system

Abstract

The system according to this embodiment aims to detect signs of vehicle malfunction early and respond quickly. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a notification unit, a search unit, and a reservation unit. The data collection unit collects data from various sensors of the vehicle. The analysis unit analyzes the data collected by the data collection unit in real time and detects signs of malfunction. The notification unit notifies the driver of the signs of malfunction detected by the analysis unit. The search unit searches for the nearest repair shop based on the information notified by the notification unit. The reservation unit makes a reservation for repairs at the repair shop found by the search unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to detect early signs of vehicle failure and take appropriate actions promptly.

[0005] The system according to the embodiment aims to detect early signs of vehicle failure and respond promptly.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a notification unit, a search unit, and a reservation unit. The data collection unit collects data from various sensors in the vehicle. The analysis unit analyzes the data collected by the data collection unit in real time and detects signs of malfunction. The notification unit notifies the driver of the signs of malfunction detected by the analysis unit. The search unit searches for the nearest repair shop based on the information notified by the notification unit. The reservation unit makes a repair reservation at the repair shop found by the search unit. [Effects of the Invention]

[0007] The system according to this embodiment can detect signs of vehicle malfunction early and respond quickly. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that analyzes data from various sensors in a vehicle in real time, detects signs of malfunction, and notifies the driver. This AI agent system collects data from various sensors in the vehicle, and the AI ​​analyzes it in real time. Next, the AI ​​detects signs of malfunction and notifies the driver. Furthermore, if necessary, it searches for the nearest repair shop and makes a repair reservation. This mechanism is expected to reduce the risk of malfunction through preventive maintenance, reduce maintenance costs, and extend the lifespan of the vehicle. For example, the AI ​​agent system collects data from various sensors in the vehicle. For example, it collects data such as engine temperature, oil condition, and tire pressure. This data is input to the AI. Next, the AI ​​analyzes the collected data in real time. The AI ​​analyzes the data and detects signs of malfunction. For example, it detects signs of malfunction such as abnormally high engine temperature, poor oil condition, or low tire pressure. When signs of malfunction are detected, the AI ​​notifies the driver. For example, it displays a warning message on the dashboard or sends a notification to the smartphone. This allows the driver to be aware of signs of malfunction in advance. Furthermore, the system searches for the nearest repair shop and makes an appointment as needed. The AI ​​searches for the nearest repair shop based on the current location and makes an appointment. For example, a driver can select a repair shop and confirm the appointment on their smartphone. This system is expected to reduce the risk of breakdowns through preventative maintenance, lower maintenance costs, and extend the lifespan of the vehicle. For example, by detecting signs of failure in advance, expensive repair costs can be avoided. Also, regular maintenance can extend the life of the vehicle. This AI agent system has a user-friendly interface, making it easy for drivers to operate. For example, through a smartphone app, drivers can check for signs of failure, search for repair shops, and make appointments. As an innovator in vehicle maintenance, this AI agent system enables seamless collaboration between drivers and repair shops. For example, if signs of failure are detected, the AI ​​automatically notifies the repair shop so that they can prepare for repairs.This reduces the hassle of repairs and enables quick responses. This AI agent system is offered to car owners of all ages and is particularly suitable for people interested in new technologies. For example, it is a very useful tool for drivers who want to know the risk of breakdowns in advance and for car owners who want to reduce repair costs. This AI agent system achieves increased efficiency and cost reduction in vehicle maintenance through advancements in AI technology and improved data analysis capabilities. For example, by analyzing data in real time and detecting signs of malfunction, quick responses are possible. It also automatically searches for the nearest repair shop and makes reservations, saving the hassle of repairs. This AI agent system aims to realize a sustainable automotive society by reducing the risk of accidents and extending the lifespan of vehicles through preventive maintenance. For example, by detecting signs of malfunctions in advance and addressing them quickly, the risk of accidents can be reduced. Also, by performing regular maintenance, the lifespan of vehicles can be extended, realizing a sustainable automotive society. As such, the AI ​​agent system can analyze data from various vehicle sensors in real time, detect signs of malfunction and notify the driver, find the nearest repair shop if necessary, and even support the reservation of repairs.

[0029] The AI ​​agent system according to the embodiment comprises a data collection unit, an analysis unit, a notification unit, a search unit, and a reservation unit. The data collection unit collects data from various sensors of the vehicle. The data collection unit collects data such as engine temperature, oil condition, and tire pressure. The data collection unit can, for example, measure engine temperature using a temperature sensor. The data collection unit can, for example, measure oil condition using an oil sensor. The data collection unit can, for example, measure tire pressure using a pressure sensor. The analysis unit analyzes the data collected by the data collection unit in real time and detects signs of failure. The analysis unit, for example, uses AI to analyze the collected data in real time and detects signs of failure such as abnormal temperature rises or abnormal vibration patterns. The analysis unit, for example, uses AI to analyze engine temperature data and detect abnormal temperature rises. The analysis unit, for example, uses AI to analyze oil condition data and detect oil deterioration. The analysis unit, for example, uses AI to analyze tire pressure data and detect pressure drops. The notification unit notifies the driver of the signs of failure detected by the analysis unit. The notification unit can, for example, display a warning message on the dashboard. The notification unit can, for example, send a notification to a smartphone. The notification unit can, for example, issue an audio alert. The search unit searches for the nearest repair shop based on the information notified by the notification unit. The search unit can, for example, search for the nearest repair shop based on the current location. The search unit can, for example, provide search results considering the repair shop's rating and services. The search unit can, for example, provide search results based on the distance to the repair shop. The reservation unit makes a repair reservation at the repair shop found by the search unit. The reservation unit can, for example, allow the driver to select a repair shop on their smartphone and confirm the reservation. The reservation unit can, for example, automatically send the reservation to the repair shop. The reservation unit can, for example, adjust the date and time of the repair. As a result, the AI ​​agent system according to the embodiment can analyze data from various sensors in the vehicle in real time, detect signs of malfunction and notify the driver, find the nearest repair shop if necessary, and support the driver through to making a repair reservation.

[0030] The data collection unit collects data from various sensors in the vehicle. Specifically, it collects data such as engine temperature, oil condition, and tire pressure. The data collection unit can measure engine temperature using a temperature sensor. The engine temperature sensor is attached to the engine's cooling system and monitors the engine temperature in real time. The oil sensor measures the condition of the engine oil and detects oil degradation or contamination. The oil sensor predicts when to change the oil by measuring the oil viscosity and the concentration of contaminants. The pressure sensor measures tire pressure and verifies that the tire pressure is within the appropriate range. The pressure sensor is attached to the tire valve and monitors the tire pressure in real time. The data collected from these sensors is transmitted to the vehicle's central control unit and centrally managed by the data collection unit. The data collection unit collects this data in real time and transmits it to the analysis unit. By adjusting the frequency and accuracy of data collection, the data collection unit can respond flexibly to specific situations and conditions. For example, if the engine temperature rises rapidly, the data collection unit increases the frequency of data collection from the temperature sensor to help detect abnormalities early. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes data collected by the data collection unit in real time to detect signs of failure. The analysis uses AI to process data in real time and detect signs of failure such as abnormal temperature increases and abnormal vibration patterns. Specifically, the AI ​​can analyze engine temperature data and detect abnormal temperature increases. The AI ​​evaluates current temperature data by comparing it with past data and identifies abnormal patterns. For example, if the engine temperature rises rapidly under normal operating conditions, the AI ​​will determine this as abnormal and detect it as a sign of failure. The AI ​​can also analyze oil condition data and detect oil degradation. It evaluates the oil viscosity and contaminant concentration to predict when the oil needs to be changed. The AI ​​can also analyze tire pressure data and detect pressure drops. It evaluates whether the tire pressure is within the appropriate range and issues a warning if the pressure drops. This allows the analysis unit to quickly and accurately analyze collected data and grasp signs of failure in real time. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term failure prediction and trend analysis. For example, based on past failure data, it can evaluate the failure risk of specific parts or systems and develop preventive maintenance plans. This allows the analysis unit to handle not only real-time fault detection but also long-term fault prevention and risk management, thereby improving the reliability and safety of the entire system.

[0032] The notification unit alerts the driver to signs of malfunction detected by the analysis unit. Specifically, it can display warning messages on the dashboard. The warning messages vary depending on the type and urgency of the malfunction. For example, if the engine temperature rises abnormally, a "Engine Temperature Abnormality" warning message will be displayed on the dashboard. The notification unit can also send notifications to a smartphone. Detailed information about the malfunction and recommended actions are provided through a smartphone app. For example, if oil degradation is detected, a notification saying "Oil change required" will be sent to the smartphone. The notification unit can also issue voice alerts. Voice alerts are used to attract the driver's attention, and different voice messages are played depending on the type and urgency of the malfunction. For example, if tire pressure drops, a voice alert will say, "Tire pressure is low. Please check." This allows the notification unit to provide drivers with quick and appropriate information, supporting early detection and countermeasures for malfunctions. Furthermore, the notification unit can collect driver feedback and continuously improve the accuracy and effectiveness of its notifications. For example, the content and display method of warning messages can be reviewed based on driver feedback. Furthermore, the notification unit can reliably transmit information using multiple communication methods. This allows the notification unit to provide information to drivers quickly and reliably, supporting early detection and countermeasures for malfunctions.

[0033] The search unit searches for the nearest repair shop based on information notified by the notification unit. Specifically, it can search for the nearest repair shop based on the current location. The search unit uses GPS data to determine the vehicle's current location and searches for repair shops in the vicinity. The search unit can provide search results considering the repair shop's ratings and services. For example, it can recommend the best repair shop based on customer ratings, the types of services offered, and prices. The search unit can also provide search results based on the distance to the repair shop. For example, it can prioritize displaying the repair shop closest to the current location to ensure a quick response in emergencies. The search unit also considers the repair shop's business hours and the types of repairs it can perform when providing search results. This allows the search unit to quickly provide the driver with the best repair shop and support early repairs. Furthermore, the search unit can learn past search history and driver preferences to provide personalized search results. For example, it can display repair shops that are recommended for use again based on previously used repair shops and driver ratings. This allows the search unit to provide flexible search results tailored to the driver's needs and support early repairs.

[0034] The reservation department makes repair reservations at repair shops found by the search department. Specifically, drivers can select a repair shop on their smartphone and confirm the reservation. The reservation department can automatically send the reservation to the repair shop. For example, when a driver selects a repair shop through a smartphone app and enters their desired repair date and time, the reservation department sends that information to the repair shop and confirms the reservation. The reservation department can adjust the repair date and time. For example, it checks the availability of the repair shop and adjusts whether the repair is possible at the date and time requested by the driver. The reservation department notifies the driver of a confirmation message from the repair shop, allowing them to check the reservation details. This enables the reservation department to quickly and reliably make repair reservations for drivers and support the early repair of malfunctions. Furthermore, the reservation department can notify the driver of the repair progress. For example, it sends a notification to the smartphone when the repair is completed to inform the driver that the repair is finished. This enables the reservation department to provide drivers with real-time updates on the repair progress and support the early repair of malfunctions.

[0035] The data collection unit can collect data such as engine temperature, oil condition, and tire pressure. For example, the data collection unit uses a temperature sensor to measure engine temperature. For example, the data collection unit uses an oil sensor to measure oil condition. For example, the data collection unit uses a pressure sensor to measure tire pressure. In this way, the data collection unit can provide data for detecting signs of malfunction by collecting data from various sensors in the vehicle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input engine temperature data into a generating AI and have the generating AI perform analysis of the temperature data.

[0036] The analysis unit can analyze the collected data in real time and detect signs of failure. For example, the analysis unit can use AI to analyze the collected data in real time and detect signs of failure such as abnormal temperature rises or abnormal vibration patterns. For example, the analysis unit can use AI to analyze engine temperature data and detect abnormal temperature rises. For example, the analysis unit can use AI to analyze oil condition data and detect oil degradation. For example, the analysis unit can use AI to analyze tire pressure data and detect pressure drops. In this way, the analysis unit can quickly detect signs of failure by analyzing the collected data in real time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the detection of signs of failure.

[0037] The notification unit can display warning messages on the dashboard or send notifications to a smartphone. For example, the notification unit can display warning messages on the dashboard. For example, the notification unit can send notifications to a smartphone. For example, the notification unit can issue voice alerts. This allows the notification unit to quickly notify the driver of signs of a malfunction. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data detecting signs of a malfunction into a generating AI and have the generating AI generate a notification.

[0038] The search unit can search for the nearest repair shop based on the current location. For example, the search unit searches for the nearest repair shop based on the current location. The search unit provides search results considering, for example, the repair shop's rating and services. The search unit provides search results based on, for example, the distance to the repair shop. This allows the search unit to quickly find the nearest repair shop. Some or all of the above processing in the search unit may be performed using, for example, AI, or not using AI. For example, the search unit can input current location data into a generating AI and have the generating AI perform the search for the nearest repair shop.

[0039] The reservation system allows drivers to select a repair shop and confirm a reservation using their smartphone. For example, the reservation system allows the driver to select a repair shop and confirm a reservation using their smartphone. The reservation system automatically sends the reservation to the repair shop. The reservation system adjusts the date and time of the repair. This allows the reservation system to make repair reservations quickly. Some or all of the above processes in the reservation system may be performed using AI, for example, or not using AI. For example, the reservation system can input the repair shop's reservation data into a generating AI and have the generating AI confirm the reservation.

[0040] The data collection unit can refer to past vehicle data and enhance data collection under specific conditions. For example, if the engine temperature was abnormally high in the past, the data collection unit will enhance data collection under similar conditions. For example, if the oil condition deteriorated in the past, the data collection unit will enhance data collection regarding the oil condition. For example, if the tire pressure dropped in the past, the data collection unit will enhance data collection regarding tire pressure. In this way, by referring to past vehicle data, the data collection unit can enhance data collection under specific conditions and more accurately detect signs of failure. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past vehicle data into a generating AI and have the generating AI perform enhanced data collection under specific conditions.

[0041] The data collection unit can dynamically change the type of data it collects depending on the vehicle's usage during data collection. For example, during long-distance driving, the data collection unit enhances the collection of data related to engine temperature and oil condition. For example, during city driving, the data collection unit enhances the collection of data related to tire pressure and brake condition. For example, during highway driving, the data collection unit enhances the collection of data related to fuel consumption and engine speed. In this way, the data collection unit can collect more appropriate data by dynamically changing the type of data it collects according to the vehicle's usage. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input vehicle usage data into a generating AI and cause the generating AI to dynamically change the type of data to collect.

[0042] The data collection unit can enhance data collection in specific regions by considering the vehicle's geographical location during data collection. For example, in mountainous areas, the data collection unit can enhance data collection regarding engine temperature and brake status. In urban areas, for example, the data collection unit can enhance data collection regarding tire pressure and fuel consumption. In highways, for example, the data collection unit can enhance data collection regarding engine speed and oil status. By enhancing data collection in specific regions by considering the vehicle's geographical location, the data collection unit can more accurately detect region-specific signs of malfunction. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location data into a generating AI and have the generating AI perform enhanced data collection in specific regions.

[0043] The data collection unit can analyze the vehicle's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on issues reported on the vehicle's social media accounts. For example, the data collection unit can analyze user feedback on social media to enhance data collection regarding specific parts. For example, the data collection unit can analyze trends on social media to collect data on common issues. This allows the data collection unit to collect data based on user feedback and trends by analyzing the vehicle's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0044] The analysis unit can optimize its analysis algorithm by referring to past failure data during analysis. For example, the analysis unit can optimize its analysis algorithm regarding engine temperature based on past engine failure data. For example, the analysis unit can optimize its analysis algorithm regarding oil condition based on past oil degradation data. For example, the analysis unit can optimize its analysis algorithm regarding tire pressure based on past tire pressure drop data. In this way, the analysis unit can optimize its analysis algorithm by referring to past failure data and more accurately detect signs of failure. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past failure data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0045] The analysis unit can apply different analysis methods depending on the vehicle's usage during analysis. For example, during long-distance driving, the analysis unit applies analysis methods related to engine temperature and oil condition. For example, during city driving, the analysis unit applies analysis methods related to tire pressure and brake condition. For example, during highway driving, the analysis unit applies analysis methods related to fuel consumption and engine speed. In this way, the analysis unit can perform a more appropriate analysis by applying different analysis methods depending on the vehicle's usage. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input vehicle usage data into a generating AI and have the generating AI execute the application of different analysis methods.

[0046] The analysis unit can analyze failure trends in a specific region by considering the vehicle's geographical location during analysis. For example, in mountainous areas, the analysis unit can analyze failure trends related to engine temperature and brake condition. In urban areas, for example, the analysis unit can analyze failure trends related to tire pressure and fuel consumption. In highways, for example, the analysis unit can analyze failure trends related to engine speed and oil condition. By doing so, the analysis unit can more accurately detect region-specific signs of failure by analyzing failure trends in a specific region by considering the vehicle's geographical location. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical location data into a generating AI and have the generating AI perform an analysis of failure trends in a specific region.

[0047] The analysis unit can improve the accuracy of its analysis by referring to relevant vehicle literature during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on engine temperature. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on oil condition. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on tire pressure. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant vehicle literature and more accurately detect signs of failure. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0048] The notification unit can adjust the level of detail in the notification based on the severity of the malfunction. For example, in the case of a major malfunction, the notification unit provides a notification containing detailed information. For example, in the case of a minor malfunction, the notification unit provides a concise notification. The notification unit dynamically adjusts the level of detail in the notification according to the severity of the malfunction. This allows the notification unit to provide appropriate information to the driver by adjusting the level of detail in the notification according to the severity of the malfunction. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input malfunction severity data into a generating AI and have the generating AI perform the adjustment of the level of detail in the notification.

[0049] The notification unit can select the optimal notification method by referring to the driver's past response history when sending a notification. For example, the notification unit may prioritize providing notification methods that the driver has previously preferred to use. For example, the notification unit may select the optimal notification method from the driver's past response history. For example, the notification unit may analyze the driver's past response history and provide the most effective notification method. In this way, the notification unit can provide the optimal notification method by referring to the driver's past response history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input past response history data into a generating AI and have the generating AI perform the selection of the optimal notification method.

[0050] The notification unit can select the optimal notification method when issuing a notification, taking into account the vehicle's geographical location information. For example, the notification unit may provide a highly visible notification in mountainous areas. For example, it may prioritize voice notifications in urban areas. For example, it may provide a concise notification on highways. In this way, the notification unit can provide the optimal notification method by taking into account the vehicle's geographical location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input geographical location data into a generating AI and have the generating AI select the optimal notification method.

[0051] The notification unit can analyze the driver's social media activity and customize the notification content when sending a notification. For example, the notification unit can analyze the driver's social media activity to determine their preferred notification method and customize it. For example, the notification unit can customize the notification content based on feedback on social media. For example, the notification unit can analyze social media trends and customize the notification content. In this way, the notification unit can customize the notification content by analyzing the driver's social media activity and provide more appropriate notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input social media data into a generating AI and have the generating AI perform the customization of the notification content.

[0052] The search unit can prioritize displaying the most suitable repair shops by referring to past repair history during a search. For example, the search unit prioritizes displaying the most suitable repair shops based on evaluations of repair shops used in the past. For example, the search unit prioritizes displaying repair shops that are strong in specific repairs based on past repair history. For example, the search unit analyzes past repair history and prioritizes displaying the most reliable repair shops. In this way, the search unit can prioritize displaying the most suitable repair shops by referring to past repair history. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input past repair history data into a generating AI and have the generating AI perform the display of the most suitable repair shops.

[0053] The search unit can apply different search algorithms depending on the vehicle's usage during a search. For example, during long-distance driving, the search unit prioritizes displaying repair shops that can respond quickly. For example, during city driving, the search unit prioritizes displaying repair shops with good access. For example, during highway driving, the search unit prioritizes displaying the nearest repair shop. In this way, the search unit can provide more appropriate search results by applying different search algorithms depending on the vehicle's usage. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input vehicle usage data into a generating AI and have the generating AI execute the application of different search algorithms.

[0054] The search unit can display the most suitable repair shop by considering the vehicle's geographical location during the search. For example, the search unit may prioritize displaying the repair shop closest to the current location. For example, the search unit may prioritize displaying the repair shop with good access based on geographical location information. For example, the search unit may prioritize displaying the repair shop that can respond quickly, taking geographical location information into consideration. In this way, the search unit can display the most suitable repair shop by considering the vehicle's geographical location information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit may input geographical location data into a generating AI and have the generating AI display the most suitable repair shop.

[0055] The search unit can customize search results by referring to repair shop review information during a search. For example, the search unit may prioritize displaying highly-rated repair shops based on their review information. For example, the search unit may analyze repair shop review information and prioritize displaying repair shops that specialize in specific repairs. For example, the search unit may refer to repair shop review information and prioritize displaying highly reliable repair shops. In this way, the search unit can display more reliable repair shops by referring to repair shop review information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit may input repair shop review information into a generating AI and have the generating AI perform the customization of search results.

[0056] The reservation department can suggest the optimal reservation method by referring to past reservation history when a reservation is made. For example, the reservation department can suggest the optimal reservation method based on reservation methods used in the past. For example, the reservation department can prioritize suggesting a specific repair shop based on past reservation history. For example, the reservation department can analyze past reservation history and suggest the most efficient reservation method. In this way, the reservation department can suggest the optimal reservation method by referring to past reservation history. Some or all of the above processes in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can input past reservation history data into a generating AI and have the generating AI perform the task of suggesting the optimal reservation method.

[0057] The reservation unit can adjust the timing of reservations according to the vehicle's usage. For example, when driving long distances, the reservation unit suggests a time when the reservation can be completed quickly. For example, when driving in urban areas, the reservation unit suggests a time with good access. For example, when driving on highways, the reservation unit suggests a time to book the nearest repair shop. In this way, the reservation unit can provide more appropriate reservation timings by adjusting the timing according to the vehicle's usage. Some or all of the above processing in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input vehicle usage data into a generating AI and have the generating AI perform the adjustment of reservation timing.

[0058] The reservation department can suggest the most suitable repair shop when a reservation is made, taking into account the vehicle's geographical location. For example, the reservation department may prioritize suggesting the repair shop closest to the current location. For example, the reservation department may prioritize suggesting a repair shop with good access based on geographical location information. For example, the reservation department may prioritize suggesting a repair shop that can respond quickly, taking into account geographical location information. In this way, the reservation department can suggest the most suitable repair shop by taking into account the vehicle's geographical location information. Some or all of the above processing in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can input geographical location data into a generating AI and have the generating AI suggest the most suitable repair shop.

[0059] The reservation department can analyze the driver's social media activity at the time of booking and customize the reservation details. For example, the reservation department can analyze the driver's social media activity to determine their preferred repair shop and customize the reservation. For example, the reservation department can customize the reservation details based on feedback on social media. For example, the reservation department can analyze social media trends and customize the reservation details. In this way, the reservation department can customize the reservation details by analyzing the driver's social media activity and provide more appropriate reservations. Some or all of the above processes in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can input social media data into a generating AI and have the generating AI perform the customization of the reservation details.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The analysis unit can refer to the vehicle's driving history and detect signs of malfunction based on specific driving patterns. For example, if there is a high frequency of sudden acceleration or sudden braking, the analysis unit can detect signs of malfunction related to the brake system or tire wear. For example, if there is a lot of prolonged idling, the analysis unit can detect signs of malfunction related to engine overheating or oil degradation. For example, if there is a lot of driving on highways, the analysis unit can detect signs of malfunction related to engine speed or fuel consumption. This allows the analysis unit to detect signs of malfunction more accurately based on the driving history.

[0062] The search unit can refer to the vehicle's maintenance history and suggest the most suitable repair shop. For example, the search unit prioritizes suggesting highly reliable repair shops based on evaluations of previously used repair shops. For example, the search unit suggests repair shops specializing in specific parts based on past maintenance history. For example, the search unit analyzes past maintenance history to suggest the most efficient repair shop. In this way, the search unit can suggest the most suitable repair shop by referring to the maintenance history.

[0063] The data collection unit can dynamically adjust the frequency of data collection according to the vehicle's usage. For example, during long-distance driving, the unit enhances data collection regarding engine temperature and oil condition. For example, during city driving, the unit enhances data collection regarding tire pressure and brake condition. For example, during highway driving, the unit enhances data collection regarding fuel consumption and engine speed. By dynamically adjusting the data collection frequency according to the vehicle's usage, the unit can collect more appropriate data.

[0064] The notification unit can select the optimal notification method by considering the vehicle's geographical location. For example, in mountainous areas, the notification unit provides highly visible notifications. In urban areas, for example, the notification unit prioritizes voice notifications. In highways, for example, the notification unit provides concise notifications. In this way, the notification unit can provide the optimal notification method by considering the vehicle's geographical location.

[0065] The reservation department can suggest the most suitable repair shop by considering the vehicle's geographical location. For example, the reservation department can prioritize suggesting the repair shop closest to the current location. For example, the reservation department can prioritize suggesting a repair shop with good access based on geographical location information. For example, the reservation department can prioritize suggesting a repair shop that can respond quickly, taking geographical location information into consideration. In this way, the reservation department can suggest the most suitable repair shop by considering the vehicle's geographical location information.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects data from various sensors in the vehicle. For example, it collects data such as engine temperature, oil condition, and tire pressure. The data collection unit can measure engine temperature using a temperature sensor, measure oil condition using an oil sensor, and measure tire pressure using a pressure sensor. Step 2: The analysis unit analyzes the data collected by the collection unit in real time to detect signs of failure. For example, the AI ​​analyzes the collected data to detect signs of failure such as abnormal temperature increases or abnormal vibration patterns. Step 3: The notification unit notifies the driver of any signs of malfunction detected by the analysis unit. For example, it can display a warning message on the dashboard, send a notification to the driver's smartphone, or issue an audio alert. Step 4: The search unit searches for the nearest repair shop based on the information notified by the notification unit. For example, it searches for the nearest repair shop based on the current location and provides search results considering the repair shop's rating, services offered, and distance. Step 5: The reservation unit makes a repair reservation at the repair shop found by the search unit. For example, the driver can select a repair shop on their smartphone and confirm the reservation. The reservation can also be automatically sent to the repair shop, and the date and time of the repair can be adjusted.

[0068] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that analyzes data from various sensors in a vehicle in real time, detects signs of malfunction, and notifies the driver. This AI agent system collects data from various sensors in the vehicle, and the AI ​​analyzes it in real time. Next, the AI ​​detects signs of malfunction and notifies the driver. Furthermore, if necessary, it searches for the nearest repair shop and makes a repair reservation. This mechanism is expected to reduce the risk of malfunction through preventive maintenance, reduce maintenance costs, and extend the lifespan of the vehicle. For example, the AI ​​agent system collects data from various sensors in the vehicle. For example, it collects data such as engine temperature, oil condition, and tire pressure. This data is input to the AI. Next, the AI ​​analyzes the collected data in real time. The AI ​​analyzes the data and detects signs of malfunction. For example, it detects signs of malfunction such as abnormally high engine temperature, poor oil condition, or low tire pressure. When signs of malfunction are detected, the AI ​​notifies the driver. For example, it displays a warning message on the dashboard or sends a notification to the smartphone. This allows the driver to be aware of signs of malfunction in advance. Furthermore, the system searches for the nearest repair shop and makes an appointment as needed. The AI ​​searches for the nearest repair shop based on the current location and makes an appointment. For example, a driver can select a repair shop and confirm the appointment on their smartphone. This system is expected to reduce the risk of breakdowns through preventative maintenance, lower maintenance costs, and extend the lifespan of the vehicle. For example, by detecting signs of failure in advance, expensive repair costs can be avoided. Also, regular maintenance can extend the life of the vehicle. This AI agent system has a user-friendly interface, making it easy for drivers to operate. For example, through a smartphone app, drivers can check for signs of failure, search for repair shops, and make appointments. As an innovator in vehicle maintenance, this AI agent system enables seamless collaboration between drivers and repair shops. For example, if signs of failure are detected, the AI ​​automatically notifies the repair shop so that they can prepare for repairs.This reduces the hassle of repairs and enables quick responses. This AI agent system is offered to car owners of all ages and is particularly suitable for people interested in new technologies. For example, it is a very useful tool for drivers who want to know the risk of breakdowns in advance and for car owners who want to reduce repair costs. This AI agent system achieves increased efficiency and cost reduction in vehicle maintenance through advancements in AI technology and improved data analysis capabilities. For example, by analyzing data in real time and detecting signs of malfunction, quick responses are possible. It also automatically searches for the nearest repair shop and makes reservations, saving the hassle of repairs. This AI agent system aims to realize a sustainable automotive society by reducing the risk of accidents and extending the lifespan of vehicles through preventive maintenance. For example, by detecting signs of malfunctions in advance and addressing them quickly, the risk of accidents can be reduced. Also, by performing regular maintenance, the lifespan of vehicles can be extended, realizing a sustainable automotive society. As such, the AI ​​agent system can analyze data from various vehicle sensors in real time, detect signs of malfunction and notify the driver, find the nearest repair shop if necessary, and even support the reservation of repairs.

[0069] The AI ​​agent system according to the embodiment comprises a data collection unit, an analysis unit, a notification unit, a search unit, and a reservation unit. The data collection unit collects data from various sensors of the vehicle. The data collection unit collects data such as engine temperature, oil condition, and tire pressure. The data collection unit can, for example, measure engine temperature using a temperature sensor. The data collection unit can, for example, measure oil condition using an oil sensor. The data collection unit can, for example, measure tire pressure using a pressure sensor. The analysis unit analyzes the data collected by the data collection unit in real time and detects signs of failure. The analysis unit, for example, uses AI to analyze the collected data in real time and detects signs of failure such as abnormal temperature rises or abnormal vibration patterns. The analysis unit, for example, uses AI to analyze engine temperature data and detect abnormal temperature rises. The analysis unit, for example, uses AI to analyze oil condition data and detect oil deterioration. The analysis unit, for example, uses AI to analyze tire pressure data and detect pressure drops. The notification unit notifies the driver of the signs of failure detected by the analysis unit. The notification unit can, for example, display a warning message on the dashboard. The notification unit can, for example, send a notification to a smartphone. The notification unit can, for example, issue an audio alert. The search unit searches for the nearest repair shop based on the information notified by the notification unit. The search unit can, for example, search for the nearest repair shop based on the current location. The search unit can, for example, provide search results considering the repair shop's rating and services. The search unit can, for example, provide search results based on the distance to the repair shop. The reservation unit makes a repair reservation at the repair shop found by the search unit. The reservation unit can, for example, allow the driver to select a repair shop on their smartphone and confirm the reservation. The reservation unit can, for example, automatically send the reservation to the repair shop. The reservation unit can, for example, adjust the date and time of the repair. As a result, the AI ​​agent system according to the embodiment can analyze data from various sensors in the vehicle in real time, detect signs of malfunction and notify the driver, find the nearest repair shop if necessary, and support the driver through to making a repair reservation.

[0070] The data collection unit collects data from various sensors in the vehicle. Specifically, it collects data such as engine temperature, oil condition, and tire pressure. The data collection unit can measure engine temperature using a temperature sensor. The engine temperature sensor is attached to the engine's cooling system and monitors the engine temperature in real time. The oil sensor measures the condition of the engine oil and detects oil degradation or contamination. The oil sensor predicts when to change the oil by measuring the oil viscosity and the concentration of contaminants. The pressure sensor measures tire pressure and verifies that the tire pressure is within the appropriate range. The pressure sensor is attached to the tire valve and monitors the tire pressure in real time. The data collected from these sensors is transmitted to the vehicle's central control unit and centrally managed by the data collection unit. The data collection unit collects this data in real time and transmits it to the analysis unit. By adjusting the frequency and accuracy of data collection, the data collection unit can respond flexibly to specific situations and conditions. For example, if the engine temperature rises rapidly, the data collection unit increases the frequency of data collection from the temperature sensor to help detect abnormalities early. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0071] The analysis unit analyzes data collected by the data collection unit in real time to detect signs of failure. The analysis uses AI to process data in real time and detect signs of failure such as abnormal temperature increases and abnormal vibration patterns. Specifically, the AI ​​can analyze engine temperature data and detect abnormal temperature increases. The AI ​​evaluates current temperature data by comparing it with past data and identifies abnormal patterns. For example, if the engine temperature rises rapidly under normal operating conditions, the AI ​​will determine this as abnormal and detect it as a sign of failure. The AI ​​can also analyze oil condition data and detect oil degradation. It evaluates the oil viscosity and contaminant concentration to predict when the oil needs to be changed. The AI ​​can also analyze tire pressure data and detect pressure drops. It evaluates whether the tire pressure is within the appropriate range and issues a warning if the pressure drops. This allows the analysis unit to quickly and accurately analyze collected data and grasp signs of failure in real time. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term failure prediction and trend analysis. For example, based on past failure data, it can evaluate the failure risk of specific parts or systems and develop preventive maintenance plans. This allows the analysis unit to handle not only real-time fault detection but also long-term fault prevention and risk management, thereby improving the reliability and safety of the entire system.

[0072] The notification unit alerts the driver to signs of malfunction detected by the analysis unit. Specifically, it can display warning messages on the dashboard. The warning messages vary depending on the type and urgency of the malfunction. For example, if the engine temperature rises abnormally, a "Engine Temperature Abnormality" warning message will be displayed on the dashboard. The notification unit can also send notifications to a smartphone. Detailed information about the malfunction and recommended actions are provided through a smartphone app. For example, if oil degradation is detected, a notification saying "Oil change required" will be sent to the smartphone. The notification unit can also issue voice alerts. Voice alerts are used to attract the driver's attention, and different voice messages are played depending on the type and urgency of the malfunction. For example, if tire pressure drops, a voice alert will say, "Tire pressure is low. Please check." This allows the notification unit to provide drivers with quick and appropriate information, supporting early detection and countermeasures for malfunctions. Furthermore, the notification unit can collect driver feedback and continuously improve the accuracy and effectiveness of its notifications. For example, the content and display method of warning messages can be reviewed based on driver feedback. Furthermore, the notification unit can reliably transmit information using multiple communication methods. This allows the notification unit to provide information to drivers quickly and reliably, supporting early detection and countermeasures for malfunctions.

[0073] The search unit searches for the nearest repair shop based on information notified by the notification unit. Specifically, it can search for the nearest repair shop based on the current location. The search unit uses GPS data to determine the vehicle's current location and searches for repair shops in the vicinity. The search unit can provide search results considering the repair shop's ratings and services. For example, it can recommend the best repair shop based on customer ratings, the types of services offered, and prices. The search unit can also provide search results based on the distance to the repair shop. For example, it can prioritize displaying the repair shop closest to the current location to ensure a quick response in emergencies. The search unit also considers the repair shop's business hours and the types of repairs it can perform when providing search results. This allows the search unit to quickly provide the driver with the best repair shop and support early repairs. Furthermore, the search unit can learn past search history and driver preferences to provide personalized search results. For example, it can display repair shops that are recommended for use again based on previously used repair shops and driver ratings. This allows the search unit to provide flexible search results tailored to the driver's needs and support early repairs.

[0074] The reservation department makes repair reservations at repair shops found by the search department. Specifically, drivers can select a repair shop on their smartphone and confirm the reservation. The reservation department can automatically send the reservation to the repair shop. For example, when a driver selects a repair shop through a smartphone app and enters their desired repair date and time, the reservation department sends that information to the repair shop and confirms the reservation. The reservation department can adjust the repair date and time. For example, it checks the availability of the repair shop and adjusts whether the repair is possible at the date and time requested by the driver. The reservation department notifies the driver of a confirmation message from the repair shop, allowing them to check the reservation details. This enables the reservation department to quickly and reliably make repair reservations for drivers and support the early repair of malfunctions. Furthermore, the reservation department can notify the driver of the repair progress. For example, it sends a notification to the smartphone when the repair is completed to inform the driver that the repair is finished. This enables the reservation department to provide drivers with real-time updates on the repair progress and support the early repair of malfunctions.

[0075] The data collection unit can collect data such as engine temperature, oil condition, and tire pressure. For example, the data collection unit uses a temperature sensor to measure engine temperature. For example, the data collection unit uses an oil sensor to measure oil condition. For example, the data collection unit uses a pressure sensor to measure tire pressure. In this way, the data collection unit can provide data for detecting signs of malfunction by collecting data from various sensors in the vehicle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input engine temperature data into a generating AI and have the generating AI perform analysis of the temperature data.

[0076] The analysis unit can analyze the collected data in real time and detect signs of failure. For example, the analysis unit can use AI to analyze the collected data in real time and detect signs of failure such as abnormal temperature rises or abnormal vibration patterns. For example, the analysis unit can use AI to analyze engine temperature data and detect abnormal temperature rises. For example, the analysis unit can use AI to analyze oil condition data and detect oil degradation. For example, the analysis unit can use AI to analyze tire pressure data and detect pressure drops. In this way, the analysis unit can quickly detect signs of failure by analyzing the collected data in real time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the detection of signs of failure.

[0077] The notification unit can display warning messages on the dashboard or send notifications to a smartphone. For example, the notification unit can display warning messages on the dashboard. For example, the notification unit can send notifications to a smartphone. For example, the notification unit can issue voice alerts. This allows the notification unit to quickly notify the driver of signs of a malfunction. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data detecting signs of a malfunction into a generating AI and have the generating AI generate a notification.

[0078] The search unit can search for the nearest repair shop based on the current location. For example, the search unit searches for the nearest repair shop based on the current location. The search unit provides search results considering, for example, the repair shop's rating and services. The search unit provides search results based on, for example, the distance to the repair shop. This allows the search unit to quickly find the nearest repair shop. Some or all of the above processing in the search unit may be performed using, for example, AI, or not using AI. For example, the search unit can input current location data into a generating AI and have the generating AI perform the search for the nearest repair shop.

[0079] The reservation system allows drivers to select a repair shop and confirm a reservation using their smartphone. For example, the reservation system allows the driver to select a repair shop and confirm a reservation using their smartphone. The reservation system automatically sends the reservation to the repair shop. The reservation system adjusts the date and time of the repair. This allows the reservation system to make repair reservations quickly. Some or all of the above processes in the reservation system may be performed using AI, for example, or not using AI. For example, the reservation system can input the repair shop's reservation data into a generating AI and have the generating AI confirm the reservation.

[0080] The data collection unit can estimate the driver's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the driver is stressed, the data collection unit can increase the frequency of data collection to detect anomalies early. For example, if the driver is relaxed, the data collection unit can decrease the frequency of data collection to reduce the system load. For example, if the driver is in a hurry, the data collection unit can prioritize collecting only important data. In this way, the data collection unit can reduce the system load while detecting anomalies early by adjusting the frequency of data collection according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the driver's emotion data into the generative AI and have the generative AI adjust the frequency of data collection.

[0081] The data collection unit can refer to past vehicle data and enhance data collection under specific conditions. For example, if the engine temperature was abnormally high in the past, the data collection unit will enhance data collection under similar conditions. For example, if the oil condition deteriorated in the past, the data collection unit will enhance data collection regarding the oil condition. For example, if the tire pressure dropped in the past, the data collection unit will enhance data collection regarding tire pressure. In this way, by referring to past vehicle data, the data collection unit can enhance data collection under specific conditions and more accurately detect signs of failure. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past vehicle data into a generating AI and have the generating AI perform enhanced data collection under specific conditions.

[0082] The data collection unit can dynamically change the type of data it collects depending on the vehicle's usage during data collection. For example, during long-distance driving, the data collection unit enhances the collection of data related to engine temperature and oil condition. For example, during city driving, the data collection unit enhances the collection of data related to tire pressure and brake condition. For example, during highway driving, the data collection unit enhances the collection of data related to fuel consumption and engine speed. In this way, the data collection unit can collect more appropriate data by dynamically changing the type of data it collects according to the vehicle's usage. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input vehicle usage data into a generating AI and cause the generating AI to dynamically change the type of data to collect.

[0083] The data collection unit can estimate the driver's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the driver is stressed, the data collection unit will prioritize collecting data on engine temperature and oil condition. For example, if the driver is relaxed, the data collection unit will prioritize collecting data on tire pressure and fuel consumption. For example, if the driver is in a hurry, the data collection unit will prioritize collecting data on brake status and engine RPM. In this way, the data collection unit can prioritize collecting more important data by determining the priority of data to collect according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the driver's emotion data into a generative AI and have the generative AI determine the priority of data to collect.

[0084] The data collection unit can enhance data collection in specific regions by considering the vehicle's geographical location during data collection. For example, in mountainous areas, the data collection unit can enhance data collection regarding engine temperature and brake status. In urban areas, for example, the data collection unit can enhance data collection regarding tire pressure and fuel consumption. In highways, for example, the data collection unit can enhance data collection regarding engine speed and oil status. By enhancing data collection in specific regions by considering the vehicle's geographical location, the data collection unit can more accurately detect region-specific signs of malfunction. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location data into a generating AI and have the generating AI perform enhanced data collection in specific regions.

[0085] The data collection unit can analyze the vehicle's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on issues reported on the vehicle's social media accounts. For example, the data collection unit can analyze user feedback on social media to enhance data collection regarding specific parts. For example, the data collection unit can analyze trends on social media to collect data on common issues. This allows the data collection unit to collect data based on user feedback and trends by analyzing the vehicle's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0086] The analysis unit can estimate the driver's emotions and adjust the display method of the analysis results based on the estimated emotions of the driver. For example, if the driver is tense, the analysis unit provides a simple and highly visible display method. For example, if the driver is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the driver is in a hurry, the analysis unit provides a display method that gets straight to the point. In this way, the analysis unit can provide more appropriate information by adjusting the display method of the analysis results according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the driver's emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0087] The analysis unit can optimize its analysis algorithm by referring to past failure data during analysis. For example, the analysis unit can optimize its analysis algorithm regarding engine temperature based on past engine failure data. For example, the analysis unit can optimize its analysis algorithm regarding oil condition based on past oil degradation data. For example, the analysis unit can optimize its analysis algorithm regarding tire pressure based on past tire pressure drop data. In this way, the analysis unit can optimize its analysis algorithm by referring to past failure data and more accurately detect signs of failure. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past failure data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0088] The analysis unit can apply different analysis methods depending on the vehicle's usage during analysis. For example, during long-distance driving, the analysis unit applies analysis methods related to engine temperature and oil condition. For example, during city driving, the analysis unit applies analysis methods related to tire pressure and brake condition. For example, during highway driving, the analysis unit applies analysis methods related to fuel consumption and engine speed. In this way, the analysis unit can perform a more appropriate analysis by applying different analysis methods depending on the vehicle's usage. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input vehicle usage data into a generating AI and have the generating AI execute the application of different analysis methods.

[0089] The analysis unit can estimate the driver's emotions and determine the priority of analysis results based on the estimated emotions. For example, if the driver is stressed, the analysis unit will prioritize displaying analysis results related to engine temperature and oil status. For example, if the driver is relaxed, the analysis unit will prioritize displaying analysis results related to tire pressure and fuel consumption. For example, if the driver is in a hurry, the analysis unit will prioritize displaying analysis results related to brake status and engine speed. In this way, the analysis unit can prioritize providing more important information by determining the priority of analysis results according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the driver's emotion data into the generative AI and have the generative AI perform the determination of the priority of analysis results.

[0090] The analysis unit can analyze failure trends in a specific region by considering the vehicle's geographical location during analysis. For example, in mountainous areas, the analysis unit can analyze failure trends related to engine temperature and brake condition. In urban areas, for example, the analysis unit can analyze failure trends related to tire pressure and fuel consumption. In highways, for example, the analysis unit can analyze failure trends related to engine speed and oil condition. By doing so, the analysis unit can more accurately detect region-specific signs of failure by analyzing failure trends in a specific region by considering the vehicle's geographical location. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical location data into a generating AI and have the generating AI perform an analysis of failure trends in a specific region.

[0091] The analysis unit can improve the accuracy of its analysis by referring to relevant vehicle literature during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on engine temperature. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on oil condition. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on tire pressure. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant vehicle literature and more accurately detect signs of failure. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0092] The notification unit can estimate the driver's emotions and adjust the way notifications are presented based on the estimated emotions. For example, if the driver is tense, the notification unit provides a simple and easily visible notification. If the driver is relaxed, the notification unit provides a notification containing detailed information. If the driver is in a hurry, the notification unit provides a notification that gets straight to the point. In this way, the notification unit can provide more appropriate notifications by adjusting the way notifications are presented according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input driver emotion data into a generative AI and have the generative AI adjust the way notifications are presented.

[0093] The notification unit can adjust the level of detail in the notification based on the severity of the malfunction. For example, in the case of a major malfunction, the notification unit provides a notification containing detailed information. For example, in the case of a minor malfunction, the notification unit provides a concise notification. The notification unit dynamically adjusts the level of detail in the notification according to the severity of the malfunction. This allows the notification unit to provide appropriate information to the driver by adjusting the level of detail in the notification according to the severity of the malfunction. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input malfunction severity data into a generating AI and have the generating AI perform the adjustment of the level of detail in the notification.

[0094] The notification unit can select the optimal notification method by referring to the driver's past response history when sending a notification. For example, the notification unit may prioritize providing notification methods that the driver has previously preferred to use. For example, the notification unit may select the optimal notification method from the driver's past response history. For example, the notification unit may analyze the driver's past response history and provide the most effective notification method. In this way, the notification unit can provide the optimal notification method by referring to the driver's past response history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input past response history data into a generating AI and have the generating AI perform the selection of the optimal notification method.

[0095] The notification unit can estimate the driver's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the driver is stressed, the notification unit can delay the notification to allow the driver to concentrate on driving. For example, if the driver is relaxed, the notification unit can provide an immediate notification. For example, if the driver is in a hurry, the notification unit can prioritize only important notifications. In this way, the notification unit can provide notifications at a more appropriate time by adjusting the timing according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input driver emotion data into a generative AI and have the generative AI adjust the timing of notifications.

[0096] The notification unit can select the optimal notification method when issuing a notification, taking into account the vehicle's geographical location information. For example, the notification unit may provide a highly visible notification in mountainous areas. For example, it may prioritize voice notifications in urban areas. For example, it may provide a concise notification on highways. In this way, the notification unit can provide the optimal notification method by taking into account the vehicle's geographical location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input geographical location data into a generating AI and have the generating AI select the optimal notification method.

[0097] The notification unit can analyze the driver's social media activity and customize the notification content when sending a notification. For example, the notification unit can analyze the driver's social media activity to determine their preferred notification method and customize it. For example, the notification unit can customize the notification content based on feedback on social media. For example, the notification unit can analyze social media trends and customize the notification content. In this way, the notification unit can customize the notification content by analyzing the driver's social media activity and provide more appropriate notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input social media data into a generating AI and have the generating AI perform the customization of the notification content.

[0098] The search unit can estimate the driver's emotions and adjust how search results are displayed based on the estimated emotions. For example, if the driver is tense, the search unit provides simple and highly visible search results. If the driver is relaxed, the search unit provides search results containing detailed information. If the driver is in a hurry, the search unit provides concise search results. In this way, the search unit can provide more appropriate search results by adjusting how search results are displayed according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input driver emotion data into a generative AI and have the generative AI adjust how search results are displayed.

[0099] The search unit can prioritize displaying the most suitable repair shops by referring to past repair history during a search. For example, the search unit prioritizes displaying the most suitable repair shops based on evaluations of repair shops used in the past. For example, the search unit prioritizes displaying repair shops that are strong in specific repairs based on past repair history. For example, the search unit analyzes past repair history and prioritizes displaying the most reliable repair shops. In this way, the search unit can prioritize displaying the most suitable repair shops by referring to past repair history. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input past repair history data into a generating AI and have the generating AI perform the display of the most suitable repair shops.

[0100] The search unit can apply different search algorithms depending on the vehicle's usage during a search. For example, during long-distance driving, the search unit prioritizes displaying repair shops that can respond quickly. For example, during city driving, the search unit prioritizes displaying repair shops with good access. For example, during highway driving, the search unit prioritizes displaying the nearest repair shop. In this way, the search unit can provide more appropriate search results by applying different search algorithms depending on the vehicle's usage. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input vehicle usage data into a generating AI and have the generating AI execute the application of different search algorithms.

[0101] The search unit can estimate the driver's emotions and prioritize search results based on the estimated emotions. For example, if the driver is stressed, the search unit will prioritize displaying repair shops that can respond quickly. For example, if the driver is relaxed, the search unit will prioritize displaying highly-rated repair shops. For example, if the driver is in a hurry, the search unit will prioritize displaying the nearest repair shop. In this way, the search unit can prioritize more important search results by prioritizing them according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI or not using AI. For example, the search unit can input driver emotion data into a generative AI and have the generative AI determine the priority of search results.

[0102] The search unit can display the most suitable repair shop by considering the vehicle's geographical location during the search. For example, the search unit may prioritize displaying the repair shop closest to the current location. For example, the search unit may prioritize displaying the repair shop with good access based on geographical location information. For example, the search unit may prioritize displaying the repair shop that can respond quickly, taking geographical location information into consideration. In this way, the search unit can display the most suitable repair shop by considering the vehicle's geographical location information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit may input geographical location data into a generating AI and have the generating AI display the most suitable repair shop.

[0103] The search unit can customize search results by referring to repair shop review information during a search. For example, the search unit may prioritize displaying highly-rated repair shops based on their review information. For example, the search unit may analyze repair shop review information and prioritize displaying repair shops that specialize in specific repairs. For example, the search unit may refer to repair shop review information and prioritize displaying highly reliable repair shops. In this way, the search unit can display more reliable repair shops by referring to repair shop review information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit may input repair shop review information into a generating AI and have the generating AI perform the customization of search results.

[0104] The booking unit can estimate the driver's emotions and adjust the booking procedure based on the estimated emotions. For example, if the driver is stressed, the booking unit provides a simple booking procedure. If the driver is relaxed, the booking unit provides detailed booking options. If the driver is in a hurry, the booking unit provides a procedure that allows for quick booking completion. In this way, the booking unit can provide a more appropriate booking procedure by adjusting it according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the booking unit may be performed using AI or not using AI. For example, the booking unit can input driver emotion data into a generative AI and have the generative AI perform the adjustment of the booking procedure.

[0105] The reservation department can suggest the optimal reservation method by referring to past reservation history when a reservation is made. For example, the reservation department can suggest the optimal reservation method based on reservation methods used in the past. For example, the reservation department can prioritize suggesting a specific repair shop based on past reservation history. For example, the reservation department can analyze past reservation history and suggest the most efficient reservation method. In this way, the reservation department can suggest the optimal reservation method by referring to past reservation history. Some or all of the above processes in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can input past reservation history data into a generating AI and have the generating AI perform the task of suggesting the optimal reservation method.

[0106] The reservation unit can adjust the timing of reservations according to the vehicle's usage. For example, when driving long distances, the reservation unit suggests a time when the reservation can be completed quickly. For example, when driving in urban areas, the reservation unit suggests a time with good access. For example, when driving on highways, the reservation unit suggests a time to book the nearest repair shop. In this way, the reservation unit can provide more appropriate reservation timings by adjusting the timing according to the vehicle's usage. Some or all of the above processing in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input vehicle usage data into a generating AI and have the generating AI perform the adjustment of reservation timing.

[0107] The booking system can estimate the driver's emotions and determine booking priorities based on those emotions. For example, if the driver is stressed, the booking system will prioritize suggesting repair shops that can complete the booking quickly. If the driver is relaxed, the booking system will prioritize suggesting highly-rated repair shops. If the driver is in a hurry, the booking system will prioritize suggesting the nearest repair shop. This allows the booking system to prioritize more important bookings by determining booking priorities according to the driver's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the booking system may be performed using AI or not. For example, the booking system can input driver emotion data into a generative AI and have the generative AI determine booking priorities.

[0108] The reservation department can suggest the most suitable repair shop when a reservation is made, taking into account the vehicle's geographical location. For example, the reservation department may prioritize suggesting the repair shop closest to the current location. For example, the reservation department may prioritize suggesting a repair shop with good access based on geographical location information. For example, the reservation department may prioritize suggesting a repair shop that can respond quickly, taking into account geographical location information. In this way, the reservation department can suggest the most suitable repair shop by taking into account the vehicle's geographical location information. Some or all of the above processing in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can input geographical location data into a generating AI and have the generating AI suggest the most suitable repair shop.

[0109] The reservation department can analyze the driver's social media activity at the time of booking and customize the reservation details. For example, the reservation department can analyze the driver's social media activity to determine their preferred repair shop and customize the reservation. For example, the reservation department can customize the reservation details based on feedback on social media. For example, the reservation department can analyze social media trends and customize the reservation details. In this way, the reservation department can customize the reservation details by analyzing the driver's social media activity and provide more appropriate reservations. Some or all of the above processes in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can input social media data into a generating AI and have the generating AI perform the customization of the reservation details.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The analysis unit can refer to the vehicle's driving history and detect signs of malfunction based on specific driving patterns. For example, if there is a high frequency of sudden acceleration or sudden braking, the analysis unit can detect signs of malfunction related to the brake system or tire wear. For example, if there is a lot of prolonged idling, the analysis unit can detect signs of malfunction related to engine overheating or oil degradation. For example, if there is a lot of driving on highways, the analysis unit can detect signs of malfunction related to engine speed or fuel consumption. This allows the analysis unit to detect signs of malfunction more accurately based on the driving history.

[0112] The notification unit can estimate the driver's emotions and adjust the content of the notification based on those emotions. For example, if the driver is stressed, the notification unit will provide a concise and to-the-point notification. If the driver is relaxed, the notification unit will provide a notification with more detailed information. If the driver is in a hurry, the notification unit will prioritize notifying only the most important information. This allows the notification unit to provide more appropriate information by adjusting the content of the notification according to the driver's emotions.

[0113] The search unit can refer to the vehicle's maintenance history and suggest the most suitable repair shop. For example, the search unit prioritizes suggesting highly reliable repair shops based on evaluations of previously used repair shops. For example, the search unit suggests repair shops specializing in specific parts based on past maintenance history. For example, the search unit analyzes past maintenance history to suggest the most efficient repair shop. In this way, the search unit can suggest the most suitable repair shop by referring to the maintenance history.

[0114] The reservation system can estimate the driver's emotions and adjust the timing of reservations based on those emotions. For example, if the driver is feeling stressed, the system might suggest a time when the reservation can be completed quickly. If the driver is relaxed, the system might offer more detailed reservation options. If the driver is in a hurry, the system might suggest a time to book an appointment at the nearest repair shop. In this way, the reservation system can provide more appropriate reservation timings by adjusting them according to the driver's emotions.

[0115] The data collection unit can dynamically adjust the frequency of data collection according to the vehicle's usage. For example, during long-distance driving, the unit enhances data collection regarding engine temperature and oil condition. For example, during city driving, the unit enhances data collection regarding tire pressure and brake condition. For example, during highway driving, the unit enhances data collection regarding fuel consumption and engine speed. By dynamically adjusting the data collection frequency according to the vehicle's usage, the unit can collect more appropriate data.

[0116] The analysis unit can estimate the driver's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the driver is tense, the analysis unit provides a simple and highly visible display method. For example, if the driver is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the driver is in a hurry, the analysis unit provides a display method that gets straight to the point. In this way, the analysis unit can provide more appropriate information by adjusting the display method of the analysis results according to the driver's emotions.

[0117] The notification unit can select the optimal notification method by considering the vehicle's geographical location. For example, in mountainous areas, the notification unit provides highly visible notifications. In urban areas, for example, the notification unit prioritizes voice notifications. In highways, for example, the notification unit provides concise notifications. In this way, the notification unit can provide the optimal notification method by considering the vehicle's geographical location.

[0118] The search engine can estimate the driver's emotions and prioritize search results based on that estimation. For example, if the driver is stressed, the search engine will prioritize repair shops that can respond quickly. If the driver is relaxed, the search engine will prioritize highly-rated repair shops. If the driver is in a hurry, the search engine will prioritize the nearest repair shop. In this way, the search engine can prioritize more important search results by determining the priority of search results according to the driver's emotions.

[0119] The reservation department can suggest the most suitable repair shop by considering the vehicle's geographical location. For example, the reservation department can prioritize suggesting the repair shop closest to the current location. For example, the reservation department can prioritize suggesting a repair shop with good access based on geographical location information. For example, the reservation department can prioritize suggesting a repair shop that can respond quickly, taking geographical location information into consideration. In this way, the reservation department can suggest the most suitable repair shop by considering the vehicle's geographical location information.

[0120] The data collection unit can estimate the driver's emotions and prioritize the data to collect based on those emotions. For example, if the driver is stressed, the unit will prioritize collecting data on engine temperature and oil condition. If the driver is relaxed, the unit will prioritize collecting data on tire pressure and fuel consumption. If the driver is in a hurry, the unit will prioritize collecting data on brake status and engine speed. This allows the data collection unit to prioritize collecting more important data by prioritizing the data to be collected according to the driver's emotions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects data from various sensors in the vehicle. For example, it collects data such as engine temperature, oil condition, and tire pressure. The data collection unit can measure engine temperature using a temperature sensor, measure oil condition using an oil sensor, and measure tire pressure using a pressure sensor. Step 2: The analysis unit analyzes the data collected by the collection unit in real time to detect signs of failure. For example, the AI ​​analyzes the collected data to detect signs of failure such as abnormal temperature increases or abnormal vibration patterns. Step 3: The notification unit notifies the driver of any signs of malfunction detected by the analysis unit. For example, it can display a warning message on the dashboard, send a notification to the driver's smartphone, or issue an audio alert. Step 4: The search unit searches for the nearest repair shop based on the information notified by the notification unit. For example, it searches for the nearest repair shop based on the current location and provides search results considering the repair shop's rating, services offered, and distance. Step 5: The reservation unit makes a repair reservation at the repair shop found by the search unit. For example, the driver can select a repair shop on their smartphone and confirm the reservation. The reservation can also be automatically sent to the repair shop, and the date and time of the repair can be adjusted.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the data collection unit, analysis unit, notification unit, search unit, and reservation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects various vehicle data using the sensors of the smart device 14. The analysis unit analyzes the collected data in real time using the identification processing unit 290 of the data processing unit 12 to detect signs of failure. The notification unit notifies the driver using the control unit 46A of the smart device 14. The search unit searches for the nearest repair shop using the identification processing unit 290 of the data processing unit 12. The reservation unit makes a repair reservation using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the data collection unit, analysis unit, notification unit, search unit, and reservation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects various vehicle data using the sensors of the smart glasses 214. The analysis unit analyzes the collected data in real time, for example, by the identification processing unit 290 of the data processing unit 12, and detects signs of malfunction. The notification unit notifies the driver, for example, by the control unit 46A of the smart glasses 214. The search unit searches for the nearest repair shop, for example, by the identification processing unit 290 of the data processing unit 12. The reservation unit makes a repair reservation, for example, by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the data collection unit, analysis unit, notification unit, search unit, and reservation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects various vehicle data using the sensors of the headset terminal 314. The analysis unit analyzes the collected data in real time using the identification processing unit 290 of the data processing unit 12 to detect signs of failure. The notification unit notifies the driver using the control unit 46A of the headset terminal 314. The search unit searches for the nearest repair shop using the identification processing unit 290 of the data processing unit 12. The reservation unit makes a repair reservation using the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the data collection unit, analysis unit, notification unit, search unit, and reservation unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects various vehicle data using the sensors of the robot 414. The analysis unit analyzes the collected data in real time using, for example, the identification processing unit 290 of the data processing unit 12 to detect signs of failure. The notification unit notifies the driver using, for example, the control unit 46A of the robot 414. The search unit searches for the nearest repair shop using, for example, the identification processing unit 290 of the data processing unit 12. The reservation unit makes a repair reservation using, for example, the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A data collection unit that collects data from various sensors in the vehicle, An analysis unit analyzes the data collected by the aforementioned collection unit in real time and detects signs of failure, A notification unit that notifies the driver of the signs of failure detected by the analysis unit, A search unit that searches for the nearest repair shop based on the information notified by the aforementioned notification unit, The system includes a reservation unit that makes a reservation for repairs at a repair shop found by the search unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects data such as engine temperature, oil condition, and tire pressure. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed in real time to detect signs of failure. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, Display warning messages on the dashboard or send notifications to your smartphone. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned search unit, Search for the nearest repair shop based on your current location. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reservation section is, The driver selects a repair shop and confirms the reservation using their smartphone. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the driver's emotions and adjusts the frequency of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Referencing past vehicle data enhances data collection under specific conditions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, the type of data collected is dynamically changed according to the vehicle's usage. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the driver's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, we enhance data collection in specific areas by considering the geographical location information of the vehicles. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the vehicle's social media activity is analyzed, and relevant data is collected. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the driver's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past failure data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, different analysis methods are applied depending on the vehicle's usage. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the driver's emotions and prioritizes the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the vehicle's geographical location information is taken into consideration to analyze failure trends in specific regions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant vehicle literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, The system estimates the driver's emotions and adjusts the way notifications are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, When a notification is sent, the level of detail in the notification will be adjusted based on the severity of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, When a notification is sent, the system will refer to the driver's past response history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, It estimates the driver's emotions and adjusts the timing of notifications based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, When sending a notification, the system will select the most suitable notification method, taking into account the vehicle's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, When sending notifications, the system analyzes the driver's social media activity to customize the notification content. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned search unit, It estimates the driver's emotions and adjusts how search results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned search unit, When searching, the system prioritizes displaying the most suitable repair shops by referencing past repair history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned search unit, When searching, different search algorithms are applied depending on the vehicle's usage. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned search unit, The system estimates the driver's emotions and prioritizes search results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned search unit, When searching, the system will display the most suitable repair shop, taking into account the vehicle's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned search unit, Customize your search results by referencing reviews of repair shops during your search. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned reservation section is, The system estimates the driver's emotions and adjusts the booking process based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned reservation section is, When you make a reservation, we will refer to your past reservation history and suggest the most suitable reservation method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned reservation section is, When making a reservation, we will adjust the timing of your reservation according to the vehicle's availability. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned reservation section is, The system estimates the driver's emotions and prioritizes bookings based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned reservation section is, When you make a reservation, we will suggest the most suitable repair shop, taking into account the vehicle's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned reservation section is, When booking, the driver's social media activity is analyzed to customize the booking details. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data from various sensors in the vehicle, An analysis unit analyzes the data collected by the aforementioned collection unit in real time and detects signs of failure, A notification unit that notifies the driver of the signs of failure detected by the analysis unit, A search unit that searches for the nearest repair shop based on the information notified by the aforementioned notification unit, The system includes a reservation unit that makes a reservation for repairs at a repair shop found by the search unit. A system characterized by the following features.

2. The aforementioned collection unit is It collects data such as engine temperature, oil condition, and tire pressure. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed in real time to detect signs of failure. The system according to feature 1.

4. The aforementioned notification unit, Display warning messages on the dashboard or send notifications to your smartphone. The system according to feature 1.

5. The aforementioned search unit, Search for the nearest repair shop based on your current location. The system according to feature 1.

6. The aforementioned reservation section is, The driver selects a repair shop and confirms the reservation using their smartphone. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the driver's emotions and adjusts the frequency of data collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Referencing past vehicle data enhances data collection under specific conditions. The system according to feature 1.

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