system

Abstract

The system according to this embodiment aims to detect signs of vehicle failure in real time and respond quickly. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a notification unit, a search unit, and a reservation unit. The collection unit collects vehicle sensor data in real time. The analysis unit analyzes the sensor data collected by the 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 the repair shop selected 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to detect a fault omen of a vehicle in real time and respond quickly.

[0005] The system according to the embodiment aims to detect a fault omen of a vehicle in real time and respond quickly.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a notification unit, a search unit, and a reservation unit. The collection unit collects vehicle sensor data in real time. The analysis unit analyzes the sensor data collected by the 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 the repair shop selected by the search unit. [Effects of the Invention]

[0007] The system according to this embodiment can detect signs of vehicle malfunction in real time 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, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 system according to an embodiment of the present invention is a system that analyzes vehicle sensor data in real time, detects signs of malfunction, and notifies the driver. This AI system collects vehicle sensor data in real time, including data such as engine status, tire pressure, and brake status. Next, the AI ​​system analyzes the collected sensor data in real time and uses a machine learning algorithm to detect signs of malfunction. If signs of malfunction are detected, the AI ​​system notifies the driver. The notification is made through a smartphone app. Furthermore, if repairs are needed, the AI ​​system searches for the nearest repair shop, selects the most suitable repair shop based on the repair shop's location information and ratings, and automatically handles the reservation process. For example, the AI ​​system collects vehicle sensor data in real time. The sensor data includes engine status, tire pressure, brake status, etc. Next, the AI ​​system analyzes the collected sensor data in real time and uses a machine learning algorithm to detect signs of malfunction. If signs of malfunction are detected, the AI ​​system notifies the driver. The notification is made through a smartphone app. Furthermore, if repairs are needed, the AI ​​system searches for the nearest repair shop, selects the best one based on its location and ratings, and automatically handles the booking process. This system extends the lifespan of the vehicle, reduces maintenance costs, and improves driver safety. This enables the AI ​​system to collect, analyze, notify, search, and book appointments using vehicle sensor data in real time.

[0029] The AI ​​system according to this embodiment comprises a collection unit, an analysis unit, a notification unit, a search unit, and a reservation unit. The collection unit collects vehicle sensor data in real time. The collection unit collects sensor data such as engine status, tire pressure, and brake status. The collection unit can collect data such as engine temperature, rotational speed, tire pressure, brake pad wear, and brake fluid pressure. The collection unit can collect data using pressure sensors and temperature sensors, for example. The collection unit can collect sensor data in real time and transmit it to the AI ​​system, for example. The analysis unit analyzes the sensor data collected by the collection unit in real time and detects signs of failure. The analysis unit can detect signs of failure using machine learning algorithms, for example. The analysis unit can detect signs of failure using machine learning algorithms such as deep learning and support vector machines, for example. The analysis unit can detect abnormal values ​​or abnormal patterns in sensor data, for example. The analysis unit can analyze sensor data in real time and detect signs of failure. The notification unit notifies the driver of any signs of failure detected by the analysis unit. The notification unit notifies the driver, for example, through a smartphone app. The notification unit can notify the driver, for example, using push notifications or alert sounds. The notification unit can notify the driver of signs of failure through a smartphone app. The notification unit can notify the driver, for example, when signs of failure are detected. The search unit searches for the nearest repair shop based on the information notified by the notification unit. The search unit selects the most suitable repair shop, for example, based on the location information and ratings of the repair shops. The search unit can select the most suitable repair shop, for example, based on the location information and rating scores of the repair shops. The search unit can select the most suitable repair shop, for example, based on the location information and ratings of the repair shops. The search unit can select the most suitable repair shop, for example, based on the location information and ratings of the repair shops. The reservation unit makes a reservation for the repair shop selected by the search unit. The reservation unit automatically makes a reservation for the selected repair shop, for example.The reservation unit can, for example, automatically perform procedures such as automatically filling out reservation forms and sending confirmation emails. The reservation unit can, for example, automatically perform reservation procedures for selected repair shops. The reservation unit can, for example, automatically perform reservation procedures for selected repair shops. As a result, the AI ​​system according to the embodiment can collect, analyze, notify, search, and reserve vehicle sensor data in real time.

[0030] The data collection unit collects vehicle sensor data in real time. Specifically, it collects sensor data such as engine status, tire pressure, and brake status. For example, it can collect data such as engine temperature and rotation speed, tire pressure, brake pad wear, and brake fluid pressure. This data is collected using various sensors such as pressure sensors and temperature sensors. The data collection unit collects this sensor data in real time and transmits it to the AI ​​system. The collected data is constantly updated during vehicle operation to reflect the latest status. For example, it can detect abnormalities in real time, such as when the engine temperature rises abnormally or when the tire pressure drops. The data collection unit centrally manages this data and can cooperate with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the analysis unit and notification unit. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance. In addition, the data collection unit can optimize sensor operation according to the vehicle's operating status and environmental conditions. For example, safety can be ensured by more frequent monitoring of tire pressure and brake status during adverse weather conditions. This allows the collection unit to support the safe operation of the vehicle and contribute to the prevention and early detection of breakdowns.

[0031] The analysis unit analyzes sensor data collected by the data collection unit in real time to detect signs of failure. Specifically, it uses machine learning algorithms to detect signs of failure. For example, it can use machine learning algorithms such as deep learning and support vector machines to detect signs of failure. This allows it to detect abnormal values ​​and patterns in sensor data. The analysis unit analyzes the collected sensor data in real time to detect signs of failure. For example, it can detect abnormal data such as when the engine temperature is higher than normal or when the tire pressure drops sharply. Based on this abnormal data, the analysis unit can detect signs of failure early and take appropriate countermeasures. Furthermore, the analysis unit can also utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past failure data, it can assess the failure risk of specific parts or systems and plan preventive maintenance. In addition, the analysis unit can use anomaly detection algorithms to detect patterns that are different from normal or abnormal data and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system. Furthermore, the analysis department can achieve more accurate fault prediction by utilizing advanced AI-based analysis techniques. For example, by building an anomaly detection model using deep learning and capturing subtle changes in sensor data, it can detect signs of failure with high accuracy. As a result, the analysis department can improve vehicle safety and contribute to the prevention and early detection of failures.

[0032] The notification unit notifies the driver of any signs of malfunction detected by the analysis unit. Specifically, it notifies the driver via a smartphone app. For example, it can notify the driver using push notifications or alert sounds. The notification unit can quickly notify the driver when signs of malfunction are detected. For example, if the engine temperature rises abnormally or the tire pressure drops, it can immediately notify the driver so that appropriate measures can be taken. The notification unit can notify the driver of signs of malfunction via a smartphone app. For example, it can use push notifications to provide the driver with detailed information about the signs of malfunction and recommended measures. It can also use alert sounds to draw the driver's attention. The notification unit can quickly notify the driver when signs of malfunction are detected. This allows the driver to grasp signs of malfunction early and take appropriate measures. Furthermore, the notification unit can collect driver feedback and continuously improve the accuracy and effectiveness of the notification content. For example, it can review and improve the notification content based on the driver's response to the signs of malfunction and the results of the measures taken. In addition, the notification unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications but also through voice calls, SMS, and email. This allows the notification unit to quickly and reliably notify drivers of potential breakdowns and take appropriate measures. Furthermore, the notification unit can provide personalized notification functions using AI. For example, it can automatically adjust the optimal notification timing and content based on the driver's driving history and vehicle usage. This allows the notification unit to provide drivers with more effective notifications, contributing to the prevention and early detection of breakdowns.

[0033] The search unit searches for the nearest repair shop based on the information notified by the notification unit. Specifically, it selects the optimal repair shop based on the location information and ratings of the repair shops. For example, it can select the optimal repair shop based on the location information and rating score of the repair shops. When a malfunction is notified, the search unit can quickly find the best repair shop for the driver. For example, it can prioritize searching for the repair shop closest to the current location or a repair shop with a high rating. The search unit can select the optimal repair shop based on the location information and ratings of the repair shops. For example, it can evaluate the distance from the current location and ease of access based on the location information of the repair shops and select the optimal repair shop. It can also evaluate the quality of service and reliability based on the rating score of the repair shops and select the optimal repair shop. The search unit can select the optimal repair shop based on the location information and ratings of the repair shops. This allows drivers to quickly find the best repair shop when a malfunction is notified. Furthermore, the search unit can provide advanced search algorithms using AI. For example, it can automatically recommend the best repair shop based on the driver's past repair history and vehicle usage. This allows the search unit to provide drivers with a more effective repair shop search function, contributing to the prevention of breakdowns and early repairs. Furthermore, the search unit can respond to the latest situation based on repair shop information that is updated in real time. For example, it can check the business hours and reservation status of repair shops in real time and select the most suitable repair shop. In this way, the search unit can provide drivers with a quick and accurate repair shop search function, contributing to the prevention of breakdowns and early repairs.

[0034] The reservation department handles the reservation process for repair shops selected by the search department. Specifically, it automatically handles the reservation process for selected repair shops. For example, it can automatically fill in reservation forms and send confirmation emails. The reservation department can automatically handle the reservation process for selected repair shops. For example, it can quickly handle the reservation process by accessing the repair shop's reservation system and automatically entering the necessary information. It can also send reservation confirmation emails and notifications to the driver, allowing them to confirm the reservation details. The reservation department can automatically handle the reservation process for selected repair shops. This allows drivers to quickly make reservations for repair shops when they are notified of signs of a breakdown. Furthermore, the reservation department can provide advanced reservation management functions using AI. For example, it can automatically adjust the optimal reservation date and time based on the driver's schedule and the repair shop's reservation status. This allows the reservation department to provide drivers with more effective reservation management functions, contributing to breakdown prevention and early repair. In addition, the reservation department can respond to the latest situation based on repair shop information that is updated in real time. For example, it can check the repair shop's reservation status and business hours in real time and select the optimal reservation date and time. This allows the reservation department to provide drivers with quick and accurate reservation procedures, contributing to the prevention and early repair of breakdowns. Furthermore, the reservation department can monitor the progress of reservation procedures in real time and notify drivers as needed. For example, it can notify drivers if reservation confirmation or changes are necessary, enabling a quick response. This allows the reservation department to provide drivers with quick and reliable reservation procedures, contributing to the prevention and early repair of breakdowns.

[0035] The data collection unit can collect sensor data such as engine status, tire pressure, and brake status. For example, the data collection unit can collect sensor data indicating engine status. For example, the data collection unit can collect data such as engine temperature and rotational speed. For example, the data collection unit can collect sensor data indicating tire pressure. For example, the data collection unit can collect data using a tire pressure sensor. For example, the data collection unit can collect sensor data indicating brake status. For example, the data collection unit can collect data such as brake pad wear and brake fluid pressure. By collecting important sensor data for the vehicle, the accuracy of fault prediction is improved. 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 sensor data indicating engine status into AI, and the AI ​​can analyze and collect the data.

[0036] The analysis unit can detect signs of failure using machine learning algorithms. For example, the analysis unit can detect signs of failure using deep learning. For example, the analysis unit can detect signs of failure using support vector machines. For example, the analysis unit can detect abnormal values ​​or patterns in sensor data. For example, the analysis unit can analyze sensor data in real time and detect signs of failure. This improves the accuracy of fault prediction by using machine learning algorithms. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input sensor data into AI, and the AI ​​can analyze the data to detect signs of failure.

[0037] The notification unit can notify the driver through a smartphone app. The notification unit can notify the driver using, for example, push notifications. The notification unit can notify the driver using, for example, an alert sound. The notification unit can notify the driver of signs of a malfunction through a smartphone app. The notification unit can notify the driver when signs of a malfunction are detected. This allows the driver to quickly become aware of signs of a malfunction by notifying them through a smartphone app. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input a notification of a malfunction into the AI, and the AI ​​can generate the notification content and send it to the driver.

[0038] The search unit can select the optimal repair shop based on the location information and ratings of the repair shops. For example, the search unit can select the optimal repair shop based on the location information of the repair shops. For example, the search unit can select the optimal repair shop based on the evaluation score of the repair shops. For example, the search unit can select the optimal repair shop based on the location information and ratings of the repair shops. For example, the search unit can select the optimal repair shop based on the location information and ratings of the repair shops. In this way, the optimal repair shop can be selected by selecting based on the location information and ratings of the repair shops. Some or all of the above processing in the search unit may be performed using AI, for example, or without using AI. For example, the search unit can input the location information and ratings of the repair shops into the AI, and the AI ​​can select the optimal repair shop.

[0039] The reservation department can automatically perform the reservation process for selected repair shops. The reservation department can, for example, automatically fill in the reservation form. The reservation department can, for example, send confirmation emails. The reservation department can, for example, automatically perform the reservation process for selected repair shops. The reservation department can, for example, automatically perform the reservation process for selected repair shops. This reduces the effort required of drivers by automating the reservation process. 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 the reservation procedure into AI, and the AI ​​can perform the reservation procedure automatically.

[0040] The data collection unit can analyze past sensor data and optimize data collection under specific conditions. For example, the data collection unit can analyze past sensor data and adjust the data collection frequency under specific weather conditions. For example, the data collection unit can optimize data collection under specific road conditions based on past sensor data. For example, the data collection unit can refer to past sensor data to collect data according to specific driving patterns. This allows for the optimization of data collection under specific conditions by analyzing past sensor data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past sensor data into AI, which can then analyze the data and optimize collection.

[0041] The data collection unit can filter sensor data based on vehicle usage and environmental conditions. For example, when the vehicle is traveling on a highway, the data collection unit can collect only important sensor data. For example, when the vehicle is traveling in an urban area, the data collection unit can collect detailed sensor data. For example, when the vehicle is stationary, the data collection unit can prioritize the collection of data related to the engine status. This allows for the priority collection of important data by filtering the data based on vehicle usage and environmental conditions. 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 and environmental conditions into the AI, which can then filter the data.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the vehicle's geographical location when collecting sensor data. For example, if the vehicle is traveling in a mountainous area, the data collection unit can prioritize the collection of data related to engine temperature. For example, if the vehicle is traveling in an urban area, the data collection unit can prioritize the collection of data related to brake status. For example, if the vehicle is traveling on a highway, the data collection unit can prioritize the collection of data related to tire pressure. This allows for the priority collection of highly relevant data by considering the vehicle's geographical location. 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 vehicle's geographical location information into the AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0043] The data collection unit can analyze the vehicle's driving history and collect relevant data when collecting sensor data. For example, the data collection unit can analyze the vehicle's driving history and prioritize collecting data from locations where malfunctions have occurred in the past. For example, the data collection unit can collect data related to specific driving patterns based on the vehicle's driving history. For example, the data collection unit can refer to the vehicle's driving history and collect data corresponding to specific driving conditions. This allows for the priority collection of relevant data by analyzing the vehicle's driving history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the vehicle's driving history into AI, which can then analyze the data and collect relevant data.

[0044] The analysis unit can optimize the detection algorithm by referring to past failure data when detecting signs of failure. For example, the analysis unit can optimize the algorithm for detecting specific failure patterns based on past failure data. For example, the analysis unit can improve the algorithm for detecting signs of failure in specific parts by referring to past failure data. For example, the analysis unit can analyze past failure data and optimize the detection algorithm for signs of failure according to specific operating conditions. In this way, the detection algorithm can be optimized by referring to past failure data. Some or all of the above processes 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 AI, and the AI ​​can analyze the data to optimize the algorithm.

[0045] The analysis unit can apply different analysis methods depending on the vehicle's usage and environmental conditions when detecting signs of failure. For example, when the vehicle is traveling on a highway, the analysis unit can detect signs of failure based on specific sensor data. For example, when the vehicle is traveling in an urban area, the analysis unit can detect signs of failure based on different sensor data. For example, when the vehicle is stationary, the analysis unit can detect signs of failure based on the engine's condition. This improves the accuracy of detecting signs of failure by applying different analysis methods depending on the vehicle's usage and environmental conditions. 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 and environmental conditions into the AI, which can then analyze the data and apply different analysis methods.

[0046] The analysis unit can improve the accuracy of fault detection by considering the vehicle's driving history when detecting fault precursors. For example, the analysis unit can analyze the vehicle's driving history and improve the accuracy of fault precursor detection based on specific driving patterns. For example, the analysis unit can improve the accuracy of detecting fault precursors for specific parts by referring to the vehicle's driving history. For example, the analysis unit can improve the accuracy of fault precursor detection according to specific driving conditions based on the vehicle's driving history. In this way, the accuracy of fault precursor detection is improved by considering the vehicle's driving history. 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 vehicle's driving history into AI, and the AI ​​can analyze the data to improve detection accuracy.

[0047] The analysis unit can improve its detection algorithm by referring to relevant vehicle documentation when detecting signs of failure. For example, the analysis unit can improve its algorithm for detecting specific failure patterns based on relevant vehicle documentation. For example, the analysis unit can improve its algorithm for detecting signs of failure in specific parts by referring to relevant vehicle documentation. For example, the analysis unit can analyze relevant vehicle documentation and improve its algorithm for detecting signs of failure according to specific driving conditions. In this way, the detection algorithm can be improved by referring to relevant vehicle documentation. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant vehicle documentation into AI, and the AI ​​can analyze the data to improve the algorithm.

[0048] The notification unit can adjust the level of detail of a notification based on the importance of the notification when it notifies of a potential failure. For example, the notification unit can provide a notification containing detailed information in the case of a highly important potential failure. For example, the notification unit can provide a concise notification in the case of a less important potential failure. The notification unit can adjust the level of detail of the notification according to its importance. This allows for the provision of appropriate information by adjusting the level of detail according to the importance of the notification. 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 the importance of the potential failure into the AI, and the AI ​​can adjust the level of detail of the notification.

[0049] The notification unit can optimize the timing of notifications regarding malfunction warnings according to the driver's driving conditions. For example, if the driver is driving on a highway, the notification unit will only notify of important malfunction warnings. If the driver is driving in an urban area, the notification unit can notify of detailed malfunction warnings. If the driver is stopped, the notification unit can notify of all malfunction warnings. By optimizing the timing of notifications according to the driver's driving conditions, notifications can be sent at the appropriate time. 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 the driver's driving conditions into the AI, which can then optimize the timing of notifications.

[0050] The notification unit can select a notification method by referring to the driver's past response history when notifying of a malfunction. For example, the notification unit can select the optimal notification method based on the driver's past response history. For example, the notification unit can adjust the level of detail of the notification by referring to the driver's past response history. For example, the notification unit can analyze the driver's past response history and select the optimal notification timing. In this way, the optimal notification method can be selected 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 using AI. For example, the notification unit can input the driver's past response history into AI, and the AI ​​can analyze the data and select a notification method.

[0051] The notification unit can select the optimal notification method when notifying of a malfunction, taking into account the driver's device information. For example, if the driver is using a smartphone, the notification unit can provide a notification method optimized for the smartphone. For example, if the driver is using a tablet, the notification unit can provide a notification method optimized for the tablet. For example, if the driver is using a smartwatch, the notification unit can provide a notification method optimized for the smartwatch. In this way, the optimal notification method can be provided by taking into account the driver's device 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 can input the driver's device information into the AI, and the AI ​​can analyze the data to select a notification method.

[0052] The search unit can select the most suitable repair shop by referring to past repair history when searching for a repair shop. For example, the search unit can select the most suitable repair shop based on evaluations of repair shops used in the past. For example, the search unit can select a repair shop that can handle a specific malfunction by referring to past repair history. For example, the search unit can analyze past repair history and select the most efficient repair shop. In this way, the optimal repair shop can be selected by referring to past repair history. Some or all of the above processes in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input past repair history into AI, and the AI ​​can analyze the data to select the most suitable repair shop.

[0053] The search unit can apply different search algorithms depending on the nature of the vehicle's malfunction when searching for a repair shop. For example, in the case of an engine malfunction, the search unit will prioritize searching for engine repair shops. For example, in the case of a tire malfunction, the search unit can prioritize searching for tire repair shops. For example, in the case of a brake malfunction, the search unit can prioritize searching for brake repair shops. In this way, by applying different search algorithms depending on the nature of the vehicle's malfunction, the optimal repair shop can be found. 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 the vehicle's malfunction details into AI, and the AI ​​can analyze the data and apply different search algorithms.

[0054] The search unit can select the most suitable repair shop by considering the geographical distribution of repair shops when searching for a repair shop. For example, the search unit can select the nearest repair shop based on the geographical distribution of repair shops. For example, the search unit can select a highly-rated repair shop by referring to the geographical distribution of repair shops. For example, the search unit can analyze the geographical distribution of repair shops and select the most efficient repair shop. In this way, the optimal repair shop can be selected by considering the geographical distribution of repair shops. 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 the geographical distribution of repair shops into AI, and the AI ​​can analyze the data and select the most suitable shop.

[0055] The search unit can improve the accuracy of its search by referring to the evaluation data of repair shops when searching for repair shops. For example, the search unit can select the most suitable repair shop based on the evaluation data of repair shops. For example, the search unit can select a repair shop that can handle a specific type of malfunction by referring to the evaluation data of repair shops. For example, the search unit can analyze the evaluation data of repair shops and select the most efficient repair shop. In this way, the accuracy of the search is improved by referring to the evaluation data of repair shops. Some or all of the above processes in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the evaluation data of repair shops into AI, and the AI ​​can analyze the data to improve the accuracy of the search.

[0056] The reservation department can select the optimal reservation method when a repair shop is booked by referring to past reservation history. For example, the reservation department can select the optimal reservation method based on reservation methods used in the past. For example, the reservation department can select the reservation method for a specific repair shop by referring to past reservation history. For example, the reservation department can analyze past reservation history and select the most efficient reservation method. In this way, the optimal reservation method can be selected 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 without AI. For example, the reservation department can input past reservation history into AI, and the AI ​​can analyze the data and select the optimal reservation method.

[0057] The reservation unit can optimize the timing of repair shop reservations based on vehicle usage. For example, the reservation unit can select the optimal reservation timing based on vehicle usage. For example, the reservation unit can select the reservation timing for a specific repair shop by referring to vehicle usage. For example, the reservation unit can analyze vehicle usage and select the most efficient reservation timing. This makes it possible to make appropriate reservations by optimizing the timing of reservations based on vehicle usage. Some or all of the above processes in the reservation unit may be performed using AI, for example, or not using AI. For example, the reservation unit can input vehicle usage into AI, and the AI ​​can analyze the data to optimize the timing of reservations.

[0058] The reservation department can select the optimal reservation method when a repair shop is booked, taking into account the driver's schedule information. For example, the reservation department can select the optimal reservation method based on the driver's schedule information. For example, the reservation department can select the reservation method for a specific repair shop by referring to the driver's schedule information. For example, the reservation department can analyze the driver's schedule information and select the most efficient reservation method. In this way, the optimal reservation method can be provided by taking the driver's schedule information into consideration. 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 the driver's schedule information into AI, and the AI ​​can analyze the data and select the optimal reservation method.

[0059] The reservation department can adjust the timing of a reservation by referring to the repair shop's congestion status when a reservation is made. For example, the reservation department can select the optimal reservation timing based on the repair shop's congestion status. For example, the reservation department can select the reservation timing for a specific repair shop by referring to the repair shop's congestion status. For example, the reservation department can analyze the repair shop's congestion status and select the most efficient reservation timing. In this way, the optimal reservation timing can be selected by referring to the repair shop's congestion status. 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 the repair shop's congestion status into AI, and the AI ​​can analyze the data and adjust the reservation timing.

[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 data collection unit not only collects vehicle sensor data in real time, but also collects the vehicle's driving history, and can analyze driving patterns based on this data. For example, the data collection unit can prioritize the collection of sensor data related to specific driving patterns based on past driving history. Furthermore, the data collection unit can adjust the data collection frequency for specific driving situations based on the driving history. For example, it can increase the data collection frequency for frequently occurring driving patterns and decrease it for rarely occurring driving patterns. In this way, the data collection unit can optimize data collection by taking driving history into consideration.

[0062] The analysis unit can not only detect signs of failure using machine learning algorithms, but can also improve the accuracy of failure detection based on the vehicle's driving history. For example, the analysis unit can prioritize the detection of failure signs associated with specific driving patterns based on past driving history. Furthermore, the analysis unit can adjust the detection sensitivity of failure signs in specific driving situations based on the driving history. For example, it can increase the detection sensitivity for frequently occurring driving patterns and decrease it for rarely occurring driving patterns. In this way, the analysis unit can optimize the accuracy of failure detection by taking driving history into consideration.

[0063] The notification unit not only informs the driver of potential malfunctions, but can also optimize the timing of notifications according to the driver's driving conditions. For example, if the driver is driving on a highway, the notification unit can only notify of important malfunction signs. Furthermore, if the driver is driving in an urban area, the notification unit can notify of detailed malfunction signs. For example, if the driver is stopped, it can notify of all malfunction signs. In this way, the notification unit can optimize the timing of notifications according to driving conditions, enabling notifications to be sent at the appropriate time.

[0064] The search unit can select the optimal repair shop not only based on the location and ratings of repair shops, but also based on the driver's driving history. For example, the search unit can select the optimal repair shop based on the ratings of repair shops used in the past. Furthermore, the search unit can select a repair shop capable of handling a specific malfunction based on past repair history. For example, it can analyze past repair history and select the most efficient repair shop. In this way, the search unit can select the optimal repair shop while taking driving history into consideration.

[0065] The reservation department can not only process reservations with selected repair shops, but also optimize the reservation process based on the driver's driving history. For example, the reservation department can select the optimal reservation method based on past reservation history. Furthermore, the reservation department can select the reservation method for a specific repair shop based on past reservation history. For example, it can analyze past reservation history and select the most efficient reservation method. In this way, the reservation department can optimize the reservation process by taking driving history into consideration.

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

[0067] Step 1: The data collection unit collects vehicle sensor data in real time. The data collection unit collects sensor data such as engine status, tire pressure, and brake status. The data collection unit can collect data such as engine temperature, rotational speed, tire pressure, brake pad wear, and brake fluid pressure. The data collection unit can collect data using pressure sensors and temperature sensors, for example. The data collection unit can collect sensor data in real time and transmit it to an AI system, for example. Step 2: The analysis unit analyzes the sensor data collected by the collection unit in real time to detect signs of failure. The analysis unit can detect signs of failure using, for example, machine learning algorithms. The analysis unit can detect signs of failure using, for example, machine learning algorithms such as deep learning or support vector machines. The analysis unit can detect, for example, abnormal values ​​or abnormal patterns in the sensor data. Step 3: The notification unit notifies the driver of the fault indicators detected by the analysis unit. The notification unit notifies the driver, for example, through a smartphone app. The notification unit can also notify the driver using, for example, push notifications or alert sounds. Step 4: The search unit searches for the nearest repair shop based on the information notified by the notification unit. The search unit selects the most suitable repair shop based, for example, on the location information and ratings of the repair shops. Step 5: The reservation department makes a reservation for the repair shop selected by the search department. The reservation department can, for example, automatically make a reservation for the selected repair shop. The reservation department can also automate procedures such as automatically filling out the reservation form and sending confirmation emails.

[0068] (Example of form 2) An AI system according to an embodiment of the present invention is a system that analyzes vehicle sensor data in real time, detects signs of malfunction, and notifies the driver. This AI system collects vehicle sensor data in real time, including data such as engine status, tire pressure, and brake status. Next, the AI ​​system analyzes the collected sensor data in real time and uses a machine learning algorithm to detect signs of malfunction. If signs of malfunction are detected, the AI ​​system notifies the driver. The notification is made through a smartphone app. Furthermore, if repairs are needed, the AI ​​system searches for the nearest repair shop, selects the most suitable repair shop based on the repair shop's location information and ratings, and automatically handles the reservation process. For example, the AI ​​system collects vehicle sensor data in real time. The sensor data includes engine status, tire pressure, brake status, etc. Next, the AI ​​system analyzes the collected sensor data in real time and uses a machine learning algorithm to detect signs of malfunction. If signs of malfunction are detected, the AI ​​system notifies the driver. The notification is made through a smartphone app. Furthermore, if repairs are needed, the AI ​​system searches for the nearest repair shop, selects the best one based on its location and ratings, and automatically handles the booking process. This system extends the lifespan of the vehicle, reduces maintenance costs, and improves driver safety. This enables the AI ​​system to collect, analyze, notify, search, and book appointments using vehicle sensor data in real time.

[0069] The AI ​​system according to this embodiment comprises a collection unit, an analysis unit, a notification unit, a search unit, and a reservation unit. The collection unit collects vehicle sensor data in real time. The collection unit collects sensor data such as engine status, tire pressure, and brake status. The collection unit can collect data such as engine temperature, rotational speed, tire pressure, brake pad wear, and brake fluid pressure. The collection unit can collect data using pressure sensors and temperature sensors, for example. The collection unit can collect sensor data in real time and transmit it to the AI ​​system, for example. The analysis unit analyzes the sensor data collected by the collection unit in real time and detects signs of failure. The analysis unit can detect signs of failure using machine learning algorithms, for example. The analysis unit can detect signs of failure using machine learning algorithms such as deep learning and support vector machines, for example. The analysis unit can detect abnormal values ​​or abnormal patterns in sensor data, for example. The analysis unit can analyze sensor data in real time and detect signs of failure. The notification unit notifies the driver of any signs of failure detected by the analysis unit. The notification unit notifies the driver, for example, through a smartphone app. The notification unit can notify the driver, for example, using push notifications or alert sounds. The notification unit can notify the driver of signs of failure through a smartphone app. The notification unit can notify the driver, for example, when signs of failure are detected. The search unit searches for the nearest repair shop based on the information notified by the notification unit. The search unit selects the most suitable repair shop, for example, based on the location information and ratings of the repair shops. The search unit can select the most suitable repair shop, for example, based on the location information and rating scores of the repair shops. The search unit can select the most suitable repair shop, for example, based on the location information and ratings of the repair shops. The search unit can select the most suitable repair shop, for example, based on the location information and ratings of the repair shops. The reservation unit makes a reservation for the repair shop selected by the search unit. The reservation unit automatically makes a reservation for the selected repair shop, for example.The reservation unit can, for example, automatically perform procedures such as automatically filling out reservation forms and sending confirmation emails. The reservation unit can, for example, automatically perform reservation procedures for selected repair shops. The reservation unit can, for example, automatically perform reservation procedures for selected repair shops. As a result, the AI ​​system according to the embodiment can collect, analyze, notify, search, and reserve vehicle sensor data in real time.

[0070] The data collection unit collects vehicle sensor data in real time. Specifically, it collects sensor data such as engine status, tire pressure, and brake status. For example, it can collect data such as engine temperature and rotation speed, tire pressure, brake pad wear, and brake fluid pressure. This data is collected using various sensors such as pressure sensors and temperature sensors. The data collection unit collects this sensor data in real time and transmits it to the AI ​​system. The collected data is constantly updated during vehicle operation to reflect the latest status. For example, it can detect abnormalities in real time, such as when the engine temperature rises abnormally or when the tire pressure drops. The data collection unit centrally manages this data and can cooperate with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the analysis unit and notification unit. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance. In addition, the data collection unit can optimize sensor operation according to the vehicle's operating status and environmental conditions. For example, safety can be ensured by more frequent monitoring of tire pressure and brake status during adverse weather conditions. This allows the collection unit to support the safe operation of the vehicle and contribute to the prevention and early detection of breakdowns.

[0071] The analysis unit analyzes sensor data collected by the data collection unit in real time to detect signs of failure. Specifically, it uses machine learning algorithms to detect signs of failure. For example, it can use machine learning algorithms such as deep learning and support vector machines to detect signs of failure. This allows it to detect abnormal values ​​and patterns in sensor data. The analysis unit analyzes the collected sensor data in real time to detect signs of failure. For example, it can detect abnormal data such as when the engine temperature is higher than normal or when the tire pressure drops sharply. Based on this abnormal data, the analysis unit can detect signs of failure early and take appropriate countermeasures. Furthermore, the analysis unit can also utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past failure data, it can assess the failure risk of specific parts or systems and plan preventive maintenance. In addition, the analysis unit can use anomaly detection algorithms to detect patterns that are different from normal or abnormal data and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system. Furthermore, the analysis department can achieve more accurate fault prediction by utilizing advanced AI-based analysis techniques. For example, by building an anomaly detection model using deep learning and capturing subtle changes in sensor data, it can detect signs of failure with high accuracy. As a result, the analysis department can improve vehicle safety and contribute to the prevention and early detection of failures.

[0072] The notification unit notifies the driver of any signs of malfunction detected by the analysis unit. Specifically, it notifies the driver via a smartphone app. For example, it can notify the driver using push notifications or alert sounds. The notification unit can quickly notify the driver when signs of malfunction are detected. For example, if the engine temperature rises abnormally or the tire pressure drops, it can immediately notify the driver so that appropriate measures can be taken. The notification unit can notify the driver of signs of malfunction via a smartphone app. For example, it can use push notifications to provide the driver with detailed information about the signs of malfunction and recommended measures. It can also use alert sounds to draw the driver's attention. The notification unit can quickly notify the driver when signs of malfunction are detected. This allows the driver to grasp signs of malfunction early and take appropriate measures. Furthermore, the notification unit can collect driver feedback and continuously improve the accuracy and effectiveness of the notification content. For example, it can review and improve the notification content based on the driver's response to the signs of malfunction and the results of the measures taken. In addition, the notification unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications but also through voice calls, SMS, and email. This allows the notification unit to quickly and reliably notify drivers of potential breakdowns and take appropriate measures. Furthermore, the notification unit can provide personalized notification functions using AI. For example, it can automatically adjust the optimal notification timing and content based on the driver's driving history and vehicle usage. This allows the notification unit to provide drivers with more effective notifications, contributing to the prevention and early detection of breakdowns.

[0073] The search unit searches for the nearest repair shop based on the information notified by the notification unit. Specifically, it selects the optimal repair shop based on the location information and ratings of the repair shops. For example, it can select the optimal repair shop based on the location information and rating score of the repair shops. When a malfunction is notified, the search unit can quickly find the best repair shop for the driver. For example, it can prioritize searching for the repair shop closest to the current location or a repair shop with a high rating. The search unit can select the optimal repair shop based on the location information and ratings of the repair shops. For example, it can evaluate the distance from the current location and ease of access based on the location information of the repair shops and select the optimal repair shop. It can also evaluate the quality of service and reliability based on the rating score of the repair shops and select the optimal repair shop. The search unit can select the optimal repair shop based on the location information and ratings of the repair shops. This allows drivers to quickly find the best repair shop when a malfunction is notified. Furthermore, the search unit can provide advanced search algorithms using AI. For example, it can automatically recommend the best repair shop based on the driver's past repair history and vehicle usage. This allows the search unit to provide drivers with a more effective repair shop search function, contributing to the prevention of breakdowns and early repairs. Furthermore, the search unit can respond to the latest situation based on repair shop information that is updated in real time. For example, it can check the business hours and reservation status of repair shops in real time and select the most suitable repair shop. In this way, the search unit can provide drivers with a quick and accurate repair shop search function, contributing to the prevention of breakdowns and early repairs.

[0074] The reservation department handles the reservation process for repair shops selected by the search department. Specifically, it automatically handles the reservation process for selected repair shops. For example, it can automatically fill in reservation forms and send confirmation emails. The reservation department can automatically handle the reservation process for selected repair shops. For example, it can quickly handle the reservation process by accessing the repair shop's reservation system and automatically entering the necessary information. It can also send reservation confirmation emails and notifications to the driver, allowing them to confirm the reservation details. The reservation department can automatically handle the reservation process for selected repair shops. This allows drivers to quickly make reservations for repair shops when they are notified of signs of a breakdown. Furthermore, the reservation department can provide advanced reservation management functions using AI. For example, it can automatically adjust the optimal reservation date and time based on the driver's schedule and the repair shop's reservation status. This allows the reservation department to provide drivers with more effective reservation management functions, contributing to breakdown prevention and early repair. In addition, the reservation department can respond to the latest situation based on repair shop information that is updated in real time. For example, it can check the repair shop's reservation status and business hours in real time and select the optimal reservation date and time. This allows the reservation department to provide drivers with quick and accurate reservation procedures, contributing to the prevention and early repair of breakdowns. Furthermore, the reservation department can monitor the progress of reservation procedures in real time and notify drivers as needed. For example, it can notify drivers if reservation confirmation or changes are necessary, enabling a quick response. This allows the reservation department to provide drivers with quick and reliable reservation procedures, contributing to the prevention and early repair of breakdowns.

[0075] The data collection unit can collect sensor data such as engine status, tire pressure, and brake status. For example, the data collection unit can collect sensor data indicating engine status. For example, the data collection unit can collect data such as engine temperature and rotational speed. For example, the data collection unit can collect sensor data indicating tire pressure. For example, the data collection unit can collect data using a tire pressure sensor. For example, the data collection unit can collect sensor data indicating brake status. For example, the data collection unit can collect data such as brake pad wear and brake fluid pressure. By collecting important sensor data for the vehicle, the accuracy of fault prediction is improved. 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 sensor data indicating engine status into AI, and the AI ​​can analyze and collect the data.

[0076] The analysis unit can detect signs of failure using machine learning algorithms. For example, the analysis unit can detect signs of failure using deep learning. For example, the analysis unit can detect signs of failure using support vector machines. For example, the analysis unit can detect abnormal values ​​or patterns in sensor data. For example, the analysis unit can analyze sensor data in real time and detect signs of failure. This improves the accuracy of fault prediction by using machine learning algorithms. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input sensor data into AI, and the AI ​​can analyze the data to detect signs of failure.

[0077] The notification unit can notify the driver through a smartphone app. The notification unit can notify the driver using, for example, push notifications. The notification unit can notify the driver using, for example, an alert sound. The notification unit can notify the driver of signs of a malfunction through a smartphone app. The notification unit can notify the driver when signs of a malfunction are detected. This allows the driver to quickly become aware of signs of a malfunction by notifying them through a smartphone app. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input a notification of a malfunction into the AI, and the AI ​​can generate the notification content and send it to the driver.

[0078] The search unit can select the optimal repair shop based on the location information and ratings of the repair shops. For example, the search unit can select the optimal repair shop based on the location information of the repair shops. For example, the search unit can select the optimal repair shop based on the evaluation score of the repair shops. For example, the search unit can select the optimal repair shop based on the location information and ratings of the repair shops. For example, the search unit can select the optimal repair shop based on the location information and ratings of the repair shops. In this way, the optimal repair shop can be selected by selecting based on the location information and ratings of the repair shops. Some or all of the above processing in the search unit may be performed using AI, for example, or without using AI. For example, the search unit can input the location information and ratings of the repair shops into the AI, and the AI ​​can select the optimal repair shop.

[0079] The reservation department can automatically perform the reservation process for selected repair shops. The reservation department can, for example, automatically fill in the reservation form. The reservation department can, for example, send confirmation emails. The reservation department can, for example, automatically perform the reservation process for selected repair shops. The reservation department can, for example, automatically perform the reservation process for selected repair shops. This reduces the effort required of drivers by automating the reservation process. 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 the reservation procedure into AI, and the AI ​​can perform the reservation procedure automatically.

[0080] The data collection unit can estimate the driver's emotions and adjust the frequency of sensor data collection based on the estimated emotions. For example, if the driver is stressed, the data collection unit can increase the frequency of sensor data collection to monitor the vehicle's condition in more detail. For example, if the driver is relaxed, the data collection unit can decrease the frequency of sensor data collection to reduce the system load. For example, if the driver is in a hurry, the data collection unit can prioritize the collection of only important sensor data. This optimizes the system load by adjusting the frequency of sensor data collection 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 data collection unit may be performed using AI or not. For example, the data collection unit can input the driver's emotion data into an AI, which can analyze the emotions and adjust the collection frequency.

[0081] The data collection unit can analyze past sensor data and optimize data collection under specific conditions. For example, the data collection unit can analyze past sensor data and adjust the data collection frequency under specific weather conditions. For example, the data collection unit can optimize data collection under specific road conditions based on past sensor data. For example, the data collection unit can refer to past sensor data to collect data according to specific driving patterns. This allows for the optimization of data collection under specific conditions by analyzing past sensor data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past sensor data into AI, which can then analyze the data and optimize collection.

[0082] The data collection unit can filter sensor data based on vehicle usage and environmental conditions. For example, when the vehicle is traveling on a highway, the data collection unit can collect only important sensor data. For example, when the vehicle is traveling in an urban area, the data collection unit can collect detailed sensor data. For example, when the vehicle is stationary, the data collection unit can prioritize the collection of data related to the engine status. This allows for the priority collection of important data by filtering the data based on vehicle usage and environmental conditions. 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 and environmental conditions into the AI, which can then filter the data.

[0083] The data collection unit can estimate the driver's emotions and determine the priority of sensor data to collect based on the estimated emotions. For example, if the driver is stressed, the data collection unit may prioritize collecting data on engine status. For example, if the driver is relaxed, the data collection unit may prioritize collecting data on tire pressure. For example, if the driver is in a hurry, the data collection unit may prioritize collecting data on brake status. This allows for the priority collection of important data by prioritizing sensor data 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 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 an AI, which can analyze the emotions and determine the priorities.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the vehicle's geographical location when collecting sensor data. For example, if the vehicle is traveling in a mountainous area, the data collection unit can prioritize the collection of data related to engine temperature. For example, if the vehicle is traveling in an urban area, the data collection unit can prioritize the collection of data related to brake status. For example, if the vehicle is traveling on a highway, the data collection unit can prioritize the collection of data related to tire pressure. This allows for the priority collection of highly relevant data by considering the vehicle's geographical location. 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 vehicle's geographical location information into the AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0085] The data collection unit can analyze the vehicle's driving history and collect relevant data when collecting sensor data. For example, the data collection unit can analyze the vehicle's driving history and prioritize collecting data from locations where malfunctions have occurred in the past. For example, the data collection unit can collect data related to specific driving patterns based on the vehicle's driving history. For example, the data collection unit can refer to the vehicle's driving history and collect data corresponding to specific driving conditions. This allows for the priority collection of relevant data by analyzing the vehicle's driving history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the vehicle's driving history into AI, which can then analyze the data and collect relevant data.

[0086] The analysis unit can estimate the driver's emotions and adjust the fault detection method based on the estimated emotions. For example, if the driver is stressed, the analysis unit can increase the sensitivity of fault detection. For example, if the driver is relaxed, the analysis unit can decrease the sensitivity of fault detection. For example, if the driver is in a hurry, the analysis unit can prioritize the detection of only important faults. This optimizes detection accuracy by adjusting the fault detection method 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 or not using AI. For example, the analysis unit can input driver emotion data into an AI, which can analyze the emotions and adjust the detection method.

[0087] The analysis unit can optimize the detection algorithm by referring to past failure data when detecting signs of failure. For example, the analysis unit can optimize the algorithm for detecting specific failure patterns based on past failure data. For example, the analysis unit can improve the algorithm for detecting signs of failure in specific parts by referring to past failure data. For example, the analysis unit can analyze past failure data and optimize the detection algorithm for signs of failure according to specific operating conditions. In this way, the detection algorithm can be optimized by referring to past failure data. Some or all of the above processes 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 AI, and the AI ​​can analyze the data to optimize the algorithm.

[0088] The analysis unit can apply different analysis methods depending on the vehicle's usage and environmental conditions when detecting signs of failure. For example, when the vehicle is traveling on a highway, the analysis unit can detect signs of failure based on specific sensor data. For example, when the vehicle is traveling in an urban area, the analysis unit can detect signs of failure based on different sensor data. For example, when the vehicle is stationary, the analysis unit can detect signs of failure based on the engine's condition. This improves the accuracy of detecting signs of failure by applying different analysis methods depending on the vehicle's usage and environmental conditions. 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 and environmental conditions into the AI, which can then analyze the data and apply different analysis methods.

[0089] The analysis unit can estimate the driver's emotions and adjust the display method of the fault prediction detection results based on the estimated driver's emotions. For example, if the driver is stressed, the analysis unit can provide a simple and highly visible display method. For example, if the driver is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the driver is in a hurry, the analysis unit can provide a display method that gets straight to the point. This allows for highly visible displays by adjusting the display method 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 an AI, which can analyze the emotions and adjust the display method.

[0090] The analysis unit can improve the accuracy of fault detection by considering the vehicle's driving history when detecting fault precursors. For example, the analysis unit can analyze the vehicle's driving history and improve the accuracy of fault precursor detection based on specific driving patterns. For example, the analysis unit can improve the accuracy of detecting fault precursors for specific parts by referring to the vehicle's driving history. For example, the analysis unit can improve the accuracy of fault precursor detection according to specific driving conditions based on the vehicle's driving history. In this way, the accuracy of fault precursor detection is improved by considering the vehicle's driving history. 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 vehicle's driving history into AI, and the AI ​​can analyze the data to improve detection accuracy.

[0091] The analysis unit can improve its detection algorithm by referring to relevant vehicle documentation when detecting signs of failure. For example, the analysis unit can improve its algorithm for detecting specific failure patterns based on relevant vehicle documentation. For example, the analysis unit can improve its algorithm for detecting signs of failure in specific parts by referring to relevant vehicle documentation. For example, the analysis unit can analyze relevant vehicle documentation and improve its algorithm for detecting signs of failure according to specific driving conditions. In this way, the detection algorithm can be improved by referring to relevant vehicle documentation. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant vehicle documentation into AI, and the AI ​​can analyze the data to improve the algorithm.

[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 stressed, the notification unit can provide a simple and easily visible notification. For example, if the driver is relaxed, the notification unit can provide a notification that includes detailed information. For example, if the driver is in a hurry, the notification unit can provide a notification that gets straight to the point. This allows for appropriate notifications by adjusting the way notifications are presented 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 an AI, which can analyze the emotions and adjust the way notifications are presented.

[0093] The notification unit can adjust the level of detail of a notification based on the importance of the notification when it notifies of a potential failure. For example, the notification unit can provide a notification containing detailed information in the case of a highly important potential failure. For example, the notification unit can provide a concise notification in the case of a less important potential failure. The notification unit can adjust the level of detail of the notification according to its importance. This allows for the provision of appropriate information by adjusting the level of detail according to the importance of the notification. 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 the importance of the potential failure into the AI, and the AI ​​can adjust the level of detail of the notification.

[0094] The notification unit can optimize the timing of notifications regarding malfunction warnings according to the driver's driving conditions. For example, if the driver is driving on a highway, the notification unit will only notify of important malfunction warnings. If the driver is driving in an urban area, the notification unit can notify of detailed malfunction warnings. If the driver is stopped, the notification unit can notify of all malfunction warnings. By optimizing the timing of notifications according to the driver's driving conditions, notifications can be sent at the appropriate time. 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 the driver's driving conditions into the AI, which can then optimize the timing of notifications.

[0095] The notification unit can estimate the driver's emotions and determine the priority of notifications based on the estimated emotions. For example, if the driver is stressed, the notification unit will prioritize only important notifications. For example, if the driver is relaxed, the notification unit can send all notifications. For example, if the driver is in a hurry, the notification unit can prioritize only important notifications. This allows important notifications to be prioritized by determining the priority of notifications 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 an AI, which can analyze the emotions and determine the priority of notifications.

[0096] The notification unit can select a notification method by referring to the driver's past response history when notifying of a malfunction. For example, the notification unit can select the optimal notification method based on the driver's past response history. For example, the notification unit can adjust the level of detail of the notification by referring to the driver's past response history. For example, the notification unit can analyze the driver's past response history and select the optimal notification timing. In this way, the optimal notification method can be selected 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 using AI. For example, the notification unit can input the driver's past response history into AI, and the AI ​​can analyze the data and select a notification method.

[0097] The notification unit can select the optimal notification method when notifying of a malfunction, taking into account the driver's device information. For example, if the driver is using a smartphone, the notification unit can provide a notification method optimized for the smartphone. For example, if the driver is using a tablet, the notification unit can provide a notification method optimized for the tablet. For example, if the driver is using a smartwatch, the notification unit can provide a notification method optimized for the smartwatch. In this way, the optimal notification method can be provided by taking into account the driver's device 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 can input the driver's device information into the AI, and the AI ​​can analyze the data to select a notification method.

[0098] The search unit can estimate the driver's emotions and adjust the search criteria for repair shops based on the estimated emotions. For example, if the driver is stressed, the search unit may prioritize finding the nearest repair shop. If the driver is relaxed, the search unit may prioritize finding a highly-rated repair shop. If the driver is in a hurry, the search unit may prioritize finding a repair shop that can respond quickly. In this way, the optimal repair shop can be found by adjusting the search criteria 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 the driver's emotion data into an AI, which can analyze the emotions and adjust the search criteria.

[0099] The search unit can select the most suitable repair shop by referring to past repair history when searching for a repair shop. For example, the search unit can select the most suitable repair shop based on evaluations of repair shops used in the past. For example, the search unit can select a repair shop that can handle a specific malfunction by referring to past repair history. For example, the search unit can analyze past repair history and select the most efficient repair shop. In this way, the optimal repair shop can be selected by referring to past repair history. Some or all of the above processes in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input past repair history into AI, and the AI ​​can analyze the data to select the most suitable repair shop.

[0100] The search unit can apply different search algorithms depending on the nature of the vehicle's malfunction when searching for a repair shop. For example, in the case of an engine malfunction, the search unit will prioritize searching for engine repair shops. For example, in the case of a tire malfunction, the search unit can prioritize searching for tire repair shops. For example, in the case of a brake malfunction, the search unit can prioritize searching for brake repair shops. In this way, by applying different search algorithms depending on the nature of the vehicle's malfunction, the optimal repair shop can be found. 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 the vehicle's malfunction details into AI, and the AI ​​can analyze the data and apply different search algorithms.

[0101] The search unit can estimate the driver's emotions and adjust the display method of repair shop search results based on the estimated emotions of the driver. For example, if the driver is stressed, the search unit can provide a simple and highly visible display method. For example, if the driver is relaxed, the search unit can provide a display method that includes detailed information. For example, if the driver is in a hurry, the search unit can provide a display method that gets straight to the point. This allows for highly visible displays by adjusting the display method 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 AI, and the AI ​​can analyze the emotions and adjust the display method.

[0102] The search unit can select the most suitable repair shop by considering the geographical distribution of repair shops when searching for a repair shop. For example, the search unit can select the nearest repair shop based on the geographical distribution of repair shops. For example, the search unit can select a highly-rated repair shop by referring to the geographical distribution of repair shops. For example, the search unit can analyze the geographical distribution of repair shops and select the most efficient repair shop. In this way, the optimal repair shop can be selected by considering the geographical distribution of repair shops. 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 the geographical distribution of repair shops into AI, and the AI ​​can analyze the data and select the most suitable shop.

[0103] The search unit can improve the accuracy of its search by referring to the evaluation data of repair shops when searching for repair shops. For example, the search unit can select the most suitable repair shop based on the evaluation data of repair shops. For example, the search unit can select a repair shop that can handle a specific type of malfunction by referring to the evaluation data of repair shops. For example, the search unit can analyze the evaluation data of repair shops and select the most efficient repair shop. In this way, the accuracy of the search is improved by referring to the evaluation data of repair shops. Some or all of the above processes in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the evaluation data of repair shops into AI, and the AI ​​can analyze the data to improve the accuracy of the search.

[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 can provide a simple and quick booking procedure. For example, if the driver is relaxed, the booking unit can provide a booking procedure that includes detailed information. For example, if the driver is in a hurry, the booking unit can provide a booking procedure that gets straight to the point. This allows for appropriate booking by adjusting the booking procedure 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 AI, and the AI ​​can analyze the emotions and adjust the booking procedure.

[0105] The reservation department can select the optimal reservation method when a repair shop is booked by referring to past reservation history. For example, the reservation department can select the optimal reservation method based on reservation methods used in the past. For example, the reservation department can select the reservation method for a specific repair shop by referring to past reservation history. For example, the reservation department can analyze past reservation history and select the most efficient reservation method. In this way, the optimal reservation method can be selected 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 without AI. For example, the reservation department can input past reservation history into AI, and the AI ​​can analyze the data and select the optimal reservation method.

[0106] The reservation unit can optimize the timing of repair shop reservations based on vehicle usage. For example, the reservation unit can select the optimal reservation timing based on vehicle usage. For example, the reservation unit can select the reservation timing for a specific repair shop by referring to vehicle usage. For example, the reservation unit can analyze vehicle usage and select the most efficient reservation timing. This makes it possible to make appropriate reservations by optimizing the timing of reservations based on vehicle usage. Some or all of the above processes in the reservation unit may be performed using AI, for example, or not using AI. For example, the reservation unit can input vehicle usage into AI, and the AI ​​can analyze the data to optimize the timing of reservations.

[0107] The booking unit can estimate the driver's emotions and determine the priority of booking procedures based on the estimated emotions. For example, if the driver is stressed, the booking unit will prioritize important booking procedures. For example, if the driver is relaxed, the booking unit can perform all booking procedures. For example, if the driver is in a hurry, the booking unit can prioritize important booking procedures. In this way, by determining the priority of booking procedures according to the driver's emotions, important booking procedures can be prioritized. 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 an AI, and the AI ​​can analyze the emotions and determine the priority of booking procedures.

[0108] The reservation department can select the optimal reservation method when a repair shop is booked, taking into account the driver's schedule information. For example, the reservation department can select the optimal reservation method based on the driver's schedule information. For example, the reservation department can select the reservation method for a specific repair shop by referring to the driver's schedule information. For example, the reservation department can analyze the driver's schedule information and select the most efficient reservation method. In this way, the optimal reservation method can be provided by taking the driver's schedule information into consideration. 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 the driver's schedule information into AI, and the AI ​​can analyze the data and select the optimal reservation method.

[0109] The reservation department can adjust the timing of a reservation by referring to the repair shop's congestion status when a reservation is made. For example, the reservation department can select the optimal reservation timing based on the repair shop's congestion status. For example, the reservation department can select the reservation timing for a specific repair shop by referring to the repair shop's congestion status. For example, the reservation department can analyze the repair shop's congestion status and select the most efficient reservation timing. In this way, the optimal reservation timing can be selected by referring to the repair shop's congestion status. 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 the repair shop's congestion status into AI, and the AI ​​can analyze the data and adjust the reservation timing.

[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 data collection unit not only collects vehicle sensor data in real time, but also collects the vehicle's driving history, and can analyze driving patterns based on this data. For example, the data collection unit can prioritize the collection of sensor data related to specific driving patterns based on past driving history. Furthermore, the data collection unit can adjust the data collection frequency for specific driving situations based on the driving history. For example, it can increase the data collection frequency for frequently occurring driving patterns and decrease it for rarely occurring driving patterns. In this way, the data collection unit can optimize data collection by taking driving history into consideration.

[0112] The analysis unit can not only detect signs of failure using machine learning algorithms, but can also improve the accuracy of failure detection based on the vehicle's driving history. For example, the analysis unit can prioritize the detection of failure signs associated with specific driving patterns based on past driving history. Furthermore, the analysis unit can adjust the detection sensitivity of failure signs in specific driving situations based on the driving history. For example, it can increase the detection sensitivity for frequently occurring driving patterns and decrease it for rarely occurring driving patterns. In this way, the analysis unit can optimize the accuracy of failure detection by taking driving history into consideration.

[0113] The notification unit not only informs the driver of potential malfunctions, but can also optimize the timing of notifications according to the driver's driving conditions. For example, if the driver is driving on a highway, the notification unit can only notify of important malfunction signs. Furthermore, if the driver is driving in an urban area, the notification unit can notify of detailed malfunction signs. For example, if the driver is stopped, it can notify of all malfunction signs. In this way, the notification unit can optimize the timing of notifications according to driving conditions, enabling notifications to be sent at the appropriate time.

[0114] The search unit can select the optimal repair shop not only based on the location and ratings of repair shops, but also based on the driver's driving history. For example, the search unit can select the optimal repair shop based on the ratings of repair shops used in the past. Furthermore, the search unit can select a repair shop capable of handling a specific malfunction based on past repair history. For example, it can analyze past repair history and select the most efficient repair shop. In this way, the search unit can select the optimal repair shop while taking driving history into consideration.

[0115] The reservation department can not only process reservations with selected repair shops, but also optimize the reservation process based on the driver's driving history. For example, the reservation department can select the optimal reservation method based on past reservation history. Furthermore, the reservation department can select the reservation method for a specific repair shop based on past reservation history. For example, it can analyze past reservation history and select the most efficient reservation method. In this way, the reservation department can optimize the reservation process by taking driving history into consideration.

[0116] The data collection unit can estimate the driver's emotions and adjust the frequency of sensor data collection based on the estimated emotions. For example, if the driver is stressed, the frequency of sensor data collection can be increased to monitor the vehicle's condition in more detail. Furthermore, if the driver is relaxed, the frequency of sensor data collection can be decreased to reduce the system load. For example, if the driver is in a hurry, only important sensor data can be collected preferentially. In this way, the system load can be optimized by adjusting the frequency of sensor data collection according to the driver's emotions.

[0117] The analysis unit can estimate the driver's emotions and adjust the fault warning detection method based on the estimated emotions. For example, if the driver is stressed, the sensitivity of fault warning detection can be increased. Conversely, if the driver is relaxed, the sensitivity of fault warning detection can be decreased. For example, if the driver is in a hurry, only important fault warnings can be detected preferentially. In this way, the detection accuracy can be optimized by adjusting the fault warning detection method according to the driver's emotions.

[0118] The notification unit can estimate the driver's emotions and adjust the way notifications are presented based on those emotions. For example, if the driver is stressed, it can provide a simple and highly visible notification. Furthermore, if the driver is relaxed, it can provide a notification that includes more detailed information. For example, if the driver is in a hurry, it can provide a notification that gets straight to the point. By adjusting the way notifications are presented according to the driver's emotions, appropriate notifications can be provided.

[0119] The search unit can estimate the driver's emotions and adjust the search criteria for repair shops based on those emotions. For example, if the driver is stressed, the system can prioritize finding the nearest repair shop. Furthermore, if the driver is relaxed, it can prioritize finding highly-rated repair shops. For example, if the driver is in a hurry, it can prioritize finding repair shops that can respond quickly. In this way, by adjusting the search criteria for repair shops according to the driver's emotions, the system can find the most suitable repair shop.

[0120] The reservation system can estimate the driver's emotions and adjust the reservation process based on those estimates. For example, if the driver is stressed, it can offer a simple and quick reservation process. If the driver is relaxed, it can offer a reservation process with more detailed information. If the driver is in a hurry, it can offer a concise reservation process. By adjusting the reservation process according to the driver's emotions, it enables a more appropriate reservation process.

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

[0122] Step 1: The data collection unit collects vehicle sensor data in real time. The data collection unit collects sensor data such as engine status, tire pressure, and brake status. The data collection unit can collect data such as engine temperature, rotational speed, tire pressure, brake pad wear, and brake fluid pressure. The data collection unit can collect data using pressure sensors and temperature sensors, for example. The data collection unit can collect sensor data in real time and transmit it to an AI system, for example. Step 2: The analysis unit analyzes the sensor data collected by the collection unit in real time to detect signs of failure. The analysis unit can detect signs of failure using, for example, machine learning algorithms. The analysis unit can detect signs of failure using, for example, machine learning algorithms such as deep learning or support vector machines. The analysis unit can detect, for example, abnormal values ​​or abnormal patterns in the sensor data. Step 3: The notification unit notifies the driver of the fault indicators detected by the analysis unit. The notification unit notifies the driver, for example, through a smartphone app. The notification unit can also notify the driver using, for example, push notifications or alert sounds. Step 4: The search unit searches for the nearest repair shop based on the information notified by the notification unit. The search unit selects the most suitable repair shop based, for example, on the location information and ratings of the repair shops. Step 5: The reservation department makes a reservation for the repair shop selected by the search department. The reservation department can, for example, automatically make a reservation for the selected repair shop. The reservation department can also automate procedures such as automatically filling out the reservation form and sending confirmation emails.

[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 collection unit, analysis unit, notification unit, search unit, and reservation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects vehicle sensor data in real time using the pressure sensor and temperature sensor of the smart device 14 and transmits it to the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and detects signs of failure using a machine learning algorithm. The notification unit is implemented, for example, by the control unit 46A of the smart device 14 and notifies the driver through a smartphone app. The search unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and selects the optimal repair shop based on the location information and evaluation of the repair shop. The reservation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and automatically performs the reservation procedure for the selected repair shop. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed 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 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 collection unit collects vehicle sensor data in real time using the pressure and temperature sensors of the smart glasses 214 and transmits it to the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and detects signs of failure using a machine learning algorithm. The notification unit is implemented, for example, by the control unit 46A of the smart glasses 214 and notifies the driver through a smartphone app. The search unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and selects the optimal repair shop based on the location information and evaluation of the repair shop. The reservation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and automatically performs the reservation procedure for the selected repair shop. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[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 collection unit, analysis unit, notification unit, search unit, and reservation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects vehicle sensor data in real time using the pressure sensor and temperature sensor of the headset terminal 314 and transmits it to the data processing unit 12. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and detects signs of failure using a machine learning algorithm. The notification unit is implemented by, for example, the control unit 46A of the headset terminal 314 and notifies the driver through a smartphone app. The search unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and selects the optimal repair shop based on the location information and evaluation of the repair shop. The reservation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and automatically performs the reservation procedure for the selected repair shop. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed 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 collection unit, analysis unit, notification unit, search unit, and reservation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects vehicle sensor data in real time using the pressure and temperature sensors of the robot 414 and transmits it to the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and detects signs of failure using a machine learning algorithm. The notification unit is implemented, for example, by the control unit 46A of the robot 414 and notifies the driver via a smartphone app. The search unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and selects the optimal repair shop based on the location information and evaluation of the repair shop. The reservation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and automatically performs the reservation procedure for the selected repair shop. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed 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 vehicle sensor data in real time, An analysis unit analyzes the sensor data collected by the aforementioned collection unit in real time and detects signs of failure, A notification unit that notifies the driver of the fault indicators 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 performs reservation procedures for repair shops selected by the search unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects sensor data such as engine status, tire pressure, and brake status. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Using machine learning algorithms to detect signs of failure. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, Notify the driver via a smartphone app. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned search unit, We select the optimal repair shop based on its location and ratings. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reservation section is, The system automatically makes reservations with the selected repair shops. 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 sensor 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 Analyze past sensor data to optimize data collection under specific conditions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting sensor data, the data is filtered based on vehicle usage and environmental conditions. 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 sensor data to collect based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting sensor data, the system prioritizes the collection of highly relevant data, taking into account the vehicle's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting sensor data, the vehicle's driving history 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 is The system estimates the driver's emotions and adjusts the fault prediction method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is When detecting signs of failure, the detection 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 is When detecting signs of a malfunction, different analytical methods are applied depending on the vehicle's usage and environmental conditions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is The system estimates the driver's emotions and adjusts how the fault warning results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is When detecting signs of a malfunction, the accuracy of the detection is improved by considering the vehicle's driving history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is When detecting signs of a malfunction, we improve the detection algorithm by referring to relevant vehicle documentation. 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 fault warning is issued, the level of detail in the notification is adjusted based on the importance of the notification. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, When a malfunction is detected, the timing of the notification is optimized according to the driver's driving conditions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, The system estimates the driver's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, When a malfunction is detected, the system selects the notification method by referring to the driver's past response history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, When a malfunction is predicted, the driver's device information is taken into consideration to select the most appropriate notification method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned search unit, The system estimates the driver's emotions and adjusts the search criteria for repair shops 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 for a repair shop, refer to past repair history to select the most suitable repair shop. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned search unit, When searching for a repair shop, different search algorithms are applied depending on the nature of the vehicle's malfunction. 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 adjusts how repair shop search results are displayed 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 for a repair shop, the geographical distribution of repair shops is taken into consideration to select the most suitable shop. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned search unit, When searching for a repair shop, refer to the shop's rating data to improve search accuracy. 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 booking a repair shop appointment, refer to past booking history to select the most suitable booking method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned reservation section is, When booking a repair shop appointment, the timing of the appointment will be optimized based on the vehicle's usage. 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 booking procedures based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned reservation section is, When booking a repair shop appointment, the optimal booking method is selected considering the driver's schedule information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned reservation section is, When booking an appointment at a repair shop, check the shop's current congestion status to adjust the timing of your appointment. 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 vehicle sensor data in real time, An analysis unit analyzes the sensor data collected by the aforementioned collection unit in real time and detects signs of failure, A notification unit that notifies the driver of the fault indicators 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 performs reservation procedures for repair shops selected by the search unit. A system characterized by the following features.

2. The aforementioned collection unit is It collects sensor data such as engine status, tire pressure, and brake status. The system according to feature 1.

3. The aforementioned analysis unit is Using machine learning algorithms to detect signs of failure. The system according to feature 1.

4. The aforementioned notification unit, Notify the driver via a smartphone app. The system according to feature 1.

5. The aforementioned search unit, We select the optimal repair shop based on its location and ratings. The system according to feature 1.

6. The aforementioned reservation section is, The system automatically makes reservations with the selected repair shops. The system according to feature 1.

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

8. The aforementioned collection unit is Analyze past sensor data to optimize data collection under specific conditions. The system according to feature 1.

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