system

Abstract

The system according to the embodiment aims to automate vehicle fault diagnosis and maintenance prediction, thereby realizing efficient management. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a monitoring unit, a presentation unit, a prediction unit, and a reservation unit. The collection unit collects sensor data from the vehicle. The analysis unit analyzes the data collected by the collection unit and identifies the location of the fault. The provision unit provides a repair procedure based on the location of the fault identified by the analysis unit. The monitoring unit monitors real-time data from the vehicle. The presentation unit detects abnormalities based on the data monitored by the monitoring unit and presents countermeasures. The unit collects vehicle usage and maintenance history. The prediction unit predicts the next maintenance date based on the data collected by the collection unit. The reservation unit automatically makes a reservation based on the maintenance date predicted by the prediction unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, vehicle fault diagnosis and maintenance predictions were often performed manually, making efficient management difficult.

[0005] The system according to the embodiment aims to automate vehicle fault diagnosis and maintenance prediction, thereby realizing efficient management. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a monitoring unit, a presentation unit, a prediction unit, and a reservation unit. The collection unit collects sensor data from the vehicle. The analysis unit analyzes the data collected by the collection unit and identifies the fault location. The provision unit provides a repair procedure based on the fault location identified by the analysis unit. The monitoring unit monitors real-time data from the vehicle. The presentation unit detects anomalies based on the data monitored by the monitoring unit and presents countermeasures. The provision unit collects vehicle usage status and maintenance history. The prediction unit predicts the next maintenance date based on the data collected by the collection unit. The reservation unit automatically makes a reservation based on the maintenance date predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment automates vehicle fault diagnosis and maintenance prediction, thereby realizing efficient management. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An automobile repair system according to an embodiment of the present invention collects vehicle sensor data, diagnoses faults, provides repair guidance, performs real-time troubleshooting, automatically schedules and performs predictive maintenance, visualizes the repair process, and optimally selects used parts. This system utilizes AI to improve efficiency and accuracy in the automobile repair industry. For example, the system analyzes vehicle sensor data, identifies faults, and provides repair procedures. The system also monitors real-time vehicle data and immediately proposes countermeasures if an abnormality is detected. Furthermore, the system predicts the next maintenance date based on the vehicle's usage status and past maintenance history, and automatically schedules the next maintenance appointment. To visualize the repair process, the system monitors the repair progress in real time and provides a visual representation of the repair progress to the user. Finally, the system evaluates the condition of used parts and selects reusable parts. This reduces costs and protects the environment. This enables the automobile repair system to diagnose vehicle faults, provide repair guidance, perform real-time troubleshooting, automatically schedules and performs predictive maintenance.

[0029] An automobile repair system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a monitoring unit, a presentation unit, a prediction unit, and a reservation unit. The collection unit collects sensor data from the vehicle. The collection unit can collect sensor data such as temperature data, speed data, and vibration data. The collection unit acquires data from various sensors in the vehicle and transmits the data to the analysis unit. The analysis unit analyzes the data collected by the collection unit and identifies the fault location. The analysis unit, for example, sets an abnormal value threshold and identifies the fault location when the threshold is exceeded. The analysis unit can also identify the fault location using a diagnostic algorithm. The provision unit provides a repair procedure based on the fault location identified by the analysis unit. The provision unit provides, for example, the steps of the repair procedure and the tools and parts to be used. The provision unit displays the repair procedure to a user and supports the repair work. The monitoring unit monitors real-time data from the vehicle. The monitoring unit, for example, sets the data update frequency and the type of data to be collected and monitors the data in real time. If the monitoring unit detects an abnormality, it transmits data to the presentation unit. The presentation unit detects an abnormality based on the data monitored by the monitoring unit and presents a countermeasure. The presentation unit presents countermeasures such as repair procedures and emergency response procedures. The presentation unit displays details of the abnormality and the countermeasure to the user. The prediction unit collects vehicle usage status and maintenance history. The prediction unit collects usage status data such as mileage and engine operating time. The prediction unit predicts the next maintenance date based on the collected data. The prediction unit predicts the maintenance date using a prediction algorithm based on usage status and past maintenance data. The reservation unit automatically makes a reservation based on the maintenance date predicted by the prediction unit. The reservation unit cooperates with, for example, an online reservation system and automatically makes a maintenance reservation. As a result, the automobile repair system according to the embodiment is capable of vehicle fault diagnosis, provision of repair guides, real-time troubleshooting, automatic reservations, and predictive maintenance.

[0030] The system is equipped with a monitoring unit that monitors the progress of repairs. The monitoring unit monitors the progress of repairs in real time. For example, the monitoring unit monitors the completion status of repair steps and the status of parts used. The monitoring unit updates data and records the progress status each time a step of the repair work is completed. The monitoring unit has a function to display the progress of repairs to the user. For example, the monitoring unit displays the progress of repairs as a graph or progress bar, allowing the user to grasp the progress of repairs at a glance. This allows the progress of repairs to be monitored in real time.

[0031] The monitoring unit includes a visualization unit that visualizes the repair progress. The visualization unit visualizes the repair progress to the user. For example, the visualization unit displays the repair progress in the form of a graph, a progress bar, a dashboard, or the like. The visualization unit updates data each time a repair step is completed and displays the progress in real time. The visualization unit provides an interface that allows the user to grasp the repair progress at a glance. For example, the visualization unit displays the repair progress in a color-coded manner to visually distinguish between completed steps and incomplete steps. This allows the repair progress to be visualized to the user.

[0032] The system includes an evaluation unit that evaluates the condition of used parts. The evaluation unit evaluates the condition of used parts using evaluation criteria such as the degree of wear and the remaining rate of functionality. The evaluation unit collects various data on used parts and performs evaluation based on this data. The evaluation unit quantifies the condition of used parts and provides criteria for selecting reusable parts. For example, the evaluation unit determines that parts with a degree of wear below a certain standard are reusable and lists them as candidates for reuse. This makes it possible to evaluate the condition of used parts and select reusable parts.

[0033] The evaluation unit includes a selection unit that selects reusable parts. The selection unit selects reusable parts. The selection unit selects reusable parts using evaluation criteria, for example, the lifespan and functionality of the parts. The selection unit lists reusable parts based on data on used parts evaluated by the evaluation unit. The selection unit selects optimal parts using an algorithm for selecting reusable parts. For example, the selection unit determines that parts whose lifespan exceeds a certain standard are reusable and lists them as candidates for reuse. In this way, the selection of reusable parts achieves cost reduction and environmental protection.

[0034] The collection unit can analyze the vehicle's past failure history and select the optimal sensor data collection method. The collection unit analyzes the vehicle's past failure history and selects the optimal sensor data collection method. For example, the collection unit increases the frequency of sensor data collection for specific parts based on the past failure history. The collection unit analyzes past failure patterns and strengthens sensor data collection under specific driving conditions. The collection unit optimizes sensor data collection under specific seasons and weather conditions based on the past failure history. This makes it possible to select the optimal sensor data collection method based on the past failure history.

[0035] The collection unit can filter the sensor data based on the current driving situation and environmental conditions of the vehicle when collecting the sensor data. The collection unit filters the sensor data based on the current driving situation and environmental conditions of the vehicle when collecting the sensor data. For example, when the vehicle is traveling on a highway, the collection unit prioritizes collecting sensor data on the engine and brakes. When it is raining, the collection unit collects sensor data related to tire grip condition and braking performance. When traveling at night, the collection unit collects sensor data related to the light status and visibility. This allows the sensor data to be filtered based on the driving situation and environmental conditions.

[0036] When collecting sensor data, the collection unit can prioritize collection of highly relevant data based on the geographical position information of the vehicle. When collecting sensor data, the collection unit prioritizes collection of highly relevant data based on the geographical position information of the vehicle. For example, when the vehicle is traveling in a mountainous area, the collection unit prioritizes collection of sensor data related to engine load and fuel efficiency. When the vehicle is traveling in an urban area, the collection unit prioritizes collection of brake and steering sensor data. When the vehicle is traveling on a highway, the collection unit prioritizes collection of sensor data related to tire condition and speed. This makes it possible to prioritize collection of highly relevant data taking geographical position information into consideration.

[0037] The collection unit can analyze the social media activity of the vehicle when collecting sensor data and collect related data. The collection unit analyzes the social media activity of the vehicle when collecting sensor data and collects related data. For example, if a vehicle owner reports a specific problem on social media, the collection unit prioritizes collecting sensor data related to that problem. The collection unit analyzes trends on social media and collects sensor data related to general problems. The collection unit collects sensor data related to specific parts or systems based on user feedback on social media. This makes it possible to analyze social media activity and collect related data.

[0038] The analysis unit can optimize the analysis algorithm based on the vehicle's past failure data during analysis. The analysis unit optimizes the analysis algorithm by referring to the vehicle's past failure data during analysis. For example, the analysis unit optimizes the analysis algorithm for a specific component or system based on the past failure data. The analysis unit analyzes past failure patterns and optimizes the analysis algorithm under specific driving conditions. The analysis unit optimizes the analysis algorithm under specific seasons or weather conditions based on the past failure data. This makes it possible to optimize the analysis algorithm based on past failure data.

[0039] The analysis unit can apply different analysis methods depending on the vehicle category during analysis. The analysis unit applies different analysis methods depending on the vehicle category during analysis. For example, in the case of a passenger car, the analysis unit applies analysis methods for the engine and brakes. In the case of a truck, the analysis unit applies analysis methods for load and fuel efficiency. In the case of a bus, the analysis unit applies analysis methods related to passenger comfort and safety. This makes it possible to apply different analysis methods depending on the vehicle category.

[0040] The analysis unit can determine the priority of the analysis based on the usage status of the vehicle during analysis. The analysis unit determines the priority of the analysis based on the usage status of the vehicle during analysis. For example, if the vehicle is used frequently, the analysis unit prioritizes the analysis of important parts and systems. If the vehicle has not been used for a long period of time, the analysis unit prioritizes the analysis of the battery and tires. If the vehicle is used under specific driving conditions, the analysis unit prioritizes the analysis of parts and systems related to those conditions. This makes it possible to determine the priority of the analysis based on the usage status of the vehicle.

[0041] The analysis unit can improve the accuracy of the analysis by referring to vehicle-related literature during the analysis. The analysis unit improves the accuracy of the analysis by referring to vehicle-related literature during the analysis. The analysis unit improves the accuracy of the analysis by referring to, for example, vehicle manuals and technical documents. The analysis unit improves the accuracy of the analysis by referring to past research papers and technical reports. The analysis unit improves the accuracy of the analysis by referring to technical information from vehicle manufacturers. This improves the accuracy of the analysis by referring to related literature.

[0042] The providing unit can adjust the level of detail of the repair procedure based on the importance of the fault location when providing the procedure. The providing unit adjusts the level of detail of the repair procedure based on the importance of the fault location when providing the procedure. For example, in the case of an important fault location, the providing unit provides detailed repair procedures. In the case of a minor fault location, the providing unit provides simple repair procedures. In the case of a fault location that is related to safety, the providing unit emphasizes procedures that require particular attention. In this way, the level of detail of the repair procedure can be adjusted based on the importance of the fault location.

[0043] The providing unit can apply different repair procedures depending on the category of the faulty part when providing the repair procedures. The providing unit applies different repair procedures depending on the category of the faulty part when providing the repair procedures. For example, in the case of an engine fault, the providing unit provides a repair procedure specialized for the engine. In the case of a brake fault, the providing unit provides a repair procedure specialized for the brake. In the case of an electrical system fault, the providing unit provides a repair procedure specialized for the electrical system. This makes it possible to apply an appropriate repair procedure depending on the category of the faulty part.

[0044] The providing unit can, at the time of providing, determine the priority of repair procedures based on the time when the fault occurred. The providing unit, at the time of providing, determine the priority of repair procedures based on the time when the fault occurred. For example, the providing unit provides repair procedures by giving priority to a fault that occurred recently. The providing unit provides repair procedures by giving priority to a fault that has been left unattended for a long period of time. The providing unit provides repair procedures by giving priority to a fault that is related to the season or weather conditions. In this way, the priority of repair procedures can be determined based on the time when the fault occurred.

[0045] The providing unit can adjust the order of repair procedures based on the relevance of the failure locations when providing the procedures. The providing unit adjusts the order of repair procedures based on the relevance of the failure locations when providing the procedures. For example, the providing unit provides a procedure for simultaneously repairing failure locations that are related to each other. The providing unit provides a procedure for repairing important failure locations with priority. The providing unit provides a procedure for repairing safety-related failure locations with top priority. This makes it possible to adjust the order of repair procedures based on the relevance of the failure locations.

[0046] The monitoring unit can optimize the monitoring algorithm based on past vehicle data during monitoring. The monitoring unit optimizes the monitoring algorithm by referring to past vehicle data during monitoring. For example, the monitoring unit optimizes the monitoring algorithm for a specific component or system based on past data. The monitoring unit analyzes past failure patterns and optimizes the monitoring algorithm under specific driving conditions. The monitoring unit optimizes the monitoring algorithm under specific seasons or weather conditions based on past data. This makes it possible to optimize the monitoring algorithm based on past data.

[0047] The monitoring unit can apply different monitoring methods depending on the vehicle category during monitoring. The monitoring unit applies different monitoring methods depending on the vehicle category during monitoring. For example, in the case of a passenger car, the monitoring unit applies monitoring methods for the engine and brakes. In the case of a truck, the monitoring unit applies monitoring methods for the load and fuel efficiency. In the case of a bus, the monitoring unit applies monitoring methods related to passenger comfort and safety. This makes it possible to apply an appropriate monitoring method depending on the vehicle category.

[0048] The monitoring unit can perform monitoring based on the geographical position information of the vehicle during monitoring. The monitoring unit performs monitoring based on the geographical position information of the vehicle during monitoring. For example, when the vehicle is traveling in a mountainous area, the monitoring unit prioritizes monitoring data related to engine load and fuel efficiency. When the vehicle is traveling in an urban area, the monitoring unit prioritizes monitoring data related to braking and steering. When the vehicle is traveling on a highway, the monitoring unit prioritizes monitoring data related to tire condition and speed. In this way, monitoring taking geographical position information into consideration enables more accurate monitoring.

[0049] The monitoring unit can improve the accuracy of monitoring by referring to literature related to the vehicle during monitoring. The monitoring unit improves the accuracy of monitoring by referring to literature related to the vehicle during monitoring. The monitoring unit improves the accuracy of monitoring by referring to, for example, a vehicle manual or technical documentation. The monitoring unit improves the accuracy of monitoring by referring to past research papers or technical reports. The monitoring unit improves the accuracy of monitoring by referring to technical information from the vehicle manufacturer. This improves the accuracy of monitoring by referring to related literature.

[0050] The presentation unit can adjust the level of detail of the countermeasures based on the importance of the anomaly when presenting them. The presentation unit adjusts the level of detail of the countermeasures based on the importance of the anomaly when presenting them. For example, in the case of a serious anomaly, the presentation unit presents detailed countermeasures. In the case of a minor anomaly, the presentation unit presents simple countermeasures. In the case of an anomaly related to safety, the presentation unit emphasizes countermeasures that require particular attention. In this way, the level of detail of the countermeasures can be adjusted based on the importance of the anomaly.

[0051] The presentation unit can apply different countermeasures depending on the category of the abnormality when presenting the abnormality. The presentation unit applies different countermeasures depending on the category of the abnormality when presenting the abnormality. For example, in the case of an engine abnormality, the presentation unit presents a countermeasure specialized for the engine. In the case of a brake abnormality, the presentation unit presents a countermeasure specialized for the brake. In the case of an electrical system abnormality, the presentation unit presents a countermeasure specialized for the electrical system. This makes it possible to apply an appropriate countermeasure depending on the category of the abnormality.

[0052] The presentation unit can determine the priority of countermeasures based on the time of occurrence of the abnormality when presenting the countermeasures. The presentation unit determines the priority of countermeasures based on the time of occurrence of the abnormality when presenting the countermeasures. For example, the presentation unit prioritizes presenting countermeasures for recently occurred abnormalities. The presentation unit prioritizes presenting countermeasures for abnormalities that have been left untreated for a long period of time. The presentation unit prioritizes presenting countermeasures for abnormalities related to seasons or weather conditions. This makes it possible to determine the priority of countermeasures based on the time of occurrence of the abnormality.

[0053] The presentation unit can adjust the order of countermeasures based on the relevance of the anomalies when presenting them. The presentation unit adjusts the order of countermeasures based on the relevance of the anomalies when presenting them. The presentation unit, for example, presents procedures for simultaneously dealing with anomalies that are related to each other. The presentation unit presents procedures for preferentially dealing with important anomalies. The presentation unit presents procedures for preferentially dealing with anomalies that relate to safety. This makes it possible to adjust the order of countermeasures based on the relevance of the anomalies.

[0054] The prediction unit can optimize the prediction algorithm based on the vehicle's past maintenance data when making predictions. The prediction unit optimizes the prediction algorithm by referring to the vehicle's past maintenance data when making predictions. The prediction unit optimizes the prediction algorithm for a specific part or system, for example, based on the past maintenance data. The prediction unit analyzes past maintenance patterns and optimizes the prediction algorithm under specific driving conditions. The prediction unit optimizes the prediction algorithm under specific seasons or weather conditions based on the past maintenance data. This makes it possible to optimize the prediction algorithm based on past maintenance data.

[0055] The prediction unit can apply different prediction methods depending on the vehicle category when making predictions. The prediction unit applies different prediction methods depending on the vehicle category when making predictions. For example, in the case of a passenger car, the prediction unit applies a prediction method for the engine or brakes. In the case of a truck, the prediction unit applies a prediction method for the load or fuel efficiency. In the case of a bus, the prediction unit applies a prediction method related to passenger comfort and safety. This makes it possible to apply an appropriate prediction method depending on the vehicle category.

[0056] The prediction unit can determine the priority of predictions based on the usage status of the vehicle when making predictions. The prediction unit determines the priority of predictions based on the usage status of the vehicle when making predictions. For example, if the vehicle is used frequently, the prediction unit prioritizes predictions of important parts and systems. If the vehicle has not been used for a long period of time, the prediction unit prioritizes predictions of batteries and tires. If the vehicle is used under specific driving conditions, the prediction unit prioritizes predictions of parts and systems related to those conditions. This makes it possible to determine the priority of predictions based on the usage status of the vehicle.

[0057] The prediction unit can improve the accuracy of the prediction by referring to literature related to the vehicle when making a prediction. The prediction unit improves the accuracy of the prediction by referring to literature related to the vehicle when making a prediction. The prediction unit improves the accuracy of the prediction by referring to, for example, a vehicle manual or technical documentation. The prediction unit improves the accuracy of the prediction by referring to past research papers or technical reports. The prediction unit improves the accuracy of the prediction by referring to technical information from the vehicle manufacturer. This improves the accuracy of the prediction by referring to related literature.

[0058] The reservation unit can optimize the reservation algorithm based on the vehicle's past reservation data at the time of reservation. The reservation unit optimizes the reservation algorithm by referring to the vehicle's past reservation data at the time of reservation. The reservation unit optimizes the reservation algorithm for a specific part or system, for example, based on the past reservation data. The reservation unit analyzes past reservation patterns and optimizes the reservation algorithm under specific driving conditions. The reservation unit optimizes the reservation algorithm under specific seasons or weather conditions based on the past reservation data. This makes it possible to optimize the reservation algorithm based on the past reservation data.

[0059] The reservation unit can apply different reservation methods depending on the vehicle category at the time of reservation. The reservation unit applies different reservation methods depending on the vehicle category at the time of reservation. For example, in the case of a passenger car, the reservation unit applies a reservation method based on engine or brakes. In the case of a truck, the reservation unit applies a reservation method based on load or fuel efficiency. In the case of a bus, the reservation unit applies a reservation method related to passenger comfort and safety. This makes it possible to apply an appropriate reservation method depending on the vehicle category.

[0060] The reservation unit can make a reservation based on the geographical location information of the vehicle when making a reservation. The reservation unit makes a reservation based on the geographical location information of the vehicle when making a reservation. For example, if the vehicle is in a specific area, the reservation unit reserves the service center in that area with priority. If the vehicle is moving, the reservation unit reserves the nearest service center. If the vehicle is in a specific city, the reservation unit reserves the service center in that city with priority. In this way, by making a reservation taking the geographical location information into consideration, the optimal service center can be selected.

[0061] The reservation unit can improve the accuracy of the reservation by referring to literature related to the vehicle when making a reservation. The reservation unit improves the accuracy of the reservation by referring to literature related to the vehicle when making a reservation. The reservation unit improves the accuracy of the reservation by referring to, for example, the vehicle manual or technical documentation. The reservation unit improves the accuracy of the reservation by referring to past research papers or technical reports. The reservation unit improves the accuracy of the reservation by referring to technical information from the vehicle manufacturer. This improves the accuracy of the reservation by referring to related literature.

[0062] The visualization unit can optimize the visualization algorithm based on past repair data during visualization. The visualization unit optimizes the visualization algorithm by referring to the past repair data during visualization. For example, the visualization unit optimizes the visualization algorithm for a specific part or system based on past repair data. The visualization unit analyzes past repair patterns and optimizes the visualization algorithm under specific operating conditions. The visualization unit optimizes the visualization algorithm under specific seasons or weather conditions based on past repair data. This makes it possible to optimize the visualization algorithm based on past repair data.

[0063] The visualization unit can apply different visualization techniques depending on the category of repair during visualization. The visualization unit applies different visualization techniques depending on the category of repair during visualization. For example, in the case of engine repair, the visualization unit applies a visualization technique specialized for the engine. In the case of brake repair, the visualization unit applies a visualization technique specialized for the brake. In the case of electrical system repair, the visualization unit applies a visualization technique specialized for the electrical system. This makes it possible to apply an appropriate visualization technique depending on the category of repair.

[0064] The visualization unit can perform visualization based on the geographical location information of the repair when visualizing. The visualization unit performs visualization based on the geographical location information of the repair when visualizing. The visualization unit displays, for example, information on the area where the repair is being performed. The visualization unit displays information on the service center where the repair is being performed. The visualization unit displays the geographical features of the location where the repair is being performed. This makes it possible to visualize the repair progress taking into account the geographical location information.

[0065] The visualization unit can improve the accuracy of the visualization by referring to literature related to repairs during visualization. The visualization unit improves the accuracy of the visualization by referring to literature related to repairs during visualization. The visualization unit improves the accuracy of the visualization by referring to, for example, repair manuals and technical documents. The visualization unit improves the accuracy of the visualization by referring to past research papers and technical reports. The visualization unit improves the accuracy of the visualization by referring to technical information from vehicle manufacturers. This improves the accuracy of the visualization by referring to related literature.

[0066] The evaluation unit can optimize the evaluation algorithm based on past data of used parts during evaluation. The evaluation unit optimizes the evaluation algorithm by referring to the past data of used parts during evaluation. The evaluation unit, for example, optimizes the evaluation algorithm for a specific part or system based on past data. The evaluation unit analyzes past usage patterns and optimizes the evaluation algorithm under specific operating conditions. The evaluation unit optimizes the evaluation algorithm for specific seasons or weather conditions based on past data. This makes it possible to optimize the evaluation algorithm based on past data.

[0067] The evaluation unit can apply different evaluation methods depending on the category of the used part during evaluation. The evaluation unit applies different evaluation methods depending on the category of the used part during evaluation. For example, in the case of an engine part, the evaluation unit applies an evaluation method specialized for engines. In the case of a brake part, the evaluation unit applies an evaluation method specialized for brakes. In the case of an electrical system part, the evaluation unit applies an evaluation method specialized for electrical systems. This makes it possible to apply an appropriate evaluation method depending on the category of the used part.

[0068] The evaluation unit can perform the evaluation based on the geographical location information of the used part when evaluating the used part. The evaluation unit performs the evaluation based on the geographical location information of the used part when evaluating the used part. For example, if the used part was used in a specific region, the evaluation unit performs the evaluation taking into account the environmental conditions of the region. If the used part was used in a specific city, the evaluation unit performs the evaluation taking into account the driving conditions of the city. If the used part was used in specific climatic conditions, the evaluation unit performs the evaluation taking into account the climatic conditions. In this way, by evaluating the used part taking into account the geographical location information, a more accurate evaluation is possible.

[0069] The evaluation unit can improve the accuracy of the evaluation by referring to literature related to the used part during the evaluation. The evaluation unit improves the accuracy of the evaluation by referring to literature related to the used part during the evaluation. The evaluation unit improves the accuracy of the evaluation by referring to, for example, manuals and technical documents for the used part. The evaluation unit improves the accuracy of the evaluation by referring to past research papers and technical reports. The evaluation unit improves the accuracy of the evaluation by referring to technical information from vehicle manufacturers. In this way, the accuracy of the evaluation of the used part is improved by referring to related literature.

[0070] The selection unit can optimize the selection algorithm based on past data of used parts when making a selection. The selection unit optimizes the selection algorithm by referring to past data of used parts when making a selection. For example, the selection unit optimizes the selection algorithm for a specific part or system based on past data. The selection unit analyzes past usage patterns and optimizes the selection algorithm under specific operating conditions. The selection unit optimizes the selection algorithm under specific seasons or weather conditions based on past data. This makes it possible to optimize the selection algorithm based on past data.

[0071] The selection unit can apply different selection methods depending on the category of the used part during selection. The selection unit applies different selection methods depending on the category of the used part during selection. For example, in the case of engine parts, the selection unit applies a selection method specialized for engines. In the case of brake parts, the selection unit applies a selection method specialized for brakes. In the case of electrical system parts, the selection unit applies a selection method specialized for electrical systems. This makes it possible to apply an appropriate selection method depending on the category of the used part.

[0072] The selection unit can make the selection based on the geographical location information of the used parts when selecting the parts. The selection unit makes the selection based on the geographical location information of the used parts when selecting the parts. For example, if the used parts were used in a specific region, the selection unit makes the selection taking into account the environmental conditions of the region. If the used parts were used in a specific city, the selection unit makes the selection taking into account the driving conditions of the city. If the used parts were used in specific climatic conditions, the selection unit makes the selection taking into account the climatic conditions. This allows for more accurate selection by selecting reusable parts taking into account the geographical location information.

[0073] The selection unit can improve the accuracy of the selection by referring to literature related to the used parts when making a selection. The selection unit improves the accuracy of the selection by referring to literature related to the used parts when making a selection. The selection unit improves the accuracy of the selection by referring to, for example, manuals and technical documents for the used parts. The selection unit improves the accuracy of the selection by referring to past research papers and technical reports. The selection unit improves the accuracy of the selection by referring to technical information from vehicle manufacturers. This improves the accuracy of the selection of reusable parts by referring to related literature.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] When analyzing the vehicle's sensor data, the analysis unit can adjust the analysis algorithm taking into account the vehicle's driving history. For example, if the vehicle frequently drives on highways, the analysis unit can focus on data from high-speed driving in its analysis. Also, if the vehicle mainly drives in urban areas, the analysis unit can focus on data from low-speed driving in its analysis. Furthermore, if the vehicle drives in mountainous areas, the analysis unit can perform analysis based on engine load and frequency of brake use. This enables more accurate fault diagnosis based on the vehicle's driving history.

[0076] When monitoring real-time data of the vehicle, the monitoring unit can adjust the monitoring algorithm taking into account the driving style of the vehicle driver. For example, if the driver frequently accelerates or brakes suddenly, the monitoring unit can prioritize monitoring sensor data related to these actions. In addition, if the driver has a moderate driving style, the monitoring unit can prioritize monitoring data related to fuel economy and engine efficiency. Furthermore, if the driver frequently drives long distances, the monitoring unit can prioritize monitoring data related to tire wear and engine temperature. This makes it possible to monitor real-time data according to the driver's driving style.

[0077] When providing repair procedures, the provider can customize the repair procedures based on the vehicle's year and model. For example, for older vehicle models, the provider can provide instructions on using alternative parts in case parts are difficult to obtain. For newer vehicle models, the provider can provide repair procedures using the latest technology and tools. Furthermore, the provider can optimize the repair procedures by taking into account failure patterns unique to specific models. This makes it possible to provide appropriate repair procedures according to the vehicle's year and model.

[0078] When evaluating the condition of a used part, the evaluation unit can adjust the evaluation criteria taking into account the environment in which the part was used. For example, if the part was used in a high-temperature, high-humidity environment, the evaluation unit can evaluate the part by focusing on the progression of corrosion and deterioration. If the part was used in a low-temperature, dry environment, the evaluation unit can evaluate the part by focusing on wear and mechanical damage. Furthermore, if the part was used in an area susceptible to salt damage, the evaluation unit can evaluate the part by focusing on the progression of rust and corrosion. This enables accurate evaluation according to the environment in which the part was used.

[0079] The processing flow of the first embodiment will be briefly explained below.

[0080] Step 1: The collection unit collects sensor data from the vehicle. The collection unit can collect sensor data such as temperature data, speed data, and vibration data. The collection unit acquires data from various sensors in the vehicle and transmits it to the analysis unit. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies the fault location. For example, the analysis unit sets a threshold for abnormal values ​​and identifies the fault location when this threshold is exceeded. The analysis unit can also identify the fault location using a diagnostic algorithm. Step 3: The provider provides a repair procedure based on the fault location identified by the analyzer. The provider provides, for example, the steps of the repair procedure and the tools and parts to be used. The provider displays the repair procedure to the user to assist in the repair work. Step 4: The monitoring unit monitors real-time data of the vehicle. The monitoring unit sets, for example, the data update frequency and the type of data to be collected, and monitors the data in real time. If the monitoring unit detects an abnormality, it sends the data to the presentation unit. Step 5: The presentation unit detects an anomaly based on the data monitored by the monitoring unit and presents a countermeasure. The presentation unit presents a countermeasure, such as a repair procedure or an emergency response procedure. The presentation unit displays the details of the anomaly and the countermeasure to the user. Step 6: The prediction unit collects the vehicle's usage status and maintenance history. The prediction unit collects usage status data such as mileage and engine operating hours. The prediction unit predicts the next maintenance date based on the collected data. The prediction unit predicts the maintenance date using a prediction algorithm based on the usage status and past maintenance data. Step 7: The reservation unit automatically makes a reservation based on the maintenance time predicted by the prediction unit. The reservation unit, for example, works in conjunction with an online reservation system to automatically make a reservation for maintenance.

[0081] (Example 2) An automobile repair system according to an embodiment of the present invention collects vehicle sensor data, diagnoses faults, provides repair guidance, performs real-time troubleshooting, automatically schedules and performs predictive maintenance, visualizes the repair process, and optimally selects used parts. This system utilizes AI to improve efficiency and accuracy in the automobile repair industry. For example, the system analyzes vehicle sensor data, identifies faults, and provides repair procedures. The system also monitors real-time vehicle data and immediately proposes countermeasures if an abnormality is detected. Furthermore, the system predicts the next maintenance date based on the vehicle's usage status and past maintenance history, and automatically schedules the next maintenance appointment. To visualize the repair process, the system monitors the repair progress in real time and provides a visual representation of the repair progress to the user. Finally, the system evaluates the condition of used parts and selects reusable parts. This reduces costs and protects the environment. This enables the automobile repair system to diagnose vehicle faults, provide repair guidance, perform real-time troubleshooting, automatically schedules and performs predictive maintenance.

[0082] An automobile repair system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a monitoring unit, a presentation unit, a prediction unit, and a reservation unit. The collection unit collects sensor data from the vehicle. The collection unit can collect sensor data such as temperature data, speed data, and vibration data. The collection unit acquires data from various sensors in the vehicle and transmits the data to the analysis unit. The analysis unit analyzes the data collected by the collection unit and identifies the fault location. The analysis unit, for example, sets an abnormal value threshold and identifies the fault location when the threshold is exceeded. The analysis unit can also identify the fault location using a diagnostic algorithm. The provision unit provides a repair procedure based on the fault location identified by the analysis unit. The provision unit provides, for example, the steps of the repair procedure and the tools and parts to be used. The provision unit displays the repair procedure to a user and supports the repair work. The monitoring unit monitors real-time data from the vehicle. The monitoring unit, for example, sets the data update frequency and the type of data to be collected and monitors the data in real time. If the monitoring unit detects an abnormality, it transmits data to the presentation unit. The presentation unit detects an abnormality based on the data monitored by the monitoring unit and presents a countermeasure. The presentation unit presents countermeasures such as repair procedures and emergency response procedures. The presentation unit displays details of the abnormality and the countermeasure to the user. The prediction unit collects vehicle usage status and maintenance history. The prediction unit collects usage status data such as mileage and engine operating time. The prediction unit predicts the next maintenance date based on the collected data. The prediction unit predicts the maintenance date using a prediction algorithm based on usage status and past maintenance data. The reservation unit automatically makes a reservation based on the maintenance date predicted by the prediction unit. The reservation unit cooperates with, for example, an online reservation system and automatically makes a maintenance reservation. As a result, the automobile repair system according to the embodiment is capable of vehicle fault diagnosis, provision of repair guides, real-time troubleshooting, automatic reservations, and predictive maintenance.

[0083] The system is equipped with a monitoring unit that monitors the progress of repairs. The monitoring unit monitors the progress of repairs in real time. For example, the monitoring unit monitors the completion status of repair steps and the status of parts used. The monitoring unit updates data and records the progress status each time a step of the repair work is completed. The monitoring unit has a function to display the progress of repairs to the user. For example, the monitoring unit displays the progress of repairs as a graph or progress bar, allowing the user to grasp the progress of repairs at a glance. This allows the progress of repairs to be monitored in real time.

[0084] The monitoring unit includes a visualization unit that visualizes the repair progress. The visualization unit visualizes the repair progress to the user. For example, the visualization unit displays the repair progress in the form of a graph, a progress bar, a dashboard, or the like. The visualization unit updates data each time a repair step is completed and displays the progress in real time. The visualization unit provides an interface that allows the user to grasp the repair progress at a glance. For example, the visualization unit displays the repair progress in a color-coded manner to visually distinguish between completed steps and incomplete steps. This allows the repair progress to be visualized to the user.

[0085] The system includes an evaluation unit that evaluates the condition of used parts. The evaluation unit evaluates the condition of used parts using evaluation criteria such as the degree of wear and the remaining rate of functionality. The evaluation unit collects various data on used parts and performs evaluation based on this data. The evaluation unit quantifies the condition of used parts and provides criteria for selecting reusable parts. For example, the evaluation unit determines that parts with a degree of wear below a certain standard are reusable and lists them as candidates for reuse. This makes it possible to evaluate the condition of used parts and select reusable parts.

[0086] The evaluation unit includes a selection unit that selects reusable parts. The selection unit selects reusable parts. The selection unit selects reusable parts using evaluation criteria, for example, the lifespan and functionality of the parts. The selection unit lists reusable parts based on data on used parts evaluated by the evaluation unit. The selection unit selects optimal parts using an algorithm for selecting reusable parts. For example, the selection unit determines that parts whose lifespan exceeds a certain standard are reusable and lists them as candidates for reuse. In this way, the selection of reusable parts achieves cost reduction and environmental protection.

[0087] The collection unit can estimate the user's emotions and adjust the timing of collecting sensor data based on the estimated emotions. The collection unit estimates the user's emotions and adjusts the timing of collecting sensor data based on the estimated emotions. The collection unit estimates the user's emotions using, for example, facial expression recognition, voice analysis, questionnaire results, etc. The collection unit reduces the frequency of collecting sensor data when the user is feeling stressed, thereby reducing the burden on the user. The collection unit increases the frequency of collecting sensor data when the user is relaxed, in order to collect detailed data. When the user is in a hurry, the collection unit prioritizes collecting only important data and processes it quickly. This makes it possible to adjust the timing of collecting sensor data according to the user's emotions.

[0088] The collection unit can analyze the vehicle's past failure history and select the optimal sensor data collection method. The collection unit analyzes the vehicle's past failure history and selects the optimal sensor data collection method. For example, the collection unit increases the frequency of sensor data collection for specific parts based on the past failure history. The collection unit analyzes past failure patterns and strengthens sensor data collection under specific driving conditions. The collection unit optimizes sensor data collection under specific seasons and weather conditions based on the past failure history. This makes it possible to select the optimal sensor data collection method based on the past failure history.

[0089] The collection unit can filter the sensor data based on the current driving situation and environmental conditions of the vehicle when collecting the sensor data. The collection unit filters the sensor data based on the current driving situation and environmental conditions of the vehicle when collecting the sensor data. For example, when the vehicle is traveling on a highway, the collection unit prioritizes collecting sensor data on the engine and brakes. When it is raining, the collection unit collects sensor data related to tire grip condition and braking performance. When traveling at night, the collection unit collects sensor data related to the light status and visibility. This allows the sensor data to be filtered based on the driving situation and environmental conditions.

[0090] The collection unit can estimate the user's emotions and determine the priority of the sensor data to be collected based on the estimated emotions. The collection unit estimates the user's emotions and determines the priority of the sensor data to be collected based on the estimated emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting engine and brake sensor data. When the user is relaxed, the collection unit prioritizes collecting sensor data related to comfort. When the user is in a hurry, the collection unit prioritizes collecting sensor data related to important safety. In this way, the priority of the sensor data can be determined according to the user's emotions.

[0091] When collecting sensor data, the collection unit can prioritize collection of highly relevant data based on the geographical position information of the vehicle. When collecting sensor data, the collection unit prioritizes collection of highly relevant data based on the geographical position information of the vehicle. For example, when the vehicle is traveling in a mountainous area, the collection unit prioritizes collection of sensor data related to engine load and fuel efficiency. When the vehicle is traveling in an urban area, the collection unit prioritizes collection of brake and steering sensor data. When the vehicle is traveling on a highway, the collection unit prioritizes collection of sensor data related to tire condition and speed. This makes it possible to prioritize collection of highly relevant data taking geographical position information into consideration.

[0092] The collection unit can analyze the social media activity of the vehicle when collecting sensor data and collect related data. The collection unit analyzes the social media activity of the vehicle when collecting sensor data and collects related data. For example, if a vehicle owner reports a specific problem on social media, the collection unit prioritizes collecting sensor data related to that problem. The collection unit analyzes trends on social media and collects sensor data related to general problems. The collection unit collects sensor data related to specific parts or systems based on user feedback on social media. This makes it possible to analyze social media activity and collect related data.

[0093] The analysis unit can estimate the user's emotions and adjust the method for identifying the fault location based on the estimated emotions. The analysis unit estimates the user's emotions and adjusts the method for identifying the fault location based on the estimated emotions. For example, when the user is feeling stressed, the analysis unit uses a simple analysis method to quickly identify the fault location. When the user is relaxed, the analysis unit uses a detailed analysis method to identify the fault location. When the user is in a hurry, the analysis unit prioritizes identifying important fault locations. In this way, the method for identifying the fault location can be adjusted according to the user's emotions.

[0094] The analysis unit can optimize the analysis algorithm based on the vehicle's past failure data during analysis. The analysis unit optimizes the analysis algorithm by referring to the vehicle's past failure data during analysis. For example, the analysis unit optimizes the analysis algorithm for a specific component or system based on the past failure data. The analysis unit analyzes past failure patterns and optimizes the analysis algorithm under specific driving conditions. The analysis unit optimizes the analysis algorithm under specific seasons or weather conditions based on the past failure data. This makes it possible to optimize the analysis algorithm based on past failure data.

[0095] The analysis unit can apply different analysis methods depending on the vehicle category during analysis. The analysis unit applies different analysis methods depending on the vehicle category during analysis. For example, in the case of a passenger car, the analysis unit applies analysis methods for the engine and brakes. In the case of a truck, the analysis unit applies analysis methods for load and fuel efficiency. In the case of a bus, the analysis unit applies analysis methods related to passenger comfort and safety. This makes it possible to apply different analysis methods depending on the vehicle category.

[0096] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit provides a display method including detailed information. If the user is in a hurry, the analysis unit provides a display method that focuses on the main points. This makes it possible to adjust the display method of the analysis results according to the user's emotions.

[0097] The analysis unit can determine the priority of the analysis based on the usage status of the vehicle during analysis. The analysis unit determines the priority of the analysis based on the usage status of the vehicle during analysis. For example, if the vehicle is used frequently, the analysis unit prioritizes the analysis of important parts and systems. If the vehicle has not been used for a long period of time, the analysis unit prioritizes the analysis of the battery and tires. If the vehicle is used under specific driving conditions, the analysis unit prioritizes the analysis of parts and systems related to those conditions. This makes it possible to determine the priority of the analysis based on the usage status of the vehicle.

[0098] The analysis unit can improve the accuracy of the analysis by referring to vehicle-related literature during the analysis. The analysis unit improves the accuracy of the analysis by referring to vehicle-related literature during the analysis. The analysis unit improves the accuracy of the analysis by referring to, for example, vehicle manuals and technical documents. The analysis unit improves the accuracy of the analysis by referring to past research papers and technical reports. The analysis unit improves the accuracy of the analysis by referring to technical information from vehicle manufacturers. This improves the accuracy of the analysis by referring to related literature.

[0099] The providing unit can estimate the user's emotions and adjust the method of providing repair procedures based on the estimated emotions. The providing unit estimates the user's emotions and adjusts the method of providing repair procedures based on the estimated emotions. For example, when the user is feeling stressed, the providing unit provides simple and easy-to-understand repair procedures. When the user is relaxed, the providing unit provides detailed repair procedures. When the user is in a hurry, the providing unit provides only important procedures with priority. In this way, the method of providing repair procedures can be adjusted according to the user's emotions.

[0100] The providing unit can adjust the level of detail of the repair procedure based on the importance of the fault location when providing the procedure. The providing unit adjusts the level of detail of the repair procedure based on the importance of the fault location when providing the procedure. For example, in the case of an important fault location, the providing unit provides detailed repair procedures. In the case of a minor fault location, the providing unit provides simple repair procedures. In the case of a fault location that is related to safety, the providing unit emphasizes procedures that require particular attention. In this way, the level of detail of the repair procedure can be adjusted based on the importance of the fault location.

[0101] The providing unit can apply different repair procedures depending on the category of the faulty part when providing the repair procedures. The providing unit applies different repair procedures depending on the category of the faulty part when providing the repair procedures. For example, in the case of an engine fault, the providing unit provides a repair procedure specialized for the engine. In the case of a brake fault, the providing unit provides a repair procedure specialized for the brake. In the case of an electrical system fault, the providing unit provides a repair procedure specialized for the electrical system. This makes it possible to apply an appropriate repair procedure depending on the category of the faulty part.

[0102] The providing unit can estimate the user's emotions and adjust the length of the repair procedure based on the estimated emotions. The providing unit estimates the user's emotions and adjusts the length of the repair procedure based on the estimated emotions. For example, if the user is feeling stressed, the providing unit provides short, to-the-point repair procedures. If the user is relaxed, the providing unit provides longer repair procedures with detailed explanations. If the user is in a hurry, the providing unit provides procedures for quickly completing the repair. This allows the length of the repair procedure to be adjusted according to the user's emotions.

[0103] The providing unit can, at the time of providing, determine the priority of repair procedures based on the time when the fault occurred. The providing unit, at the time of providing, determine the priority of repair procedures based on the time when the fault occurred. For example, the providing unit provides repair procedures by giving priority to a fault that occurred recently. The providing unit provides repair procedures by giving priority to a fault that has been left unattended for a long period of time. The providing unit provides repair procedures by giving priority to a fault that is related to the season or weather conditions. In this way, the priority of repair procedures can be determined based on the time when the fault occurred.

[0104] The providing unit can adjust the order of repair procedures based on the relevance of the failure locations when providing the procedures. The providing unit adjusts the order of repair procedures based on the relevance of the failure locations when providing the procedures. For example, the providing unit provides a procedure for simultaneously repairing failure locations that are related to each other. The providing unit provides a procedure for repairing important failure locations with priority. The providing unit provides a procedure for repairing safety-related failure locations with top priority. This makes it possible to adjust the order of repair procedures based on the relevance of the failure locations.

[0105] The monitoring unit can estimate the user's emotions and adjust the method of monitoring real-time data based on the estimated emotions. The monitoring unit estimates the user's emotions and adjusts the method of monitoring real-time data based on the estimated emotions. For example, when the user is feeling stressed, the monitoring unit prioritizes monitoring only important data. When the user is relaxed, the monitoring unit monitors detailed data. When the user is in a hurry, the monitoring unit prioritizes monitoring data that requires a quick response. This makes it possible to adjust the method of monitoring real-time data according to the user's emotions.

[0106] The monitoring unit can optimize the monitoring algorithm based on past vehicle data during monitoring. The monitoring unit optimizes the monitoring algorithm by referring to past vehicle data during monitoring. For example, the monitoring unit optimizes the monitoring algorithm for a specific component or system based on past data. The monitoring unit analyzes past failure patterns and optimizes the monitoring algorithm under specific driving conditions. The monitoring unit optimizes the monitoring algorithm under specific seasons or weather conditions based on past data. This makes it possible to optimize the monitoring algorithm based on past data.

[0107] The monitoring unit can apply different monitoring methods depending on the vehicle category during monitoring. The monitoring unit applies different monitoring methods depending on the vehicle category during monitoring. For example, in the case of a passenger car, the monitoring unit applies monitoring methods for the engine and brakes. In the case of a truck, the monitoring unit applies monitoring methods for the load and fuel efficiency. In the case of a bus, the monitoring unit applies monitoring methods related to passenger comfort and safety. This makes it possible to apply an appropriate monitoring method depending on the vehicle category.

[0108] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated emotions. The monitoring unit estimates the user's emotions and adjusts the display method of the monitoring results based on the estimated emotions. For example, if the user is nervous, the monitoring unit provides a simple, highly visible display method. If the user is relaxed, the monitoring unit provides a display method including detailed information. If the user is in a hurry, the monitoring unit provides a display method that focuses on the main points. This makes it possible to adjust the display method of the monitoring results according to the user's emotions.

[0109] The monitoring unit can perform monitoring based on the geographical position information of the vehicle during monitoring. The monitoring unit performs monitoring based on the geographical position information of the vehicle during monitoring. For example, when the vehicle is traveling in a mountainous area, the monitoring unit prioritizes monitoring data related to engine load and fuel efficiency. When the vehicle is traveling in an urban area, the monitoring unit prioritizes monitoring data related to braking and steering. When the vehicle is traveling on a highway, the monitoring unit prioritizes monitoring data related to tire condition and speed. In this way, monitoring taking geographical position information into consideration enables more accurate monitoring.

[0110] The monitoring unit can improve the accuracy of monitoring by referring to literature related to the vehicle during monitoring. The monitoring unit improves the accuracy of monitoring by referring to literature related to the vehicle during monitoring. The monitoring unit improves the accuracy of monitoring by referring to, for example, a vehicle manual or technical documentation. The monitoring unit improves the accuracy of monitoring by referring to past research papers or technical reports. The monitoring unit improves the accuracy of monitoring by referring to technical information from the vehicle manufacturer. This improves the accuracy of monitoring by referring to related literature.

[0111] The presentation unit can estimate the user's emotions and adjust the method of presenting countermeasures based on the estimated emotions. The presentation unit estimates the user's emotions and adjusts the method of presenting countermeasures based on the estimated emotions. For example, when the user is feeling stressed, the presentation unit presents simple and easy-to-understand countermeasures. When the user is relaxed, the presentation unit presents detailed countermeasures. When the user is in a hurry, the presentation unit presents countermeasures that require a quick response. This makes it possible to adjust the method of presenting countermeasures according to the user's emotions.

[0112] The presentation unit can adjust the level of detail of the countermeasures based on the importance of the anomaly when presenting them. The presentation unit adjusts the level of detail of the countermeasures based on the importance of the anomaly when presenting them. For example, in the case of a serious anomaly, the presentation unit presents detailed countermeasures. In the case of a minor anomaly, the presentation unit presents simple countermeasures. In the case of an anomaly related to safety, the presentation unit emphasizes countermeasures that require particular attention. In this way, the level of detail of the countermeasures can be adjusted based on the importance of the anomaly.

[0113] The presentation unit can apply different countermeasures depending on the category of the abnormality when presenting the abnormality. The presentation unit applies different countermeasures depending on the category of the abnormality when presenting the abnormality. For example, in the case of an engine abnormality, the presentation unit presents a countermeasure specialized for the engine. In the case of a brake abnormality, the presentation unit presents a countermeasure specialized for the brake. In the case of an electrical system abnormality, the presentation unit presents a countermeasure specialized for the electrical system. This makes it possible to apply an appropriate countermeasure depending on the category of the abnormality.

[0114] The presentation unit can estimate the user's emotions and adjust the length of the countermeasures based on the estimated emotions. The presentation unit estimates the user's emotions and adjusts the length of the countermeasures based on the estimated emotions. For example, when the user is feeling stressed, the presentation unit presents short and to-the-point countermeasures. When the user is relaxed, the presentation unit presents longer countermeasures including detailed explanations. When the user is in a hurry, the presentation unit presents countermeasures that require a quick response. This allows the length of the countermeasures to be adjusted according to the user's emotions.

[0115] The presentation unit can determine the priority of countermeasures based on the time of occurrence of the abnormality when presenting the countermeasures. The presentation unit determines the priority of countermeasures based on the time of occurrence of the abnormality when presenting the countermeasures. For example, the presentation unit prioritizes presenting countermeasures for recently occurred abnormalities. The presentation unit prioritizes presenting countermeasures for abnormalities that have been left untreated for a long period of time. The presentation unit prioritizes presenting countermeasures for abnormalities related to seasons or weather conditions. This makes it possible to determine the priority of countermeasures based on the time of occurrence of the abnormality.

[0116] The presentation unit can adjust the order of countermeasures based on the relevance of the anomalies when presenting them. The presentation unit adjusts the order of countermeasures based on the relevance of the anomalies when presenting them. The presentation unit, for example, presents procedures for simultaneously dealing with anomalies that are related to each other. The presentation unit presents procedures for preferentially dealing with important anomalies. The presentation unit presents procedures for preferentially dealing with anomalies that relate to safety. This makes it possible to adjust the order of countermeasures based on the relevance of the anomalies.

[0117] The prediction unit can estimate the user's emotions and adjust the maintenance timing prediction method based on the estimated emotions. The prediction unit estimates the user's emotions and adjusts the maintenance timing prediction method based on the estimated emotions. For example, when the user is stressed, the prediction unit uses a simple method to quickly predict the maintenance timing. When the user is relaxed, the prediction unit predicts the maintenance timing using a detailed method. When the user is in a hurry, the prediction unit prioritizes predicting important maintenance items. This makes it possible to adjust the maintenance timing prediction method according to the user's emotions.

[0118] The prediction unit can optimize the prediction algorithm based on the vehicle's past maintenance data when making predictions. The prediction unit optimizes the prediction algorithm by referring to the vehicle's past maintenance data when making predictions. The prediction unit optimizes the prediction algorithm for a specific part or system, for example, based on the past maintenance data. The prediction unit analyzes past maintenance patterns and optimizes the prediction algorithm under specific driving conditions. The prediction unit optimizes the prediction algorithm under specific seasons or weather conditions based on the past maintenance data. This makes it possible to optimize the prediction algorithm based on past maintenance data.

[0119] The prediction unit can apply different prediction methods depending on the vehicle category when making predictions. The prediction unit applies different prediction methods depending on the vehicle category when making predictions. For example, in the case of a passenger car, the prediction unit applies a prediction method for the engine or brakes. In the case of a truck, the prediction unit applies a prediction method for the load or fuel efficiency. In the case of a bus, the prediction unit applies a prediction method related to passenger comfort and safety. This makes it possible to apply an appropriate prediction method depending on the vehicle category.

[0120] The prediction unit can estimate the user's emotion and adjust the display method of the prediction result based on the estimated emotion. The prediction unit estimates the user's emotion and adjusts the display method of the prediction result based on the estimated emotion. For example, if the user is nervous, the prediction unit provides a simple, highly visible display method. If the user is relaxed, the prediction unit provides a display method including detailed information. If the user is in a hurry, the prediction unit provides a display method that focuses on the main points. This makes it possible to adjust the display method of the prediction result according to the user's emotion.

[0121] The prediction unit can determine the priority of predictions based on the usage status of the vehicle when making predictions. The prediction unit determines the priority of predictions based on the usage status of the vehicle when making predictions. For example, if the vehicle is used frequently, the prediction unit prioritizes predictions of important parts and systems. If the vehicle has not been used for a long period of time, the prediction unit prioritizes predictions of batteries and tires. If the vehicle is used under specific driving conditions, the prediction unit prioritizes predictions of parts and systems related to those conditions. This makes it possible to determine the priority of predictions based on the usage status of the vehicle.

[0122] The prediction unit can improve the accuracy of the prediction by referring to literature related to the vehicle when making a prediction. The prediction unit improves the accuracy of the prediction by referring to literature related to the vehicle when making a prediction. The prediction unit improves the accuracy of the prediction by referring to, for example, a vehicle manual or technical documentation. The prediction unit improves the accuracy of the prediction by referring to past research papers or technical reports. The prediction unit improves the accuracy of the prediction by referring to technical information from the vehicle manufacturer. This improves the accuracy of the prediction by referring to related literature.

[0123] The reservation unit can estimate the user's emotions and adjust the reservation method based on the estimated emotions. The reservation unit can estimate the user's emotions and adjust the reservation method based on the estimated emotions. For example, if the user is feeling stressed, the reservation unit provides a simple and quick reservation method. If the user is relaxed, the reservation unit provides detailed reservation options. If the user is in a hurry, the reservation unit prioritizes voice input to enable the reservation to be completed quickly. In this way, the reservation method can be adjusted according to the user's emotions.

[0124] The reservation unit can optimize the reservation algorithm based on the vehicle's past reservation data at the time of reservation. The reservation unit optimizes the reservation algorithm by referring to the vehicle's past reservation data at the time of reservation. The reservation unit optimizes the reservation algorithm for a specific part or system, for example, based on the past reservation data. The reservation unit analyzes past reservation patterns and optimizes the reservation algorithm under specific driving conditions. The reservation unit optimizes the reservation algorithm under specific seasons or weather conditions based on the past reservation data. This makes it possible to optimize the reservation algorithm based on the past reservation data.

[0125] The reservation unit can apply different reservation methods depending on the vehicle category at the time of reservation. The reservation unit applies different reservation methods depending on the vehicle category at the time of reservation. For example, in the case of a passenger car, the reservation unit applies a reservation method based on engine or brakes. In the case of a truck, the reservation unit applies a reservation method based on load or fuel efficiency. In the case of a bus, the reservation unit applies a reservation method related to passenger comfort and safety. This makes it possible to apply an appropriate reservation method depending on the vehicle category.

[0126] The reservation unit can estimate the user's emotions and determine the priority of reservations based on the estimated emotions. The reservation unit estimates the user's emotions and determines the priority of reservations based on the estimated emotions. For example, if the user is feeling stressed, the reservation unit adjusts the priority so that reservations can be completed quickly. If the user is relaxed, the reservation unit provides detailed reservation options. If the user is in a hurry, the reservation unit prioritizes important reservation items. In this way, the priority of reservations can be determined according to the user's emotions.

[0127] The reservation unit can make a reservation based on the geographical location information of the vehicle when making a reservation. The reservation unit makes a reservation based on the geographical location information of the vehicle when making a reservation. For example, if the vehicle is in a specific area, the reservation unit reserves the service center in that area with priority. If the vehicle is moving, the reservation unit reserves the nearest service center. If the vehicle is in a specific city, the reservation unit reserves the service center in that city with priority. In this way, by making a reservation taking the geographical location information into consideration, the optimal service center can be selected.

[0128] The reservation unit can improve the accuracy of the reservation by referring to literature related to the vehicle when making a reservation. The reservation unit improves the accuracy of the reservation by referring to literature related to the vehicle when making a reservation. The reservation unit improves the accuracy of the reservation by referring to, for example, the vehicle manual or technical documentation. The reservation unit improves the accuracy of the reservation by referring to past research papers or technical reports. The reservation unit improves the accuracy of the reservation by referring to technical information from the vehicle manufacturer. This improves the accuracy of the reservation by referring to related literature.

[0129] The visualization unit can estimate the user's emotions and adjust the display method of the repair progress based on the estimated emotions. The visualization unit estimates the user's emotions and adjusts the display method of the repair progress based on the estimated emotions. For example, when the user is feeling stressed, the visualization unit provides a simple, highly visible display method. When the user is relaxed, the visualization unit provides a display method including detailed information. When the user is in a hurry, the visualization unit provides a display method that focuses on the main points. This makes it possible to adjust the display method of the repair progress according to the user's emotions.

[0130] The visualization unit can optimize the visualization algorithm based on past repair data during visualization. The visualization unit optimizes the visualization algorithm by referring to the past repair data during visualization. For example, the visualization unit optimizes the visualization algorithm for a specific part or system based on past repair data. The visualization unit analyzes past repair patterns and optimizes the visualization algorithm under specific operating conditions. The visualization unit optimizes the visualization algorithm under specific seasons or weather conditions based on past repair data. This makes it possible to optimize the visualization algorithm based on past repair data.

[0131] The visualization unit can apply different visualization techniques depending on the category of repair during visualization. The visualization unit applies different visualization techniques depending on the category of repair during visualization. For example, in the case of engine repair, the visualization unit applies a visualization technique specialized for the engine. In the case of brake repair, the visualization unit applies a visualization technique specialized for the brake. In the case of electrical system repair, the visualization unit applies a visualization technique specialized for the electrical system. This makes it possible to apply an appropriate visualization technique depending on the category of repair.

[0132] The visualization unit can estimate the user's emotions and adjust the display order of the repair progress based on the estimated emotions. The visualization unit estimates the user's emotions and adjusts the display order of the repair progress based on the estimated emotions. For example, if the user is feeling stressed, the visualization unit prioritizes displaying important repair progress. If the user is relaxed, the visualization unit displays detailed repair progress. If the user is in a hurry, the visualization unit displays repair progress that focuses on the main points. This makes it possible to adjust the display order of the repair progress according to the user's emotions.

[0133] The visualization unit can perform visualization based on the geographical location information of the repair when visualizing. The visualization unit performs visualization based on the geographical location information of the repair when visualizing. The visualization unit displays, for example, information on the area where the repair is being performed. The visualization unit displays information on the service center where the repair is being performed. The visualization unit displays the geographical features of the location where the repair is being performed. This makes it possible to visualize the repair progress taking into account the geographical location information.

[0134] The visualization unit can improve the accuracy of the visualization by referring to literature related to repairs during visualization. The visualization unit improves the accuracy of the visualization by referring to literature related to repairs during visualization. The visualization unit improves the accuracy of the visualization by referring to, for example, repair manuals and technical documents. The visualization unit improves the accuracy of the visualization by referring to past research papers and technical reports. The visualization unit improves the accuracy of the visualization by referring to technical information from vehicle manufacturers. This improves the accuracy of the visualization by referring to related literature.

[0135] The evaluation unit can estimate the user's emotions and adjust the evaluation method for used parts based on the estimated emotions. The evaluation unit estimates the user's emotions and adjusts the evaluation method for used parts based on the estimated emotions. For example, when the user is feeling stressed, the evaluation unit uses a simple method to perform an evaluation quickly. When the user is relaxed, the evaluation unit performs an evaluation using a detailed method. When the user is in a hurry, the evaluation unit prioritizes evaluation of important parts. In this way, the evaluation method for used parts can be adjusted according to the user's emotions.

[0136] The evaluation unit can optimize the evaluation algorithm based on past data of used parts during evaluation. The evaluation unit optimizes the evaluation algorithm by referring to the past data of used parts during evaluation. The evaluation unit, for example, optimizes the evaluation algorithm for a specific part or system based on past data. The evaluation unit analyzes past usage patterns and optimizes the evaluation algorithm under specific operating conditions. The evaluation unit optimizes the evaluation algorithm for specific seasons or weather conditions based on past data. This makes it possible to optimize the evaluation algorithm based on past data.

[0137] The evaluation unit can apply different evaluation methods depending on the category of the used part during evaluation. The evaluation unit applies different evaluation methods depending on the category of the used part during evaluation. For example, in the case of an engine part, the evaluation unit applies an evaluation method specialized for engines. In the case of a brake part, the evaluation unit applies an evaluation method specialized for brakes. In the case of an electrical system part, the evaluation unit applies an evaluation method specialized for electrical systems. This makes it possible to apply an appropriate evaluation method depending on the category of the used part.

[0138] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results of used parts based on the estimated emotions. The evaluation unit estimates the user's emotions and adjusts the display method of the evaluation results of used parts based on the estimated emotions. For example, if the user is nervous, the evaluation unit provides a simple, highly visible display method. If the user is relaxed, the evaluation unit provides a display method including detailed information. If the user is in a hurry, the evaluation unit provides a display method that focuses on the main points. This makes it possible to adjust the display method of the evaluation results of used parts according to the user's emotions.

[0139] The evaluation unit can perform the evaluation based on the geographical location information of the used part when evaluating the used part. The evaluation unit performs the evaluation based on the geographical location information of the used part when evaluating the used part. For example, if the used part was used in a specific region, the evaluation unit performs the evaluation taking into account the environmental conditions of the region. If the used part was used in a specific city, the evaluation unit performs the evaluation taking into account the driving conditions of the city. If the used part was used in specific climatic conditions, the evaluation unit performs the evaluation taking into account the climatic conditions. In this way, by evaluating the used part taking into account the geographical location information, a more accurate evaluation is possible.

[0140] The evaluation unit can improve the accuracy of the evaluation by referring to literature related to the used part during the evaluation. The evaluation unit improves the accuracy of the evaluation by referring to literature related to the used part during the evaluation. The evaluation unit improves the accuracy of the evaluation by referring to, for example, manuals and technical documents for the used part. The evaluation unit improves the accuracy of the evaluation by referring to past research papers and technical reports. The evaluation unit improves the accuracy of the evaluation by referring to technical information from vehicle manufacturers. In this way, the accuracy of the evaluation of the used part is improved by referring to related literature.

[0141] The selection unit can estimate the user's emotion and adjust the selection method for reusable parts based on the estimated emotion. The selection unit estimates the user's emotion and adjusts the selection method for reusable parts based on the estimated emotion. For example, when the user is stressed, the selection unit uses a simple technique to make a quick selection. When the user is relaxed, the selection unit makes a selection using a detailed technique. When the user is in a hurry, the selection unit prioritizes the selection of important parts. In this way, the selection method for reusable parts can be adjusted according to the user's emotion.

[0142] The selection unit can optimize the selection algorithm based on past data of used parts when making a selection. The selection unit optimizes the selection algorithm by referring to past data of used parts when making a selection. For example, the selection unit optimizes the selection algorithm for a specific part or system based on past data. The selection unit analyzes past usage patterns and optimizes the selection algorithm under specific operating conditions. The selection unit optimizes the selection algorithm under specific seasons or weather conditions based on past data. This makes it possible to optimize the selection algorithm based on past data.

[0143] The selection unit can apply different selection methods depending on the category of the used part during selection. The selection unit applies different selection methods depending on the category of the used part during selection. For example, in the case of engine parts, the selection unit applies a selection method specialized for engines. In the case of brake parts, the selection unit applies a selection method specialized for brakes. In the case of electrical system parts, the selection unit applies a selection method specialized for electrical systems. This makes it possible to apply an appropriate selection method depending on the category of the used part.

[0144] The selection unit can estimate the user's emotion and adjust the display method of the reusable part selection results based on the estimated emotion. The selection unit estimates the user's emotion and adjusts the display method of the reusable part selection results based on the estimated emotion. For example, when the user is nervous, the selection unit provides a simple, highly visible display method. When the user is relaxed, the selection unit provides a display method including detailed information. When the user is in a hurry, the selection unit provides a display method that focuses on the main points. In this way, the display method of the reusable part selection results can be adjusted according to the user's emotion.

[0145] The selection unit can make the selection based on the geographical location information of the used parts when selecting the parts. The selection unit makes the selection based on the geographical location information of the used parts when selecting the parts. For example, if the used parts were used in a specific region, the selection unit makes the selection taking into account the environmental conditions of the region. If the used parts were used in a specific city, the selection unit makes the selection taking into account the driving conditions of the city. If the used parts were used in specific climatic conditions, the selection unit makes the selection taking into account the climatic conditions. This allows for more accurate selection by selecting reusable parts taking into account the geographical location information.

[0146] The selection unit can improve the accuracy of the selection by referring to literature related to the used parts when making a selection. The selection unit improves the accuracy of the selection by referring to literature related to the used parts when making a selection. The selection unit improves the accuracy of the selection by referring to, for example, manuals and technical documents for the used parts. The selection unit improves the accuracy of the selection by referring to past research papers and technical reports. The selection unit improves the accuracy of the selection by referring to technical information from vehicle manufacturers. This improves the accuracy of the selection of reusable parts by referring to related literature. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, monitoring unit, presentation unit, prediction unit, and reservation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects vehicle sensor data using the camera 42 and sensors of the smart device 14 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit, for example, is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected data to identify a fault location. The provision unit, for example, is realized by the identification processing unit 290 of the data processing device 12 and provides a repair procedure based on the identified fault location. The monitoring unit, for example, is realized by the control unit 46A of the smart device 14 and monitors real-time vehicle data. The presentation unit, for example, is realized by the display 40A of the smart device 14 and displays details of the abnormality and countermeasures. The prediction unit, for example, is realized by the identification processing unit 290 of the data processing device 12 and predicts the next maintenance date based on usage status and maintenance history. The reservation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and automatically makes a reservation based on the predicted maintenance time. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, monitoring unit, presentation unit, prediction unit, and reservation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects vehicle sensor data using the camera 42 and sensors of the smart glasses 214 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit, for example, is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected data to identify a fault location. The provision unit, for example, is realized by the identification processing unit 290 of the data processing device 12 and provides a repair procedure based on the identified fault location. The monitoring unit, for example, is realized by the control unit 46A of the smart glasses 214 and monitors real-time vehicle data. The presentation unit, for example, is realized by the display of the smart glasses 214 and displays details of the abnormality and countermeasures. The prediction unit, for example, is realized by the identification processing unit 290 of the data processing device 12 and predicts the next maintenance date based on usage status and maintenance history. The reservation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and automatically makes a reservation based on the predicted maintenance time. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, monitoring unit, presentation unit, prediction unit, and reservation unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects vehicle sensor data using the camera 42 or sensor of the headset terminal 314 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the collected data to identify the fault location. The provision unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and provides a repair procedure based on the identified fault location. The monitoring unit is realized, for example, by the control unit 46A of the headset terminal 314 and monitors real-time vehicle data. The presentation unit is realized, for example, by the display 343 of the headset terminal 314 and displays details of the abnormality and countermeasures. The prediction unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and predicts the next maintenance date based on usage status and maintenance history. The reservation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and automatically makes a reservation based on the predicted maintenance time. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, monitoring unit, presentation unit, prediction unit, and reservation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects vehicle sensor data using the camera 42 and sensors of the robot 414 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the collected data to identify the fault location. The provision unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and provides a repair procedure based on the identified fault location. The monitoring unit is realized, for example, by the control unit 46A of the robot 414 and monitors real-time vehicle data. The presentation unit is realized, for example, by a display of the robot 414 and displays details of the abnormality and countermeasures. The prediction unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and predicts the next maintenance date based on usage status and maintenance history. The reservation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and automatically makes a reservation based on the predicted maintenance time.

[0147] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0148] When analyzing the vehicle's sensor data, the analysis unit can adjust the analysis algorithm taking into account the vehicle's driving history. For example, if the vehicle frequently drives on highways, the analysis unit can focus on data from high-speed driving in its analysis. Also, if the vehicle mainly drives in urban areas, the analysis unit can focus on data from low-speed driving in its analysis. Furthermore, if the vehicle drives in mountainous areas, the analysis unit can perform analysis based on engine load and frequency of brake use. This enables more accurate fault diagnosis based on the vehicle's driving history.

[0149] When monitoring real-time data of the vehicle, the monitoring unit can adjust the monitoring algorithm taking into account the driving style of the vehicle driver. For example, if the driver frequently accelerates or brakes suddenly, the monitoring unit can prioritize monitoring sensor data related to these actions. In addition, if the driver has a moderate driving style, the monitoring unit can prioritize monitoring data related to fuel economy and engine efficiency. Furthermore, if the driver frequently drives long distances, the monitoring unit can prioritize monitoring data related to tire wear and engine temperature. This makes it possible to monitor real-time data according to the driver's driving style.

[0150] When providing repair procedures, the provider can customize the repair procedures based on the vehicle's year and model. For example, for older vehicle models, the provider can provide instructions on using alternative parts in case parts are difficult to obtain. For newer vehicle models, the provider can provide repair procedures using the latest technology and tools. Furthermore, the provider can optimize the repair procedures by taking into account failure patterns unique to specific models. This makes it possible to provide appropriate repair procedures according to the vehicle's year and model.

[0151] When evaluating the condition of a used part, the evaluation unit can adjust the evaluation criteria taking into account the environment in which the part was used. For example, if the part was used in a high-temperature, high-humidity environment, the evaluation unit can evaluate the part by focusing on the progression of corrosion and deterioration. If the part was used in a low-temperature, dry environment, the evaluation unit can evaluate the part by focusing on wear and mechanical damage. Furthermore, if the part was used in an area susceptible to salt damage, the evaluation unit can evaluate the part by focusing on the progression of rust and corrosion. This enables accurate evaluation according to the environment in which the part was used.

[0152] When collecting vehicle sensor data, the collection unit can adjust the frequency of data collection taking into account the health state of the vehicle driver. For example, if the driver feels fatigued, the collection unit can increase the frequency of data collection to enable early detection of abnormalities. Furthermore, if the driver is healthy, the collection unit can reduce the frequency of data collection to reduce the load on the system. Furthermore, if the driver feels stressed, the collection unit can prioritize collecting data related to stress and provide appropriate countermeasures. This makes it possible to collect data according to the driver's health state.

[0153] The analysis unit can estimate the user's emotions and adjust the method for identifying the fault location based on the estimated emotions. For example, if the user is feeling stressed, a simple analysis method can be used to quickly identify the fault location. If the user is relaxed, a detailed analysis method can be used to identify the fault location. Furthermore, if the user is in a hurry, important fault locations can be identified with priority. In this way, the method for identifying the fault location can be adjusted according to the user's emotions.

[0154] The providing unit can estimate the user's emotions and adjust the method of providing repair instructions based on the estimated emotions. For example, if the user is feeling stressed, simple and easy-to-understand repair instructions can be provided. If the user is relaxed, detailed repair instructions can be provided. Furthermore, if the user is in a hurry, only important instructions can be provided preferentially. In this way, the method of providing repair instructions can be adjusted according to the user's emotions.

[0155] The monitoring unit can estimate the user's emotions and adjust the method of monitoring real-time data based on the estimated emotions. For example, if the user is feeling stressed, it is possible to prioritize monitoring only important data. Also, if the user is relaxed, it is possible to monitor detailed data. Furthermore, if the user is in a hurry, it is possible to prioritize monitoring data that requires a quick response. In this way, it is possible to adjust the method of monitoring real-time data according to the user's emotions.

[0156] The presentation unit can estimate the user's emotions and adjust the method of presenting countermeasures based on the estimated emotions. For example, if the user is feeling stressed, simple and easy-to-understand countermeasures can be presented. If the user is relaxed, detailed countermeasures can be presented. Furthermore, if the user is in a hurry, countermeasures that require a quick response can be presented. In this way, the method of presenting countermeasures can be adjusted according to the user's emotions.

[0157] The prediction unit can estimate the user's emotions and adjust the maintenance timing prediction method based on the estimated emotions. For example, if the user is stressed, a simple method can be used to quickly predict the maintenance timing. If the user is relaxed, a detailed method can be used to predict the maintenance timing. Furthermore, if the user is in a hurry, important maintenance items can be predicted with priority. This makes it possible to adjust the maintenance timing prediction method according to the user's emotions.

[0158] The processing flow of the second embodiment will be briefly explained below.

[0159] Step 1: The collection unit collects sensor data from the vehicle. The collection unit can collect sensor data such as temperature data, speed data, and vibration data. The collection unit acquires data from various sensors in the vehicle and transmits it to the analysis unit. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies the fault location. For example, the analysis unit sets a threshold for abnormal values ​​and identifies the fault location when this threshold is exceeded. The analysis unit can also identify the fault location using a diagnostic algorithm. Step 3: The provider provides a repair procedure based on the fault location identified by the analyzer. The provider provides, for example, the steps of the repair procedure and the tools and parts to be used. The provider displays the repair procedure to the user to assist in the repair work. Step 4: The monitoring unit monitors real-time data of the vehicle. The monitoring unit sets, for example, the data update frequency and the type of data to be collected, and monitors the data in real time. If the monitoring unit detects an abnormality, it sends the data to the presentation unit. Step 5: The presentation unit detects an anomaly based on the data monitored by the monitoring unit and presents a countermeasure. The presentation unit presents a countermeasure, such as a repair procedure or an emergency response procedure. The presentation unit displays the details of the anomaly and the countermeasure to the user. Step 6: The prediction unit collects the vehicle's usage status and maintenance history. The prediction unit collects usage status data such as mileage and engine operating hours. The prediction unit predicts the next maintenance date based on the collected data. The prediction unit predicts the maintenance date using a prediction algorithm based on the usage status and past maintenance data. Step 7: The reservation unit automatically makes a reservation based on the maintenance time predicted by the prediction unit. The reservation unit, for example, works in conjunction with an online reservation system to automatically make a reservation for maintenance.

[0160] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0162] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0165] 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.

[0166] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0167] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0168] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0170] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0171] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0172] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0173] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0174] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0175] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0176] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0177] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0178] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0179] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0180] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0181] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0182] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0183] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0184] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0185] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0186] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0187] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0188] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0189] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0190] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0191] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0192] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0193] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0194] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0195] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0196] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0197] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0198] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0199] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0200] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0201] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0202] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0203] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0204] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0205] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0206] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0207] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0208] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0209] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0210] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0211] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0212] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0213] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0214] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0215] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0216] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0217] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0218] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0219] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0220] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0221] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0222] 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.

[0223] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0224] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0225] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0226] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0227] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0228] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0229] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0230] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0231] [Explanation of symbols]

[0232] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects sensor data of a vehicle; an analysis unit that analyzes the data collected by the collection unit and identifies a fault location; a provision unit that provides a repair procedure based on the fault location identified by the analysis unit; a monitoring unit that monitors real-time data of the vehicle; a presentation unit that detects an abnormality based on the data monitored by the monitoring unit and presents a countermeasure; A department that collects vehicle usage and maintenance history, a prediction unit that predicts the next maintenance timing based on the data collected by the collection unit; a reservation unit that automatically makes a reservation based on the maintenance time predicted by the prediction unit; Equipped with A system characterized by:

2. Equipped with a monitoring unit to monitor the progress of repairs 2. The system of claim 1.

3. The monitoring unit Equipped with a visualization section that visualizes the progress of repairs 3. The system of claim 2.

4. Equipped with an evaluation unit that evaluates the condition of used parts 2. The system of claim 1.

5. The evaluation unit Equipped with a selection unit that selects reusable parts 5. The system of claim 4.

6. The collecting unit Estimate the user's emotions and adjust the timing of sensor data collection based on the estimated emotions.

2. The system of claim 1.

7. The collecting unit Analyze the vehicle's past failure history and select the appropriate sensor data collection method 2. The system of claim 1.

8. The collecting unit As sensor data is collected, it is filtered based on the vehicle's current driving situation and environmental conditions.

2. The system of claim 1.

9. The collecting unit Estimate the user's emotions and prioritize the sensor data to be collected based on the estimated emotions.

2. The system of claim 1.

Images