System

Abstract

An object of a system according to an embodiment is to evaluate a driving habit of a driver and propose an optimal maintenance schedule.SOLUTION: A system according to an embodiment includes a driving data acquirer, a driving habit analyzer, a maintenance suggester, and an advice provider. The driving data acquisition unit acquires driving data of a driver. The driving habit analyzer analyzes the driving data acquired by the driving data acquirer to evaluate the driving habit. The maintenance suggestion unit suggests an optimal maintenance schedule based on the driving habit evaluated by the driving habit analysis unit. The advice provider provides intelligent advice based on the maintenance schedule suggested by the maintenance suggester.SELECTED DRAWING: Figure 1

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] Conventional technology has difficulty proposing optimal maintenance schedules based on a driver's driving habits, and there is room for improvement.

[0005] The system according to the embodiment aims to evaluate the driving habits of a driver and propose an optimal maintenance schedule. [Means for solving the problem]

[0006] The system according to the embodiment includes a driving data acquisition unit, a driving habit analysis unit, a maintenance suggestion unit, and an advice provision unit. The driving data acquisition unit acquires driving data of a driver. The driving habit analysis unit analyzes the driving data acquired by the driving data acquisition unit to evaluate the driving habits. The maintenance suggestion unit proposes an optimal maintenance schedule based on the driving habits evaluated by the driving habit analysis unit. The advice provision unit provides intelligent advice based on the maintenance schedule proposed by the maintenance suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can evaluate the driving habits of the driver and suggest an optimal maintenance schedule. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 mobile app according to an embodiment of the present invention is a system that uses cutting-edge AI technology to analyze a driver's driving habits and streamline car maintenance. The system incorporates gamification elements to provide intelligent advice for making driving more enjoyable and extending the life of a car. This allows the mobile app to improve a driver's driving habits and streamline car maintenance. For example, reducing sudden braking and acceleration can improve fuel efficiency, and performing regular maintenance can extend the life of a car. Furthermore, the gamification elements make driving more enjoyable and increase the driver's motivation.

[0029] A mobile app according to an embodiment includes a driving data acquisition unit, a driving habit analysis unit, a maintenance suggestion unit, and an advice provision unit. The driving data acquisition unit acquires driving data of a driver. For example, the driving data acquisition unit collects data such as speed, braking frequency, and acceleration patterns. The driving data acquisition unit can also acquire driving data using in-vehicle sensors or a smartphone's GPS function. The driving habit analysis unit analyzes the driving data acquired by the driving data acquisition unit to evaluate driving habits. For example, the generation AI analyzes the frequency of sudden braking and sudden acceleration and suggests driving improvements. The generation AI also detects speeding and unnecessary idling to improve driving efficiency. The maintenance suggestion unit proposes an optimal maintenance schedule based on the driving habits evaluated by the driving habit analysis unit. For example, the generation AI analyzes the vehicle's mileage and usage status to predict when oil and tire changes are required. The generation AI also performs a detailed analysis of the deterioration status of each vehicle part and suggests the optimal timing for part replacement. The advice provision unit provides intelligent advice based on the maintenance schedule proposed by the maintenance suggestion unit. For example, the generating AI suggests driving methods to improve fuel efficiency and maintenance methods to extend the life of the car. The generating AI also provides specific advice to the driver based on driving habits and maintenance data. This allows the mobile app according to the embodiment to improve the driver's driving habits and streamline car maintenance. For example, the output unit displays driving data and advice within the app to provide feedback to the driver. A notification function can also be used to notify the driver when maintenance is due. Furthermore, the app stores driving data in the cloud, making it accessible from other devices.

[0030] The driving data acquisition unit can monitor the driver's heart rate and stress level in real time while driving and acquire driving data. For example, the driving data acquisition unit collects the driver's heart rate data in real time, and the generation AI analyzes the data to estimate the stress level. For example, if the heart rate rises sharply, it is determined that stress is increasing and suggests driving methods to help the driver relax. The driving data acquisition unit can also collect the driver's electrodermal activity using a sensor to measure the stress level. For example, it can analyze changes in electrodermal activity to evaluate the stress level. The driving data acquisition unit can also monitor the driver's breathing pattern to estimate the stress level. For example, if breathing becomes shallow and rapid, it is determined that stress is increasing. This makes it possible to suggest improvements to driving habits by monitoring the driver's heart rate and stress level while driving.

[0031] The driving habit analysis unit can provide driving advice optimized for individual driving styles based on driving data. The driving habit analysis unit, for example, analyzes the driver's driving data (speed, frequency of braking, acceleration patterns, etc.) to identify individual driving styles. For example, for a driver who frequently accelerates suddenly, the unit can suggest smooth acceleration methods. The driving habit analysis unit can also suggest fuel-efficient driving methods to encourage eco-driving. For example, it can recommend maintaining a constant speed and avoiding unnecessary idling. The driving habit analysis unit can also suggest safe and efficient driving methods for drivers who prefer sporty driving. For example, it can advise on appropriate braking on curves and the timing of acceleration. This allows the quality of driving to be improved by providing driving advice optimized for individual driving styles.

[0032] The driving data acquisition unit can analyze in-car voice data and evaluate the impact of conversations and music while driving. For example, the driving data acquisition unit collects in-car voice data, and the generation AI analyzes that data to evaluate the impact of conversations and music while driving. For example, if there is a lot of conversation, it can point out the possibility of a decrease in concentration and advise the driver to concentrate on driving. The driving data acquisition unit can also analyze the genre and tempo of music to evaluate the impact of music. For example, it can evaluate the impact of fast-tempo music on driving and recommend relaxing music. The driving data acquisition unit can also monitor the noise level inside the car and evaluate the impact on driving. For example, it can point out the possibility of a decrease in concentration if the noise level is high. This allows the system to analyze in-car voice data and evaluate the impact of conversations and music while driving, thereby suggesting areas for improvement in driving habits.

[0033] The driving habit analysis unit can add a function for sharing the analysis results of driving habits with other drivers and receiving feedback within the community. For example, the driving habit analysis unit can add a function for sharing the analysis results of driving habits within the app and receiving feedback from other drivers. For example, driving scores and areas for improvement can be shared and advice can be received from other drivers. The driving habit analysis unit can also provide a ranking function within the community to promote competition between drivers. For example, driving scores can be displayed in a ranking format and top drivers can be rewarded. The driving habit analysis unit can also provide a chat function within the community to promote information exchange between drivers. For example, driving tips and maintenance information can be shared. This allows drivers to share the analysis results of driving habits with other drivers and receive feedback, thereby promoting the improvement of driving habits.

[0034] The maintenance proposal unit can analyze the deterioration status of each car part in detail and propose the optimal timing for part replacement. For example, the maintenance proposal unit monitors the deterioration status of each car part in real time, and the generation AI analyzes the data to propose the optimal timing for part replacement. For example, it analyzes the wear status of tires and notifies the user when it is time to replace them. The maintenance proposal unit can also analyze the deterioration status of engine oil and propose the timing for replacement. For example, it can analyze the viscosity and dirtiness of the oil and notify the user when it is time to replace it. The maintenance proposal unit can also analyze the wear status of brake pads and propose the timing for replacement. For example, it can monitor the thickness of the brake pads and notify the user when it is time to replace them. This allows for the efficient analysis of the deterioration status of each car part and the proposal of the optimal timing for part replacement.

[0035] The maintenance suggestion unit can predict future breakdown risks based on the vehicle's maintenance data and suggest preventive maintenance. The maintenance suggestion unit, for example, analyzes the vehicle's maintenance data and predicts future breakdown risks. For example, it analyzes the deterioration status of engine oil and suggests an oil change before the risk of breakdown increases. The maintenance suggestion unit can also analyze the wear status of brake pads and predict breakdown risks. For example, it monitors the thickness of brake pads and notifies the user when they need to be replaced. The maintenance suggestion unit can also analyze the wear status of tires and predict breakdown risks. For example, it monitors the depth of tire treads and notifies the user when they need to be replaced. In this way, it is possible to predict future breakdown risks and suggest preventive maintenance, thereby preventing vehicle breakdowns from occurring.

[0036] When analyzing maintenance data, the maintenance suggestion unit takes into account the vehicle's usage environment and can propose a maintenance schedule that suits the environment. For example, the maintenance suggestion unit collects data on the vehicle's usage environment (urban area, mountainous area, etc.), and the generation AI analyzes that data to propose a maintenance schedule that suits the environment. For example, if the vehicle is used frequently in mountainous areas, the frequency of brake pad replacement may be increased. The maintenance suggestion unit may also increase the frequency of engine oil replacement if the vehicle is used frequently in cold regions. For example, the timing of replacement may be suggested, taking into account oil deterioration at low temperatures. The maintenance suggestion unit may also increase the frequency of air filter replacement if the vehicle is used frequently in urban areas. For example, the timing of filter replacement may be suggested, taking into account pollutants in urban areas. This allows for more efficient vehicle maintenance by proposing a maintenance schedule that takes into account the vehicle's usage environment.

[0037] The maintenance suggestion unit can add a function to compare the maintenance data with data of other vehicles of the same type and evaluate the relative maintenance status. The maintenance suggestion unit can add a function to compare the maintenance data with data of other vehicles of the same type and evaluate the relative maintenance status. For example, the maintenance status of the vehicle is evaluated by comparing it with the average maintenance frequency of vehicles of the same type. The maintenance suggestion unit can also compare it with failure data of other vehicles of the same type and evaluate the failure risk. For example, the failure risk of the vehicle is evaluated by comparing it with the failure frequency of vehicles of the same type. The maintenance suggestion unit can also compare it with the usage status of other vehicles of the same type and evaluate the need for maintenance. For example, the need for maintenance of the vehicle is evaluated by comparing it with the mileage and usage environment of vehicles of the same type. This makes it possible to improve the efficiency of vehicle maintenance by evaluating the relative maintenance status by comparing it with data of other vehicles of the same type.

[0038] The advice providing unit can suggest specific driving methods to improve fuel efficiency from driving data. The advice providing unit, for example, analyzes the driver's driving data and suggests specific driving methods to improve fuel efficiency. For example, it recommends maintaining a constant speed and avoiding sudden acceleration. The advice providing unit can also suggest eco-driving techniques. For example, it can recommend utilizing engine braking and avoiding unnecessary idling. The advice providing unit can also suggest improvements to accelerator operation to improve fuel efficiency. For example, it can recommend smooth accelerator operation to improve fuel efficiency. In this way, by suggesting specific driving methods to improve fuel efficiency, improvements in fuel efficiency can be expected.

[0039] The advice providing unit can evaluate the driver's driving skills based on the driving data and provide a training program for skill improvement. The advice providing unit, for example, analyzes the driver's driving data and evaluates the driving skills. For example, it determines the skill level based on the frequency of sudden braking and sudden acceleration and provides a training program for skill improvement. The advice providing unit can also provide simulation training. For example, it can improve driving skills through a driving simulation in a virtual environment. The advice providing unit can also provide practical training. For example, it can improve driving skills through training in an actual driving environment. In this way, the driving skills of the driver can be improved by evaluating the driving skills and providing a training program for skill improvement.

[0040] When analyzing driving data, the advice providing unit also takes into account external data such as weather and traffic conditions, and can provide more accurate driving advice. For example, when analyzing driving data, the advice providing unit takes into account weather data and provides more accurate driving advice. For example, the advice providing unit analyzes driving data in rainy weather and suggests driving methods on slippery roads. The advice providing unit can also take into account traffic condition data and provide more accurate driving advice. For example, the advice providing unit analyzes driving data in traffic jams and suggests an efficient route. The advice providing unit can also provide driving advice according to the driving environment based on external data. For example, the temperature and wind speed are taken into account to adjust the driving method. In this way, by taking into account external data such as weather and traffic conditions, more accurate driving advice can be provided.

[0041] The advice providing unit can add a function to compare the driver's driving data with other drivers and evaluate the relative driving skills. The advice providing unit, for example, adds a function to compare the driver's driving data with other drivers and evaluate the relative driving skills. For example, the advice providing unit displays driving scores in a ranking format and compares the driver with other drivers. The advice providing unit can also compare the driving data with that of other drivers and suggest areas for improving driving skills. For example, the advice providing unit can advise the driver to refer to the driving methods of top drivers. The advice providing unit can also provide feedback to promote the improvement of driving skills through comparison with other drivers. For example, the advice providing unit can provide detailed analysis results of the driving scores and specifically indicate areas for improvement. This allows the driver's driving technique to be improved by evaluating the driver's relative driving skills in comparison with other drivers.

[0042] The advice providing unit can add a function that allows a driver to share driving data with other drivers and receive feedback. The advice providing unit adds a function that allows a driver to share driving data with other drivers and receive feedback, for example, through an app. For example, a driver can share driving scores and areas for improvement and receive advice from other drivers. The advice providing unit can also promote competition between drivers through the sharing of driving data. For example, driving scores can be displayed in a ranking format and top drivers can be rewarded. The advice providing unit can also promote information exchange between drivers through the sharing of driving data. For example, drivers can share driving tips and maintenance information. This allows drivers to share driving data with other drivers and receive feedback, thereby promoting the improvement of driving habits.

[0043] The advice providing unit can generate a customized driving report based on the driving data and provide it to the user. The advice providing unit can generate a customized driving report based on the driving data within an app, for example, and provide it to the user. For example, a detailed report including a driving score and areas for improvement can be provided. The advice providing unit can also analyze the driving data and provide individual advice based on the user's driving style. For example, it can provide specific suggestions for eco-driving and advice for safe driving. The advice providing unit can also provide statistical data based on the driving data. For example, it can provide graphs showing monthly mileage and fuel efficiency trends. In this way, generating a customized driving report and providing it to the user can promote improvement of driving habits.

[0044] The advice providing unit can add a function that allows a driver to share driving data with other drivers and receive feedback. The advice providing unit adds a function that allows a driver to share driving data with other drivers and receive feedback, for example, through an app. For example, a driver can share driving scores and areas for improvement and receive advice from other drivers. The advice providing unit can also promote competition between drivers through the sharing of driving data. For example, driving scores can be displayed in a ranking format and top drivers can be rewarded. The advice providing unit can also promote information exchange between drivers through the sharing of driving data. For example, drivers can share driving tips and maintenance information. This allows drivers to share driving data with other drivers and receive feedback, thereby promoting the improvement of driving habits.

[0045] The advice providing unit can generate a customized driving report based on the driving data and provide it to the user. The advice providing unit can generate a customized driving report based on the driving data within an app, for example, and provide it to the user. For example, a detailed report including a driving score and areas for improvement can be provided. The advice providing unit can also analyze the driving data and provide individual advice based on the user's driving style. For example, it can provide specific suggestions for eco-driving and advice for safe driving. The advice providing unit can also provide statistical data based on the driving data. For example, it can provide graphs showing monthly mileage and fuel efficiency trends. In this way, generating a customized driving report and providing it to the user can promote improvement of driving habits.

[0046] The advice providing unit can add a function that allows a driver to share driving data with other drivers and receive feedback. The advice providing unit adds a function that allows a driver to share driving data with other drivers and receive feedback, for example, through an app. For example, a driver can share driving scores and areas for improvement and receive advice from other drivers. The advice providing unit can also promote competition between drivers through the sharing of driving data. For example, driving scores can be displayed in a ranking format and top drivers can be rewarded. The advice providing unit can also promote information exchange between drivers through the sharing of driving data. For example, drivers can share driving tips and maintenance information. This allows drivers to share driving data with other drivers and receive feedback, thereby promoting the improvement of driving habits.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The driving data acquisition unit can monitor the temperature and humidity inside the vehicle and evaluate the comfort of the driving environment. For example, if the temperature inside the vehicle is too high, it can recommend using the air conditioner. Also, if the humidity is high, it can suggest opening the windows. Furthermore, the driving data acquisition unit can monitor the air quality inside the vehicle and suggest using an air purifier. This can improve the comfort of the driving environment.

[0049] The driving habit analysis unit can evaluate energy consumption while driving based on the driver's driving data and provide specific advice for eco-driving. For example, it can recommend avoiding sudden acceleration and braking. It can also suggest maintaining a constant speed and making use of engine braking. Furthermore, the driving habit analysis unit can provide advice on avoiding unnecessary idling. This reduces energy consumption and promotes environmentally friendly driving.

[0050] The maintenance suggestion unit can suggest maintenance methods to improve the fuel efficiency of the vehicle based on the vehicle's maintenance data. For example, it can recommend maintaining proper tire pressure and regularly changing engine oil. It can also suggest when to change the air filter. Furthermore, the maintenance suggestion unit can suggest upgrading parts to improve fuel efficiency. This can improve the vehicle's fuel efficiency and reduce operating costs.

[0051] The advice providing unit can evaluate the driver's driving skills based on the driving data and provide a training program to improve the skills. For example, the advice providing unit can determine the skill level based on the frequency of sudden braking and sudden acceleration, and provide a training program to improve the skills. The advice providing unit can also provide simulation training. For example, driving skills can be improved through a driving simulation in a virtual environment. The advice providing unit can also provide on-the-job training. For example, driving skills can be improved through training in an actual driving environment. In this way, the driving skills can be evaluated and a training program to improve the skills can be provided, thereby improving the driver's driving skills.

[0052] The advice providing unit can provide customized driving advice based on the driving data according to the driver's driving style. For example, it can suggest smooth acceleration methods to a driver who frequently accelerates suddenly. It can also suggest fuel-efficient driving methods to encourage eco-driving. For example, it can recommend maintaining a constant speed and avoiding unnecessary idling. It can also suggest safe and efficient driving methods to a driver who prefers sporty driving. For example, it can advise on appropriate braking on curves and the timing of acceleration. This allows the quality of driving to be improved by providing driving advice optimized for individual driving styles.

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

[0054] Step 1: The driving data acquisition unit acquires the driver's driving data. For example, it collects data such as speed, frequency of braking, and acceleration patterns. The driving data acquisition unit can also acquire driving data using sensors inside the vehicle or the GPS function of a smartphone. Step 2: The driving habit analysis unit analyzes the driving data acquired by the driving data acquisition unit and evaluates driving habits. For example, the generation AI analyzes the frequency of sudden braking and sudden acceleration and suggests ways to improve driving. The generation AI also detects speeding and unnecessary idling, aiming to improve driving efficiency. Step 3: The maintenance suggestion unit proposes an optimal maintenance schedule based on the driving habits evaluated by the driving habit analysis unit. For example, the generation AI analyzes the vehicle's mileage and usage status to predict when oil and tire changes are required. The generation AI also analyzes the deterioration status of each vehicle part in detail and proposes the optimal timing for part replacement. Step 4: The advice provider provides intelligent advice based on the maintenance schedule proposed by the maintenance suggestor. For example, the generator AI suggests driving methods to improve fuel efficiency and maintenance methods to extend the vehicle's lifespan. The generator AI also provides specific advice to the driver based on driving habits and maintenance data.

[0055] (Example 2) A mobile app according to an embodiment of the present invention is a system that uses cutting-edge AI technology to analyze a driver's driving habits and streamline car maintenance. The system incorporates gamification elements to provide intelligent advice for making driving more enjoyable and extending the life of a car. This allows the mobile app to improve a driver's driving habits and streamline car maintenance. For example, reducing sudden braking and acceleration can improve fuel efficiency, and performing regular maintenance can extend the life of a car. Furthermore, the gamification elements make driving more enjoyable and increase the driver's motivation.

[0056] A mobile app according to an embodiment includes a driving data acquisition unit, a driving habit analysis unit, a maintenance suggestion unit, and an advice provision unit. The driving data acquisition unit acquires driving data of a driver. For example, the driving data acquisition unit collects data such as speed, braking frequency, and acceleration patterns. The driving data acquisition unit can also acquire driving data using in-vehicle sensors or a smartphone's GPS function. The driving habit analysis unit analyzes the driving data acquired by the driving data acquisition unit to evaluate driving habits. For example, the generation AI analyzes the frequency of sudden braking and sudden acceleration and suggests driving improvements. The generation AI also detects speeding and unnecessary idling to improve driving efficiency. The maintenance suggestion unit proposes an optimal maintenance schedule based on the driving habits evaluated by the driving habit analysis unit. For example, the generation AI analyzes the vehicle's mileage and usage status to predict when oil and tire changes are required. The generation AI also performs a detailed analysis of the deterioration status of each vehicle part and suggests the optimal timing for part replacement. The advice provision unit provides intelligent advice based on the maintenance schedule proposed by the maintenance suggestion unit. For example, the generating AI suggests driving methods to improve fuel efficiency and maintenance methods to extend the life of the car. The generating AI also provides specific advice to the driver based on driving habits and maintenance data. This allows the mobile app according to the embodiment to improve the driver's driving habits and streamline car maintenance. For example, the output unit displays driving data and advice within the app to provide feedback to the driver. A notification function can also be used to notify the driver when maintenance is due. Furthermore, the app stores driving data in the cloud, making it accessible from other devices.

[0057] The driving data acquisition unit can monitor the driver's heart rate and stress level in real time while driving and acquire driving data. For example, the driving data acquisition unit collects the driver's heart rate data in real time, and the generation AI analyzes the data to estimate the stress level. For example, if the heart rate rises sharply, it is determined that stress is increasing and suggests driving methods to help the driver relax. The driving data acquisition unit can also collect the driver's electrodermal activity using a sensor to measure the stress level. For example, it can analyze changes in electrodermal activity to evaluate the stress level. The driving data acquisition unit can also monitor the driver's breathing pattern to estimate the stress level. For example, if breathing becomes shallow and rapid, it is determined that stress is increasing. This makes it possible to suggest improvements to driving habits by monitoring the driver's heart rate and stress level while driving.

[0058] The driving habit analysis unit can provide driving advice optimized for individual driving styles based on driving data. The driving habit analysis unit, for example, analyzes the driver's driving data (speed, frequency of braking, acceleration patterns, etc.) to identify individual driving styles. For example, for a driver who frequently accelerates suddenly, the unit can suggest smooth acceleration methods. The driving habit analysis unit can also suggest fuel-efficient driving methods to encourage eco-driving. For example, it can recommend maintaining a constant speed and avoiding unnecessary idling. The driving habit analysis unit can also suggest safe and efficient driving methods for drivers who prefer sporty driving. For example, it can advise on appropriate braking on curves and the timing of acceleration. This allows the quality of driving to be improved by providing driving advice optimized for individual driving styles.

[0059] The driving habit analysis unit can use the emotion estimation function to analyze the driver's emotional state and suggest driving methods to help the driver relax when stress levels rise. The driving habit analysis unit, for example, analyzes the driver's facial expressions and voice to estimate the driver's emotional state in real time. For example, it can detect stress or fatigue from facial expressions and suggest driving methods to help the driver relax. The driving habit analysis unit can also analyze the driver's voice data to evaluate the driver's emotional state. For example, it can analyze the tone and speed of the voice to estimate the stress level. The driving habit analysis unit can also analyze the driver's biometric data (heart rate and electrodermal activity) to evaluate the driver's emotional state. For example, it can estimate the stress level based on fluctuations in heart rate. This allows the emotion estimation function to analyze the driver's emotional state and suggest driving methods to help the driver relax, thereby improving the quality of driving.

[0060] The driving data acquisition unit can analyze in-car voice data and evaluate the impact of conversations and music while driving. For example, the driving data acquisition unit collects in-car voice data, and the generation AI analyzes that data to evaluate the impact of conversations and music while driving. For example, if there is a lot of conversation, it can point out the possibility of a decrease in concentration and advise the driver to concentrate on driving. The driving data acquisition unit can also analyze the genre and tempo of music to evaluate the impact of music. For example, it can evaluate the impact of fast-tempo music on driving and recommend relaxing music. The driving data acquisition unit can also monitor the noise level inside the car and evaluate the impact on driving. For example, it can point out the possibility of a decrease in concentration if the noise level is high. This allows the system to analyze in-car voice data and evaluate the impact of conversations and music while driving, thereby suggesting areas for improvement in driving habits.

[0061] The driving habit analysis unit can add a function for sharing the analysis results of driving habits with other drivers and receiving feedback within the community. For example, the driving habit analysis unit can add a function for sharing the analysis results of driving habits within the app and receiving feedback from other drivers. For example, driving scores and areas for improvement can be shared and advice can be received from other drivers. The driving habit analysis unit can also provide a ranking function within the community to promote competition between drivers. For example, driving scores can be displayed in a ranking format and top drivers can be rewarded. The driving habit analysis unit can also provide a chat function within the community to promote information exchange between drivers. For example, driving tips and maintenance information can be shared. This allows drivers to share the analysis results of driving habits with other drivers and receive feedback, thereby promoting the improvement of driving habits.

[0062] The maintenance proposal unit can analyze the deterioration status of each car part in detail and propose the optimal timing for part replacement. For example, the maintenance proposal unit monitors the deterioration status of each car part in real time, and the generation AI analyzes the data to propose the optimal timing for part replacement. For example, it analyzes the wear status of tires and notifies the user when it is time to replace them. The maintenance proposal unit can also analyze the deterioration status of engine oil and propose the timing for replacement. For example, it can analyze the viscosity and dirtiness of the oil and notify the user when it is time to replace it. The maintenance proposal unit can also analyze the wear status of brake pads and propose the timing for replacement. For example, it can monitor the thickness of the brake pads and notify the user when it is time to replace them. This allows for the efficient analysis of the deterioration status of each car part and the proposal of the optimal timing for part replacement.

[0063] The maintenance suggestion unit can predict future breakdown risks based on the vehicle's maintenance data and suggest preventive maintenance. The maintenance suggestion unit, for example, analyzes the vehicle's maintenance data and predicts future breakdown risks. For example, it analyzes the deterioration status of engine oil and suggests an oil change before the risk of breakdown increases. The maintenance suggestion unit can also analyze the wear status of brake pads and predict breakdown risks. For example, it monitors the thickness of brake pads and notifies the user when they need to be replaced. The maintenance suggestion unit can also analyze the wear status of tires and predict breakdown risks. For example, it monitors the depth of tire treads and notifies the user when they need to be replaced. In this way, it is possible to predict future breakdown risks and suggest preventive maintenance, thereby preventing vehicle breakdowns from occurring.

[0064] The maintenance suggestion unit can use the emotion estimation function to analyze the driver's emotions regarding maintenance and suggest approaches to help the driver understand the importance of maintenance. The maintenance suggestion unit can, for example, use the emotion estimation function to analyze the driver's emotions regarding maintenance in real time and suggest approaches to help the driver understand the importance of maintenance. For example, it can provide information to reduce anxiety regarding maintenance. The maintenance suggestion unit can also analyze the driver's emotional state and suggest educational approaches to help the driver understand the importance of maintenance. For example, it can provide videos or articles explaining the necessity and effects of maintenance. The maintenance suggestion unit can also analyze the driver's emotional state and suggest psychological approaches. For example, it can provide positive feedback regarding maintenance to increase the driver's motivation. In this way, by using the emotion estimation function to analyze the driver's emotions regarding maintenance and suggesting approaches to help the driver understand the importance of maintenance, the maintenance implementation rate can be improved.

[0065] When analyzing maintenance data, the maintenance suggestion unit takes into account the vehicle's usage environment and can propose a maintenance schedule that suits the environment. For example, the maintenance suggestion unit collects data on the vehicle's usage environment (urban area, mountainous area, etc.), and the generation AI analyzes that data to propose a maintenance schedule that suits the environment. For example, if the vehicle is used frequently in mountainous areas, the frequency of brake pad replacement may be increased. The maintenance suggestion unit may also increase the frequency of engine oil replacement if the vehicle is used frequently in cold regions. For example, the timing of replacement may be suggested, taking into account oil deterioration at low temperatures. The maintenance suggestion unit may also increase the frequency of air filter replacement if the vehicle is used frequently in urban areas. For example, the timing of filter replacement may be suggested, taking into account pollutants in urban areas. This allows for more efficient vehicle maintenance by proposing a maintenance schedule that takes into account the vehicle's usage environment.

[0066] The maintenance suggestion unit can add a function to compare the maintenance data with data of other vehicles of the same type and evaluate the relative maintenance status. The maintenance suggestion unit can add a function to compare the maintenance data with data of other vehicles of the same type and evaluate the relative maintenance status. For example, the maintenance status of the vehicle is evaluated by comparing it with the average maintenance frequency of vehicles of the same type. The maintenance suggestion unit can also compare it with failure data of other vehicles of the same type and evaluate the failure risk. For example, the failure risk of the vehicle is evaluated by comparing it with the failure frequency of vehicles of the same type. The maintenance suggestion unit can also compare it with the usage status of other vehicles of the same type and evaluate the need for maintenance. For example, the need for maintenance of the vehicle is evaluated by comparing it with the mileage and usage environment of vehicles of the same type. This makes it possible to improve the efficiency of vehicle maintenance by evaluating the relative maintenance status by comparing it with data of other vehicles of the same type.

[0067] The advice providing unit can suggest specific driving methods to improve fuel efficiency from driving data. The advice providing unit, for example, analyzes the driver's driving data and suggests specific driving methods to improve fuel efficiency. For example, it recommends maintaining a constant speed and avoiding sudden acceleration. The advice providing unit can also suggest eco-driving techniques. For example, it can recommend utilizing engine braking and avoiding unnecessary idling. The advice providing unit can also suggest improvements to accelerator operation to improve fuel efficiency. For example, it can recommend smooth accelerator operation to improve fuel efficiency. In this way, by suggesting specific driving methods to improve fuel efficiency, improvements in fuel efficiency can be expected.

[0068] The advice providing unit can evaluate the driver's driving skills based on the driving data and provide a training program for skill improvement. The advice providing unit, for example, analyzes the driver's driving data and evaluates the driving skills. For example, it determines the skill level based on the frequency of sudden braking and sudden acceleration and provides a training program for skill improvement. The advice providing unit can also provide simulation training. For example, it can improve driving skills through a driving simulation in a virtual environment. The advice providing unit can also provide practical training. For example, it can improve driving skills through training in an actual driving environment. In this way, the driving skills of the driver can be improved by evaluating the driving skills and providing a training program for skill improvement.

[0069] The advice providing unit can use the emotion estimation function to evaluate the emotional state of the driver and provide driving advice according to the emotion. The advice providing unit, for example, uses the emotion estimation function to evaluate the emotional state of the driver in real time and provide driving advice according to the emotion. For example, when stress increases, the advice providing unit can suggest a driving method to help the driver relax. The advice providing unit can also analyze the emotional state of the driver and suggest driving techniques according to the emotion. For example, when the driver is nervous, the advice providing unit can suggest advice to encourage deep breathing. The advice providing unit can also analyze the emotional state of the driver and suggest adjustments to the driving environment according to the emotion. For example, the advice providing unit can recommend relaxing music. In this way, by using the emotion estimation function to evaluate the emotional state of the driver and providing driving advice according to the emotion, the quality of driving can be improved.

[0070] When analyzing driving data, the advice providing unit also takes into account external data such as weather and traffic conditions, and can provide more accurate driving advice. For example, when analyzing driving data, the advice providing unit takes into account weather data and provides more accurate driving advice. For example, the advice providing unit analyzes driving data in rainy weather and suggests driving methods on slippery roads. The advice providing unit can also take into account traffic condition data and provide more accurate driving advice. For example, the advice providing unit analyzes driving data in traffic jams and suggests an efficient route. The advice providing unit can also provide driving advice according to the driving environment based on external data. For example, the temperature and wind speed are taken into account to adjust the driving method. In this way, by taking into account external data such as weather and traffic conditions, more accurate driving advice can be provided.

[0071] The advice providing unit can add a function to compare the driver's driving data with other drivers and evaluate the relative driving skills. The advice providing unit, for example, adds a function to compare the driver's driving data with other drivers and evaluate the relative driving skills. For example, the advice providing unit displays driving scores in a ranking format and compares the driver with other drivers. The advice providing unit can also compare the driving data with that of other drivers and suggest areas for improving driving skills. For example, the advice providing unit can advise the driver to refer to the driving methods of top drivers. The advice providing unit can also provide feedback to promote the improvement of driving skills through comparison with other drivers. For example, the advice providing unit can provide detailed analysis results of the driving scores and specifically indicate areas for improvement. This allows the driver's driving technique to be improved by evaluating the driver's relative driving skills in comparison with other drivers.

[0072] The advice providing unit can use the emotion estimation function to analyze emotional changes while driving and provide driving advice according to the emotions. For example, the advice providing unit can use the emotion estimation function to monitor emotional changes while driving in real time and provide driving advice according to the emotions. For example, when stress increases, the advice providing unit can suggest a driving method to help the driver relax. The advice providing unit can also analyze emotional changes while driving and suggest driving techniques according to the emotions. For example, when the driver is nervous, the advice providing unit can suggest advice to take deep breaths. The advice providing unit can also analyze emotional changes while driving and suggest adjustments to the driving environment according to the emotions. For example, the advice providing unit can recommend relaxing music. In this way, the quality of driving can be improved by using the emotion estimation function to analyze emotional changes while driving and providing driving advice according to the emotions.

[0073] The advice providing unit can add a function that allows a driver to share driving data with other drivers and receive feedback. The advice providing unit adds a function that allows a driver to share driving data with other drivers and receive feedback, for example, through an app. For example, a driver can share driving scores and areas for improvement and receive advice from other drivers. The advice providing unit can also promote competition between drivers through the sharing of driving data. For example, driving scores can be displayed in a ranking format and top drivers can be rewarded. The advice providing unit can also promote information exchange between drivers through the sharing of driving data. For example, drivers can share driving tips and maintenance information. This allows drivers to share driving data with other drivers and receive feedback, thereby promoting the improvement of driving habits.

[0074] The advice providing unit can use the emotion estimation function to monitor the driver's emotional state in real time and provide feedback according to the emotion. The advice providing unit can, for example, use the emotion estimation function to monitor the driver's emotional state in real time and provide feedback according to the emotion. For example, when stress increases, a message to relax is displayed. The advice providing unit can also analyze the driver's emotional state and provide driving advice according to the emotion. For example, when the driver is nervous, advice to take deep breaths is provided. The advice providing unit can also analyze the driver's emotional state and suggest adjustments to the driving environment according to the emotion. For example, relaxing music is recommended. In this way, the quality of driving can be improved by using the emotion estimation function to monitor the driver's emotional state in real time and providing feedback according to the emotion.

[0075] The advice providing unit can generate a customized driving report based on the driving data and provide it to the user. The advice providing unit can generate a customized driving report based on the driving data within an app, for example, and provide it to the user. For example, a detailed report including a driving score and areas for improvement can be provided. The advice providing unit can also analyze the driving data and provide individual advice based on the user's driving style. For example, it can provide specific suggestions for eco-driving and advice for safe driving. The advice providing unit can also provide statistical data based on the driving data. For example, it can provide graphs showing monthly mileage and fuel efficiency trends. In this way, generating a customized driving report and providing it to the user can promote improvement of driving habits.

[0076] The advice providing unit can add a function that allows a driver to share driving data with other drivers and receive feedback. The advice providing unit adds a function that allows a driver to share driving data with other drivers and receive feedback, for example, through an app. For example, a driver can share driving scores and areas for improvement and receive advice from other drivers. The advice providing unit can also promote competition between drivers through the sharing of driving data. For example, driving scores can be displayed in a ranking format and top drivers can be rewarded. The advice providing unit can also promote information exchange between drivers through the sharing of driving data. For example, drivers can share driving tips and maintenance information. This allows drivers to share driving data with other drivers and receive feedback, thereby promoting the improvement of driving habits.

[0077] The advice providing unit can use the emotion estimation function to monitor the driver's emotional state in real time and provide feedback according to the emotion. The advice providing unit can, for example, use the emotion estimation function to monitor the driver's emotional state in real time and provide feedback according to the emotion. For example, when stress increases, a message to relax is displayed. The advice providing unit can also analyze the driver's emotional state and provide driving advice according to the emotion. For example, when the driver is nervous, advice to take deep breaths is provided. The advice providing unit can also analyze the driver's emotional state and suggest adjustments to the driving environment according to the emotion. For example, relaxing music is recommended. In this way, the quality of driving can be improved by using the emotion estimation function to monitor the driver's emotional state in real time and providing feedback according to the emotion.

[0078] The advice providing unit can generate a customized driving report based on the driving data and provide it to the user. The advice providing unit can generate a customized driving report based on the driving data within an app, for example, and provide it to the user. For example, a detailed report including a driving score and areas for improvement can be provided. The advice providing unit can also analyze the driving data and provide individual advice based on the user's driving style. For example, it can provide specific suggestions for eco-driving and advice for safe driving. The advice providing unit can also provide statistical data based on the driving data. For example, it can provide graphs showing monthly mileage and fuel efficiency trends. In this way, generating a customized driving report and providing it to the user can promote improvement of driving habits.

[0079] The advice providing unit can add a function that allows a driver to share driving data with other drivers and receive feedback. The advice providing unit adds a function that allows a driver to share driving data with other drivers and receive feedback, for example, through an app. For example, a driver can share driving scores and areas for improvement and receive advice from other drivers. The advice providing unit can also promote competition between drivers through the sharing of driving data. For example, driving scores can be displayed in a ranking format and top drivers can be rewarded. The advice providing unit can also promote information exchange between drivers through the sharing of driving data. For example, drivers can share driving tips and maintenance information. This allows drivers to share driving data with other drivers and receive feedback, thereby promoting the improvement of driving habits.

[0080] The advice providing unit can use the emotion estimation function to monitor the driver's emotional state in real time and provide feedback according to the emotion. The advice providing unit can, for example, use the emotion estimation function to monitor the driver's emotional state in real time and provide feedback according to the emotion. For example, when stress increases, a message to relax is displayed. The advice providing unit can also analyze the driver's emotional state and provide driving advice according to the emotion. For example, when the driver is nervous, advice to take deep breaths is provided. The advice providing unit can also analyze the driver's emotional state and suggest adjustments to the driving environment according to the emotion. For example, relaxing music is recommended. In this way, the quality of driving can be improved by using the emotion estimation function to monitor the driver's emotional state in real time and providing feedback according to the emotion.

[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 driving data acquisition unit can monitor the temperature and humidity inside the vehicle and evaluate the comfort of the driving environment. For example, if the temperature inside the vehicle is too high, it can recommend using the air conditioner. Also, if the humidity is high, it can suggest opening the windows. Furthermore, the driving data acquisition unit can monitor the air quality inside the vehicle and suggest using an air purifier. This can improve the comfort of the driving environment.

[0083] The driving habit analysis unit can evaluate energy consumption while driving based on the driver's driving data and provide specific advice for eco-driving. For example, it can recommend avoiding sudden acceleration and braking. It can also suggest maintaining a constant speed and making use of engine braking. Furthermore, the driving habit analysis unit can provide advice on avoiding unnecessary idling. This reduces energy consumption and promotes environmentally friendly driving.

[0084] The maintenance suggestion unit can suggest maintenance methods to improve the fuel efficiency of the vehicle based on the vehicle's maintenance data. For example, it can recommend maintaining proper tire pressure and regularly changing engine oil. It can also suggest when to change the air filter. Furthermore, the maintenance suggestion unit can suggest upgrading parts to improve fuel efficiency. This can improve the vehicle's fuel efficiency and reduce operating costs.

[0085] The advice providing unit can evaluate the driver's driving skills based on the driving data and provide a training program to improve the skills. For example, the advice providing unit can determine the skill level based on the frequency of sudden braking and sudden acceleration, and provide a training program to improve the skills. The advice providing unit can also provide simulation training. For example, driving skills can be improved through a driving simulation in a virtual environment. The advice providing unit can also provide on-the-job training. For example, driving skills can be improved through training in an actual driving environment. In this way, the driving skills can be evaluated and a training program to improve the skills can be provided, thereby improving the driver's driving skills.

[0086] The advice providing unit can provide customized driving advice based on the driving data according to the driver's driving style. For example, it can suggest smooth acceleration methods to a driver who frequently accelerates suddenly. It can also suggest fuel-efficient driving methods to encourage eco-driving. For example, it can recommend maintaining a constant speed and avoiding unnecessary idling. It can also suggest safe and efficient driving methods to a driver who prefers sporty driving. For example, it can advise on appropriate braking on curves and the timing of acceleration. This allows the quality of driving to be improved by providing driving advice optimized for individual driving styles.

[0087] The driving habit analysis unit can use the emotion estimation function to analyze the driver's emotional state and suggest driving methods to help the driver relax when stress levels rise. For example, it can analyze the driver's facial expressions and voice to estimate the driver's emotional state in real time. For example, it can detect stress or fatigue from facial expressions and suggest driving methods to help the driver relax. The driving habit analysis unit can also analyze the driver's voice data to evaluate the driver's emotional state. For example, it can analyze the tone and speed of the voice to estimate the stress level. The driving habit analysis unit can also analyze the driver's biometric data (heart rate and electrodermal activity) to evaluate the driver's emotional state. For example, it can estimate the stress level based on fluctuations in heart rate. This allows the emotion estimation function to analyze the driver's emotional state and suggest driving methods to help the driver relax, thereby improving the quality of driving.

[0088] The advice providing unit can use the emotion estimation function to evaluate the driver's emotional state and provide driving advice according to the emotion. For example, the emotion estimation function can be used to evaluate the driver's emotional state in real time and provide driving advice according to the emotion. For example, when stress increases, the advice providing unit can suggest a driving method to help the driver relax. The advice providing unit can also analyze the driver's emotional state and suggest driving techniques according to the emotion. For example, when the driver is nervous, the advice providing unit can suggest advice to take deep breaths. The advice providing unit can also analyze the driver's emotional state and suggest adjustments to the driving environment according to the emotion. For example, the advice providing unit can recommend relaxing music. In this way, by using the emotion estimation function to evaluate the driver's emotional state and providing driving advice according to the emotion, the quality of driving can be improved.

[0089] The maintenance suggestion unit can use the emotion estimation function to analyze the driver's emotions regarding maintenance and suggest approaches to help the driver understand the importance of maintenance. For example, the emotion estimation function can be used to analyze the driver's emotions regarding maintenance in real time and suggest approaches to help the driver understand the importance of maintenance. For example, information can be provided to reduce anxiety regarding maintenance. The maintenance suggestion unit can also analyze the driver's emotional state and suggest educational approaches to help the driver understand the importance of maintenance. For example, videos or articles explaining the necessity and effects of maintenance can be provided. The maintenance suggestion unit can also analyze the driver's emotional state and suggest psychological approaches. For example, positive feedback regarding maintenance can be provided to increase the driver's motivation. In this way, by using the emotion estimation function to analyze the driver's emotions regarding maintenance and suggesting approaches to help the driver understand the importance of maintenance, the maintenance implementation rate can be improved.

[0090] The advice providing unit can use the emotion estimation function to analyze emotional changes while driving and provide driving advice according to the emotions. For example, the emotion estimation function can be used to monitor emotional changes while driving in real time and provide driving advice according to the emotions. For example, when stress increases, the advice providing unit can suggest a driving method to relax. The advice providing unit can also analyze emotional changes while driving and suggest driving techniques according to the emotions. For example, if the driver is nervous, the advice providing unit can suggest advice to take deep breaths. The advice providing unit can also analyze emotional changes while driving and suggest adjustments to the driving environment according to the emotions. For example, the advice providing unit can recommend relaxing music. In this way, the quality of driving can be improved by using the emotion estimation function to analyze emotional changes while driving and providing driving advice according to the emotions.

[0091] The advice providing unit can use the emotion estimation function to monitor the driver's emotional state in real time and provide feedback according to the emotion. For example, the emotion estimation function can be used to monitor the driver's emotional state in real time and provide feedback according to the emotion. For example, when stress increases, a message to relax can be displayed. The advice providing unit can also analyze the driver's emotional state and provide driving advice according to the emotion. For example, when the driver is nervous, advice to take deep breaths can be provided. The advice providing unit can also analyze the driver's emotional state and suggest adjustments to the driving environment according to the emotion. For example, relaxing music can be recommended. In this way, the quality of driving can be improved by using the emotion estimation function to monitor the driver's emotional state in real time and providing feedback according to the emotion.

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

[0093] Step 1: The driving data acquisition unit acquires the driver's driving data. For example, it collects data such as speed, frequency of braking, and acceleration patterns. The driving data acquisition unit can also acquire driving data using sensors inside the vehicle or the GPS function of a smartphone. Step 2: The driving habit analysis unit analyzes the driving data acquired by the driving data acquisition unit and evaluates driving habits. For example, the generation AI analyzes the frequency of sudden braking and sudden acceleration and suggests ways to improve driving. The generation AI also detects speeding and unnecessary idling, aiming to improve driving efficiency. Step 3: The maintenance suggestion unit proposes an optimal maintenance schedule based on the driving habits evaluated by the driving habit analysis unit. For example, the generation AI analyzes the vehicle's mileage and usage status to predict when oil and tire changes are required. The generation AI also analyzes the deterioration status of each vehicle part in detail and proposes the optimal timing for part replacement. Step 4: The advice provider provides intelligent advice based on the maintenance schedule proposed by the maintenance suggestor. For example, the generator AI suggests driving methods to improve fuel efficiency and maintenance methods to extend the vehicle's lifespan. The generator AI also provides specific advice to the driver based on driving habits and maintenance data.

[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 (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] 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.

[0109] 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.

[0110] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] 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.

[0124] 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.

[0125] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] 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.

[0140] 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.

[0141] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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. [Explanation of symbols]

[0161] 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. a driving data acquisition unit that acquires driving data of a driver; a driving habit analysis unit that analyzes the driving data acquired by the driving data acquisition unit and evaluates driving habits; a maintenance suggestion unit that suggests an optimal maintenance schedule based on the driving habits evaluated by the driving habit analysis unit; an advice providing unit that provides intelligent advice based on the maintenance schedule proposed by the maintenance suggestion unit. A system characterized by:

2. The driving data acquisition unit Monitor heart rate and stress levels in real time while driving and obtain driving data.

2. The system of claim 1.

3. The driving habit analysis unit Based on the driving data, driving advice optimized for individual driving styles is provided.

2. The system of claim 1.

4. The driving habit analysis unit Add the ability to share your driving habits analysis with other drivers and receive feedback from the community 2. The system of claim 1.

5. The maintenance suggestion unit Using emotion estimation, we analyze drivers' feelings about maintenance and propose approaches to help them understand the importance of maintenance.

2. The system of claim 1.

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