system

Abstract

The system according to the embodiment aims to quickly and appropriately deal with any problems that the driver may have. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit inputs the driver's troubles. The analysis unit analyzes the information input by the reception unit. The provision unit provides support based on the information analyzed by the analysis 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] Conventional technology has had the problem of making it difficult to respond quickly and appropriately to driver problems.

[0005] The system according to the embodiment aims to quickly and appropriately deal with any problems that the driver may have. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit inputs the driver's troubles. The analysis unit analyzes the information input by the reception unit. The provision unit provides support based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and appropriately respond to any problem the driver may have. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A driver support system according to an embodiment of the present invention utilizes AI to address various vehicle problems. This driver support system allows drivers to input their problems, and AI analyzes them and provides appropriate support. For example, in the event of a breakdown, the system identifies the problem and suggests repair methods. It also provides advice on improving fuel economy, ensuring a safe, secure, and comfortable driving experience for drivers. First, the driver inputs the problem. For example, the AI ​​inputs a specific problem, such as "the engine won't start." This information is then input into the AI. The AI ​​then analyzes the input information. The AI ​​identifies the problem and suggests repair methods. For example, if the engine won't start, the AI ​​checks the battery status and suggests replacing it if necessary. Furthermore, the AI ​​provides advice on improving fuel economy. For example, it provides specific advice on driving habits and car maintenance. This improves fuel economy. This system allows drivers to enjoy a safe, secure, and comfortable driving experience. For example, it can respond quickly to breakdowns and provide advice on improving fuel economy, enabling more economical driving. It also provides tire pressure checks, notifications for oil changes, and safe driving advice. AI learns from a driver's past driving data and provides optimal advice for each individual driver, enabling more personalized support. This allows the driver support system to provide quick and appropriate support for drivers when they are in trouble.

[0029] A driver support system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit inputs a driver's problem. Examples of the driver's problem include, but are not limited to, a problem with the engine not starting, low tire pressure, or poor fuel economy. The reception unit can provide multiple input methods, such as voice input, text input, and input via an application. The analysis unit analyzes the information input by the reception unit. The analysis unit, for example, uses AI to identify the fault and propose a repair method. For example, if the engine will not start, the analysis unit checks the battery condition and proposes replacement if necessary. The analysis unit can also analyze driving and maintenance methods to improve fuel economy. The provision unit provides support based on the information analyzed by the analysis unit. For example, the provision unit proposes a repair method for the fault and provides a list of necessary parts and tools. The provision unit can also provide specific advice for improving fuel economy. For example, the provision unit provides advice on driving techniques and vehicle maintenance methods. This allows the driver support system to provide prompt and appropriate support for the driver's problem.

[0030] The driver support system includes a check unit that checks tire pressure. The check unit checks tire pressure. For example, the check unit measures tire pressure using a tire pressure sensor. For example, the check unit issues a warning when tire pressure drops. The check unit can also periodically check tire pressure and suggest refilling tires with air as needed. For example, the check unit can also guide the driver to the nearest gas station or repair shop when tire pressure drops. In this way, checking tire pressure can ensure the driver's safety.

[0031] The driver support system includes a notification unit that notifies the driver when it is time to change the oil. The notification unit notifies the driver when it is time to change the oil. For example, the notification unit calculates the timing of the oil change based on the vehicle's mileage and the engine's usage time. For example, the notification unit notifies the driver when it is time to change the oil. The notification unit can also issue a warning when the oil change is overdue. For example, the notification unit can also guide the driver to the nearest repair shop or service station when it is time to change the oil. In this way, by notifying the driver when it is time to change the oil, the vehicle can be properly maintained.

[0032] The driver support system includes an advice unit that provides advice on safe driving. The advice unit provides the advice on safe driving. For example, the advice unit analyzes the driver's driving data and provides specific advice for safe driving. For example, the advice unit advises the driver to avoid sudden braking and sudden acceleration. The advice unit can also advise the driver to maintain an appropriate distance between vehicles. Furthermore, the advice unit can also provide points to pay attention to while driving and hints for safe driving. For example, the advice unit provides points to pay attention to while driving and specific guidelines for safe driving. In this way, by providing advice on safe driving, it is possible to promote safe driving by the driver.

[0033] The analysis unit can identify the location of the failure and propose a repair method. The analysis unit can identify the location of the failure using, for example, sensors and diagnostic algorithms. For example, the analysis unit can check the battery status if the engine will not start and propose replacement if necessary. The analysis unit can also detect abnormal engine sounds or vibrations and identify the location of the failure. Furthermore, the analysis unit can analyze vehicle diagnostic data and identify the location of the failure. For example, the analysis unit can analyze engine diagnostic data and detect a failure in a specific part. This allows the analysis unit to identify the location of the failure and propose a repair method, enabling quick repairs.

[0034] The providing unit can provide advice on improving fuel efficiency. The providing unit can provide specific advice on, for example, changing driving style or vehicle maintenance methods. For example, the providing unit can advise avoiding sudden acceleration and braking. The providing unit can also advise maintaining appropriate tire pressure. Furthermore, the providing unit can recommend regular oil changes and air filter replacements. For example, the providing unit can provide specific maintenance methods for improving fuel efficiency. As a result, providing advice on improving fuel efficiency enables economical driving.

[0035] The providing unit can learn the driver's past driving data and provide optimal advice to each individual driver. For example, the providing unit collects the driver's driving data and learns using AI. For example, the providing unit analyzes the driver's driving style and driving habits and provides optimal advice to each individual driver. The providing unit can also provide advice for specific driving patterns based on the past driving data. Furthermore, the providing unit can analyze the driver's driving data and point out areas for improvement in driving. For example, the providing unit can advise the driver to reduce the frequency of sudden braking and sudden acceleration. In this way, personalized advice can be provided by learning the driver's past driving data.

[0036] The reception unit can analyze the driver's past problem input history and suggest the optimal input method. For example, the reception unit collects the driver's past input data and analyzes it using AI. For example, the reception unit can automatically display problems that the driver has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the driver has used in the past. Furthermore, the reception unit can predict and suggest problems that will occur during specific time periods based on the driver's past input history. For example, the reception unit can provide the optimal input method based on the driver's past input history. This makes it possible to suggest the optimal input method by analyzing the past input history.

[0037] When a problem is input, the reception unit can filter the input content based on the driver's current vehicle state and driving situation. The reception unit, for example, uses vehicle sensors and driving data to understand the current vehicle state and driving situation. For example, when the driver is driving, the reception unit provides simple options to avoid detailed input. In addition, when the vehicle is stopped, the reception unit can provide detailed input options to collect accurate information. Furthermore, the reception unit can prioritize the input of related problems taking into account the vehicle state (engine state, fuel level, etc.). For example, the reception unit provides optimal input content based on the driver's current vehicle state and driving situation. This makes it possible to provide more appropriate support by filtering the input content based on the current vehicle state and driving situation.

[0038] When inputting a problem, the reception unit can prioritize inputting highly relevant problems by taking into account the driver's geographical location information. The reception unit grasps the driver's geographical location information using, for example, GPS data or map information. For example, if the driver is in a specific area, the reception unit can prioritize inputting problems that frequently occur in that area. In addition, if the driver is driving long distances, the reception unit can also prioritize inputting problems related to rest areas and service areas. Furthermore, if the driver is in an urban area, the reception unit can prioritize inputting problems related to traffic congestion and parking. For example, the reception unit provides optimal input content based on the driver's geographical location information. This allows highly relevant problems to be input preferentially by taking into account the geographical location information.

[0039] When a problem is input, the reception unit can analyze the driver's social media activity and input related problems. The reception unit, for example, analyzes the driver's social media posts and comments. For example, if the driver mentions a specific problem on social media, the reception unit automatically inputs that problem. The reception unit can also analyze problems the driver has experienced in the past from the driver's social media posts and input related problems. Furthermore, the reception unit can predict and input problems related to the driver's current situation from the driver's social media activity. For example, the reception unit provides optimal input content based on the driver's social media activity. This makes it possible to automatically input related problems by analyzing social media activity.

[0040] During analysis, the analysis unit can improve the accuracy of identifying the failure location by referring to past failure data. The analysis unit, for example, collects past failure data and analyzes it using AI. For example, the analysis unit can identify similar failure patterns based on the past failure data and quickly identify the failure location. The analysis unit can also identify failure locations common to specific car types and models from the past failure data. Furthermore, the analysis unit can analyze the past failure data and optimize the algorithm for improving the accuracy of identifying the failure location. For example, the analysis unit provides an optimal analysis method based on the past failure data. In this way, the accuracy of identifying the failure location can be improved by referring to the past failure data.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the vehicle category and model. For example, the analysis unit selects an optimal analysis algorithm depending on the vehicle category (passenger car, truck, bus, etc.). For example, the analysis unit applies an analysis algorithm that takes into account a specific failure pattern depending on the vehicle model (manufacturer, year, etc.). The analysis unit can also customize the analysis algorithm based on the vehicle category and model to improve accuracy. Furthermore, the analysis unit can provide an optimal analysis method depending on the vehicle category and model. For example, the analysis unit provides an optimal analysis algorithm based on the vehicle category and model. In this way, analysis accuracy can be improved by applying an analysis algorithm depending on the vehicle category and model.

[0042] The analysis unit can perform analysis taking into account the geographical distribution of vehicles. For example, the analysis unit collects geographical distribution data of vehicles and analyzes it using AI. For example, when a vehicle is in a specific region, the analysis unit performs analysis taking into account the climate and road conditions of that region. In addition, when a vehicle is driving long distances, the analysis unit can also integrate data from different regions and perform analysis. Furthermore, the analysis unit can perform analysis taking into account regional failure patterns based on the geographical distribution of vehicles. For example, the analysis unit provides an optimal analysis method based on the geographical distribution of vehicles. As a result, by taking into account the geographical distribution of vehicles, analysis taking into account regional failure patterns becomes possible.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to vehicle-related literature. The analysis unit, for example, refers to the vehicle's instruction manual or repair manual. For example, the analysis unit improves the accuracy of identifying the fault location based on the vehicle's instruction manual or repair manual. The analysis unit can also optimize the analysis algorithm based on the vehicle's technical literature. Furthermore, the analysis unit can improve the reliability of the analysis results by referring to vehicle-related literature. For example, the analysis unit provides an optimal analysis method based on vehicle-related literature. In this way, the accuracy of the analysis can be improved by referring to vehicle-related literature.

[0044] When providing support, the support providing unit can select the optimal support method by referring to the driver's past support history. The support providing unit, for example, collects the driver's past support history and analyzes it using AI. For example, the support providing unit selects the optimal support method based on the support content the driver has received in the past. The support providing unit can also suggest an effective support method for a specific problem based on the driver's past support history. Furthermore, the support providing unit can analyze the driver's past support history and provide the optimal support method for each individual driver. For example, the support providing unit provides the optimal support method based on the driver's past support history. In this way, the optimal support method can be selected by referring to the past support history.

[0045] When providing support, the providing unit can customize the support content based on the driver's current driving situation. The providing unit, for example, grasps the driver's driving situation (highway, urban area, etc.) and provides optimal support content. For example, the providing unit provides concise and highly visible support when the driver is driving. Furthermore, the providing unit can also provide support including detailed explanations when the driver is stopped. Furthermore, the providing unit can also provide optimal support content according to the driver's driving situation. For example, the providing unit provides an optimal support method based on the driver's driving situation. In this way, more appropriate support can be provided by customizing the support content based on the current driving situation.

[0046] When providing support, the providing unit can select the optimal support method by taking into account the driver's geographical location information. The providing unit grasps the driver's geographical location information, for example, using GPS data or map information. For example, if the driver is in a specific area, the providing unit can provide support for problems that commonly occur in that area. In addition, if the driver is driving a long distance, the providing unit can provide support related to rest areas and service areas. Furthermore, if the driver is in an urban area, the providing unit can provide support related to traffic congestion and parking. For example, the providing unit provides the optimal support method based on the driver's geographical location information. In this way, the optimal support method can be selected by taking the geographical location information into account.

[0047] When providing support, the providing unit can analyze the driver's social media activity and suggest support content. The providing unit, for example, analyzes the driver's social media posts and comments. For example, if the driver mentions a specific problem on social media, the providing unit provides support for that problem. The providing unit can also provide support for problems the driver has experienced in the past from the driver's social media posts. Furthermore, the providing unit can predict and provide support relevant to the current situation from the driver's social media activity. For example, the providing unit provides optimal support content based on the driver's social media activity. In this way, relevant support content can be suggested by analyzing social media activity.

[0048] The checking unit can improve the accuracy of tire pressure checks by referring to past check data. For example, the checking unit collects past tire pressure check data and analyzes it using AI. For example, the checking unit can quickly identify abnormal values ​​based on past tire pressure check data. The checking unit can also analyze the deterioration patterns of specific tires from the past check data to improve check accuracy. Furthermore, the checking unit can also optimize the frequency of tire pressure checks by referring to past check data. For example, the checking unit provides an optimal check method based on past check data. In this way, checking accuracy can be improved by referring to past check data.

[0049] When checking tire pressure, the checking unit can select the optimal checking method by taking into account the driver's geographical location information. The checking unit, for example, uses GPS data or map information to grasp the driver's geographical location information. For example, if the driver is in a specific area, the checking unit checks tire pressure by taking into account the local climate and road conditions. In addition, if the driver is driving long distances, the checking unit can also check tire pressure at rest areas or service areas. Furthermore, if the driver is in an urban area, the checking unit can also check tire pressure by taking into account traffic congestion and parking conditions. For example, the checking unit provides the optimal checking method based on the driver's geographical location information. This allows the optimal checking method to be selected by taking into account the geographical location information.

[0050] When notifying an oil change, the notification unit can select the optimal notification timing by referring to past oil change history. The notification unit, for example, collects past oil change history and analyzes it using AI. For example, the notification unit calculates the optimal notification timing based on the past oil change history. The notification unit can also identify the optimal notification timing common to a specific car type or model from the past oil change history. Furthermore, the notification unit can analyze the past oil change history to improve the accuracy of the notification timing. For example, the notification unit provides the optimal notification timing based on the past oil change history. This makes it possible to select the optimal notification timing by referring to the past oil change history.

[0051] When notifying the driver of an oil change, the notification unit can select the optimal notification method by taking into account the driver's geographical location information. The notification unit, for example, uses GPS data or map information to grasp the driver's geographical location information. For example, if the driver is in a specific area, the notification unit can provide the oil change notification by taking into account the climate and road conditions of the area. In addition, if the driver is driving a long distance, the notification unit can also provide the oil change notification at a rest area or service area. Furthermore, if the driver is in an urban area, the notification unit can provide the oil change notification by taking into account traffic congestion and parking conditions. For example, the notification unit provides the optimal notification method based on the driver's geographical location information. As a result, the optimal notification method can be selected by taking into account the geographical location information.

[0052] When giving safe driving advice, the advice unit can provide optimal advice by referring to past driving data. The advice unit, for example, collects the driver's past driving data and analyzes it using AI. For example, the advice unit provides advice for a specific driving pattern based on the past driving data. The advice unit can also suggest the optimal driving method for a specific situation based on the past driving data. Furthermore, the advice unit can analyze the past driving data and provide optimal advice to each individual driver. For example, the advice unit provides the optimal advice method based on the past driving data. In this way, optimal advice can be provided by referring to the past driving data.

[0053] When giving safe driving advice, the advice unit can provide optimal advice by taking into account the driver's geographical location information. The advice unit, for example, uses GPS data or map information to grasp the driver's geographical location information. For example, if the driver is in a specific area, the advice unit can provide safe driving advice by taking into account the local climate and road conditions. In addition, if the driver is driving a long distance, the advice unit can provide advice related to rest areas and service areas. Furthermore, if the driver is in an urban area, the advice unit can provide safe driving advice by taking into account traffic congestion and parking conditions. For example, the advice unit provides optimal advice methods based on the driver's geographical location information. In this way, optimal advice can be provided by taking into account the geographical location information.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The reception unit can predict and suggest problems to be input based on the driver's past driving data. For example, the reception unit can learn specific patterns from past driving data and make suggestions in advance if there is a high possibility that the driver will input a problem in a specific situation. The reception unit can also automatically display problems that the driver frequently experiences as candidates based on past driving data. Furthermore, the reception unit can analyze past driving data and predict and suggest problems that are likely to occur during specific time periods or locations. This allows the driver to input problems quickly and efficiently.

[0056] When checking tire pressure, the checking unit can adjust the frequency of checks taking into account the driver's driving style. For example, the checking unit may suggest that a driver who frequently accelerates or brakes suddenly should check tire pressure more frequently. The checking unit may also recommend regular checks for drivers who frequently drive long distances. Furthermore, the checking unit may optimize the timing of tire pressure checks based on the driving style. This makes it possible to check tire pressure appropriately according to the driver's driving style.

[0057] When notifying the driver of the timing of an oil change, the notification unit can customize the notification content taking into account the driver's driving habits. For example, the notification unit may recommend more frequent oil changes for a driver who frequently drives short distances. The notification unit may also suggest oil changes at specific mileage intervals for a driver who frequently uses expressways. Furthermore, the notification unit may provide notifications that emphasize the importance of oil changes based on the driver's driving habits. This makes it possible to provide appropriate oil change notifications according to the driver's driving habits.

[0058] When providing safe driving advice, the advice unit can customize the advice to each individual based on the driver's past driving data. For example, the advice unit can provide specific advice on how to use the brakes to a driver who has frequently braked suddenly in the past. The advice unit can also provide advice emphasizing the importance of speed management to a driver who has frequently exceeded the speed limit in the past. Furthermore, the advice unit can point out areas for improvement in specific driving patterns based on past driving data. This allows drivers to receive specific advice tailored to their own driving style.

[0059] When identifying the location of a failure, the analysis unit can perform analysis taking into account the vehicle's usage environment. For example, if the vehicle is used in a cold region, the analysis unit can perform analysis taking into account failure patterns specific to that region. Also, if the vehicle is used in a hot and humid region, the analysis unit can perform analysis taking into account failure patterns specific to that region. Furthermore, the analysis unit can predict the deterioration of specific parts based on the vehicle's usage environment and identify the location of the failure. This enables highly accurate failure analysis that is tailored to the vehicle's usage environment.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit inputs the driver's troubles. Examples of the driver's troubles include, but are not limited to, the engine not starting, low tire pressure, poor fuel economy, etc. The reception unit can provide multiple input methods, such as voice input, text input, and input through an application. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit uses AI to identify the fault and propose repair methods. For example, if the engine won't start, the analysis unit checks the battery condition and suggests replacing it if necessary. It can also analyze driving and maintenance methods to improve fuel efficiency. Step 3: The support section provides support based on the information analyzed by the analysis section. The support section proposes repair methods for the faulty part and provides a list of necessary parts and tools. It can also provide specific advice for improving fuel efficiency. For example, it can provide advice on how to drive and maintain the car.

[0062] (Example 2) A driver support system according to an embodiment of the present invention utilizes AI to address various vehicle problems. This driver support system allows drivers to input their problems, and AI analyzes them and provides appropriate support. For example, in the event of a breakdown, the system identifies the problem and suggests repair methods. It also provides advice on improving fuel economy, ensuring a safe, secure, and comfortable driving experience for drivers. First, the driver inputs the problem. For example, the AI ​​inputs a specific problem, such as "the engine won't start." This information is then input into the AI. The AI ​​then analyzes the input information. The AI ​​identifies the problem and suggests repair methods. For example, if the engine won't start, the AI ​​checks the battery status and suggests replacing it if necessary. Furthermore, the AI ​​provides advice on improving fuel economy. For example, it provides specific advice on driving habits and car maintenance. This improves fuel economy. This system allows drivers to enjoy a safe, secure, and comfortable driving experience. For example, it can respond quickly to breakdowns and provide advice on improving fuel economy, enabling more economical driving. It also provides tire pressure checks, notifications for oil changes, and safe driving advice. AI learns from a driver's past driving data and provides optimal advice for each individual driver, enabling more personalized support. This allows the driver support system to provide quick and appropriate support for drivers when they are in trouble.

[0063] A driver support system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit inputs a driver's problem. Examples of the driver's problem include, but are not limited to, a problem with the engine not starting, low tire pressure, or poor fuel economy. The reception unit can provide multiple input methods, such as voice input, text input, and input via an application. The analysis unit analyzes the information input by the reception unit. The analysis unit, for example, uses AI to identify the fault and propose a repair method. For example, if the engine will not start, the analysis unit checks the battery condition and proposes replacement if necessary. The analysis unit can also analyze driving and maintenance methods to improve fuel economy. The provision unit provides support based on the information analyzed by the analysis unit. For example, the provision unit proposes a repair method for the fault and provides a list of necessary parts and tools. The provision unit can also provide specific advice for improving fuel economy. For example, the provision unit provides advice on driving techniques and vehicle maintenance methods. This allows the driver support system to provide prompt and appropriate support for the driver's problem.

[0064] The driver support system includes a check unit that checks tire pressure. The check unit checks tire pressure. For example, the check unit measures tire pressure using a tire pressure sensor. For example, the check unit issues a warning when tire pressure drops. The check unit can also periodically check tire pressure and suggest refilling tires with air as needed. For example, the check unit can also guide the driver to the nearest gas station or repair shop when tire pressure drops. In this way, checking tire pressure can ensure the driver's safety.

[0065] The driver support system includes a notification unit that notifies the driver when it is time to change the oil. The notification unit notifies the driver when it is time to change the oil. For example, the notification unit calculates the timing of the oil change based on the vehicle's mileage and the engine's usage time. For example, the notification unit notifies the driver when it is time to change the oil. The notification unit can also issue a warning when the oil change is overdue. For example, the notification unit can also guide the driver to the nearest repair shop or service station when it is time to change the oil. In this way, by notifying the driver when it is time to change the oil, the vehicle can be properly maintained.

[0066] The driver support system includes an advice unit that provides advice on safe driving. The advice unit provides the advice on safe driving. For example, the advice unit analyzes the driver's driving data and provides specific advice for safe driving. For example, the advice unit advises the driver to avoid sudden braking and sudden acceleration. The advice unit can also advise the driver to maintain an appropriate distance between vehicles. Furthermore, the advice unit can also provide points to pay attention to while driving and hints for safe driving. For example, the advice unit provides points to pay attention to while driving and specific guidelines for safe driving. In this way, by providing advice on safe driving, it is possible to promote safe driving by the driver.

[0067] The analysis unit can identify the location of the failure and propose a repair method. The analysis unit can identify the location of the failure using, for example, sensors and diagnostic algorithms. For example, the analysis unit can check the battery status if the engine will not start and propose replacement if necessary. The analysis unit can also detect abnormal engine sounds or vibrations and identify the location of the failure. Furthermore, the analysis unit can analyze vehicle diagnostic data and identify the location of the failure. For example, the analysis unit can analyze engine diagnostic data and detect a failure in a specific part. This allows the analysis unit to identify the location of the failure and propose a repair method, enabling quick repairs.

[0068] The providing unit can provide advice on improving fuel efficiency. The providing unit can provide specific advice on, for example, changing driving style or vehicle maintenance methods. For example, the providing unit can advise avoiding sudden acceleration and braking. The providing unit can also advise maintaining appropriate tire pressure. Furthermore, the providing unit can recommend regular oil changes and air filter replacements. For example, the providing unit can provide specific maintenance methods for improving fuel efficiency. As a result, providing advice on improving fuel efficiency enables economical driving.

[0069] The providing unit can learn the driver's past driving data and provide optimal advice to each individual driver. For example, the providing unit collects the driver's driving data and learns using AI. For example, the providing unit analyzes the driver's driving style and driving habits and provides optimal advice to each individual driver. The providing unit can also provide advice for specific driving patterns based on the past driving data. Furthermore, the providing unit can analyze the driver's driving data and point out areas for improvement in driving. For example, the providing unit can advise the driver to reduce the frequency of sudden braking and sudden acceleration. In this way, personalized advice can be provided by learning the driver's past driving data.

[0070] The reception unit can estimate the driver's emotions and adjust the problem input method based on the estimated driver's emotions. The reception unit estimates emotions using, for example, facial expression recognition or voice analysis of the driver. For example, if the driver is feeling stressed, the reception unit can provide a simple interface and minimize input steps. In addition, if the driver is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the driver is in a hurry, the reception unit can prioritize voice input to enable the driver to quickly input the problem. For example, the reception unit can provide the optimal input method depending on the driver's emotional state. This makes it possible to provide more appropriate support by adjusting the input method according to the driver's emotions.

[0071] The reception unit can analyze the driver's past problem input history and suggest the optimal input method. For example, the reception unit collects the driver's past input data and analyzes it using AI. For example, the reception unit can automatically display problems that the driver has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the driver has used in the past. Furthermore, the reception unit can predict and suggest problems that will occur during specific time periods based on the driver's past input history. For example, the reception unit can provide the optimal input method based on the driver's past input history. This makes it possible to suggest the optimal input method by analyzing the past input history.

[0072] When a problem is input, the reception unit can filter the input content based on the driver's current vehicle state and driving situation. The reception unit, for example, uses vehicle sensors and driving data to understand the current vehicle state and driving situation. For example, when the driver is driving, the reception unit provides simple options to avoid detailed input. In addition, when the vehicle is stopped, the reception unit can provide detailed input options to collect accurate information. Furthermore, the reception unit can prioritize the input of related problems taking into account the vehicle state (engine state, fuel level, etc.). For example, the reception unit provides optimal input content based on the driver's current vehicle state and driving situation. This makes it possible to provide more appropriate support by filtering the input content based on the current vehicle state and driving situation.

[0073] The reception unit can estimate the driver's emotions and determine the priority of the input problems based on the estimated driver's emotions. The reception unit estimates the emotions using, for example, facial expression recognition or voice analysis of the driver. For example, if the driver is feeling anxious, the reception unit can prioritize problems with high urgency. Also, if the driver is relaxed, the reception unit can handle problems with normal priority. Furthermore, if the driver is in a hurry, the reception unit can prioritize problems that require a quick response. For example, the reception unit determines the optimal priority according to the driver's emotional state. In this way, by determining the priority based on the driver's emotions, it is possible to quickly respond to problems with high urgency.

[0074] When inputting a problem, the reception unit can prioritize inputting highly relevant problems by taking into account the driver's geographical location information. The reception unit grasps the driver's geographical location information using, for example, GPS data or map information. For example, if the driver is in a specific area, the reception unit can prioritize inputting problems that frequently occur in that area. In addition, if the driver is driving long distances, the reception unit can also prioritize inputting problems related to rest areas and service areas. Furthermore, if the driver is in an urban area, the reception unit can prioritize inputting problems related to traffic congestion and parking. For example, the reception unit provides optimal input content based on the driver's geographical location information. This allows highly relevant problems to be input preferentially by taking into account the geographical location information.

[0075] When a problem is input, the reception unit can analyze the driver's social media activity and input related problems. The reception unit, for example, analyzes the driver's social media posts and comments. For example, if the driver mentions a specific problem on social media, the reception unit automatically inputs that problem. The reception unit can also analyze problems the driver has experienced in the past from the driver's social media posts and input related problems. Furthermore, the reception unit can predict and input problems related to the driver's current situation from the driver's social media activity. For example, the reception unit provides optimal input content based on the driver's social media activity. This makes it possible to automatically input related problems by analyzing social media activity.

[0076] The analysis unit can estimate the driver's emotions and adjust the analysis method based on the estimated driver's emotions. The analysis unit estimates emotions using, for example, facial expression recognition or voice analysis of the driver. For example, if the driver is stressed, the analysis unit can quickly perform an analysis and provide concise results. Alternatively, if the driver is relaxed, the analysis unit can perform a detailed analysis and provide comprehensive results. Furthermore, if the driver is in a hurry, the analysis unit can prioritize the most important information and provide results quickly. For example, the analysis unit provides the optimal analysis method depending on the driver's emotional state. This allows for more appropriate analysis results to be provided by adjusting the analysis method based on the driver's emotions.

[0077] During analysis, the analysis unit can improve the accuracy of identifying the failure location by referring to past failure data. The analysis unit, for example, collects past failure data and analyzes it using AI. For example, the analysis unit can identify similar failure patterns based on the past failure data and quickly identify the failure location. The analysis unit can also identify failure locations common to specific car types and models from the past failure data. Furthermore, the analysis unit can analyze the past failure data and optimize the algorithm for improving the accuracy of identifying the failure location. For example, the analysis unit provides an optimal analysis method based on the past failure data. In this way, the accuracy of identifying the failure location can be improved by referring to the past failure data.

[0078] During analysis, the analysis unit can apply different analysis algorithms depending on the vehicle category and model. For example, the analysis unit selects an optimal analysis algorithm depending on the vehicle category (passenger car, truck, bus, etc.). For example, the analysis unit applies an analysis algorithm that takes into account a specific failure pattern depending on the vehicle model (manufacturer, year, etc.). The analysis unit can also customize the analysis algorithm based on the vehicle category and model to improve accuracy. Furthermore, the analysis unit can provide an optimal analysis method depending on the vehicle category and model. For example, the analysis unit provides an optimal analysis algorithm based on the vehicle category and model. In this way, analysis accuracy can be improved by applying an analysis algorithm depending on the vehicle category and model.

[0079] The analysis unit can estimate the driver's emotions and adjust the display method of the analysis results based on the estimated driver's emotions. The analysis unit estimates emotions using, for example, facial expression recognition or voice analysis of the driver. For example, if the driver is nervous, the analysis unit provides a simple, highly visible display method. If the driver is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the driver is in a hurry, the analysis unit can also provide a display method that focuses on the main points. For example, the analysis unit provides an optimal display method depending on the driver's emotional state. As a result, more appropriate analysis results can be provided by adjusting the display method based on the driver's emotions.

[0080] The analysis unit can perform analysis taking into account the geographical distribution of vehicles. For example, the analysis unit collects geographical distribution data of vehicles and analyzes it using AI. For example, when a vehicle is in a specific region, the analysis unit performs analysis taking into account the climate and road conditions of that region. In addition, when a vehicle is driving long distances, the analysis unit can also integrate data from different regions and perform analysis. Furthermore, the analysis unit can perform analysis taking into account regional failure patterns based on the geographical distribution of vehicles. For example, the analysis unit provides an optimal analysis method based on the geographical distribution of vehicles. As a result, by taking into account the geographical distribution of vehicles, analysis taking into account regional failure patterns becomes possible.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis by referring to vehicle-related literature. The analysis unit, for example, refers to the vehicle's instruction manual or repair manual. For example, the analysis unit improves the accuracy of identifying the fault location based on the vehicle's instruction manual or repair manual. The analysis unit can also optimize the analysis algorithm based on the vehicle's technical literature. Furthermore, the analysis unit can improve the reliability of the analysis results by referring to vehicle-related literature. For example, the analysis unit provides an optimal analysis method based on vehicle-related literature. In this way, the accuracy of the analysis can be improved by referring to vehicle-related literature.

[0082] The providing unit can estimate the driver's emotions and adjust the support provision method based on the estimated driver's emotions. The providing unit estimates the driver's emotions using, for example, facial expression recognition or voice analysis. For example, if the driver is feeling stressed, the providing unit can provide quick and concise support. Also, if the driver is relaxed, the providing unit can provide support including detailed explanations. Furthermore, if the driver is in a hurry, the providing unit can prioritize providing the most important information. For example, the providing unit provides the optimal support method depending on the driver's emotional state. As a result, more appropriate support can be provided by adjusting the support provision method based on the driver's emotions.

[0083] When providing support, the support providing unit can select the optimal support method by referring to the driver's past support history. The support providing unit, for example, collects the driver's past support history and analyzes it using AI. For example, the support providing unit selects the optimal support method based on the support content the driver has received in the past. The support providing unit can also suggest an effective support method for a specific problem based on the driver's past support history. Furthermore, the support providing unit can analyze the driver's past support history and provide the optimal support method for each individual driver. For example, the support providing unit provides the optimal support method based on the driver's past support history. In this way, the optimal support method can be selected by referring to the past support history.

[0084] When providing support, the providing unit can customize the support content based on the driver's current driving situation. The providing unit, for example, grasps the driver's driving situation (highway, urban area, etc.) and provides optimal support content. For example, the providing unit provides concise and highly visible support when the driver is driving. Furthermore, the providing unit can also provide support including detailed explanations when the driver is stopped. Furthermore, the providing unit can also provide optimal support content according to the driver's driving situation. For example, the providing unit provides an optimal support method based on the driver's driving situation. In this way, more appropriate support can be provided by customizing the support content based on the current driving situation.

[0085] The providing unit can estimate the driver's emotions and determine the priority of support based on the estimated driver's emotions. The providing unit estimates the emotions using, for example, facial expression recognition or voice analysis of the driver. For example, if the driver feels anxious, the providing unit can prioritize providing support with a high level of urgency. Also, if the driver is relaxed, the providing unit can provide support with a normal priority. Furthermore, if the driver is in a hurry, the providing unit can prioritize providing support that requires a quick response. For example, the providing unit determines an optimal priority according to the driver's emotional state. In this way, by determining the priority of support based on the driver's emotions, it is possible to prioritize support with a high level of urgency.

[0086] When providing support, the providing unit can select the optimal support method by taking into account the driver's geographical location information. The providing unit grasps the driver's geographical location information, for example, using GPS data or map information. For example, if the driver is in a specific area, the providing unit can provide support for problems that commonly occur in that area. In addition, if the driver is driving a long distance, the providing unit can provide support related to rest areas and service areas. Furthermore, if the driver is in an urban area, the providing unit can provide support related to traffic congestion and parking. For example, the providing unit provides the optimal support method based on the driver's geographical location information. In this way, the optimal support method can be selected by taking the geographical location information into account.

[0087] When providing support, the providing unit can analyze the driver's social media activity and suggest support content. The providing unit, for example, analyzes the driver's social media posts and comments. For example, if the driver mentions a specific problem on social media, the providing unit provides support for that problem. The providing unit can also provide support for problems the driver has experienced in the past from the driver's social media posts. Furthermore, the providing unit can predict and provide support relevant to the current situation from the driver's social media activity. For example, the providing unit provides optimal support content based on the driver's social media activity. In this way, relevant support content can be suggested by analyzing social media activity.

[0088] The checking unit can estimate the driver's emotions and adjust the timing of tire pressure checks based on the estimated driver's emotions. The checking unit estimates emotions using, for example, facial expression recognition or voice analysis of the driver. For example, if the driver is stressed, the checking unit can quickly check the tire pressure. Also, if the driver is relaxed, the checking unit can check the tire pressure at a normal timing. Furthermore, if the driver is in a hurry, the checking unit can postpone the tire pressure check and check it at a time when the driver is calmer. For example, the checking unit checks the tire pressure at the optimal timing depending on the driver's emotional state. In this way, by adjusting the timing of the tire pressure check based on the driver's emotions, the check can be performed at a more appropriate timing.

[0089] The checking unit can improve the accuracy of tire pressure checks by referring to past check data. For example, the checking unit collects past tire pressure check data and analyzes it using AI. For example, the checking unit can quickly identify abnormal values ​​based on past tire pressure check data. The checking unit can also analyze the deterioration patterns of specific tires from the past check data to improve check accuracy. Furthermore, the checking unit can also optimize the frequency of tire pressure checks by referring to past check data. For example, the checking unit provides an optimal check method based on past check data. In this way, checking accuracy can be improved by referring to past check data.

[0090] The check unit can estimate the driver's emotions and determine the priority of tire pressure checks based on the estimated driver's emotions. The check unit estimates emotions using, for example, facial expression recognition or voice analysis of the driver. For example, if the driver feels anxious, the check unit can prioritize checking tire pressure. Also, if the driver is relaxed, the check unit can check tire pressure with normal priority. Furthermore, if the driver is in a hurry, the check unit can postpone checking tire pressure until the driver has calmed down. For example, the check unit can check tire pressure with optimal priority depending on the driver's emotional state. In this way, by determining the priority based on the driver's emotions, it is possible to prioritize checks with high urgency.

[0091] When checking tire pressure, the checking unit can select the optimal checking method by taking into account the driver's geographical location information. The checking unit, for example, uses GPS data or map information to grasp the driver's geographical location information. For example, if the driver is in a specific area, the checking unit checks tire pressure by taking into account the local climate and road conditions. In addition, if the driver is driving long distances, the checking unit can also check tire pressure at rest areas or service areas. Furthermore, if the driver is in an urban area, the checking unit can also check tire pressure by taking into account traffic congestion and parking conditions. For example, the checking unit provides the optimal checking method based on the driver's geographical location information. This allows the optimal checking method to be selected by taking into account the geographical location information.

[0092] The notification unit can estimate the driver's emotions and adjust the oil change notification method based on the estimated driver's emotions. The notification unit estimates the driver's emotions using, for example, facial expression recognition or voice analysis. For example, if the driver is feeling stressed, the notification unit can provide a concise and highly visible notification method. Furthermore, if the driver is relaxed, the notification unit can provide a notification method that includes a detailed explanation. Furthermore, if the driver is in a hurry, the notification unit can provide a notification method that focuses on the main points. For example, the notification unit provides an optimal notification method depending on the driver's emotional state. This allows for more appropriate notification by adjusting the notification method based on the driver's emotions.

[0093] When notifying an oil change, the notification unit can select the optimal notification timing by referring to past oil change history. The notification unit, for example, collects past oil change history and analyzes it using AI. For example, the notification unit calculates the optimal notification timing based on the past oil change history. The notification unit can also identify the optimal notification timing common to a specific car type or model from the past oil change history. Furthermore, the notification unit can analyze the past oil change history to improve the accuracy of the notification timing. For example, the notification unit provides the optimal notification timing based on the past oil change history. This makes it possible to select the optimal notification timing by referring to the past oil change history.

[0094] The notification unit can estimate the driver's emotions and determine the priority of oil change notifications based on the estimated driver's emotions. The notification unit estimates the emotions using, for example, facial expression recognition or voice analysis of the driver. For example, if the driver is feeling anxious, the notification unit can prioritize the oil change notification. Also, if the driver is relaxed, the notification unit can also notify the driver of the oil change with normal priority. Furthermore, if the driver is in a hurry, the notification unit can postpone the oil change notification until the driver has calmed down. For example, the notification unit can notify the driver of the oil change with optimal priority according to the driver's emotional state. In this way, by determining the notification priority based on the driver's emotions, notifications with high urgency can be prioritized.

[0095] When notifying the driver of an oil change, the notification unit can select the optimal notification method by taking into account the driver's geographical location information. The notification unit, for example, uses GPS data or map information to grasp the driver's geographical location information. For example, if the driver is in a specific area, the notification unit can provide the oil change notification by taking into account the climate and road conditions of the area. In addition, if the driver is driving a long distance, the notification unit can also provide the oil change notification at a rest area or service area. Furthermore, if the driver is in an urban area, the notification unit can provide the oil change notification by taking into account traffic congestion and parking conditions. For example, the notification unit provides the optimal notification method based on the driver's geographical location information. As a result, the optimal notification method can be selected by taking into account the geographical location information.

[0096] The advice unit can estimate the driver's emotions and adjust the method of giving advice for safe driving based on the estimated driver's emotions. The advice unit estimates the driver's emotions using, for example, facial expression recognition or voice analysis. For example, if the driver is feeling stressed, the advice unit can provide concise and highly visible advice. Furthermore, if the driver is relaxed, the advice unit can also provide advice including detailed explanations. Furthermore, if the driver is in a hurry, the advice unit can provide advice that focuses on the main points. For example, the advice unit provides the optimal advice method depending on the driver's emotional state. As a result, more appropriate advice can be provided by adjusting the advice method based on the driver's emotions.

[0097] When giving safe driving advice, the advice unit can provide optimal advice by referring to past driving data. The advice unit, for example, collects the driver's past driving data and analyzes it using AI. For example, the advice unit provides advice for a specific driving pattern based on the past driving data. The advice unit can also suggest the optimal driving method for a specific situation based on the past driving data. Furthermore, the advice unit can analyze the past driving data and provide optimal advice to each individual driver. For example, the advice unit provides the optimal advice method based on the past driving data. In this way, optimal advice can be provided by referring to the past driving data.

[0098] The advice unit can estimate the driver's emotions and determine the priority of safe driving advice based on the estimated driver's emotions. The advice unit estimates the emotions using, for example, facial expression recognition or voice analysis of the driver. For example, if the driver feels anxious, the advice unit can provide safe driving advice with a normal priority if the driver is relaxed. Furthermore, if the driver is in a hurry, the advice unit can provide advice that requires a quick response with a priority. For example, the advice unit provides safe driving advice with an optimal priority according to the driver's emotional state. In this way, by determining the priority of advice based on the driver's emotions, it is possible to provide advice with a high degree of urgency with a priority.

[0099] When giving safe driving advice, the advice unit can provide optimal advice by taking into account the driver's geographical location information. The advice unit, for example, uses GPS data or map information to grasp the driver's geographical location information. For example, if the driver is in a specific area, the advice unit can provide safe driving advice by taking into account the local climate and road conditions. In addition, if the driver is driving a long distance, the advice unit can provide advice related to rest areas and service areas. Furthermore, if the driver is in an urban area, the advice unit can provide safe driving advice by taking into account traffic congestion and parking conditions. For example, the advice unit provides optimal advice methods based on the driver's geographical location information. In this way, optimal advice can be provided by taking into account the geographical location information. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, check unit, notification unit, and advice unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives the driver's troubles via voice input or text input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and identifies the fault location using AI and proposes a repair method. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides support based on the analysis results. The check unit, for example, checks tire pressure using the camera 42 or sensor of the smart device 14. The notification unit, for example, is realized by the output device 40 of the smart device 14 and notifies the driver of the timing of an oil change. The advice unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the driver's driving data and provides safe driving advice. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, check unit, notification unit, and advice unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives the driver's troubles via voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and identifies the fault location using AI and proposes a repair method. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides support based on the analysis results. The check unit, for example, checks tire pressure using the camera 42 or sensor of the smart glasses 214. The notification unit, for example, is realized by the speaker 240 of the smart glasses 214 and notifies the driver of the timing of an oil change. The advice unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the driver's driving data and provides safe driving advice. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, analysis unit, provision unit, check unit, notification unit, and advice unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives the driver's troubles via voice input. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and identifies the fault location using AI and proposes a repair method. The provision unit is realized by the speaker 240 of the headset-type terminal 314 and provides support based on the analysis results. The check unit checks tire pressure using the camera 42 or sensor of the headset-type terminal 314, for example. The notification unit is realized by the speaker 240 of the headset-type terminal 314 and notifies the driver of the timing of an oil change. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the driver's driving data and provides safe driving advice. === Hard Collateral 1-4 === Each of the multiple elements including the above-described reception unit, analysis unit, provision unit, check unit, notification unit, and advice unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives the driver's troubles via voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and identifies the fault location using AI and proposes a repair method. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides support based on the analysis results. The check unit, for example, checks the tire pressure using the camera 42 or sensor of the robot 414. The notification unit, for example, is realized by the speaker 240 of the robot 414 and notifies the driver of the timing of an oil change. The advice unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the driver's driving data and provides safe driving advice.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The reception unit can predict and suggest problems to be input based on the driver's past driving data. For example, the reception unit can learn specific patterns from past driving data and make suggestions in advance if there is a high possibility that the driver will input a problem in a specific situation. The reception unit can also automatically display problems that the driver frequently experiences as candidates based on past driving data. Furthermore, the reception unit can analyze past driving data and predict and suggest problems that are likely to occur during specific time periods or locations. This allows the driver to input problems quickly and efficiently.

[0102] When checking tire pressure, the checking unit can adjust the frequency of checks taking into account the driver's driving style. For example, the checking unit may suggest that a driver who frequently accelerates or brakes suddenly should check tire pressure more frequently. The checking unit may also recommend regular checks for drivers who frequently drive long distances. Furthermore, the checking unit may optimize the timing of tire pressure checks based on the driving style. This makes it possible to check tire pressure appropriately according to the driver's driving style.

[0103] When notifying the driver of the timing of an oil change, the notification unit can customize the notification content taking into account the driver's driving habits. For example, the notification unit may recommend more frequent oil changes for a driver who frequently drives short distances. The notification unit may also suggest oil changes at specific mileage intervals for a driver who frequently uses expressways. Furthermore, the notification unit may provide notifications that emphasize the importance of oil changes based on the driver's driving habits. This makes it possible to provide appropriate oil change notifications according to the driver's driving habits.

[0104] When providing safe driving advice, the advice unit can customize the advice to each individual based on the driver's past driving data. For example, the advice unit can provide specific advice on how to use the brakes to a driver who has frequently braked suddenly in the past. The advice unit can also provide advice emphasizing the importance of speed management to a driver who has frequently exceeded the speed limit in the past. Furthermore, the advice unit can point out areas for improvement in specific driving patterns based on past driving data. This allows drivers to receive specific advice tailored to their own driving style.

[0105] When identifying the location of a failure, the analysis unit can perform analysis taking into account the vehicle's usage environment. For example, if the vehicle is used in a cold region, the analysis unit can perform analysis taking into account failure patterns specific to that region. Also, if the vehicle is used in a hot and humid region, the analysis unit can perform analysis taking into account failure patterns specific to that region. Furthermore, the analysis unit can predict the deterioration of specific parts based on the vehicle's usage environment and identify the location of the failure. This enables highly accurate failure analysis that is tailored to the vehicle's usage environment.

[0106] When providing advice to improve fuel economy, the providing unit can estimate the driver's emotions and adjust the content of the advice based on the estimated emotions. For example, if the driver is feeling stressed, the providing unit can provide concise and easy-to-follow advice. If the driver is relaxed, the providing unit can also provide advice that includes detailed explanations. Furthermore, if the driver is in a hurry, the providing unit can also prioritize suggesting the most effective method to improve fuel economy. This makes it possible to provide appropriate advice to improve fuel economy according to the driver's emotions.

[0107] The reception unit can estimate the driver's emotions and adjust the input method for the problem based on the estimated emotions. For example, if the driver is feeling stressed, the reception unit can provide a simple interface and minimize the input steps. Alternatively, if the driver is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the driver is in a hurry, the reception unit can prioritize voice input, allowing the driver to quickly input the problem. This allows the system to provide more appropriate support by adjusting the input method according to the driver's emotions.

[0108] The analysis unit can estimate the driver's emotions and adjust the analysis method based on the estimated emotions. For example, if the driver is stressed, the analysis unit can perform a quick analysis and provide concise results. If the driver is relaxed, the analysis unit can perform a detailed analysis and provide comprehensive results. Furthermore, if the driver is in a hurry, the analysis unit can prioritize the most important information and provide results quickly. This allows the analysis method to be adjusted based on the driver's emotions, providing more appropriate analysis results.

[0109] The providing unit can estimate the driver's emotions and adjust the method of providing support based on the estimated emotions. For example, if the driver is feeling stressed, the providing unit can provide quick and concise support. If the driver is relaxed, the providing unit can also provide support with detailed explanations. Furthermore, if the driver is in a hurry, the providing unit can prioritize providing the most important information. This allows for more appropriate support to be provided by adjusting the method of providing support based on the driver's emotions.

[0110] The providing unit can estimate the driver's emotions and determine the priority of support based on the estimated emotions. For example, if the driver feels anxious, the providing unit can provide support with a high level of urgency with priority. Also, if the driver is relaxed, the providing unit can provide support with a normal priority. Furthermore, if the driver is in a hurry, the providing unit can provide support that requires a quick response with priority. In this way, by determining the priority of support based on the driver's emotions, it is possible to provide support with a high level of urgency with priority.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The reception unit inputs the driver's troubles. Examples of the driver's troubles include, but are not limited to, the engine not starting, low tire pressure, poor fuel economy, etc. The reception unit can provide multiple input methods, such as voice input, text input, and input through an application. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit uses AI to identify the fault and propose repair methods. For example, if the engine won't start, the analysis unit checks the battery condition and suggests replacing it if necessary. It can also analyze driving and maintenance methods to improve fuel efficiency. Step 3: The support section provides support based on the information analyzed by the analysis section. The support section proposes repair methods for the faulty part and provides a list of necessary parts and tools. It can also provide specific advice for improving fuel efficiency. For example, it can provide advice on how to drive and maintain the car.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 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 reception desk where drivers can input their concerns, an analysis unit that analyzes the information input by the reception unit; a providing unit that provides support based on the information analyzed by the analyzing unit. A system characterized by:

2. Equipped with a check unit for checking tire air pressure 2. The system of claim 1.

3. Equipped with a notification unit that notifies you when it's time to change the oil 2. The system of claim 1.

4. Equipped with an advice section that provides safe driving advice 2. The system of claim 1.

5. The analysis unit Identify the fault and propose repair methods 2. The system of claim 1.

6. The providing unit Providing advice on improving fuel economy 2. The system of claim 1.

7. The providing unit Learns from a driver's past driving data and provides advice to each individual driver 2. The system of claim 1.

8. The reception unit Estimate the driver's emotions and adjust the way the driver inputs their concerns based on the estimated emotions.

2. The system of claim 1.

9. The reception unit Analyzes the driver's past trouble entry history and suggests input methods 2. The system of claim 1.

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