This alleviates anxiety when purchasing a used car and allows consumers to purchase a used car with peace of mind. For example, the user takes an image of the used car with their smartphone. In this case, it is recommended to take pictures of the exterior and detailed parts of the interior of the car body. For example, pictures of scratches and dents on the car body, and the condition of the engine compartment. This image data is input to the generating AI. Next, the generating AI analyzes the input image data. The generating AI uses image recognition technology to analyze the exterior and interior condition of the car body in detail. For example, it identifies the presence or absence of scratches and dents on the car body, abnormal areas in the engine compartment, etc. This makes it possible to determine whether or not there are defects such as accident-damaged or flood-damaged cars. The determination result is provided to the user. For example, the determination result is displayed through a smartphone app. The determination result includes detailed information if there is a possibility that the car is an accident-damaged or flood-damaged car. This allows users to accurately understand the condition of used cars. The service is offered at an inexpensive price of a few hundred yen per use. This makes it easy for consumers to have used cars inspected. Furthermore, by using generative AI, highly accurate diagnoses are possible, alleviating consumer concerns. For example, when a user purchases a used car, they take a picture of the car with their smartphone and request an inspection through the app. The generative AI analyzes the image and determines whether or not the car may have been in an accident or flooded. The inspection result is displayed, and the user can consider purchasing the car based on that information. This allows consumers to purchase used cars with peace of mind. In short, the used car inspection system enables consumers to purchase used cars with confidence.

[0060] The used car diagnostic system according to this embodiment comprises a shooting unit, an analysis unit, and a provision unit. The shooting unit allows the user to take images of the used car with their smartphone. The shooting unit can, for example, photograph the exterior and detailed interior of the car body. For example, the shooting unit can photograph scratches and dents on the car body, the condition of the engine compartment, etc. The shooting unit can, for example, take high-resolution images using the smartphone camera. The shooting unit can also, for example, take images from multiple angles to capture an overall view of the car body. The analysis unit analyzes the images taken by the shooting unit and determines whether or not there are defects such as accident vehicles or flood-damaged vehicles. The analysis unit analyzes the images using, for example, a generative AI. The generative AI can analyze the exterior and interior condition of the car body in detail using image recognition technology. For example, the analysis unit can identify the presence or absence of scratches and dents on the car body, abnormal areas in the engine compartment, etc. The analysis unit can, for example, learn from past accident vehicle and flood-damaged vehicle data to improve the accuracy of the analysis. The provision unit provides the user with the results determined by the analysis unit. The providing unit can, for example, display the judgment results through a smartphone app. The providing unit can, for example, include detailed information if the judgment results indicate the possibility of the vehicle being involved in an accident or being submerged in water. The providing unit can, for example, provide the judgment results in text or graphical display. As a result, the used car diagnostic system according to this embodiment can determine defects in a used car by having the user take an image of the used car with their smartphone and provide the analysis results. This allows the user to accurately understand the condition of the used car.

[0061] The photography unit takes pictures of used cars using the user's smartphone. The photography unit can, for example, photograph the exterior and detailed interior of the car. Specifically, it can capture high-resolution images using the smartphone's camera. Users are encouraged to take pictures from multiple angles to capture the overall appearance of the car. For example, taking pictures from the front, sides, rear, and top of the car allows for a detailed record of the car's condition. It is also required to photograph areas requiring special attention, such as scratches, dents, and the condition of the engine compartment. When photographing the engine compartment, it is important to take pictures from multiple angles to check the condition of each engine component and wiring, and whether there are any oil leaks. Furthermore, when photographing the interior, it is recommended to photograph the condition of the seats, dashboard, and the wear on the pedals and steering wheel in detail. This allows the photography unit to accurately record the exterior and interior condition of used cars by having users take detailed pictures of them using their smartphones. The captured images are uploaded to a cloud server in real time, making them accessible to the analysis unit. This allows the photography unit to efficiently and effectively record the condition of used cars, improving the overall system performance.

[0062] The analysis unit analyzes images captured by the camera unit to determine whether or not there are defects such as those found in accident-damaged or flood-damaged vehicles. The analysis unit uses, for example, a generative AI to analyze the images. The generative AI uses image recognition technology to analyze the external and internal condition of the vehicle body in detail. Specifically, the generative AI can identify the presence or absence of scratches and dents on the vehicle body, abnormalities in the engine compartment, etc. For example, from images of the vehicle body's exterior, it can detect paint peeling, rust, and panel distortion. From images of the engine compartment, it can identify oil leaks, wiring abnormalities, and component deterioration. Furthermore, the generative AI can learn from past data on accident-damaged and flood-damaged vehicles to improve its analysis accuracy. For example, using past data, it can determine whether a particular pattern of scratches or dents is unique to accident-damaged vehicles. In the case of flood-damaged vehicles, it can analyze and determine the pattern of rust and corrosion caused by water intrusion. Based on these analysis results, the analysis unit comprehensively evaluates the condition of the vehicle and generates information to provide to the user. This allows the analysis unit to quickly and accurately analyze captured images and determine defects in used cars. Furthermore, the analysis unit can continuously revise its analysis results based on real-time updated data, enabling it to adapt to the latest situation. This allows the analysis unit to always provide highly accurate analysis based on the latest information, helping users accurately understand the condition of used cars.

[0063] The service provider provides users with the results determined by the analysis unit. The service provider can display the results, for example, through a smartphone app. Specifically, if the results indicate a potential accident or flood damage, detailed information can be included. For example, the location and extent of scratches and dents on the body, and any abnormalities in the engine compartment can be displayed in detail. The service provider can provide the results in text or graphical format. Text display provides a concise report summarizing the analysis results for easy user understanding. Graphical display overlays the analysis results onto an image of the vehicle, making it visually easier for users to understand. For example, the location of scratches and dents can be marked on the vehicle image, and detailed information can be displayed as a pop-up. Additionally, abnormalities in the engine compartment can be highlighted, and their causes and countermeasures can be explained. Furthermore, the service provider can collect user feedback to continuously improve the accuracy and effectiveness of its offerings. For example, feedback on user actions based on the provided information can be collected and used to improve the analysis algorithm and display methods. This allows the service provider to provide users with quick and accurate information, helping them accurately understand the condition of used cars.

[0064] The analysis unit can identify scratches and dents on the vehicle body, abnormalities in the engine compartment, etc., using image recognition technology. For example, the analysis unit can identify scratches and dents on the vehicle body using deep learning. For example, the analysis unit can identify abnormalities in the engine compartment using computer vision technology. For example, the analysis unit can identify abnormalities on the vehicle body using image processing algorithms. In this way, abnormalities on the vehicle body can be identified by using image recognition technology. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input an image of the vehicle body into a generative AI, and the generative AI can analyze the image to identify abnormalities.

[0065] The service provider can display the judgment results through a smartphone app. For example, the service provider can provide the judgment results in text format. For example, the service provider can provide the judgment results in a graphical format. For example, the service provider can provide the judgment results in voice format. This makes it easier for users to check the results by displaying them through a smartphone app. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the judgment results into the AI, and the AI ​​can determine how to display the results.

[0066] The analysis unit can learn based on data from past accident vehicles and flooded vehicles. The analysis unit can learn from past data using, for example, machine learning algorithms. For example, the analysis unit can learn using training datasets. For example, the analysis unit can learn using image data from past accident vehicles and flooded vehicles. This improves the accuracy of the analysis by learning from past data. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input past data into a generative AI, and the generative AI can learn from the data to improve the accuracy of the analysis.

[0067] The camera unit can photograph the exterior and detailed interior of the vehicle. For example, it can photograph the condition of the engine compartment. For example, it can photograph the underside of the vehicle. For example, it can photograph scratches and dents on the vehicle in detail. By photographing these detailed parts, more accurate analysis becomes possible. Some or all of the above processing in the camera unit may be performed using AI, or not. For example, the camera unit can input the area to be photographed into the AI, which can then suggest the optimal shooting method.

[0068] The service provider may include detailed information if the assessment result indicates a possibility of the vehicle being involved in an accident or being flooded. For example, if the vehicle is likely to be involved in an accident, the service provider may provide detailed information in text format. For example, if the vehicle is likely to be flooded, the service provider may provide detailed information in a report with images. For example, if the vehicle is likely to be involved in an accident or is flooded, the service provider may provide detailed information in audio format. This allows users to accurately understand the condition of the used car by providing detailed information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider may input the assessment result into AI, which may generate and provide detailed information.

[0069] The camera unit can estimate the user's emotions and adjust the timing of the shot based on the estimated emotions. For example, if the user is nervous, the camera unit can wait until the user relaxes before starting the shot. For example, if the user is in a hurry, the camera unit can provide guidance for taking the shot quickly. For example, if the user is excited, the camera unit can provide a countdown to ensure a stable shot. By adjusting the timing of the shot according to the user's emotions, better shooting results can be obtained. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the camera unit may be performed using AI or not using AI. For example, the camera unit can input user emotion data into an AI, which can then adjust the timing of the shot.

[0070] The camera unit can automatically detect specific parts of the vehicle body and suggest the optimal shooting angle. For example, when photographing the engine compartment, the camera unit can guide the user to the optimal angle and distance. For example, when photographing tires, the camera unit can suggest an angle that clearly shows the tire wear. For example, when photographing door gaps, the camera unit can indicate an angle that clearly shows the condition of the gaps. By suggesting the optimal shooting angle, the camera unit can accurately photograph the condition of important parts. Some or all of the above processing in the camera unit may be performed using AI or not. For example, the camera unit can input specific parts of the vehicle body into the AI, which can then suggest the optimal shooting angle.

[0071] The imaging unit can apply filtering techniques to minimize light reflection and shadows. For example, the imaging unit can automatically apply a polarizing filter to reduce light reflection. For example, the imaging unit can suggest shooting from multiple angles to minimize shadows. For example, the imaging unit can automatically adjust the exposure according to the intensity of light. This allows for the capture of sharper images by suppressing light reflection and shadows. Some or all of the above processing in the imaging unit may be performed using AI or not. For example, the imaging unit can input light reflection and shadows into the AI, which can then apply filtering techniques.

[0072] The camera unit can estimate the user's emotions and determine the priority of which parts to photograph based on the estimated emotions. For example, if the user is feeling anxious, the camera unit can prioritize photographing important parts (such as the engine compartment or tires). For example, if the user is relaxed, the camera unit can take a full-body shot. For example, if the user is in a hurry, the camera unit can photograph only the most important parts. This allows for prioritizing important parts by determining the priority of the parts to photograph according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit may be performed using AI or not. For example, the camera unit can input user emotion data into the AI, which can then determine the priority of which parts to photograph.

[0073] The camera unit can automatically select a shooting mode suitable for specific weather conditions, taking into account the user's geographical location information. For example, the camera unit can automatically select a waterproof mode in rainy weather. For example, the camera unit can select a mode to adjust brightness in sunny weather. For example, the camera unit can select a mode to prevent overexposure on snowy days. By selecting a shooting mode suitable for the weather conditions, optimal shooting results can be obtained. Some or all of the above processing in the camera unit may be performed using AI, or it may be performed without AI. For example, the camera unit can input the user's geographical location information into the AI, which can then select a shooting mode suitable for the weather conditions.

[0074] The camera unit can automatically apply the optimal shooting settings by referring to the user's past shooting history. For example, the camera unit can suggest the optimal shooting settings based on the settings the user has used in the past. For example, the camera unit can automatically apply the most successful settings from the user's past shooting history. For example, the camera unit can analyze the user's past shooting history and suggest the optimal settings. This ensures that the optimal shooting settings are applied by referring to the past shooting history. Some or all of the above processes in the camera unit may be performed using AI or not. For example, the camera unit can input the user's past shooting history into the AI, which can then apply the optimal shooting settings.

[0075] The analysis unit can estimate the user's emotions and adjust the way the analysis results are presented based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can use concise and easy-to-understand language. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can display concise results that get straight to the point. By adjusting the way the analysis results are presented according to the user's emotions, more easily understandable results are provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input the user's emotion data into the generative AI, and the generative AI can adjust the way the analysis results are presented.

[0076] The analysis unit can not only identify abnormal locations on the vehicle body but also add the function of estimating their causes. For example, if there is damage to the vehicle body, the analysis unit can estimate whether the cause is an accident or natural deterioration. For example, if there is an abnormality in the engine compartment, the analysis unit can estimate whether the cause is deterioration of parts or improper maintenance. For example, if there is tire wear, the analysis unit can estimate whether the cause is mileage or improper tire pressure. By estimating the cause of the abnormal location, more detailed information is provided. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input data on the abnormal location on the vehicle body into a generation AI, and the generation AI can estimate the cause.

[0077] The analysis unit can estimate the repair costs for abnormal parts of the vehicle body and provide this information to the user. For example, if there are scratches on the vehicle body, the analysis unit can estimate the repair costs. For example, if there are abnormalities in the engine compartment, the analysis unit can estimate the repair costs. For example, if there is tire wear, the analysis unit can estimate the replacement costs. By estimating repair costs, the user can understand the cost of repairs. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input data on abnormal parts of the vehicle body into a generation AI, and the generation AI can estimate the repair costs.

[0078] The analysis unit can estimate the user's emotions and adjust the level of detail in the analysis results based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide concise and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. By adjusting the level of detail in the analysis results according to the user's emotions, more appropriate information is provided. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generative AI or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the level of detail in the analysis results.

[0079] The analysis unit can evaluate the likelihood of recurrence of an abnormality by referring to the repair history of the abnormal part of the vehicle body. For example, the analysis unit can evaluate the likelihood of recurrence of an area that has been repaired in the past. For example, the analysis unit can estimate the likelihood of recurrence of a specific part from the repair history. For example, the analysis unit can evaluate the likelihood of recurrence of an abnormality based on the repair history. In this way, the likelihood of recurrence of an abnormality can be evaluated by referring to the repair history. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the analysis unit can input the vehicle body's repair history into a generating AI, and the generating AI can evaluate the likelihood of recurrence.

[0080] The analysis unit can display location information of abnormal areas on the vehicle body on a map and provide it to the user visually. For example, the analysis unit can map and display the abnormal areas on the vehicle body on a map. For example, the analysis unit can display location information of abnormal areas on a map and provide it to the user. For example, the analysis unit can display location information of abnormal areas on a map and provide the repair location visually. This allows the abnormal areas to be visually identified by displaying location information of abnormal areas on a map. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input location information of abnormal areas on the vehicle body into a generation AI, and the generation AI can display it on a map.

[0081] The service provider can estimate the user's emotions and adjust how the information is displayed based on the estimated emotions. For example, if the user is feeling anxious, the service provider can provide a concise and easy-to-understand display. For example, if the user is relaxed, the service provider can provide a display that includes detailed information. For example, if the user is in a hurry, the service provider can provide a display that gets straight to the point. By adjusting how the information is displayed according to the user's emotions, more easily understandable information is provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into an AI, and the AI ​​can adjust how the information is displayed.

[0082] The service provider can suggest recommended repair companies and repair methods based on the analysis results. For example, the service provider can suggest the nearest repair company based on the analysis results. For example, the service provider can suggest the optimal repair method based on the analysis results. For example, the service provider can suggest recommended repair companies based on the analysis results. This allows users to have appropriate repairs done by suggesting repair companies and repair methods based on the analysis results. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the analysis results into AI, and the AI ​​can suggest repair companies and repair methods.

[0083] The service provider can evaluate the market value of a used car based on the analysis results and provide it to the user. For example, the service provider can evaluate the market value of a used car based on the analysis results. For example, the service provider can provide the user with the market value of a used car based on the analysis results. For example, the service provider can evaluate the market value of a used car based on the analysis results and provide it to the user. This allows the user to purchase a used car at an appropriate price by evaluating its market value. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the analysis results into AI, and the AI ​​can evaluate the market value.

[0084] The service provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is feeling anxious, the service provider can prioritize providing important information. For example, if the user is relaxed, the service provider can provide detailed information. For example, if the user is in a hurry, the service provider can prioritize providing concise information. In this way, by prioritizing information according to the user's emotions, important information can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into an AI, and the AI ​​can determine the priority of the information.

[0085] The service provider can suggest the nearest repair shop or retailer, taking into account the user's geographical location. For example, the service provider can suggest the nearest repair shop based on the user's current location. For example, the service provider can suggest the nearest retailer based on the user's current location. For example, the service provider can suggest the nearest repair shop or retailer based on the user's current location. This allows the service provider to suggest the most suitable repair shop or retailer by taking into account the user's geographical location. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location information into the AI, which can then suggest the nearest repair shop or retailer.

[0086] The service provider can suggest the optimal purchase timing by referring to the user's past purchase history. For example, the service provider can suggest the optimal purchase timing based on the user's past purchase history. For example, the service provider can analyze the user's past purchase history and suggest the optimal purchase timing. For example, the service provider can suggest the optimal purchase timing by referring to the user's past purchase history. In this way, the service provider can suggest the optimal purchase timing by referring to past purchase history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past purchase history into AI, and the AI ​​can suggest the optimal purchase timing.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The used car diagnostic system can estimate the user's emotions and adjust the notification method of the diagnostic results based on the estimated emotions. For example, if the user is feeling anxious, the diagnostic results can be notified in a concise and easy-to-understand format. For example, if the user is relaxed, detailed diagnostic results can be provided. For example, if the user is in a hurry, a concise notification that gets straight to the point can be provided. By adjusting the notification method of the diagnostic results according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the delivery unit may be performed using AI or not using AI. For example, the delivery unit can input user emotion data into the AI, and the AI ​​can adjust the notification method.

[0089] The used car diagnostic system can evaluate the reliability of diagnostic results by referring to the user's past diagnostic history. For example, it can compare the results of previously diagnosed vehicles with the actual condition to evaluate the accuracy of the diagnostic results. For example, if past diagnostic results were accurate, their reliability can be highly evaluated. For example, if past diagnostic results were inaccurate, the cause can be analyzed and feedback can be provided to improve the accuracy of future diagnostics. In this way, by referring to past diagnostic history, the reliability of diagnostic results can be evaluated and more accurate information can be provided to the user. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit can input past diagnostic history into a generating AI, and the generating AI can evaluate the reliability.

[0090] The used car diagnostic system can estimate the user's emotions and adjust the speed of the diagnostic process based on the estimated emotions. For example, if the user is feeling anxious, the diagnostic process can be expedited. For example, if the user is relaxed, the diagnostic process can be expedited with detailed explanations. For example, if the user is in a hurry, only the most important parts can be quickly diagnosed. In this way, by adjusting the speed of the diagnostic process according to the user's emotions, a service tailored to the user's needs can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into the AI, and the AI ​​can adjust the speed of the diagnostic process.

[0091] The used car diagnostic system can estimate the user's emotions and adjust the display format of the diagnostic results based on the estimated emotions. For example, if the user is feeling anxious, the diagnostic results can be displayed in a concise and easy-to-understand format. For example, if the user is relaxed, detailed diagnostic results can be displayed. For example, if the user is in a hurry, a concise display that gets straight to the point can be provided. By adjusting the display format of the diagnostic results according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the delivery unit may be performed using AI or not using AI. For example, the delivery unit can input user emotion data into the AI, and the AI ​​can adjust the display format.

[0092] The used car diagnostic system can estimate the user's emotions and adjust the timing of notification of the diagnostic results based on the estimated emotions. For example, if the user is feeling anxious, the diagnostic results can be notified quickly. For example, if the user is relaxed, the diagnostic results can be notified with detailed explanations. For example, if the user is in a hurry, only the most important parts can be quickly notified. In this way, by adjusting the timing of notification of the diagnostic results according to the user's emotions, a service tailored to the user's needs can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the delivery unit may be performed using AI or not using AI. For example, the delivery unit can input user emotion data into the AI, and the AI ​​can adjust the notification timing.

[0093] The used car diagnostic system can suggest the optimal timing for purchase by referring to the user's past purchase history. For example, it can suggest the optimal timing based on the user's past purchase history. For example, it can suggest the optimal timing by analyzing the user's past purchase history. For example, it can suggest the optimal timing by referring to the user's past purchase history. In this way, the optimal timing for purchase can be suggested by referring to past purchase history. Some or all of the above processing in the service provision unit may be performed using AI or not. For example, the service provision unit can input the user's past purchase history into the AI, and the AI ​​can suggest the optimal timing for purchase.

[0094] The used car diagnostic system can automatically select a shooting mode suitable for specific weather conditions, taking into account the user's geographical location. For example, it can automatically select a waterproof mode in rainy weather. For example, it can select a mode that adjusts brightness in sunny weather. For example, it can select a mode that prevents overexposure on snowy days. By selecting a shooting mode suitable for the weather conditions, optimal shooting results can be obtained. Some or all of the above processing in the shooting unit may be performed using AI or not. For example, the shooting unit can input the user's geographical location information into the AI, which can then select a shooting mode suitable for the weather conditions.

[0095] The used car diagnostic system can estimate the repair costs for any defects in the vehicle body and provide this information to the user. For example, if there are scratches on the vehicle body, the repair costs can be estimated. For example, if there are defects in the engine compartment, the repair costs can be estimated. For example, if there is tire wear, the replacement costs can be estimated. This allows the user to understand the repair costs by estimating them. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input data on the defects in the vehicle body into a generation AI, which can then estimate the repair costs.

[0096] The used car diagnostic system can evaluate the likelihood of recurrence of an abnormality by referring to the repair history of the abnormal part of the vehicle body. For example, it can evaluate the likelihood of recurrence of an area that has been repaired in the past. For example, it can estimate the likelihood of recurrence of a specific part from the repair history. For example, it can evaluate the likelihood of recurrence of an abnormality based on the repair history. In this way, the likelihood of recurrence of an abnormality can be evaluated by referring to the repair history. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the analysis unit can input the vehicle body's repair history into a generating AI, and the generating AI can evaluate the likelihood of recurrence.

[0097] The used car diagnostic system can display the location information of vehicle defects on a map and provide it to the user visually. For example, it can map and display the vehicle defects on a map. For example, it can display the location information of defects on a map and provide it to the user. For example, it can display the location information of defects on a map and visually provide the repair location. This allows the user to visually understand the defects by displaying the location information of defects on a map. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the location information of vehicle defects into a generation AI, and the generation AI can display it on a map.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The user takes pictures of the used car with their smartphone. The camera can capture detailed shots of the car's exterior and interior. For example, it can capture scratches and dents on the car body, and the condition of the engine compartment. The camera can capture high-resolution images using the smartphone's camera. It can also capture images from multiple angles to capture the overall appearance of the car. Step 2: The analysis unit analyzes the images captured by the camera unit to determine whether or not there are defects such as those found in accident vehicles or flooded vehicles. The analysis unit uses generating AI to analyze the images and can perform a detailed analysis of the vehicle's exterior and interior condition. For example, it can identify the presence or absence of scratches and dents on the vehicle body, and abnormal areas in the engine compartment. The analysis unit can improve its analysis accuracy by learning from past data on accident and flooded vehicles. Step 3: The service provider provides the user with the results determined by the analysis unit. The service provider can display the determination results via a smartphone app. If the determination results indicate a possible accident vehicle or a flood-damaged vehicle, detailed information may be included. The service provider can provide the determination results in text or graphical format.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0102] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] Each of the multiple elements described above, including the imaging unit, analysis unit, and data provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the imaging unit can capture high-resolution images using the camera 42 of the smart device 14. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, which analyzes the images using generated AI to determine defects such as those in accidents or submerged vehicles. The data provision unit is implemented in the control unit 46A of the smart device 14, which provides the determination results to the user through a smartphone application. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0111] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the imaging unit, analysis unit, and data provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the imaging unit can capture high-resolution images using the camera 42 of the smart glasses 214. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, which analyzes the images using generated AI to determine defects such as accident vehicles or flooded vehicles. The data provision unit is implemented in the control unit 46A of the smart glasses 214, which provides the determination results to the user through a smartphone application. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the imaging unit, analysis unit, and data provision unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the imaging unit can capture high-resolution images using the camera 42 of the headset terminal 314. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, which analyzes the images using generated AI to determine defects such as those in accidents or submerged vehicles. The data provision unit is implemented in the control unit 46A of the headset terminal 314, which provides the determination results to the user through a smartphone application. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the imaging unit, analysis unit, and data provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the imaging unit can capture high-resolution images using the camera 42 of the robot 414. The analysis unit is implemented in, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the images using generated AI to determine defects such as accident vehicles or flooded vehicles. The data provision unit is implemented in, for example, the control unit 46A of the robot 414, which provides the determination results to the user through a smartphone application. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0155] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0156] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0157] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0161] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0163] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0164] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0165] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0166] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0168] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0169] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0170] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0171] (Note 1) The photography section involves users taking pictures of used cars with their smartphones, An analysis unit analyzes the images captured by the aforementioned imaging unit to determine whether or not there are defects such as those found in accident vehicles or flooded vehicles. The system includes a providing unit that provides the user with the results determined by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Image recognition technology is used to identify scratches and dents on the vehicle body, as well as abnormalities in the engine compartment. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, The results are displayed via a smartphone app. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, It learns from data on past accident and flood-damaged vehicles. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned imaging unit is Take photos of the exterior and detailed interior of the vehicle. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, If the assessment results indicate the possibility of the vehicle being involved in an accident or submerged in water, detailed information will be included. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned imaging unit is It estimates the user's emotions and adjusts the timing of the photo shoot based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned imaging unit is It automatically detects specific parts of the vehicle body and suggests the optimal shooting angle. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned imaging unit is Apply filtering techniques to minimize light reflection and shadows. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned imaging unit is The system estimates the user's emotions and determines the priority of which body parts to photograph based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned imaging unit is The system automatically selects a shooting mode suitable for specific weather conditions, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned imaging unit is The system automatically applies the optimal shooting settings by referencing the user's past shooting history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the way the analysis results are presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, In addition to identifying abnormal parts of the vehicle body, the system will be updated to include a function that estimates the cause of those abnormalities. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, We estimate the repair costs for any abnormalities in the vehicle body and provide this information to the user. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the level of detail in the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, We will assess the likelihood of the problem recurring by referring to the repair history of the vehicle's faulty parts. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, The system displays the location of any abnormalities in the vehicle on a map, providing users with visual information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, Based on the analysis results, we will suggest recommended repair companies and repair methods. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, Based on the analysis results, we evaluate the market value of used cars and provide this information to users. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, We suggest the nearest repair shop or retailer, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, We refer to the user's past purchase history to suggest the optimal timing for purchase. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The photography section involves users taking pictures of used cars with their smartphones, An analysis unit analyzes the images captured by the aforementioned imaging unit to determine whether or not there are defects such as those found in accident vehicles or flooded vehicles. The system includes a providing unit that provides the user with the results determined by the analysis unit. A system characterized by the following features.

2. The aforementioned analysis unit, Image recognition technology is used to identify scratches and dents on the vehicle body, as well as abnormalities in the engine compartment. The system according to feature 1.

3. The aforementioned supply unit is, The results are displayed via a smartphone app. The system according to feature 1.

4. The aforementioned analysis unit, It learns from data on past accident and flood-damaged vehicles. The system according to feature 1.

5. The aforementioned imaging unit is Take photos of the exterior and detailed interior of the vehicle. The system according to feature 1.

6. The aforementioned supply unit is, If the assessment results indicate the possibility of the vehicle being involved in an accident or submerged in water, detailed information will be included. The system according to feature 1.

7. The aforementioned imaging unit is It estimates the user's emotions and adjusts the timing of the photo shoot based on those emotions. The system according to feature 1.

8. The aforementioned imaging unit is It automatically detects specific parts of the vehicle body and suggests the optimal shooting angle. The system according to feature 1.

9. The aforementioned imaging unit is Apply filtering techniques to minimize light reflection and shadows. The system according to feature 1.

10. The aforementioned imaging unit is The system estimates the user's emotions and determines the priority of which body parts to photograph based on those estimated emotions. The system according to feature 1.

Images