system

Abstract

The system according to the embodiment aims to realize a fair checking system by sharing information on the vehicle's maintenance status and repair history among three parties. [Solution] The system according to the embodiment includes an acquisition unit, an analysis unit, a provision unit, and a fairness assurance unit. The acquisition unit collects data on the vehicle's maintenance status or repair history, and insurance coverage. The analysis unit analyzes the data collected by the acquisition unit and determines the need for maintenance and the progress of repairs. The provision unit provides the analysis results obtained by the analysis unit to repair shops, insurance companies, and end users. The fairness assurance unit realizes a fair checking system by having all three parties perform maintenance checks based on the same information.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, information on vehicle maintenance status and repair history was not shared among the three parties, which meant that a fair checking system was not established.

[0005] The system according to the embodiment aims to realize a fair checking system by sharing information on the vehicle's maintenance status and repair history among three parties. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a provision unit, and a fairness assurance unit. The acquisition unit collects data on the vehicle's maintenance status or repair history, and insurance coverage. The analysis unit analyzes the data collected by the acquisition unit and determines the need for maintenance and the progress of repairs. The provision unit provides the analysis results obtained by the analysis unit to repair shops, insurance companies, and end users. The fairness assurance unit ensures a fair check system by having all three parties perform maintenance checks based on the same information. [Effects of the Invention]

[0007] The system according to the embodiment allows information on the vehicle's maintenance status and repair history to be shared among three parties, enabling a fair checking system to be realized. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A maintenance check system according to an embodiment of the present invention incorporates AI to visualize and ensure fairness among repairers, insurance companies, and end users. This system collects data on vehicle maintenance status, repair history, and insurance coverage, analyzes the collected data using AI, and determines whether maintenance is necessary or not. This allows repairers, insurance companies, and end users to share the same information, thereby achieving a fair check system. For example, the maintenance check system includes four main elements: data collection, data analysis, information sharing, and fairness assurance. The data collection function collects data on vehicle maintenance status, repair history, and insurance coverage. The data analysis function analyzes the collected data using AI to determine whether maintenance is necessary or not. The information sharing function provides the analysis results to repairers, insurance companies, and end users. The fairness assurance function ensures fairness by having all three parties conduct maintenance checks based on the same information. This improves the transparency of maintenance checks and strengthens the relationship of trust between repairers, insurance companies, and end users. Furthermore, the use of AI is expected to improve the efficiency and accuracy of maintenance checks. This allows the maintenance check system to improve the transparency of maintenance checks and strengthen the trust between repairers, insurance companies, and end users.

[0029] A maintenance check system according to an embodiment includes an acquisition unit, an analysis unit, a provision unit, and a fairness assurance unit. The acquisition unit collects data on the vehicle's maintenance status or repair history and insurance coverage. For example, the acquisition unit can collect the vehicle's maintenance status using sensors or diagnostic equipment. The acquisition unit can also acquire repair history from a database. The acquisition unit can also acquire insurance coverage from an insurance company's system. The analysis unit analyzes the data collected by the acquisition unit and determines the need for maintenance and the progress of the repair. For example, the analysis unit can analyze the data using AI to determine the need for maintenance. The analysis unit can also monitor the progress of repairs in real time and suggest necessary maintenance work. The provision unit provides the analysis results obtained by the analysis unit to repair shops, insurance companies, and end users. For example, the provision unit can provide the analysis results via a web application or a mobile application. The provision unit can also provide the analysis results via email or a notification system. The fairness assurance unit ensures a fair check system by having all three parties perform maintenance checks based on the same information. For example, the fairness ensuring unit can share the analysis results, allowing repairers, insurance companies, and end users to perform maintenance checks based on the same information. The fairness ensuring unit can also monitor the maintenance check process and provide feedback to ensure fairness. As a result, the maintenance check system according to the embodiment can improve the transparency of maintenance checks and strengthen the relationship of trust between repairers, insurance companies, and end users.

[0030] The acquisition unit can analyze the vehicle's past maintenance history and select an efficient data acquisition method. For example, the acquisition unit can prioritize acquisition of data related to parts that frequently break down from the past maintenance history. The acquisition unit can also acquire detailed data related to specific maintenance items based on the past maintenance history. Furthermore, the acquisition unit can analyze the past maintenance history and selectively acquire data related to areas that require maintenance. This enables efficient data collection by selecting the optimal data acquisition method based on the past maintenance history. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input past maintenance history data into the generation AI and cause the generation AI to select the optimal data acquisition method.

[0031] When acquiring data, the acquisition unit can perform filtering based on the current state or usage status of the vehicle. The acquisition unit can filter and acquire necessary data based on, for example, the current mileage of the vehicle. The acquisition unit can also filter and acquire related data based on the usage status of the vehicle (e.g., whether the vehicle is being driven long distances or short distances). Furthermore, the acquisition unit can filter and acquire necessary data based on the current state of the vehicle (e.g., the state of the engine or the state of tire wear). This enables efficient data acquisition by filtering necessary data based on the current state or usage status of the vehicle. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input current state data of the vehicle to the generation AI and have the generation AI perform filtering.

[0032] When acquiring data, the acquisition unit can prioritize acquiring highly relevant data based on the vehicle's geographical location information. For example, if the vehicle is in a cold region, the acquisition unit can prioritize acquiring maintenance data specific to cold regions. Furthermore, if the vehicle is in an urban area, the acquisition unit can prioritize acquiring maintenance data specific to urban areas. Furthermore, if the vehicle is in a mountainous area, the acquisition unit can prioritize acquiring maintenance data specific to mountainous areas. This enables efficient data collection by prioritizing the acquisition of highly relevant data in consideration of the vehicle's geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the vehicle's geographical location information to the generation AI and cause the generation AI to select highly relevant data.

[0033] When acquiring data, the acquisition unit can analyze the vehicle's social media activity and acquire related data. For example, the acquisition unit can acquire related data based on problems reported by vehicle owners on social media. The acquisition unit can also analyze vehicle ratings and reviews on social media and acquire related data. Furthermore, the acquisition unit can analyze vehicle usage on social media and acquire related data. This enables more accurate data collection by analyzing the vehicle's social media activity and acquiring related data. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input social media data to the generation AI and cause the generation AI to acquire related data.

[0034] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0035] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply an analysis algorithm dedicated to engines to engine-related data. The analysis unit can also apply an analysis algorithm dedicated to tires to tire-related data. Furthermore, the analysis unit can also apply an analysis algorithm dedicated to electronic devices to electronic device-related data. This enables more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI select the analysis algorithm to apply.

[0036] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit can, for example, prioritize analyzing the most recent data. The analysis unit can also postpone analyzing older data. Furthermore, the analysis unit can prioritize analyzing data collected during a specific period. This enables efficient analysis by determining the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0037] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can prioritize analysis of data related to a specific category. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0038] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit can provide detailed information for information of high importance. The providing unit can also provide simplified information for information of low importance. Furthermore, the providing unit can provide an appropriate level of detail for information of medium importance. This enables efficient information provision by adjusting the level of detail of the information provided based on the importance of the information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the importance of the information to the generating AI and cause the generating AI to adjust the level of detail of the information provided.

[0039] The providing unit can apply different provision algorithms depending on the category of information when providing the information. For example, the providing unit can apply a provision algorithm dedicated to engines to engine-related information. The providing unit can also apply a provision algorithm dedicated to tires to tire-related information. Furthermore, the providing unit can also apply a provision algorithm dedicated to electronic devices to electronic device-related information. This enables more accurate information provision by applying different provision algorithms depending on the category of information. Some or all of the above-mentioned processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the category of information to the generation AI and cause the generation AI to select the provision algorithm to be applied.

[0040] The providing unit can determine the priority of provision based on the time when the information was collected at the time of provision. For example, the providing unit can provide the latest information preferentially. The providing unit can also provide older information later. Furthermore, the providing unit can also provide information collected during a specific period preferentially. This enables efficient information provision by determining the priority of provision based on the time when the information was collected. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the time when the information was collected into the generating AI and cause the generating AI to determine the priority of provision.

[0041] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. For example, the providing unit can provide highly relevant information preferentially. The providing unit can also provide less relevant information later. Furthermore, the providing unit can also provide information related to a specific category preferentially. This enables efficient information provision by adjusting the order of provision based on the relevance of the information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the relevance of the information to the generating AI and cause the generating AI to adjust the order of provision.

[0042] When ensuring fairness, the fairness ensuring unit can select the optimal fairness ensuring method by referring to past data. The fairness ensuring unit can, for example, select the most effective fairness ensuring method based on past data. The fairness ensuring unit can also analyze past data and select a fairness ensuring method that suits a specific situation. Furthermore, the fairness ensuring unit can also select the optimal fairness ensuring method by referring to past data. This enables efficient fairness ensuring by selecting the optimal fairness ensuring method based on past data. Some or all of the above-mentioned processing in the fairness ensuring unit can be performed, for example, using AI or without using AI. For example, the fairness ensuring unit can input past data into the generation AI and cause the generation AI to select the optimal fairness ensuring method.

[0043] The fairness ensuring unit can customize the fairness ensuring means based on the current situation when ensuring fairness. The fairness ensuring unit can, for example, customize the optimal fairness ensuring means based on the current situation. The fairness ensuring unit can also analyze the current situation and customize the fairness ensuring means according to the specific situation. Furthermore, the fairness ensuring unit can customize the optimal fairness ensuring means by referring to the current situation. In this way, efficient fairness can be ensured by customizing the fairness ensuring means based on the current situation. Some or all of the above-mentioned processing in the fairness ensuring unit may be performed using AI, for example, or may be performed without using AI. For example, the fairness ensuring unit can input current situation data into the generation AI and cause the generation AI to customize the fairness ensuring means.

[0044] The fairness ensuring unit can select the optimal fairness ensuring method by taking geographical location information into consideration when ensuring fairness. For example, if the vehicle is in an urban area, the fairness ensuring unit can select a fairness ensuring method specific to urban areas. Furthermore, if the vehicle is in a mountainous area, the fairness ensuring unit can select a fairness ensuring method specific to mountainous areas. Furthermore, if the vehicle is in a cold region, the fairness ensuring unit can select a fairness ensuring method specific to cold regions. This enables efficient fairness ensuring by selecting the optimal fairness ensuring method by taking geographical location information into consideration. Some or all of the above-mentioned processing in the fairness ensuring unit may be performed using AI, for example, or may be performed without using AI. For example, the fairness ensuring unit can input geographical location information to the generation AI and cause the generation AI to select the optimal fairness ensuring method.

[0045] The fairness assurance unit can analyze social media activity and propose fairness assurance measures when ensuring fairness. For example, the fairness assurance unit can analyze vehicle ratings and reviews on social media and propose fairness assurance measures. The fairness assurance unit can also analyze vehicle usage on social media and propose fairness assurance measures. Furthermore, the fairness assurance unit can propose fairness assurance measures based on problems reported on social media. In this way, efficient fairness assurance is possible by analyzing social media activity and proposing fairness assurance measures. Some or all of the above-mentioned processing in the fairness assurance unit may be performed using AI, for example, or may be performed without using AI. For example, the fairness assurance unit can input social media data into a generation AI and have the generation AI execute the proposal of fairness assurance measures.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The acquisition unit can also collect data on the vehicle's driving patterns and driver behavior, in addition to the vehicle's maintenance status and repair history. For example, the acquisition unit can use the vehicle's acceleration sensor and GPS data to record the frequency of sudden braking and sudden acceleration. The acquisition unit can also analyze the driver's driving style (e.g., aggressive or calm driving) to predict the need for maintenance. Furthermore, the acquisition unit can record the driver's driving time and rest time to evaluate the risk of overworked driving. In this way, collecting data on driving patterns and driver behavior enables more accurate maintenance checks.

[0048] The acquisition unit can also collect fuel economy data and energy consumption data of the vehicle in addition to the vehicle's maintenance status and repair history. For example, the acquisition unit can record fluctuations in fuel economy using the vehicle's fuel economy sensor. In addition, in the case of an electric vehicle, the acquisition unit can collect battery consumption data and evaluate the battery's deterioration status. Furthermore, the acquisition unit can analyze the vehicle's energy consumption pattern and propose efficient energy management. Thus, collecting fuel economy data and energy consumption data enables more comprehensive maintenance checks.

[0049] When acquiring data, the acquisition unit can adjust the data acquisition method based on the vehicle's usage environment. For example, if the vehicle is in a high-temperature environment, it can prioritize acquiring data on the engine and cooling system. Also, if the vehicle is in a humid environment, it can prioritize acquiring data on the electrical system. Furthermore, if the vehicle is in a desert area, it can prioritize acquiring data on filters and air conditioners. This allows for efficient data collection by adjusting the data acquisition method based on the vehicle's usage environment.

[0050] During analysis, the analysis unit can adjust the accuracy of the analysis based on the reliability of the data. For example, detailed analysis can be performed on highly reliable data. Also, simplified analysis can be performed on less reliable data. Furthermore, analysis with moderate accuracy can be performed on data with medium reliability. This allows for efficient analysis by adjusting the accuracy of the analysis based on the reliability of the data.

[0051] During analysis, the analysis unit can select an analysis method based on the correlation between data. For example, if there is a strong correlation between engine data and fuel economy data, these data can be combined for analysis. Also, if there is a correlation between tire wear data and mileage data, it can predict when to replace tires based on this data. Furthermore, if there is a correlation between vehicle usage environment data and failure data, it can also determine the need for maintenance based on this data. This allows for more accurate analysis by selecting an analysis method based on the correlation between data.

[0052] The providing unit can customize the method of providing information based on the user's past behavioral history when providing the information. For example, detailed information can be provided to a user who previously preferred detailed reports. Concise information can also be provided to a user who previously preferred concise notifications. Furthermore, information can be provided in a specific format (e.g., graphs or charts) to a user who previously preferred a specific format. In this way, by customizing the method of providing information based on the user's past behavioral history, information can be provided in a form that is optimal for the user.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The acquisition unit collects data on the vehicle's maintenance status or repair history and insurance coverage. For example, the acquisition unit can collect the vehicle's maintenance status using sensors or diagnostic equipment. The acquisition unit can also acquire the repair history from a database. The acquisition unit can also acquire the insurance coverage from an insurance company's system. Step 2: The analysis unit analyzes the data collected by the acquisition unit and determines the need for maintenance and the progress of repairs. For example, the analysis unit can use AI to analyze the data and determine the need for maintenance. The analysis unit can also monitor the progress of repairs in real time and suggest necessary maintenance work. Step 3: The providing unit provides the analysis results obtained by the analysis unit to repair shops, insurance companies, and end users. For example, the providing unit can provide the analysis results through a web application or a mobile application. The providing unit can also provide the analysis results via email or a notification system. Step 4: The Fairness Assurance Department ensures a fair inspection system by having all three parties perform maintenance inspections based on the same information. For example, the Fairness Assurance Department can share the analysis results, allowing repair shops, insurance companies, and end users to perform maintenance inspections based on the same information. The Fairness Assurance Department can also monitor the maintenance inspection process and provide feedback to ensure fairness.

[0055] (Example 2) A maintenance check system according to an embodiment of the present invention incorporates AI to visualize and ensure fairness among repairers, insurance companies, and end users. This system collects data on vehicle maintenance status, repair history, and insurance coverage, analyzes the collected data using AI, and determines whether maintenance is necessary or not. This allows repairers, insurance companies, and end users to share the same information, thereby achieving a fair check system. For example, the maintenance check system includes four main elements: data collection, data analysis, information sharing, and fairness assurance. The data collection function collects data on vehicle maintenance status, repair history, and insurance coverage. The data analysis function analyzes the collected data using AI to determine whether maintenance is necessary or not. The information sharing function provides the analysis results to repairers, insurance companies, and end users. The fairness assurance function ensures fairness by having all three parties conduct maintenance checks based on the same information. This improves the transparency of maintenance checks and strengthens the relationship of trust between repairers, insurance companies, and end users. Furthermore, the use of AI is expected to improve the efficiency and accuracy of maintenance checks. This allows the maintenance check system to improve the transparency of maintenance checks and strengthen the trust between repairers, insurance companies, and end users.

[0056] A maintenance check system according to an embodiment includes an acquisition unit, an analysis unit, a provision unit, and a fairness assurance unit. The acquisition unit collects data on the vehicle's maintenance status or repair history and insurance coverage. For example, the acquisition unit can collect the vehicle's maintenance status using sensors or diagnostic equipment. The acquisition unit can also acquire repair history from a database. The acquisition unit can also acquire insurance coverage from an insurance company's system. The analysis unit analyzes the data collected by the acquisition unit and determines the need for maintenance and the progress of the repair. For example, the analysis unit can analyze the data using AI to determine the need for maintenance. The analysis unit can also monitor the progress of repairs in real time and suggest necessary maintenance work. The provision unit provides the analysis results obtained by the analysis unit to repair shops, insurance companies, and end users. For example, the provision unit can provide the analysis results via a web application or a mobile application. The provision unit can also provide the analysis results via email or a notification system. The fairness assurance unit ensures a fair check system by having all three parties perform maintenance checks based on the same information. For example, the fairness ensuring unit can share the analysis results, allowing repairers, insurance companies, and end users to perform maintenance checks based on the same information. The fairness ensuring unit can also monitor the maintenance check process and provide feedback to ensure fairness. As a result, the maintenance check system according to the embodiment can improve the transparency of maintenance checks and strengthen the relationship of trust between repairers, insurance companies, and end users.

[0057] The acquisition unit can estimate the user's emotions and adjust the timing of data acquisition based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit can delay data acquisition and acquire data when the user is relaxed. Furthermore, if the user is relaxed, the acquisition unit can immediately acquire data and respond quickly. Furthermore, if the user is in a hurry, the acquisition unit can prioritize data acquisition and quickly collect necessary information. This adjusts the timing of data acquisition according to the user's emotions, reducing the user's stress and enabling efficient data acquisition. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0058] The acquisition unit can analyze the vehicle's past maintenance history and select an efficient data acquisition method. For example, the acquisition unit can prioritize acquisition of data related to parts that frequently break down from the past maintenance history. The acquisition unit can also acquire detailed data related to specific maintenance items based on the past maintenance history. Furthermore, the acquisition unit can analyze the past maintenance history and selectively acquire data related to areas that require maintenance. This enables efficient data collection by selecting the optimal data acquisition method based on the past maintenance history. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input past maintenance history data into the generation AI and cause the generation AI to select the optimal data acquisition method.

[0059] When acquiring data, the acquisition unit can perform filtering based on the current state or usage status of the vehicle. The acquisition unit can filter and acquire necessary data based on, for example, the current mileage of the vehicle. The acquisition unit can also filter and acquire related data based on the usage status of the vehicle (e.g., whether the vehicle is being driven long distances or short distances). Furthermore, the acquisition unit can filter and acquire necessary data based on the current state of the vehicle (e.g., the state of the engine or the state of tire wear). This enables efficient data acquisition by filtering necessary data based on the current state or usage status of the vehicle. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input current state data of the vehicle to the generation AI and have the generation AI perform filtering.

[0060] The acquisition unit can estimate the user's emotions and determine the priority of data to be acquired based on the estimated user emotions. For example, when the user is feeling stressed, the acquisition unit can prioritize acquiring data of high importance. Furthermore, when the user is relaxed, the acquisition unit can prioritize acquiring detailed data. Furthermore, when the user is in a hurry, the acquisition unit can prioritize acquiring data that can be acquired quickly. This enables data acquisition according to the user's needs by determining the priority of data according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0061] When acquiring data, the acquisition unit can prioritize acquiring highly relevant data based on the vehicle's geographical location information. For example, if the vehicle is in a cold region, the acquisition unit can prioritize acquiring maintenance data specific to cold regions. Furthermore, if the vehicle is in an urban area, the acquisition unit can prioritize acquiring maintenance data specific to urban areas. Furthermore, if the vehicle is in a mountainous area, the acquisition unit can prioritize acquiring maintenance data specific to mountainous areas. This enables efficient data collection by prioritizing the acquisition of highly relevant data in consideration of the vehicle's geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the vehicle's geographical location information to the generation AI and cause the generation AI to select highly relevant data.

[0062] When acquiring data, the acquisition unit can analyze the vehicle's social media activity and acquire related data. For example, the acquisition unit can acquire related data based on problems reported by vehicle owners on social media. The acquisition unit can also analyze vehicle ratings and reviews on social media and acquire related data. Furthermore, the acquisition unit can analyze vehicle usage on social media and acquire related data. This enables more accurate data collection by analyzing the vehicle's social media activity and acquiring related data. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input social media data to the generation AI and cause the generation AI to acquire related data.

[0063] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0064] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0065] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply an analysis algorithm dedicated to engines to engine-related data. The analysis unit can also apply an analysis algorithm dedicated to tires to tire-related data. Furthermore, the analysis unit can also apply an analysis algorithm dedicated to electronic devices to electronic device-related data. This enables more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI select the analysis algorithm to apply.

[0066] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a short analysis result that can be quickly understood. By adjusting the length of the analysis according to the user's emotions, it is possible to provide an analysis result that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0067] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit can, for example, prioritize analyzing the most recent data. The analysis unit can also postpone analyzing older data. Furthermore, the analysis unit can prioritize analyzing data collected during a specific period. This enables efficient analysis by determining the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0068] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can prioritize analysis of data related to a specific category. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0069] The providing unit can estimate the user's emotions and adjust the presentation method based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can adopt a simple, highly visible presentation method. Furthermore, if the user is relaxed, the providing unit can also provide detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide information that is concise. This allows the presentation method to be adjusted according to the user's emotions, making it possible to provide information that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be 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-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation method.

[0070] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit can provide detailed information for information of high importance. The providing unit can also provide simplified information for information of low importance. Furthermore, the providing unit can provide an appropriate level of detail for information of medium importance. This enables efficient information provision by adjusting the level of detail of the information provided based on the importance of the information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the importance of the information to the generating AI and cause the generating AI to adjust the level of detail of the information provided.

[0071] The providing unit can apply different provision algorithms depending on the category of information when providing the information. For example, the providing unit can apply a provision algorithm dedicated to engines to engine-related information. The providing unit can also apply a provision algorithm dedicated to tires to tire-related information. Furthermore, the providing unit can also apply a provision algorithm dedicated to electronic devices to electronic device-related information. This enables more accurate information provision by applying different provision algorithms depending on the category of information. Some or all of the above-mentioned processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the category of information to the generation AI and cause the generation AI to select the provision algorithm to be applied.

[0072] The providing unit can estimate the user's emotions and adjust the length of the information provided based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide short, concise information. Furthermore, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is in a hurry, the providing unit can provide short information that can be quickly understood. This allows the user to easily understand information by adjusting the length of the information provided according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the information provided.

[0073] The providing unit can determine the priority of provision based on the time when the information was collected at the time of provision. For example, the providing unit can provide the latest information preferentially. The providing unit can also provide older information later. Furthermore, the providing unit can also provide information collected during a specific period preferentially. This enables efficient information provision by determining the priority of provision based on the time when the information was collected. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the time when the information was collected into the generating AI and cause the generating AI to determine the priority of provision.

[0074] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. For example, the providing unit can provide highly relevant information preferentially. The providing unit can also provide less relevant information later. Furthermore, the providing unit can also provide information related to a specific category preferentially. This enables efficient information provision by adjusting the order of provision based on the relevance of the information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the relevance of the information to the generating AI and cause the generating AI to adjust the order of provision.

[0075] The fairness assurance unit can estimate a user's emotions and adjust the fairness assurance method based on the estimated user emotions. For example, if the user is stressed, the fairness assurance unit can adopt a simple, highly visible fairness assurance method. Furthermore, if the user is relaxed, the fairness assurance unit can adopt a detailed fairness assurance method. Furthermore, if the user is in a hurry, the fairness assurance unit can adopt a fairness assurance method that focuses on the main points. This allows the fairness assurance method to be adjusted according to the user's emotions, making it easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be 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-described processing in the fairness assurance unit can be performed using, for example, an AI, or without an AI. For example, the fairness assurance unit can input user emotion data into the generation AI and have the generation AI adjust the fairness assurance method.

[0076] When ensuring fairness, the fairness ensuring unit can select the optimal fairness ensuring method by referring to past data. The fairness ensuring unit can, for example, select the most effective fairness ensuring method based on past data. The fairness ensuring unit can also analyze past data and select a fairness ensuring method that suits a specific situation. Furthermore, the fairness ensuring unit can also select the optimal fairness ensuring method by referring to past data. This enables efficient fairness ensuring by selecting the optimal fairness ensuring method based on past data. Some or all of the above-mentioned processing in the fairness ensuring unit can be performed, for example, using AI or without using AI. For example, the fairness ensuring unit can input past data into the generation AI and cause the generation AI to select the optimal fairness ensuring method.

[0077] The fairness ensuring unit can customize the fairness ensuring means based on the current situation when ensuring fairness. The fairness ensuring unit can, for example, customize the optimal fairness ensuring means based on the current situation. The fairness ensuring unit can also analyze the current situation and customize the fairness ensuring means according to the specific situation. Furthermore, the fairness ensuring unit can customize the optimal fairness ensuring means by referring to the current situation. In this way, efficient fairness can be ensured by customizing the fairness ensuring means based on the current situation. Some or all of the above-mentioned processing in the fairness ensuring unit may be performed using AI, for example, or may be performed without using AI. For example, the fairness ensuring unit can input current situation data into the generation AI and cause the generation AI to customize the fairness ensuring means.

[0078] The fairness ensuring unit can estimate a user's emotions and determine a priority for ensuring fairness based on the estimated user's emotions. For example, if the user is stressed, the fairness ensuring unit can prioritize fairness ensuring measures with a high level of importance. Furthermore, if the user is relaxed, the fairness ensuring unit can prioritize detailed fairness ensuring measures. Furthermore, if the user is in a hurry, the fairness ensuring unit can prioritize fairness ensuring measures that can be implemented quickly. This enables efficient fairness ensuring by determining the priority for ensuring fairness based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the fairness ensuring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the fairness ensuring unit can input user emotion data into the generation AI and have the generation AI determine the priority for ensuring fairness.

[0079] The fairness ensuring unit can select the optimal fairness ensuring method by taking geographical location information into consideration when ensuring fairness. For example, if the vehicle is in an urban area, the fairness ensuring unit can select a fairness ensuring method specific to urban areas. Furthermore, if the vehicle is in a mountainous area, the fairness ensuring unit can select a fairness ensuring method specific to mountainous areas. Furthermore, if the vehicle is in a cold region, the fairness ensuring unit can select a fairness ensuring method specific to cold regions. This enables efficient fairness ensuring by selecting the optimal fairness ensuring method by taking geographical location information into consideration. Some or all of the above-mentioned processing in the fairness ensuring unit may be performed using AI, for example, or may be performed without using AI. For example, the fairness ensuring unit can input geographical location information to the generation AI and cause the generation AI to select the optimal fairness ensuring method.

[0080] The fairness assurance unit can analyze social media activity and propose fairness assurance measures when ensuring fairness. For example, the fairness assurance unit can analyze vehicle ratings and reviews on social media and propose fairness assurance measures. The fairness assurance unit can also analyze vehicle usage on social media and propose fairness assurance measures. Furthermore, the fairness assurance unit can propose fairness assurance measures based on problems reported on social media. In this way, efficient fairness assurance is possible by analyzing social media activity and proposing fairness assurance measures. Some or all of the above-mentioned processing in the fairness assurance unit may be performed using AI, for example, or may be performed without using AI. For example, the fairness assurance unit can input social media data into a generation AI and have the generation AI execute the proposal of fairness assurance measures. === Hard Collateral 1-1 === Each of the multiple elements, including the data collection function, data analysis function, information sharing function, and fairness assurance function, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the data collection function collects information about vehicle maintenance status using sensors and diagnostic equipment in the smart device 14 and acquires repair histories from the database 24 in the data processing device 12. The data analysis function is realized by the specific processing unit 290 in the data processing device 12, which analyzes the collected data using AI to determine the need for maintenance and the progress of repairs. The information sharing function provides the analysis results to repairers, insurance companies, and end users via the control unit 46A of the smart device 14. The fairness assurance function is realized by the specific processing unit 290 in the data processing device 12, which realizes a fair inspection system by having the three parties perform maintenance checks based on the same information. === Hard Collateral 1-2 === Each of the multiple elements, including the data collection function, data analysis function, information sharing function, and fairness assurance function, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the data collection function collects vehicle maintenance status information using sensors and diagnostic equipment in the smart glasses 214 and acquires repair histories from the database 24 in the data processing device 12. The data analysis function is realized by the specific processing unit 290 in the data processing device 12, which analyzes the collected data using AI to determine the need for maintenance and the progress of repairs. The information sharing function provides the analysis results to repairers, insurance companies, and end users via the control unit 46A of the smart glasses 214. The fairness assurance function is realized by the specific processing unit 290 in the data processing device 12, which realizes a fair inspection system by having the three parties perform maintenance checks based on the same information. === Hard Collateral 1-3 === Each of the multiple elements, including the data collection function, data analysis function, information sharing function, and fairness assurance function, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the data collection function collects information about the vehicle's maintenance status using sensors and diagnostic equipment in the headset terminal 314 and acquires repair histories from the database 24 in the data processing device 12. The data analysis function is realized by the specific processing unit 290 in the data processing device 12, which analyzes the collected data using AI to determine the need for maintenance and the progress of repairs. The information sharing function provides the analysis results to repair shops, insurance companies, and end users via the control unit 46A in the headset terminal 314. The fairness assurance function is realized by the specific processing unit 290 in the data processing device 12, which realizes a fair inspection system by having the three parties perform maintenance checks based on the same information. === Hard Collateral 1-4 === Each of the multiple elements, including the data collection function, data analysis function, information sharing function, and fairness assurance function, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the data collection function uses sensors and diagnostic equipment in the robot 414 to collect information on the vehicle's maintenance status and acquires repair histories from the database 24 in the data processing device 12. The data analysis function is realized by the specific processing unit 290 in the data processing device 12, which analyzes the collected data using AI to determine the need for maintenance and the progress of repairs. The information sharing function provides the analysis results to repair shops, insurance companies, and end users via the control unit 46A of the robot 414. The fairness assurance function is realized by the specific processing unit 290 in the data processing device 12, which realizes a fair inspection system by having the three parties perform maintenance checks based on the same information.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The acquisition unit can also collect data on the vehicle's driving patterns and driver behavior, in addition to the vehicle's maintenance status and repair history. For example, the acquisition unit can use the vehicle's acceleration sensor and GPS data to record the frequency of sudden braking and sudden acceleration. The acquisition unit can also analyze the driver's driving style (e.g., aggressive or calm driving) to predict the need for maintenance. Furthermore, the acquisition unit can record the driver's driving time and rest time to evaluate the risk of overworked driving. In this way, collecting data on driving patterns and driver behavior enables more accurate maintenance checks.

[0083] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis results can be notified as a concise message. If the user is relaxed, a detailed report can be provided. Furthermore, if the user is in a hurry, a short notification that focuses on the main points can be sent. In this way, by adjusting the notification method of the analysis results according to the user's emotions, information can be provided in a format that is easy for the user to accept.

[0084] The acquisition unit can also collect fuel economy data and energy consumption data of the vehicle in addition to the vehicle's maintenance status and repair history. For example, the acquisition unit can record fluctuations in fuel economy using the vehicle's fuel economy sensor. In addition, in the case of an electric vehicle, the acquisition unit can collect battery consumption data and evaluate the battery's deterioration status. Furthermore, the acquisition unit can analyze the vehicle's energy consumption pattern and propose efficient energy management. Thus, collecting fuel economy data and energy consumption data enables more comprehensive maintenance checks.

[0085] When acquiring data, the acquisition unit can adjust the data acquisition method based on the vehicle's usage environment. For example, if the vehicle is in a high-temperature environment, it can prioritize acquiring data on the engine and cooling system. Also, if the vehicle is in a humid environment, it can prioritize acquiring data on the electrical system. Furthermore, if the vehicle is in a desert area, it can prioritize acquiring data on filters and air conditioners. This allows for efficient data collection by adjusting the data acquisition method based on the vehicle's usage environment.

[0086] The acquisition unit can estimate the user's emotions and adjust the data acquisition frequency based on the estimated user emotions. For example, if the user is feeling stressed, the data acquisition frequency can be set low, and if the user is relaxed, the data acquisition frequency can be set high. Also, if the user is in a hurry, the minimum amount of data necessary can be quickly acquired. In this way, adjusting the data acquisition frequency according to the user's emotions reduces the burden on the user and enables efficient data acquisition.

[0087] During analysis, the analysis unit can adjust the accuracy of the analysis based on the reliability of the data. For example, detailed analysis can be performed on highly reliable data. Also, simplified analysis can be performed on less reliable data. Furthermore, analysis with moderate accuracy can be performed on data with medium reliability. This allows for efficient analysis by adjusting the accuracy of the analysis based on the reliability of the data.

[0088] During analysis, the analysis unit can select an analysis method based on the correlation between data. For example, if there is a strong correlation between engine data and fuel economy data, these data can be combined for analysis. Also, if there is a correlation between tire wear data and mileage data, it can predict when to replace tires based on this data. Furthermore, if there is a correlation between vehicle usage environment data and failure data, it can also determine the need for maintenance based on this data. This allows for more accurate analysis by selecting an analysis method based on the correlation between data.

[0089] The analysis unit can estimate the user's emotions and adjust the display format of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis results can be displayed visually in graphs or charts. If the user is relaxed, a detailed text report can be provided. Furthermore, if the user is in a hurry, key points can be displayed in bullet points. In this way, by adjusting the display format of the analysis results according to the user's emotions, information can be provided in a format that is easy for the user to understand.

[0090] The providing unit can customize the method of providing information based on the user's past behavioral history when providing the information. For example, detailed information can be provided to a user who previously preferred detailed reports. Concise information can also be provided to a user who previously preferred concise notifications. Furthermore, information can be provided in a specific format (e.g., graphs or charts) to a user who previously preferred a specific format. In this way, by customizing the method of providing information based on the user's past behavioral history, information can be provided in a form that is optimal for the user.

[0091] The providing unit can estimate the user's emotions and adjust the timing of providing information based on the estimated user's emotions. For example, if the user is feeling stressed, the provision of information can be delayed and the information can be provided at a time when the user is relaxed. Also, if the user is in a hurry, important information can be provided immediately. Furthermore, if the user is relaxed, detailed information can be provided. In this way, by adjusting the timing of providing information according to the user's emotions, the information can be provided at the optimal timing for the user.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The acquisition unit collects data on the vehicle's maintenance status or repair history and insurance coverage. For example, the acquisition unit can collect the vehicle's maintenance status using sensors or diagnostic equipment. The acquisition unit can also acquire the repair history from a database. The acquisition unit can also acquire the insurance coverage from an insurance company's system. Step 2: The analysis unit analyzes the data collected by the acquisition unit and determines the need for maintenance and the progress of repairs. For example, the analysis unit can use AI to analyze the data and determine the need for maintenance. The analysis unit can also monitor the progress of repairs in real time and suggest necessary maintenance work. Step 3: The providing unit provides the analysis results obtained by the analysis unit to repair shops, insurance companies, and end users. For example, the providing unit can provide the analysis results through a web application or a mobile application. The providing unit can also provide the analysis results via email or a notification system. Step 4: The Fairness Assurance Department ensures a fair inspection system by having all three parties perform maintenance inspections based on the same information. For example, the Fairness Assurance Department can share the analysis results, allowing repair shops, insurance companies, and end users to perform maintenance inspections based on the same information. The Fairness Assurance Department can also monitor the maintenance inspection process and provide feedback to ensure fairness.

[0094] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0116] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a 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.

[0132] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0138] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] 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.

[0157] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0165] [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that collects data on vehicle maintenance status or repair history and insurance coverage; an analysis unit that analyzes the data collected by the acquisition unit and determines the need for maintenance and the progress of repairs; a providing unit that provides the analysis results obtained by the analysis unit to repairers, insurance companies, and end users; The Fairness Assurance Department ensures a fair inspection system by having all three parties conduct maintenance inspections based on the same information. A system characterized by:

2. The acquisition unit Estimate user emotions and adjust data acquisition timing based on the estimated user emotions 2. The system of claim 1.

3. The acquisition unit Analyze the vehicle's past maintenance history and select an efficient data acquisition method 2. The system of claim 1.

4. The acquisition unit As data is acquired, it is filtered based on the vehicle's current condition or usage.

2. The system of claim 1.

5. The acquisition unit Estimate user emotions and prioritize data to be acquired based on the estimated user emotions.

2. The system of claim 1.

6. The acquisition unit When retrieving data, the vehicle's geographic location is used to prioritize the most relevant data.

2. The system of claim 1.

7. The acquisition unit Upon data capture, the vehicle's social media activity is analyzed to capture relevant data.

2. The system of claim 1.

8. The analysis unit Inferring user emotions and adjusting the presentation of analysis based on the estimated user emotions 2. The system of claim 1.

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