system

Abstract

The system according to this embodiment aims to support safe driving by taking into account the driver's physical condition and emotions. [Solution] The system according to the embodiment comprises a route generation unit, a driver assistance unit, an emotion recognition unit, a physical condition recognition unit, a safe driving support unit, and a suggestion unit. The route generation unit generates a route according to the user's purpose. The driver assistance unit drives based on the route generated by the route generation unit. The emotion recognition unit recognizes the driver's emotions. The physical condition recognition unit recognizes the driver's physical condition based on the emotions recognized by the emotion recognition unit. The safe driving support unit supports safe driving based on the physical condition recognized by the physical condition recognition unit. The suggestion unit proposes recommended routes and shops to the driver.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, driving support considering the driver's physical condition and emotions is not sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to support safe driving by considering the driver's physical condition and emotions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a route generation unit, a driver assistance unit, an emotion recognition unit, a physical condition recognition unit, a safe driving support unit, and a suggestion unit. The route generation unit generates a route according to the user's purpose. The driver assistance unit drives based on the route generated by the route generation unit. The emotion recognition unit recognizes the driver's emotions. The physical condition recognition unit recognizes the driver's physical condition based on the emotions recognized by the emotion recognition unit. The safe driving support unit supports safe driving based on the physical condition recognized by the physical condition recognition unit. The suggestion unit proposes recommended routes and shops to the driver. [Effects of the Invention]

[0007] The system according to this embodiment can support safe driving by taking into account the driver's physical condition and emotions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The driving support system according to an embodiment of the present invention is a system installed in an automobile that supports and takes over driving, similar to the AI ​​systems installed in cars that appear in American TV dramas. This driving support system can generate routes according to the user's purpose and perform driving on their behalf. Furthermore, it can recognize emotions, fatigue levels, and physical condition from the content of conversations with the driver using an emotion generation engine and image recognition technology. Using image recognition technology (rPPG), it can measure four items: body temperature, pulse rate, blood pressure, and oxygen saturation. In addition, it can avoid the impact on driving caused by sudden car cut-ins, work, personal life stress, etc., and can support safe driving or fully autonomous driving. It also has a car navigation system that suggests recommended routes and shops. For example, when the user enters a destination, the driving support system generates the optimal route and takes over the driving. For example, when the user enters, "I want you to generate a route from my home to my office and drive me there," the driving support system generates the optimal route and takes over the driving along that route. Next, the driving support system recognizes the driver's emotions and physical condition using an emotion generation engine and image recognition technology. For example, if the driver is tired, the driving support system will recognize the fatigue and suggest a break. The system also uses image recognition technology to measure the driver's body temperature, pulse, blood pressure, and oxygen saturation to understand their physical condition. Furthermore, the system supports safe driving by helping to avoid the impact of sudden lane changes and stress on driving. For instance, if a sudden lane change occurs, the system will recognize the situation and take appropriate evasive action. Also, if the driver is stressed, the system will recognize the stress and suggest ways to relax. Finally, the driving support system includes a navigation system function that suggests recommended routes and restaurants. For example, if the driver inputs "I'd like recommendations for restaurants," the system will suggest the most suitable restaurant. In this way, the driving support system uses AI to support and even take over driving, recognizing the driver's emotions and physical condition to support safe driving.Furthermore, it also features a suggestion function as a car navigation system, making it a convenient system for drivers. This allows the driving support system to recognize the driver's emotions and physical condition, thereby supporting safe driving.

[0029] The driving support system according to the embodiment comprises a route generation unit, a driving assistance unit, an emotion recognition unit, a physical condition recognition unit, a safe driving support unit, and a suggestion unit. The route generation unit generates a route according to the user's purpose. For example, when the user inputs a destination, the route generation unit generates the optimal route. For example, when the user inputs "I want you to generate a route from my home to my company," the route generation unit can generate the optimal route. The route generation unit can also generate a route considering the user's preferences and past history. For example, the route generation unit generates the optimal route based on routes that the user has preferred to use in the past. The driving assistance unit drives based on the route generated by the route generation unit. For example, when the user inputs "I want you to generate a route from my home to my company and drive me there," the driving assistance unit drives along the generated route. The driving assistance unit can also drive according to the user's physical condition and emotions. For example, when the user is tired, the driving assistance unit will drive. The emotion recognition unit recognizes the driver's emotions. The emotion recognition unit recognizes the driver's emotions, for example, by using an emotion generation engine. For example, the emotion recognition unit recognizes fatigue if the driver is tired. The emotion recognition unit can also recognize emotions by analyzing the driver's voice and facial expressions. For example, the emotion recognition unit recognizes emotions by analyzing the driver's tone of voice and facial expressions. The physical condition recognition unit recognizes the driver's physical condition based on the emotions recognized by the emotion recognition unit. The physical condition recognition unit measures the driver's body temperature, pulse, blood pressure, and oxygen saturation, for example, by using image recognition technology. For example, the physical condition recognition unit can measure the driver's body temperature, pulse, blood pressure, and oxygen saturation using image recognition technology (rPPG). The physical condition recognition unit can also monitor the driver's physical condition in real time. For example, the physical condition recognition unit measures the driver's body temperature and pulse in real time and recognizes their physical condition. The safe driving support unit supports safe driving based on the physical condition recognized by the physical condition recognition unit. For example, the safe driving support unit avoids the impact on driving caused by sudden cutting in by other vehicles or stress. For example, the safe driving support unit recognizes the situation when a sudden cut-in occurs and takes appropriate evasive action.Furthermore, the safe driving support unit recognizes when the driver is feeling stressed and offers suggestions for relaxation. The suggestion unit suggests recommended routes and restaurants to the driver. For example, the suggestion unit suggests recommended routes and restaurants as a car navigation system. For example, if the driver inputs "I want to know which restaurants you recommend," the suggestion unit will suggest the most suitable restaurant. The suggestion unit can also make suggestions considering the driver's preferences and past history. For example, the suggestion unit will suggest the most suitable restaurant based on restaurants the driver has liked to visit in the past. As a result, the driving support system according to this embodiment can provide route generation, driver assistance, emotion recognition, physical condition recognition, safe driving support, and suggestion functions according to the user's purpose.

[0030] The route generation unit generates routes tailored to the user's purpose. For example, when a user enters a destination, the route generation unit generates the optimal route. Specifically, if a user inputs "I want a route from home to work," the route generation unit can generate the optimal route considering traffic conditions, road congestion, traffic light timing, etc. The route generation unit can also generate routes considering the user's preferences and past history. For example, it can generate the optimal route based on routes the user has previously preferred or routes for enjoying specific scenery. Furthermore, the route generation unit can utilize real-time updated traffic information to recalculate routes reflecting information such as congestion and accidents. This allows users to always use the optimal route, saving time and reducing stress. The route generation unit can also generate routes that support eco-driving by considering the characteristics and fuel efficiency of the user's vehicle. For example, by suggesting routes that avoid steep slopes and frequent stops and starts, it contributes to improved fuel efficiency and reduced environmental impact. In this way, the route generation unit can achieve flexible route generation that meets the diverse needs of users, supporting comfortable and efficient driving.

[0031] The designated driver unit drives the vehicle based on a route generated by the route generation unit. For example, if a user inputs "I want you to generate a route from my home to work and drive me there," the designated driver unit will drive along the generated route. Specifically, the designated driver unit utilizes the vehicle's autonomous driving technology to safely drive the vehicle according to the route specified by the user. The designated driver unit uses the vehicle's sensors and cameras to monitor the surrounding environment in real time and avoid collisions with other vehicles and pedestrians. The designated driver unit can also drive according to the user's physical condition and emotions. For example, if the designated driver unit is tired or stressed, it will take over the driving to ensure safety. Furthermore, the designated driver unit can customize the driving according to the user's driving style and preferences. For example, if the user prefers smooth driving, it will avoid sudden acceleration and braking. The designated driver unit can also adjust music and air conditioning settings to help the user relax. In this way, the designated driver unit can provide driving support that balances the user's safety and comfort.

[0032] The emotion recognition unit recognizes the driver's emotions. For example, it uses an emotion generation engine to recognize the driver's emotions. Specifically, the emotion recognition unit analyzes the driver's voice and facial expressions to recognize emotions. For example, it can analyze the driver's tone of voice and facial expressions to determine if the driver is tired, stressed, or relaxed. The emotion recognition unit uses speech recognition technology to detect changes in the driver's tone of voice and speaking style, identifying changes in emotion. It can also use a camera to analyze the driver's facial expressions in real time, reading emotions from subtle changes in expression. Furthermore, the emotion recognition unit utilizes the driver's body movements and changes in posture as indicators of emotion. For example, if the driver moves their body frequently, it may be determined that they are experiencing stress or anxiety. This allows the emotion recognition unit to comprehensively evaluate the driver's emotional state and provide information to offer appropriate support.

[0033] The physical condition recognition unit recognizes the driver's physical condition based on the emotions recognized by the emotion recognition unit. The physical condition recognition unit measures the driver's body temperature, pulse, blood pressure, and oxygen saturation, for example, using image recognition technology. Specifically, the physical condition recognition unit uses image recognition technology (rPPG) to analyze subtle changes in the driver's complexion and skin, enabling non-contact measurement of body temperature and pulse. The physical condition recognition unit can also monitor the driver's physical condition in real time. For example, it measures the driver's body temperature and pulse in real time and immediately detects changes in physical condition. Furthermore, the physical condition recognition unit accumulates the driver's physical condition data and compares it with past data to detect trends and abnormalities in physical condition early. As a result, the physical condition recognition unit can comprehensively understand the driver's health status and provide information to support safe driving.

[0034] The Safe Driving Support Unit supports safe driving based on the driver's physical condition recognized by the Physical Condition Recognition Unit. For example, the Safe Driving Support Unit avoids the impact on driving caused by sudden lane changes or stress. Specifically, if a sudden lane change occurs, the Safe Driving Support Unit recognizes the situation and takes appropriate evasive action. For example, it uses vehicle sensors and cameras to monitor the surrounding environment in real time and automatically applies the brakes if a sudden lane change occurs. Furthermore, if the driver is feeling stressed, the Safe Driving Support Unit recognizes that stress and offers suggestions to help them relax. For example, if the driver is stressed, it may play relaxing music or adjust the air conditioning settings to reduce the driver's stress. In addition, the Safe Driving Support Unit can adjust the driving style according to the driver's physical condition and emotions. For example, if the driver is tired, it may avoid sudden acceleration and braking to ensure safety. In this way, the Safe Driving Support Unit provides flexible driving support that is tailored to the driver's physical condition and emotions, enabling safe driving.

[0035] The suggestion function proposes recommended routes and restaurants to drivers. For example, it can suggest recommended routes and restaurants as a car navigation system. Specifically, if a driver inputs "Please tell me about recommended restaurants," the suggestion function will suggest the most suitable restaurants. The suggestion function makes suggestions considering the driver's current location, destination, preferences, and past history. For example, if a driver has previously enjoyed visiting certain restaurants or has a preference for a particular type of cuisine, it will suggest the most suitable restaurants based on that information. The suggestion function can also provide information on new restaurants and events by utilizing real-time updated information. This ensures that drivers always receive suggestions based on the latest information, allowing them to enjoy their drives. Furthermore, the suggestion function can collect driver feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can collect evaluations and impressions from drivers after they visit a suggested restaurant and use that information to improve future suggestions. This allows the suggestion function to provide highly accurate suggestions tailored to the driver's needs, improving driving satisfaction.

[0036] The emotion recognition unit can recognize the driver's emotions using an emotion generation engine. For example, the emotion recognition unit can recognize fatigue if the driver is tired. The emotion recognition unit can also recognize emotions by analyzing the driver's voice and facial expressions. For example, the emotion recognition unit can recognize emotions by analyzing the driver's voice tone and facial expressions. This improves the accuracy of driver emotion recognition by using an emotion generation engine. The emotion generation engine can be implemented, for example, by a machine learning model or a rule-based system. Some or all of the above processing in the emotion recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion recognition unit can input the driver's voice data into a generative AI and have the generative AI perform emotion recognition.

[0037] The health recognition unit can measure the driver's body temperature, pulse, blood pressure, and oxygen saturation using image recognition technology. For example, the health recognition unit can measure the driver's body temperature, pulse, blood pressure, and oxygen saturation using image recognition technology. For example, the health recognition unit can measure the driver's body temperature, pulse, blood pressure, and oxygen saturation using image recognition technology (rPPG). The health recognition unit can also monitor the driver's health in real time. For example, the health recognition unit can measure the driver's body temperature and pulse in real time and recognize their health. This allows for accurate recognition of the driver's health using image recognition technology. Image recognition technology can be implemented using, for example, rPPG (remote photoplethysmography) or facial recognition technology. Some or all of the above-described processes in the health recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the health recognition unit can input the driver's image data into a generative AI and have the generative AI measure body temperature, pulse, blood pressure, and oxygen saturation.

[0038] The safe driving support unit can avoid the impact on driving caused by sudden vehicle cut-ins and stress. For example, the safe driving support unit can avoid the impact on driving caused by sudden vehicle cut-ins and stress. For example, if a sudden cut-in occurs, the safe driving support unit recognizes the situation and takes appropriate avoidance action. Also, if the driver is feeling stressed, the safe driving support unit recognizes that stress and makes suggestions for relaxation. In this way, it supports safe driving by avoiding the impact of sudden vehicle cut-ins and stress. Sudden vehicle cut-ins are realized, for example, by methods for detecting cut-ins and specific means of avoidance action. The impact on driving caused by stress is realized, for example, by methods for detecting stress, criteria for evaluating the impact, and means of avoidance. Some or all of the above processing in the safe driving support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the safe driving support unit can input driver stress data into a generative AI and have the generative AI execute suggestions to avoid the impact of stress.

[0039] The suggestion function can propose recommended routes and restaurants as a car navigation system. For example, if a driver inputs "Please tell me a recommended restaurant," the suggestion function will suggest the most suitable restaurant. The suggestion function can also make suggestions considering the driver's preferences and past history. For example, the suggestion function will suggest the most suitable restaurant based on restaurants the driver has visited and enjoyed in the past. This provides a convenient system for drivers by offering a suggestion function as a car navigation system. Recommended routes and restaurants are realized through suggestion methods based on, for example, the user's preferences, past history, and current situation. Some or all of the above processing in the suggestion function may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion function can input the driver's preference data into a generative AI and have the generative AI execute optimal suggestions.

[0040] The route generation unit can analyze past route history and select the optimal route generation algorithm. For example, the route generation unit can analyze past route history and select the optimal route generation algorithm. For example, the route generation unit can propose the optimal route based on routes frequently used by the driver in the past. The route generation unit can also propose routes that avoid congestion based on the driver's past route history. Furthermore, the route generation unit can analyze the driver's past route history and propose the most efficient route. This allows for the generation of the optimal route based on past route history. The optimal route generation algorithm can be implemented using, for example, the Dijkstra algorithm or the A* algorithm. Some or all of the above processing in the route generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the route generation unit can input past route history data into a generation AI and have the generation AI select the optimal route generation algorithm.

[0041] The route generation unit can reflect traffic conditions and weather information in real time when generating routes. For example, the route generation unit can propose the optimal route based on real-time traffic congestion information. The route generation unit can also propose the optimal route considering real-time weather information. Furthermore, the route generation unit can propose detour routes based on real-time road construction information. In this way, by reflecting real-time information, the optimal route can be proposed. Traffic conditions and weather information are realized, for example, through means of acquiring real-time data and methods of reflecting it in route generation. Some or all of the above processing in the route generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the route generation unit can input real-time traffic conditions and weather information into the generation AI and have the generation AI perform the reflection of this information in route generation.

[0042] The designated driver unit can select the optimal driving method by referring to the driver's driving history during the designated driver service. For example, the designated driver unit can select the optimal driving method by referring to the driver's driving history during the designated driver service. For example, the designated driver unit can select the optimal driving method based on the driving method the driver has preferred to use in the past. The designated driver unit can also select the safest driving method from the driver's past driving history. Furthermore, the designated driver unit can analyze the driver's past driving history and select the most efficient driving method. This allows the optimal driving method to be selected based on the driver's driving history. The driving history can be realized, for example, through past driving data or methods for analyzing driving patterns. Some or all of the above-described processes in the designated driver unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the designated driver unit can input the driver's driving history data into a generative AI and have the generative AI select the optimal driving method.

[0043] The designated driver unit can monitor the vehicle's condition and surrounding traffic conditions in real time during designated driving and adjust the driving method accordingly. For example, the designated driver unit can monitor the vehicle's fuel level in real time and adjust the optimal driving method. The designated driver unit can also monitor the surrounding traffic conditions in real time and adjust the optimal driving method. Furthermore, the designated driver unit can monitor the vehicle's tire pressure in real time and adjust the optimal driving method. This allows for adjustments to the driving method based on real-time information. The vehicle's condition and surrounding traffic conditions are realized, for example, through the type of sensor, the data acquisition method, and the adjustment means. Some or all of the above-described processes in the designated driver unit may be performed using, for example, a generative AI, or without a generative AI. For example, the designated driver unit can input vehicle condition and surrounding traffic data into a generative AI and have the generative AI perform the adjustment of the driving method.

[0044] The emotion recognition unit can select the optimal recognition method by referring to the driver's past emotional history when recognizing an emotion. For example, the emotion recognition unit can select the optimal recognition method by referring to the driver's past emotional history when recognizing an emotion. For example, the emotion recognition unit can select the optimal recognition method based on emotions the driver has felt in the past. The emotion recognition unit can also predict changes in emotion from the driver's past emotional history and select a recognition method. Furthermore, the emotion recognition unit can analyze the driver's past emotional history and select the recognition method with the highest accuracy. This allows the optimal recognition method to be selected based on past emotional history. Emotion history is realized, for example, by past emotional data and a method for saving history. Some or all of the above processing in the emotion recognition unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the emotion recognition unit can input past emotional history data into a generative AI and have the generative AI select the optimal recognition method.

[0045] The emotion recognition unit can recognize emotions by analyzing the driver's voice and facial expressions in real time during emotion recognition. For example, the emotion recognition unit can analyze the driver's voice and facial expressions in real time during emotion recognition and recognize emotions. For example, the emotion recognition unit can analyze the driver's voice tone in real time and recognize emotions. The emotion recognition unit can also analyze the driver's facial expressions in real time and recognize emotions. Furthermore, the emotion recognition unit can analyze the driver's body movements in real time and recognize emotions. This allows for accurate emotion recognition by analyzing voice and facial expressions in real time. The method for analyzing voice and facial expressions in real time is realized, for example, by the analysis algorithm and data acquisition method. Some or all of the above processing in the emotion recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion recognition unit can input the driver's voice and facial expression data into a generative AI and have the generative AI perform real-time emotion recognition.

[0046] The health recognition unit can select the optimal recognition method by referring to the driver's past health history when recognizing their health. For example, the health recognition unit can select the optimal recognition method by referring to the driver's past health history when recognizing their health. For example, the health recognition unit can select the optimal recognition method based on the driver's past body temperature measurements. The health recognition unit can also select the optimal recognition method from the driver's past pulse history. Furthermore, the health recognition unit can analyze the driver's past blood pressure history and select the most accurate recognition method. This allows the optimal recognition method to be selected based on past health history. Health history is realized, for example, by past health data and a method for saving history. Some or all of the above processing in the health recognition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the health recognition unit can input past health history data into a generating AI and have the generating AI select the optimal recognition method.

[0047] The health recognition unit can recognize the driver's health by measuring their body temperature, pulse, blood pressure, and oxygen saturation in real time when recognizing their health. For example, the health recognition unit can measure the driver's body temperature, pulse, blood pressure, and oxygen saturation in real time when recognizing their health. For example, the health recognition unit can measure the driver's body temperature in real time and recognize their health. The health recognition unit can also measure the driver's pulse in real time and recognize their health. The health recognition unit can also measure the driver's blood pressure in real time and recognize their health. This allows for accurate recognition of the driver's health by measuring body temperature, pulse, blood pressure, and oxygen saturation in real time. The method for measuring body temperature, pulse, blood pressure, and oxygen saturation in real time is achieved, for example, by the type of sensor used and the method of acquiring the data. Some or all of the above processing in the health recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the health recognition unit can input the driver's body temperature, pulse, blood pressure, and oxygen saturation data into a generative AI and have the generative AI perform real-time health recognition.

[0048] The safe driving support unit can select the optimal support method by referring to the driver's past driving history when providing safe driving support. For example, the safe driving support unit can select the optimal support method by referring to the driver's past driving history when providing safe driving support. For example, the safe driving support unit can select the optimal support method based on the driving support method the driver has preferred to use in the past. The safe driving support unit can also select the safest driving support method from the driver's past driving history. Furthermore, the safe driving support unit can analyze the driver's past driving history and select the most efficient driving support method. This allows for the selection of the optimal driving support method based on past driving history. Driving history can be realized, for example, through past driving data or methods of analyzing driving patterns. Some or all of the above-described processes in the safe driving support unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the safe driving support unit can input past driving history data into a generative AI and have the generative AI select the optimal support method.

[0049] The safe driving support unit can monitor the vehicle's condition and surrounding traffic conditions in real time during safe driving support and adjust the support method accordingly. For example, the safe driving support unit can monitor the vehicle's fuel level in real time and adjust the optimal driving support method. It can also monitor the surrounding traffic conditions in real time and adjust the optimal driving support method. Furthermore, the safe driving support unit can monitor the vehicle's tire pressure in real time and adjust the optimal driving support method. This allows the driving support method to be adjusted based on real-time information. The vehicle's condition and surrounding traffic conditions are realized, for example, by the type of sensor, the data acquisition method, and the adjustment means. Some or all of the above-described processes in the safe driving support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the safe driving support unit can input vehicle condition and surrounding traffic condition data into a generative AI and have the generative AI perform the adjustment of the driving support method.

[0050] The suggestion unit can select the most suitable suggestion by referring to the driver's past suggestion history when making suggestions. For example, the suggestion unit can select the most suitable suggestion by referring to the driver's past suggestion history when making suggestions. For example, the suggestion unit can select the most suitable suggestion based on places the driver has visited in the past. The suggestion unit can also suggest the most preferred places based on the driver's past suggestion history. Furthermore, the suggestion unit can analyze the driver's past suggestion history and select the most efficient suggestion. This allows for the selection of the most suitable suggestion based on past suggestion history. Suggestion history is realized, for example, by past suggestion data and the method of saving the history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input past suggestion history data into a generative AI and have the generative AI select the most suitable suggestion.

[0051] The suggestion unit can make optimal suggestions based on the driver's current situation and interests at the time of suggestion. For example, the suggestion unit can suggest the restaurant closest to the driver's current location. The suggestion unit can also make optimal suggestions considering the distance from the driver's current location to the destination. Furthermore, the suggestion unit can make optimal suggestions considering the traffic conditions from the driver's current location. This enables optimal suggestions tailored to the driver's current situation and interests. The current situation and interests can be realized, for example, through means of acquiring real-time data or methods of identifying interests. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the driver's current situation and interest data into a generative AI and have the generative AI execute optimal suggestions.

[0052] The suggestion unit can make optimal suggestions by considering the driver's geographical location information when making suggestions. For example, the suggestion unit can suggest the tourist spot closest to the driver's current location. The suggestion unit can also make optimal suggestions by considering the distance from the driver's current location to the destination. Furthermore, the suggestion unit can also make optimal suggestions by considering the traffic conditions from the driver's current location. This allows for optimal suggestions based on geographical location information. Geographical location information can be realized, for example, through means of acquiring GPS data or suggestion methods based on location information. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can input the driver's geographical location information data into a generative AI and have the generative AI execute optimal suggestions.

[0053] The suggestion unit can analyze the driver's social media activity and make relevant suggestions when making suggestions. For example, the suggestion unit can make optimal suggestions based on posts the driver has shared on social media. It can also make optimal suggestions based on posts from accounts the driver follows on social media. It can also make optimal suggestions based on locations the driver has checked in to on social media. This allows for relevant suggestions to be made based on social media activity. Social media activity can be realized, for example, through means of analyzing post content and related suggestion methods. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the driver's social media activity data into a generative AI and have the generative AI execute relevant suggestions.

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

[0055] The driver support system can also be equipped with a voice assistant unit. The voice assistant unit recognizes the driver's voice commands and allows the driver to operate various functions of the system by voice. For example, if the driver says, "I want to change the route," the voice assistant unit recognizes the instruction and instructs the route generation unit to regenerate the route. Also, if the driver says, "I want to check my physical condition," the voice assistant unit can instruct the physical condition recognition unit to measure the driver's physical condition. Furthermore, if the driver says, "Please recommend a restaurant," the voice assistant unit can instruct the suggestion unit to suggest the most suitable restaurant. This allows the driver to operate the system hands-free while driving, improving safety.

[0056] The driving support system can also be equipped with a learning unit. This unit can learn the driver's driving patterns and behaviors, and optimize each function of the system. For example, the learning unit can learn the driver's preferred routes and driving style, and reflect this in the route generation and driving assistance units. Furthermore, the learning unit can learn changes in the driver's emotions and physical condition, improving the accuracy of the emotion recognition and physical condition recognition units. In addition, the learning unit can learn the driver's preferences and past history, optimizing the suggestions provided by the suggestion unit. This allows for more comfortable and safer driving support for the driver.

[0057] The driving support system can also be equipped with a predictive unit. This unit can predict driver behavior and traffic conditions, optimizing each function of the system. For example, it can predict the driver's next actions based on their past driving history and reflect this in the route generation and driving assistance units. Furthermore, it can suggest the optimal route based on real-time traffic conditions. Additionally, the predictive unit can predict changes in the driver's emotions and physical condition, improving the accuracy of the emotion recognition and physical condition recognition units. This allows for more comfortable and safer driving support for the driver.

[0058] The driver support system can also be equipped with an eco-driving section. This section analyzes the driver's driving style and supports improvements in fuel efficiency and reduction of environmental impact. For example, it analyzes the driver's acceleration and braking timing and suggests the optimal driving method. It can also provide advice to improve fuel efficiency based on the driver's driving history. Furthermore, it can suggest the most eco-friendly route considering real-time traffic conditions. This optimizes the driver's driving style, leading to improved fuel efficiency and reduced environmental impact.

[0059] The driver support system can also include a maintenance unit. This unit can monitor the vehicle's condition and suggest appropriate maintenance. For example, it can regularly check the vehicle's engine oil and tire condition and suggest necessary maintenance. It can also detect signs of vehicle malfunction and suggest early repairs. Furthermore, it can suggest an optimal maintenance schedule based on the driver's driving style. This ensures optimal vehicle condition and driver safety.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The route generation unit generates a route according to the user's purpose. For example, if the user enters a destination, it generates the optimal route. It can also generate a route considering the user's preferences and past history. Step 2: The designated driver unit drives based on the route generated by the route generation unit. For example, if a user inputs, "I want you to generate a route from my home to my company and drive me there," the unit will drive along the generated route. The unit can also drive according to the user's physical condition or mood. Step 3: The emotion recognition unit recognizes the driver's emotions. For example, it can recognize the driver's emotions using an emotion generation engine, or it can recognize emotions by analyzing the driver's voice and facial expressions. Step 4: The physical condition recognition unit recognizes the driver's physical condition based on the emotions recognized by the emotion recognition unit. For example, image recognition technology can be used to measure and monitor the driver's body temperature, pulse, blood pressure, and oxygen saturation in real time. Step 5: The safe driving support unit provides support for safe driving based on the physical condition recognized by the physical condition recognition unit. For example, it makes suggestions to avoid sudden cut-ins by other vehicles or the impact of stress on driving. Step 6: The suggestion section proposes recommended routes and restaurants to the driver. For example, it can suggest recommended routes and restaurants as a car navigation system, taking into account the driver's preferences and past history.

[0062] (Example of form 2) The driving support system according to an embodiment of the present invention is a system installed in an automobile that supports and takes over driving, similar to the AI ​​systems installed in cars that appear in American TV dramas. This driving support system can generate routes according to the user's purpose and perform driving on their behalf. Furthermore, it can recognize emotions, fatigue levels, and physical condition from the content of conversations with the driver using an emotion generation engine and image recognition technology. Using image recognition technology (rPPG), it can measure four items: body temperature, pulse rate, blood pressure, and oxygen saturation. In addition, it can avoid the impact on driving caused by sudden car cut-ins, work, personal life stress, etc., and can support safe driving or fully autonomous driving. It also has a car navigation system that suggests recommended routes and shops. For example, when the user enters a destination, the driving support system generates the optimal route and takes over the driving. For example, when the user enters, "I want you to generate a route from my home to my office and drive me there," the driving support system generates the optimal route and takes over the driving along that route. Next, the driving support system recognizes the driver's emotions and physical condition using an emotion generation engine and image recognition technology. For example, if the driver is tired, the driving support system will recognize the fatigue and suggest a break. The system also uses image recognition technology to measure the driver's body temperature, pulse, blood pressure, and oxygen saturation to understand their physical condition. Furthermore, the system supports safe driving by helping to avoid the impact of sudden lane changes and stress on driving. For instance, if a sudden lane change occurs, the system will recognize the situation and take appropriate evasive action. Also, if the driver is stressed, the system will recognize the stress and suggest ways to relax. Finally, the driving support system includes a navigation system function that suggests recommended routes and restaurants. For example, if the driver inputs "I'd like recommendations for restaurants," the system will suggest the most suitable restaurant. In this way, the driving support system uses AI to support and even take over driving, recognizing the driver's emotions and physical condition to support safe driving.Furthermore, it also features a suggestion function as a car navigation system, making it a convenient system for drivers. This allows the driving support system to recognize the driver's emotions and physical condition, thereby supporting safe driving.

[0063] The driving support system according to the embodiment comprises a route generation unit, a driving assistance unit, an emotion recognition unit, a physical condition recognition unit, a safe driving support unit, and a suggestion unit. The route generation unit generates a route according to the user's purpose. For example, when the user inputs a destination, the route generation unit generates the optimal route. For example, when the user inputs "I want you to generate a route from my home to my company," the route generation unit can generate the optimal route. The route generation unit can also generate a route considering the user's preferences and past history. For example, the route generation unit generates the optimal route based on routes that the user has preferred to use in the past. The driving assistance unit drives based on the route generated by the route generation unit. For example, when the user inputs "I want you to generate a route from my home to my company and drive me there," the driving assistance unit drives along the generated route. The driving assistance unit can also drive according to the user's physical condition and emotions. For example, when the user is tired, the driving assistance unit will drive. The emotion recognition unit recognizes the driver's emotions. The emotion recognition unit recognizes the driver's emotions, for example, by using an emotion generation engine. For example, the emotion recognition unit recognizes fatigue if the driver is tired. The emotion recognition unit can also recognize emotions by analyzing the driver's voice and facial expressions. For example, the emotion recognition unit recognizes emotions by analyzing the driver's tone of voice and facial expressions. The physical condition recognition unit recognizes the driver's physical condition based on the emotions recognized by the emotion recognition unit. The physical condition recognition unit measures the driver's body temperature, pulse, blood pressure, and oxygen saturation, for example, by using image recognition technology. For example, the physical condition recognition unit can measure the driver's body temperature, pulse, blood pressure, and oxygen saturation using image recognition technology (rPPG). The physical condition recognition unit can also monitor the driver's physical condition in real time. For example, the physical condition recognition unit measures the driver's body temperature and pulse in real time and recognizes their physical condition. The safe driving support unit supports safe driving based on the physical condition recognized by the physical condition recognition unit. For example, the safe driving support unit avoids the impact on driving caused by sudden cutting in by other vehicles or stress. For example, the safe driving support unit recognizes the situation when a sudden cut-in occurs and takes appropriate evasive action.Furthermore, the safe driving support unit recognizes when the driver is feeling stressed and offers suggestions for relaxation. The suggestion unit suggests recommended routes and restaurants to the driver. For example, the suggestion unit suggests recommended routes and restaurants as a car navigation system. For example, if the driver inputs "I want to know which restaurants you recommend," the suggestion unit will suggest the most suitable restaurant. The suggestion unit can also make suggestions considering the driver's preferences and past history. For example, the suggestion unit will suggest the most suitable restaurant based on restaurants the driver has liked to visit in the past. As a result, the driving support system according to this embodiment can provide route generation, driver assistance, emotion recognition, physical condition recognition, safe driving support, and suggestion functions according to the user's purpose.

[0064] The route generation unit generates routes tailored to the user's purpose. For example, when a user enters a destination, the route generation unit generates the optimal route. Specifically, if a user inputs "I want a route from home to work," the route generation unit can generate the optimal route considering traffic conditions, road congestion, traffic light timing, etc. The route generation unit can also generate routes considering the user's preferences and past history. For example, it can generate the optimal route based on routes the user has previously preferred or routes for enjoying specific scenery. Furthermore, the route generation unit can utilize real-time updated traffic information to recalculate routes reflecting information such as congestion and accidents. This allows users to always use the optimal route, saving time and reducing stress. The route generation unit can also generate routes that support eco-driving by considering the characteristics and fuel efficiency of the user's vehicle. For example, by suggesting routes that avoid steep slopes and frequent stops and starts, it contributes to improved fuel efficiency and reduced environmental impact. In this way, the route generation unit can achieve flexible route generation that meets the diverse needs of users, supporting comfortable and efficient driving.

[0065] The designated driver unit drives the vehicle based on a route generated by the route generation unit. For example, if a user inputs "I want you to generate a route from my home to work and drive me there," the designated driver unit will drive along the generated route. Specifically, the designated driver unit utilizes the vehicle's autonomous driving technology to safely drive the vehicle according to the route specified by the user. The designated driver unit uses the vehicle's sensors and cameras to monitor the surrounding environment in real time and avoid collisions with other vehicles and pedestrians. The designated driver unit can also drive according to the user's physical condition and emotions. For example, if the designated driver unit is tired or stressed, it will take over the driving to ensure safety. Furthermore, the designated driver unit can customize the driving according to the user's driving style and preferences. For example, if the user prefers smooth driving, it will avoid sudden acceleration and braking. The designated driver unit can also adjust music and air conditioning settings to help the user relax. In this way, the designated driver unit can provide driving support that balances the user's safety and comfort.

[0066] The emotion recognition unit recognizes the driver's emotions. For example, it uses an emotion generation engine to recognize the driver's emotions. Specifically, the emotion recognition unit analyzes the driver's voice and facial expressions to recognize emotions. For example, it can analyze the driver's tone of voice and facial expressions to determine if the driver is tired, stressed, or relaxed. The emotion recognition unit uses speech recognition technology to detect changes in the driver's tone of voice and speaking style, identifying changes in emotion. It can also use a camera to analyze the driver's facial expressions in real time, reading emotions from subtle changes in expression. Furthermore, the emotion recognition unit utilizes the driver's body movements and changes in posture as indicators of emotion. For example, if the driver moves their body frequently, it may be determined that they are experiencing stress or anxiety. This allows the emotion recognition unit to comprehensively evaluate the driver's emotional state and provide information to offer appropriate support.

[0067] The physical condition recognition unit recognizes the driver's physical condition based on the emotions recognized by the emotion recognition unit. The physical condition recognition unit measures the driver's body temperature, pulse, blood pressure, and oxygen saturation, for example, using image recognition technology. Specifically, the physical condition recognition unit uses image recognition technology (rPPG) to analyze subtle changes in the driver's complexion and skin, enabling non-contact measurement of body temperature and pulse. The physical condition recognition unit can also monitor the driver's physical condition in real time. For example, it measures the driver's body temperature and pulse in real time and immediately detects changes in physical condition. Furthermore, the physical condition recognition unit accumulates the driver's physical condition data and compares it with past data to detect trends and abnormalities in physical condition early. As a result, the physical condition recognition unit can comprehensively understand the driver's health status and provide information to support safe driving.

[0068] The Safe Driving Support Unit supports safe driving based on the driver's physical condition recognized by the Physical Condition Recognition Unit. For example, the Safe Driving Support Unit avoids the impact on driving caused by sudden lane changes or stress. Specifically, if a sudden lane change occurs, the Safe Driving Support Unit recognizes the situation and takes appropriate evasive action. For example, it uses vehicle sensors and cameras to monitor the surrounding environment in real time and automatically applies the brakes if a sudden lane change occurs. Furthermore, if the driver is feeling stressed, the Safe Driving Support Unit recognizes that stress and offers suggestions to help them relax. For example, if the driver is stressed, it may play relaxing music or adjust the air conditioning settings to reduce the driver's stress. In addition, the Safe Driving Support Unit can adjust the driving style according to the driver's physical condition and emotions. For example, if the driver is tired, it may avoid sudden acceleration and braking to ensure safety. In this way, the Safe Driving Support Unit provides flexible driving support that is tailored to the driver's physical condition and emotions, enabling safe driving.

[0069] The suggestion function proposes recommended routes and restaurants to drivers. For example, it can suggest recommended routes and restaurants as a car navigation system. Specifically, if a driver inputs "Please tell me about recommended restaurants," the suggestion function will suggest the most suitable restaurants. The suggestion function makes suggestions considering the driver's current location, destination, preferences, and past history. For example, if a driver has previously enjoyed visiting certain restaurants or has a preference for a particular type of cuisine, it will suggest the most suitable restaurants based on that information. The suggestion function can also provide information on new restaurants and events by utilizing real-time updated information. This ensures that drivers always receive suggestions based on the latest information, allowing them to enjoy their drives. Furthermore, the suggestion function can collect driver feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can collect evaluations and impressions from drivers after they visit a suggested restaurant and use that information to improve future suggestions. This allows the suggestion function to provide highly accurate suggestions tailored to the driver's needs, improving driving satisfaction.

[0070] The emotion recognition unit can recognize the driver's emotions using an emotion generation engine. For example, the emotion recognition unit can recognize fatigue if the driver is tired. The emotion recognition unit can also recognize emotions by analyzing the driver's voice and facial expressions. For example, the emotion recognition unit can recognize emotions by analyzing the driver's voice tone and facial expressions. This improves the accuracy of driver emotion recognition by using an emotion generation engine. The emotion generation engine can be implemented, for example, by a machine learning model or a rule-based system. Some or all of the above processing in the emotion recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion recognition unit can input the driver's voice data into a generative AI and have the generative AI perform emotion recognition.

[0071] The health recognition unit can measure the driver's body temperature, pulse, blood pressure, and oxygen saturation using image recognition technology. For example, the health recognition unit can measure the driver's body temperature, pulse, blood pressure, and oxygen saturation using image recognition technology. For example, the health recognition unit can measure the driver's body temperature, pulse, blood pressure, and oxygen saturation using image recognition technology (rPPG). The health recognition unit can also monitor the driver's health in real time. For example, the health recognition unit can measure the driver's body temperature and pulse in real time and recognize their health. This allows for accurate recognition of the driver's health using image recognition technology. Image recognition technology can be implemented using, for example, rPPG (remote photoplethysmography) or facial recognition technology. Some or all of the above-described processes in the health recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the health recognition unit can input the driver's image data into a generative AI and have the generative AI measure body temperature, pulse, blood pressure, and oxygen saturation.

[0072] The safe driving support unit can avoid the impact on driving caused by sudden vehicle cut-ins and stress. For example, the safe driving support unit can avoid the impact on driving caused by sudden vehicle cut-ins and stress. For example, if a sudden cut-in occurs, the safe driving support unit recognizes the situation and takes appropriate avoidance action. Also, if the driver is feeling stressed, the safe driving support unit recognizes that stress and makes suggestions for relaxation. In this way, it supports safe driving by avoiding the impact of sudden vehicle cut-ins and stress. Sudden vehicle cut-ins are realized, for example, by methods for detecting cut-ins and specific means of avoidance action. The impact on driving caused by stress is realized, for example, by methods for detecting stress, criteria for evaluating the impact, and means of avoidance. Some or all of the above processing in the safe driving support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the safe driving support unit can input driver stress data into a generative AI and have the generative AI execute suggestions to avoid the impact of stress.

[0073] The suggestion function can propose recommended routes and restaurants as a car navigation system. For example, if a driver inputs "Please tell me a recommended restaurant," the suggestion function will suggest the most suitable restaurant. The suggestion function can also make suggestions considering the driver's preferences and past history. For example, the suggestion function will suggest the most suitable restaurant based on restaurants the driver has visited and enjoyed in the past. This provides a convenient system for drivers by offering a suggestion function as a car navigation system. Recommended routes and restaurants are realized through suggestion methods based on, for example, the user's preferences, past history, and current situation. Some or all of the above processing in the suggestion function may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion function can input the driver's preference data into a generative AI and have the generative AI execute optimal suggestions.

[0074] The route generation unit can estimate the driver's emotions and adjust the route selection based on the estimated emotions. For example, if the driver is relaxed, the route generation unit may prioritize suggesting a route with good scenery. If the driver is in a hurry, the route generation unit may also prioritize suggesting the shortest route. If the driver is stressed, the route generation unit may also prioritize suggesting a route with less traffic. This makes it possible to select a route that is in line with the driver's emotions. The method for estimating emotions can be implemented using data such as voice, facial expressions, and pulse rate. The method for adjusting the route selection can be implemented using criteria or adjustments for changing the route according to emotions. Some or all of the above processing in the route generation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the route generation unit can input the driver's emotion data into a generating AI and have the generating AI perform the route selection.

[0075] The route generation unit can analyze past route history and select the optimal route generation algorithm. For example, the route generation unit can analyze past route history and select the optimal route generation algorithm. For example, the route generation unit can propose the optimal route based on routes frequently used by the driver in the past. The route generation unit can also propose routes that avoid congestion based on the driver's past route history. Furthermore, the route generation unit can analyze the driver's past route history and propose the most efficient route. This allows for the generation of the optimal route based on past route history. The optimal route generation algorithm can be implemented using, for example, the Dijkstra algorithm or the A* algorithm. Some or all of the above processing in the route generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the route generation unit can input past route history data into a generation AI and have the generation AI select the optimal route generation algorithm.

[0076] The route generation unit can reflect traffic conditions and weather information in real time when generating routes. For example, the route generation unit can propose the optimal route based on real-time traffic congestion information. The route generation unit can also propose the optimal route considering real-time weather information. Furthermore, the route generation unit can propose detour routes based on real-time road construction information. In this way, by reflecting real-time information, the optimal route can be proposed. Traffic conditions and weather information are realized, for example, through means of acquiring real-time data and methods of reflecting it in route generation. Some or all of the above processing in the route generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the route generation unit can input real-time traffic conditions and weather information into the generation AI and have the generation AI perform the reflection of this information in route generation.

[0077] The designated driver unit can estimate the driver's emotions and adjust the timing of the designated driver service based on those emotions. For example, if the driver is tired, the designated driver unit may start the service earlier. Conversely, if the driver is relaxed, the designated driver unit may delay the service. Furthermore, if the driver is stressed, the designated driver unit may start the service immediately. This allows for adjustment of the timing of the designated driver service according to the driver's emotions. The method for estimating emotions is, for example, an estimation method using data such as voice, facial expressions, and pulse rate. The method for adjusting the timing of the designated driver service is, for example, a timing change criterion or adjustment method based on emotions. Some or all of the above processing in the designated driver unit may be performed using, for example, a generative AI, or without a generative AI. For example, the designated driver unit can input the driver's emotion data into a generative AI and have the generative AI perform the adjustment of the timing of the designated driver service.

[0078] The designated driver unit can select the optimal driving method by referring to the driver's driving history during the designated driver service. For example, the designated driver unit can select the optimal driving method by referring to the driver's driving history during the designated driver service. For example, the designated driver unit can select the optimal driving method based on the driving method the driver has preferred to use in the past. The designated driver unit can also select the safest driving method from the driver's past driving history. Furthermore, the designated driver unit can analyze the driver's past driving history and select the most efficient driving method. This allows the optimal driving method to be selected based on the driver's driving history. The driving history can be realized, for example, through past driving data or methods for analyzing driving patterns. Some or all of the above-described processes in the designated driver unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the designated driver unit can input the driver's driving history data into a generative AI and have the generative AI select the optimal driving method.

[0079] The designated driver unit can monitor the vehicle's condition and surrounding traffic conditions in real time during designated driving and adjust the driving method accordingly. For example, the designated driver unit can monitor the vehicle's fuel level in real time and adjust the optimal driving method. The designated driver unit can also monitor the surrounding traffic conditions in real time and adjust the optimal driving method. Furthermore, the designated driver unit can monitor the vehicle's tire pressure in real time and adjust the optimal driving method. This allows for adjustments to the driving method based on real-time information. The vehicle's condition and surrounding traffic conditions are realized, for example, through the type of sensor, the data acquisition method, and the adjustment means. Some or all of the above-described processes in the designated driver unit may be performed using, for example, a generative AI, or without a generative AI. For example, the designated driver unit can input vehicle condition and surrounding traffic data into a generative AI and have the generative AI perform the adjustment of the driving method.

[0080] The emotion recognition unit can estimate the driver's emotions and improve the accuracy of emotion recognition based on the estimated emotions. For example, the emotion recognition unit can analyze the driver's voice tone to improve the accuracy of emotion recognition when the driver is relaxed. It can also analyze facial expressions in detail to improve the accuracy of emotion recognition when the driver is stressed. Furthermore, it can analyze body movements to improve the accuracy of emotion recognition when the driver is tired. This allows the emotion recognition unit to improve the accuracy of emotion recognition based on the driver's emotions. The method for estimating emotions can be implemented using data such as voice, facial expressions, and pulse. The method for improving the accuracy of emotion recognition can be implemented using methods such as data accuracy improvement or algorithm improvement. Some or all of the above-described processes in the emotion recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion recognition unit can input the driver's emotion data into a generative AI and have the generative AI perform the emotion recognition accuracy improvement.

[0081] The emotion recognition unit can select the optimal recognition method by referring to the driver's past emotional history when recognizing an emotion. For example, the emotion recognition unit can select the optimal recognition method by referring to the driver's past emotional history when recognizing an emotion. For example, the emotion recognition unit can select the optimal recognition method based on emotions the driver has felt in the past. The emotion recognition unit can also predict changes in emotion from the driver's past emotional history and select a recognition method. Furthermore, the emotion recognition unit can analyze the driver's past emotional history and select the recognition method with the highest accuracy. This allows the optimal recognition method to be selected based on past emotional history. Emotion history is realized, for example, by past emotional data and a method for saving history. Some or all of the above processing in the emotion recognition unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the emotion recognition unit can input past emotional history data into a generative AI and have the generative AI select the optimal recognition method.

[0082] The emotion recognition unit can recognize emotions by analyzing the driver's voice and facial expressions in real time during emotion recognition. For example, the emotion recognition unit can analyze the driver's voice and facial expressions in real time during emotion recognition and recognize emotions. For example, the emotion recognition unit can analyze the driver's voice tone in real time and recognize emotions. The emotion recognition unit can also analyze the driver's facial expressions in real time and recognize emotions. Furthermore, the emotion recognition unit can analyze the driver's body movements in real time and recognize emotions. This allows for accurate emotion recognition by analyzing voice and facial expressions in real time. The method for analyzing voice and facial expressions in real time is realized, for example, by the analysis algorithm and data acquisition method. Some or all of the above processing in the emotion recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion recognition unit can input the driver's voice and facial expression data into a generative AI and have the generative AI perform real-time emotion recognition.

[0083] The physical condition recognition unit can estimate the driver's emotions and improve the accuracy of physical condition recognition based on the estimated emotions. For example, if the driver is relaxed, the physical condition recognition unit may measure body temperature in detail to improve the accuracy of physical condition recognition. If the driver is stressed, the physical condition recognition unit may also measure pulse rate in detail to improve the accuracy of physical condition recognition. If the driver is tired, the physical condition recognition unit may also measure blood pressure in detail to improve the accuracy of physical condition recognition. This allows the accuracy of physical condition recognition to be improved based on the driver's emotions. The method for estimating emotions is implemented, for example, by using estimation methods that utilize data such as voice, facial expressions, and pulse rate. The method for improving the accuracy of physical condition recognition is implemented, for example, by means of improving data accuracy or by improving the algorithm. Some or all of the above-described processes in the physical condition recognition unit may be performed, for example, using a generative AI, or without using a generative AI. For example, the physical condition recognition unit can input the driver's emotion data into a generative AI and have the generative AI perform the improvement of physical condition recognition accuracy.

[0084] The health recognition unit can select the optimal recognition method by referring to the driver's past health history when recognizing their health. For example, the health recognition unit can select the optimal recognition method by referring to the driver's past health history when recognizing their health. For example, the health recognition unit can select the optimal recognition method based on the driver's past body temperature measurements. The health recognition unit can also select the optimal recognition method from the driver's past pulse history. Furthermore, the health recognition unit can analyze the driver's past blood pressure history and select the most accurate recognition method. This allows the optimal recognition method to be selected based on past health history. Health history is realized, for example, by past health data and a method for saving history. Some or all of the above processing in the health recognition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the health recognition unit can input past health history data into a generating AI and have the generating AI select the optimal recognition method.

[0085] The health recognition unit can recognize the driver's health by measuring their body temperature, pulse, blood pressure, and oxygen saturation in real time when recognizing their health. For example, the health recognition unit can measure the driver's body temperature, pulse, blood pressure, and oxygen saturation in real time when recognizing their health. For example, the health recognition unit can measure the driver's body temperature in real time and recognize their health. The health recognition unit can also measure the driver's pulse in real time and recognize their health. The health recognition unit can also measure the driver's blood pressure in real time and recognize their health. This allows for accurate recognition of the driver's health by measuring body temperature, pulse, blood pressure, and oxygen saturation in real time. The method for measuring body temperature, pulse, blood pressure, and oxygen saturation in real time is achieved, for example, by the type of sensor used and the method of acquiring the data. Some or all of the above processing in the health recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the health recognition unit can input the driver's body temperature, pulse, blood pressure, and oxygen saturation data into a generative AI and have the generative AI perform real-time health recognition.

[0086] The safe driving support unit can estimate the driver's emotions and adjust the safe driving support method based on the estimated emotions. For example, the safe driving support unit can provide gentle driving support when the driver is relaxed. It can also provide proactive driving support when the driver is stressed. It can also provide driving support that suggests taking a break when the driver is tired. This enables safe driving support that is tailored to the driver's emotions. The method for estimating emotions can be implemented using data such as voice, facial expressions, and pulse rate. The method for adjusting the safe driving support method can be implemented using criteria or adjustment methods for changing the support method according to emotions. Some or all of the above processing in the safe driving support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the safe driving support unit can input the driver's emotion data into a generative AI and have the generative AI adjust the safe driving support method.

[0087] The safe driving support unit can select the optimal support method by referring to the driver's past driving history when providing safe driving support. For example, the safe driving support unit can select the optimal support method by referring to the driver's past driving history when providing safe driving support. For example, the safe driving support unit can select the optimal support method based on the driving support method the driver has preferred to use in the past. The safe driving support unit can also select the safest driving support method from the driver's past driving history. Furthermore, the safe driving support unit can analyze the driver's past driving history and select the most efficient driving support method. This allows for the selection of the optimal driving support method based on past driving history. Driving history can be realized, for example, through past driving data or methods of analyzing driving patterns. Some or all of the above-described processes in the safe driving support unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the safe driving support unit can input past driving history data into a generative AI and have the generative AI select the optimal support method.

[0088] The safe driving support unit can monitor the vehicle's condition and surrounding traffic conditions in real time during safe driving support and adjust the support method accordingly. For example, the safe driving support unit can monitor the vehicle's fuel level in real time and adjust the optimal driving support method. It can also monitor the surrounding traffic conditions in real time and adjust the optimal driving support method. Furthermore, the safe driving support unit can monitor the vehicle's tire pressure in real time and adjust the optimal driving support method. This allows the driving support method to be adjusted based on real-time information. The vehicle's condition and surrounding traffic conditions are realized, for example, by the type of sensor, the data acquisition method, and the adjustment means. Some or all of the above-described processes in the safe driving support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the safe driving support unit can input vehicle condition and surrounding traffic condition data into a generative AI and have the generative AI perform the adjustment of the driving support method.

[0089] The suggestion unit can estimate the driver's emotions and adjust the content of its suggestions based on those emotions. For example, if the driver is relaxed, the suggestion unit can suggest places where they can relax. If the driver is stressed, the suggestion unit can also suggest places that can help relieve stress. If the driver is tired, the suggestion unit can also suggest places where they can rest. This makes it possible to adjust the content of suggestions according to the driver's emotions. The method for estimating emotions can be implemented using data such as voice, facial expressions, and pulse rate. The method for adjusting the content of suggestions can be implemented using criteria and adjustment methods for changing the suggested content according to emotions. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the driver's emotion data into a generative AI and have the generative AI adjust the content of the suggestions.

[0090] The suggestion unit can select the most suitable suggestion by referring to the driver's past suggestion history when making suggestions. For example, the suggestion unit can select the most suitable suggestion by referring to the driver's past suggestion history when making suggestions. For example, the suggestion unit can select the most suitable suggestion based on places the driver has visited in the past. The suggestion unit can also suggest the most preferred places based on the driver's past suggestion history. Furthermore, the suggestion unit can analyze the driver's past suggestion history and select the most efficient suggestion. This allows for the selection of the most suitable suggestion based on past suggestion history. Suggestion history is realized, for example, by past suggestion data and the method of saving the history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input past suggestion history data into a generative AI and have the generative AI select the most suitable suggestion.

[0091] The suggestion unit can make optimal suggestions based on the driver's current situation and interests at the time of suggestion. For example, the suggestion unit can suggest the restaurant closest to the driver's current location. The suggestion unit can also make optimal suggestions considering the distance from the driver's current location to the destination. Furthermore, the suggestion unit can make optimal suggestions considering the traffic conditions from the driver's current location. This enables optimal suggestions tailored to the driver's current situation and interests. The current situation and interests can be realized, for example, through means of acquiring real-time data or methods of identifying interests. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the driver's current situation and interest data into a generative AI and have the generative AI execute optimal suggestions.

[0092] The suggestion unit can estimate the driver's emotions and determine the priority of suggestions based on those emotions. For example, the suggestion unit may lower the priority of suggestions if the driver is relaxed, raise the priority if the driver is stressed, or raise the priority if the driver is tired. This makes it possible to determine the priority of suggestions according to the driver's emotions. The method for estimating emotions can be implemented using data such as voice, facial expressions, and pulse rate. The method for determining the priority of suggestions can be implemented using criteria and adjustment methods for determining priority according to emotions. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the driver's emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0093] The suggestion unit can make optimal suggestions by considering the driver's geographical location information when making suggestions. For example, the suggestion unit can suggest the tourist spot closest to the driver's current location. The suggestion unit can also make optimal suggestions by considering the distance from the driver's current location to the destination. Furthermore, the suggestion unit can also make optimal suggestions by considering the traffic conditions from the driver's current location. This allows for optimal suggestions based on geographical location information. Geographical location information can be realized, for example, through means of acquiring GPS data or suggestion methods based on location information. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can input the driver's geographical location information data into a generative AI and have the generative AI execute optimal suggestions.

[0094] The suggestion unit can analyze the driver's social media activity and make relevant suggestions when making suggestions. For example, the suggestion unit can make optimal suggestions based on posts the driver has shared on social media. It can also make optimal suggestions based on posts from accounts the driver follows on social media. It can also make optimal suggestions based on locations the driver has checked in to on social media. This allows for relevant suggestions to be made based on social media activity. Social media activity can be realized, for example, through means of analyzing post content and related suggestion methods. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the driver's social media activity data into a generative AI and have the generative AI execute relevant suggestions.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The driver support system can also be equipped with a voice assistant unit. The voice assistant unit recognizes the driver's voice commands and allows the driver to operate various functions of the system by voice. For example, if the driver says, "I want to change the route," the voice assistant unit recognizes the instruction and instructs the route generation unit to regenerate the route. Also, if the driver says, "I want to check my physical condition," the voice assistant unit can instruct the physical condition recognition unit to measure the driver's physical condition. Furthermore, if the driver says, "Please recommend a restaurant," the voice assistant unit can instruct the suggestion unit to suggest the most suitable restaurant. This allows the driver to operate the system hands-free while driving, improving safety.

[0097] The driver assistance system can also be equipped with an emergency response unit. This unit can take emergency action if it detects any abnormalities in the driver's physical or emotional state. For example, if the driver suddenly loses consciousness, the emergency response unit can automatically pull over to a safe location and notify emergency contacts. If the driver is experiencing extreme stress, the emergency response unit can play relaxing music. Furthermore, if the driver complains of feeling unwell, the emergency response unit can generate a route to the nearest medical facility and have a designated driver service unit drive along that route. This ensures the driver's safety.

[0098] The driver support system can also include an entertainment section. This entertainment section can provide optimal entertainment content according to the driver's mood and physical condition. For example, if the driver is relaxed, the entertainment section can play relaxing music or podcasts. If the driver is tired, the entertainment section can suggest uplifting music or entertaining videos. Furthermore, if the driver is stressed, the entertainment section can provide content that helps relieve stress. This allows for entertainment tailored to the driver's mood and physical condition.

[0099] The driving support system can also be equipped with a learning unit. This unit can learn the driver's driving patterns and behaviors, and optimize each function of the system. For example, the learning unit can learn the driver's preferred routes and driving style, and reflect this in the route generation and driving assistance units. Furthermore, the learning unit can learn changes in the driver's emotions and physical condition, improving the accuracy of the emotion recognition and physical condition recognition units. In addition, the learning unit can learn the driver's preferences and past history, optimizing the suggestions provided by the suggestion unit. This allows for more comfortable and safer driving support for the driver.

[0100] The driving support system can also be equipped with a communication unit. This unit enables natural dialogue between the driver and the system. For example, it can provide appropriate answers to the driver's questions. Furthermore, it can engage in appropriate dialogue based on the driver's emotions and physical condition. For instance, if the driver is tired, the unit can suggest a break. If the driver is stressed, the unit can offer advice on how to relax. Additionally, the unit can provide optimal dialogue content by considering the driver's preferences and past history. This allows for more comfortable driving support through natural dialogue with the driver.

[0101] The driving support system can also be equipped with a predictive unit. This unit can predict driver behavior and traffic conditions, optimizing each function of the system. For example, it can predict the driver's next actions based on their past driving history and reflect this in the route generation and driving assistance units. Furthermore, it can suggest the optimal route based on real-time traffic conditions. Additionally, the predictive unit can predict changes in the driver's emotions and physical condition, improving the accuracy of the emotion recognition and physical condition recognition units. This allows for more comfortable and safer driving support for the driver.

[0102] The driving support system can also be equipped with a health management unit. This unit comprehensively manages the driver's health and optimizes each function of the system. For example, the health management unit can periodically measure the driver's body temperature, pulse, blood pressure, and oxygen saturation to monitor their health. It can also manage the driver's diet and exercise history to support healthy lifestyle habits. Furthermore, the health management unit can monitor changes in the driver's emotions and physical condition and suggest medical consultations as needed. This allows for comprehensive management of the driver's health and supports safer driving.

[0103] The driver support system can also be equipped with an eco-driving section. This section analyzes the driver's driving style and supports improvements in fuel efficiency and reduction of environmental impact. For example, it analyzes the driver's acceleration and braking timing and suggests the optimal driving method. It can also provide advice to improve fuel efficiency based on the driver's driving history. Furthermore, it can suggest the most eco-friendly route considering real-time traffic conditions. This optimizes the driver's driving style, leading to improved fuel efficiency and reduced environmental impact.

[0104] The driving support system can also include a safety education department. This department can provide educational content to improve drivers' driving skills and safety awareness. For example, it can analyze drivers' driving history and identify areas for improvement. It can also provide training programs to enhance drivers' driving skills. Furthermore, it can provide appropriate educational content tailored to the driver's mood and physical condition. This can improve drivers' driving skills and safety awareness, supporting safer driving.

[0105] The driver support system can also include a maintenance unit. This unit can monitor the vehicle's condition and suggest appropriate maintenance. For example, it can regularly check the vehicle's engine oil and tire condition and suggest necessary maintenance. It can also detect signs of vehicle malfunction and suggest early repairs. Furthermore, it can suggest an optimal maintenance schedule based on the driver's driving style. This ensures optimal vehicle condition and driver safety.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The route generation unit generates a route according to the user's purpose. For example, if the user enters a destination, it generates the optimal route. It can also generate a route considering the user's preferences and past history. Step 2: The designated driver unit drives based on the route generated by the route generation unit. For example, if a user inputs, "I want you to generate a route from my home to my company and drive me there," the unit will drive along the generated route. The unit can also drive according to the user's physical condition or mood. Step 3: The emotion recognition unit recognizes the driver's emotions. For example, it can recognize the driver's emotions using an emotion generation engine, or it can recognize emotions by analyzing the driver's voice and facial expressions. Step 4: The physical condition recognition unit recognizes the driver's physical condition based on the emotions recognized by the emotion recognition unit. For example, image recognition technology can be used to measure and monitor the driver's body temperature, pulse, blood pressure, and oxygen saturation in real time. Step 5: The safe driving support unit provides support for safe driving based on the physical condition recognized by the physical condition recognition unit. For example, it makes suggestions to avoid sudden cut-ins by other vehicles or the impact of stress on driving. Step 6: The suggestion section proposes recommended routes and restaurants to the driver. For example, it can suggest recommended routes and restaurants as a car navigation system, taking into account the driver's preferences and past history.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0110] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0111] Each of the multiple elements described above, including the route generation unit, driver assistance unit, emotion recognition unit, physical condition recognition unit, safe driving support unit, and suggestion unit, is implemented by, for example, at least one of the smart device 14 and the data processing device 12. For example, the route generation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The driver assistance unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The emotion recognition unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The physical condition recognition unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The safe driving support unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The suggestion unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0115] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0116] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0118] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0119] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0120] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0121] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0122] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0126] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the route generation unit, driver assistance unit, emotion recognition unit, physical condition recognition unit, safe driving support unit, and suggestion unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the route generation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The driver assistance unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The emotion recognition unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The physical condition recognition unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The safe driving support unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The suggestion unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the route generation unit, driver assistance unit, emotion recognition unit, physical condition recognition unit, safe driving support unit, and suggestion unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the route generation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The driver assistance unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The emotion recognition unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The physical condition recognition unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The safe driving support unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The suggestion function is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0152] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the route generation unit, the substitute driver unit, the emotion recognition unit, the physical condition recognition unit, the safe driving support unit, and the suggestion unit, is implemented by, for example, at least one of the robot 414 and the data processing device 12. For example, the route generation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The substitute driver unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The emotion recognition unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The physical condition recognition unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The safe driving support unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The suggestion unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0161] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0162] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0163] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0164] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0165] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0166] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0167] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0168] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0169] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0170] 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.

[0171] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0172] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0173] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0174] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0175] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0176] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0177] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0178] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0179] (Note 1) A route generation unit that generates routes according to the user's purpose, A driver substitute unit that performs driving based on the route generated by the route generation unit, An emotion recognition unit that recognizes the driver's emotions, A physical condition recognition unit recognizes the driver's physical condition based on the emotions recognized by the emotion recognition unit, A safe driving support unit that supports safe driving based on the physical condition recognized by the physical condition recognition unit, It includes a suggestion section that proposes recommended routes and shops to drivers. A system characterized by the following features. (Note 2) The emotion recognition unit, Recognizing the driver's emotions using an emotion generation engine The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned health condition recognition unit is Image recognition technology is used to measure the driver's body temperature, pulse, blood pressure, and oxygen saturation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned safe driving support unit is Avoid the impact on driving caused by sudden lane changes and stress. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned suggestion section is, Suggests recommended routes and shops as a car navigation system. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned path generation unit, It estimates the driver's emotions and adjusts route selection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned path generation unit, Analyze past route history and select the optimal route generation algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned path generation unit, When generating routes, traffic conditions and weather information are reflected in real time. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned substitute driver unit is, The system estimates the driver's emotions and adjusts the timing of the designated driver service based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned substitute driver unit is, When providing a designated driver service, the optimal driving method is selected by referring to the driver's driving history. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned substitute driver unit is, During designated driver services, the vehicle's condition and surrounding traffic conditions are monitored in real time, and the driving method is adjusted accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 12) The emotion recognition unit, It estimates the driver's emotions and improves the accuracy of emotion recognition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The emotion recognition unit, During emotion recognition, the system selects the optimal recognition method by referring to the driver's past emotional history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The emotion recognition unit, During emotion recognition, the system analyzes the driver's voice and facial expressions in real time to recognize their emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned health condition recognition unit is It estimates the driver's emotions and improves the accuracy of physical condition recognition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned health condition recognition unit is When assessing the driver's physical condition, the system selects the optimal assessment method by referring to the driver's past physical condition history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned health condition recognition unit is During health assessment, the system measures the driver's body temperature, pulse, blood pressure, and oxygen saturation in real time to recognize their physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned safe driving support unit is The system estimates the driver's emotions and adjusts safe driving support methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned safe driving support unit is During safe driving support, the system selects the optimal support method by referring to the driver's past driving history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned safe driving support unit is During safe driving support, the system monitors the vehicle's condition and surrounding traffic in real time and adjusts the support method accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned suggestion section is, It estimates the driver's emotions and adjusts the suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned suggestion section is, When suggesting options, the system selects the most suitable suggestion by referring to the driver's past suggestion history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned suggestion section is, When making suggestions, the system provides the most suitable recommendations based on the driver's current situation and interests. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned suggestion section is, It estimates the driver's emotions and prioritizes suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned suggestion section is, When suggesting options, the system takes the driver's geographical location into consideration to provide the most suitable suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned suggestion section is, When suggesting content, the system analyzes the driver's social media activity and provides relevant suggestions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A route generation unit that generates routes according to the user's purpose, A driver substitute unit that performs driving based on the route generated by the route generation unit, An emotion recognition unit that recognizes the driver's emotions, A physical condition recognition unit recognizes the driver's physical condition based on the emotions recognized by the emotion recognition unit, A safe driving support unit that supports safe driving based on the physical condition recognized by the physical condition recognition unit, It includes a suggestion section that proposes recommended routes and shops to drivers. A system characterized by the following features.

2. The emotion recognition unit, Recognizing the driver's emotions using an emotion generation engine The system according to feature 1.

3. The aforementioned health condition recognition unit is Image recognition technology is used to measure the driver's body temperature, pulse, blood pressure, and oxygen saturation. The system according to feature 1.

4. The aforementioned safe driving support unit is Avoid the impact on driving caused by sudden lane changes and stress. The system according to feature 1.

5. The aforementioned suggestion section is, Suggests recommended routes and shops as a car navigation system. The system according to feature 1.

6. The aforementioned path generation unit, It estimates the driver's emotions and adjusts route selection based on those estimated emotions. The system according to feature 1.

7. The aforementioned path generation unit, Analyze past route history and select the optimal route generation algorithm. The system according to feature 1.

8. The aforementioned path generation unit, When generating routes, traffic conditions and weather information are reflected in real time. The system according to feature 1.

9. The aforementioned substitute driver unit is, The system estimates the driver's emotions and adjusts the timing of the designated driver service based on those emotions. The system according to feature 1.

10. The aforementioned substitute driver unit is, When providing a designated driver service, the optimal driving method is selected by referring to the driver's driving history. The system according to feature 1.

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